Agneta Larsson

Anna Fredriksson

Capacity Planning at a Tactical Level in Hospital Departments

Keywords: Capacity planning, Healthcare, Tactical level, Planning process

The purpose is to describe the essential components and output of the tactical planning process and to explore context-related variations in the applicability of the planning process for hospital departments. The paper is based on a multiple-case study of three hospital departments' planning processes at a tactical level, wherein the department manager's 1 support of the planning process was found to be essential. This study illustrates how an active tactical planning process can provide for numerous potential measures to adjust capacity and how they may vary in different contexts. An active tactical planning process provides the ability to move from current short-term, costly fire-fighting measures to more proactive capacity adjustments within hospital departments, which allow the department to stay under budget while keeping waiting times and queues within limits.

Introduction

Over the years, capacity management challenges of healthcare organizations [START_REF] Smith--Daniels | Capacity management in health care services: Review and future research directions[END_REF][START_REF] Rhyne | Health care requirements planning: A conceptual framework[END_REF] have been reported in England [e.g., 2], Holland [e.g., [START_REF] Vissers | A framework for production control in health care organizations[END_REF][START_REF] Hans | A framework for health care planning and control[END_REF], Australia, Canada, England, New Zealand, Wales and Belgium [START_REF] Willcox | Measuring and reducing waiting times: a cross--national comparison of strategies[END_REF][START_REF] Cardoen | On the use of planning models in the operating theatre: results of a survey in Flanders[END_REF]. Silvester and Lendon [START_REF] Silvester | Reducing waiting times at the NHS: Is lack of capacity the problem?[END_REF] suggest that these challenges should be met by more accurate match between demand and supply of resources. The matching between resource supply and demand is made in the organizations' capacity planning processes, where tacticallevel planning dictates how operational-level matching is accomplished. Several authors (i.e., Larsson and Johansson [START_REF] Larsson | Health Care Planning-a case study of a surgery clinic[END_REF], Roth and van Dierdonk [START_REF] Roth | Hospital resource planning concepts, feasibility and framework[END_REF] and Hand et al. [START_REF] Hans | A framework for health care planning and control[END_REF]) report on operational challenges due to insufficient planning at the tactical level. As a result, high-resource utilization and long wait times for some patient groups are dealt with through "firefighting" measures, such as engaging costly excess resources [START_REF] Hans | A framework for health care planning and control[END_REF]. These issues illustrate the need for increased tactical capacity planning within healthcare organizations. Previous research regarding capacity management within hospital departments has focused on operational planning level [e.g., [START_REF] Ridge | Capacity planning for intensive care units[END_REF][START_REF] Cardoen | Operating room planning and scheduling: A literature review[END_REF][START_REF] Jack | A review and synthesis of demand management, capacity management and performance in health--care services[END_REF][START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF], such as the patient mix [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF] or scheduling practices [START_REF] Cardoen | Operating room planning and scheduling: A literature review[END_REF]. Literature that deals with the managerial aspect of the hospital planning process mainly presents planning and control frameworks that structures all planning levels [e.g., [START_REF] Vissers | A framework for production control in health care organizations[END_REF][START_REF] Hans | A framework for health care planning and control[END_REF][START_REF] Rhyne | Health care requirements planning: A conceptual framework[END_REF]). However, these studies do not describe the step-by-step planning process to assist hospital managers in matching the supply of resources with the demand for resources. There remains a need for understanding how to balance supply and demand in the tactical planning process within hospital departments. Thus, the purpose of this paper is to describe what components and output of the tactical planning process are essential and to explore contextrelated variations in the applicability of the planning process for hospital departments. The study is based on the tactical planning process within three hospital departments at a Swedish University hospital. The study will contribute to the understanding of tactical capacity planning by studying the required input, activities performed, measures used to adjust capacity and demand and the desired output within these three cases.

Theoretical Framework

This section will answer to the first part of the purpose stated above. It presents the included components of the planning process and the content of each component. A descriptive summary of theoretical planning process is stated at the en of the section, Table 1. Production planning is often described as a hierarchal structure of processes, at the strategic, tactical and operational levels [START_REF] Rhyne | Health care requirements planning: A conceptual framework[END_REF][START_REF] Roth | Hospital resource planning concepts, feasibility and framework[END_REF][START_REF] Jack | A review and synthesis of demand management, capacity management and performance in health--care services[END_REF][START_REF] De Vries | Design requirements for health care production control systems[END_REF]. Planning processes on the tactical level are the sales and operations planning (S&OP) process and the master production schedule (MPS) process [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF]. In some organizations, S&OP and MPS are not separated into two different processes, but are called Master Planning [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF][START_REF] Jonsson | Manufacturing Planning and Control[END_REF]. The responsibility for the tactical planning in hospital lies on the middle managers, however, it sometimes relies on interaction with top management [START_REF] Vissers | A framework for production control in health care organizations[END_REF].

The tactical planning process consists of different activities, including estimating the demand for products or services, deciding on a preliminary delivery plan and generating a preliminary master production schedule, reconciliation of plans and conditions and adaption of plans, as needed [START_REF] Jonsson | Manufacturing Planning and Control[END_REF][START_REF] Grimson | Sales and operations planning: an exploratory study and framework[END_REF]. When the demand and supply of capacity is balanced, the next step is to settle the prepared plans. The task of reconciling plans and conditions, and focus of this paper, is capacity planning, which at this planning level is often referred to as a rough-cut capacity plan (RCCP) [START_REF] Lapide | Sales and operations planning part I: the process[END_REF], in which the anticipated capacity variations are included [START_REF] Silvester | Reducing waiting times at the NHS: Is lack of capacity the problem?[END_REF]. According to the works of Tavares Thomé, Scavarda [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF], the planning processes can be divided into the following four parts: the planning process structure and activities, planning process input, measures that are available for balancing demand and supply, and the process delivery output.

Structure and Activities:

The form in which a planning process is performed can be described as the structure and activities [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF], which is denoted as managerial aspects consisting of meetings, activities and methods used to analyze data. The meetings can be characterized by the frequency and participants of meetings [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF][START_REF] Lapide | Sales and operations planning part I: the process[END_REF]. The planning object at the tactical level is a product or service group. The planning horizon may vary between organizations, depending on product or service lead-times [START_REF] Grimson | Sales and operations planning: an exploratory study and framework[END_REF] and may span from less than six months to over 18 months [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF]. The frequency of tactical planning is usually monthly or quarterly [START_REF] Jonsson | Manufacturing Planning and Control[END_REF].

The activities of capacity planning are described by Jonsson and Mattsson [START_REF] Jonsson | Manufacturing Planning and Control[END_REF] as calculating required and available capacity, performing capacity adjustments and making alternative actions. The manner in which data is analyzed and combined varies among organizations. Advanced IT system spreadsheets or mathematical models are analytical tools used to support capacity planning, where even the simplest method may be sufficient to fill its purpose [START_REF] Jonsson | The implications of fit between planning environment and manufacturing planning and con-trol methods[END_REF]. Present methods and calculations models can roughly be described as either focusing on how to utilize resources in an effective way [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF] or how to re-design resources to create a better flow of patients through the resources [e.g., [START_REF] Beliën | Building cyclic master surgery schedules with leveled resulting bed occupancy[END_REF][START_REF] Tai | Product line management for health care system: Theoretic capacity planning over various re-sources[END_REF]. Furthermore, literature presents different kinds of tools supporting the decision-making in the capacity planning process [e.g., [START_REF] Ridge | Capacity planning for intensive care units[END_REF][START_REF] Beliën | Building cyclic master surgery schedules with leveled resulting bed occupancy[END_REF][START_REF] Tai | Product line management for health care system: Theoretic capacity planning over various re-sources[END_REF][START_REF] Utley | Capacity Planning, Handbook of Healthcare System Scheduling[END_REF].

Input: Input to the capacity planning process is necessary to determine future needs, such as demand, available resources, restrictions placed on the system, targets and to what extent production will be allowed to deviate from plans. Information about the future demand in hospitals should be unconstrained [START_REF] Lapide | Sales and operations planning part I: the process[END_REF] and should describe the variations [START_REF] Silvester | Reducing waiting times at the NHS: Is lack of capacity the problem?[END_REF][START_REF] Vissers | A framework for production control in health care organizations[END_REF]. Future demand consists of both known and unknown demand [START_REF] Hans | A framework for health care planning and control[END_REF]. The unknown demand is forecasted demand, often based on historical data, whereas known demand includes waiting list information and "downstream" demand of patients currently under treatment [START_REF] Hans | A framework for health care planning and control[END_REF]. Demand variation in healthcare is to be found both in volume [START_REF] Utley | Capacity Planning, Handbook of Healthcare System Scheduling[END_REF][START_REF] Jack | Volume flexible strategies in health services: A research framework[END_REF] and mix of resource requirements [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF][START_REF] Tai | Product line management for health care system: Theoretic capacity planning over various re-sources[END_REF]. Furthermore, the urgency and patient-throughput time create variations regarding the required timing of resource supply. Input regarding hospital capacity generally consist of the following resources: facilities, workforce and equipment [START_REF] Jack | A review and synthesis of demand management, capacity management and performance in health--care services[END_REF]. Resource properties influence the available capacity (i.e., whether a resource is multifunctional [START_REF] De Vries | Design requirements for health care production control systems[END_REF] or specialized, if the resource is cost-intensive, resource availability [START_REF] Vissers | A framework for production control in health care organizations[END_REF] and if the resource is a shared resource or a dedicated resource [START_REF] Vissers | A framework for production control in health care organizations[END_REF][START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF]). The available capacity is often formulated in terms of available hours and number of entities.

Tactical-level planning is restricted by decisions made at the strategic level and the feasibility of options at the operational level [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF]. Strategic decisions may include the available number of units or time at the units. Other restrictions may be financial, such as budgeting or if the hospital is operating in a contracting market under planning restrictions [START_REF] Vissers | A framework for production control in health care organizations[END_REF]. Planning targets are formulated in terms of patient through-put (time, volume) [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF], patient waiting time, length of waiting lists, resource utilization rate [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF], production costs [START_REF] De Vries | Design requirements for health care production control systems[END_REF] or level of bed occupancy [START_REF] Beliën | Building cyclic master surgery schedules with leveled resulting bed occupancy[END_REF], etc. As input to the planning process, tolerance levels may also be provided.

Measures:

Measures taken to balance demand and supply include adjustments to both the demand side and the supply side [START_REF] Olhager | Long--term capacity management: linking the perspectives from manufacturing strategy and sales and operations planning[END_REF]. In the healthcare setting, measures adjusting capacity involve decisions concerning the acquisition and allocation of three types of resources: work force, equipment and facilities [START_REF] Smith--Daniels | Capacity management in health care services: Review and future research directions[END_REF][START_REF] Utley | Capacity Planning, Handbook of Healthcare System Scheduling[END_REF]. A resource acquisition at the tactical level involves, for example, decisions concerning work force changes, overtime and subcontracting [START_REF] Smith--Daniels | Capacity management in health care services: Review and future research directions[END_REF][START_REF] Olhager | Long--term capacity management: linking the perspectives from manufacturing strategy and sales and operations planning[END_REF], as well as the use of innovative shift schedules and employee cross-training to allow for movement between units [START_REF] Jack | Volume flexible strategies in health services: A research framework[END_REF]. Measures used to adjust patient demand is include prioritizing patients according to medical condition, re-scheduling, building queues, admissions planning and using scheduling rules [START_REF] Vissers | Patient mix optimisation in hospital admission planning: a case study[END_REF].

Output: According to Tenhiälä [START_REF] Tenhiälä | Contingency theory of capacity planning: The link between process types and planning methods[END_REF], the primary objective of capacity planning is to ensure the feasibility of production plans. Besides the production plan per se, the output should link lower planning levels with higher by providing feedback regarding tactical plan feasibility, which helps improve hospital management performance [START_REF] Butler | The operations management role in hospital strategic planning[END_REF] and the effectiveness and efficiency of healthcare delivery [START_REF] Hans | A framework for health care planning and control[END_REF]. Linking the lower planning levels to the higher results in changing the role of managers from one that is reactive to one that is more pro-active [START_REF] Butler | The operations management role in hospital strategic planning[END_REF].

Feasible production plan Feedback

To upper planning level To the next round of planning

Methodology

The authors of this paper selected an exploratory multiple-case research design to explain the phenomenon under investigation. The research started with constructing an analysis framework of the capacity planning at the tactical level based on existing literature [START_REF] Voss | Case research in operation managment[END_REF]. The analytical framework was built on literature from both the healthcare and manufacturing domains; thereby forming a foundation of previously developed theoretical frameworks on which to build this study [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF][START_REF] Jonsson | The implications of fit between planning environment and manufacturing planning and con-trol methods[END_REF], while systematically verifying their applicability in a new setting. The data collection was made through semistructured interviews. An interview guide based on the analytical framework was used [START_REF] Halvorsen | Samhällsvetenskaplig method[END_REF]. The studied departments were selected based on their differences in service, demand and manufacturing processes, which were chosen to obtain a wider understanding of the tactical capacity planning process within different contexts. The data collection focused on identifying the main structure and activities, input, output and measures used in the tactical capacity planning process at the studied departments. A cross-case analysis was performed to compare the ways that tactical capacity planning was conducted in the cases.

Empirical Data

Cardiology

Approximately 40 physicians, 250 nurses and administrative staff in the Cardiology department treat an average of 16,900 outpatients and 6,000 inpatients each year. The department consists of three wards, six laboratories, an outpatient policlinic, divisions that treat heart problems and patients undergoing smoking cessation as well as a teaching unit.

Structure and Activities:

There is no set planning process structure, and the focus is on costs. As such, the frequency of meetings follows the yearly budget process. The planning of available resources is not synchronized with the yearly production planning, rather the status of consumed financial means is controlled on a monthly basis, and the staff is scheduled on a quarterly basis. As a result, there is minimal connection between demand and available resources, as there is a continuous inflow of patients with treatment processes measured in days and weeks. Meeting participants represent all relevant parties for the treatment processes and financial and organizational units, including the department2 manager, the unit3 managers, the section4 manager, operational developer, 5 accountant and human resources representative. During planning, patient groups are formed according to their resource requirements, and spreadsheets facilitate the understanding of required and available capacity; however, spreadsheets are not used for all patient groups.

Input:

The anticipated demand and allocation of capacity between patient groups are based on the previous year's outcome. Data regarding patient demand and available capacity is, to some extent, available in the IT systems but is not systematically used. For example, the waiting lists are recorded and viewed by the manager, but are not used during the planning process by including the backlog in the demand. Restrictions are formulated in financial terms as limits where new funding is required and as patient waiting times. The limit is used to notify upper organizational levels that the budget is insufficient. Patient waiting times are measured but are not actively used in the planning process; rather, the patient waiting time restriction is used to depict when patients are redirected to costly sub-contractors. The objectives of the planning process are to achieve the performance measures, patient waiting time and delivered number of patients and medical priority of patients. The tolerance levels are expressed by the department as patient waiting time but, when interpreting the priority of planning focus, one could suggest that they also have indirect tolerance levels regarding costs.

Measures:

The measures taken to balance demand and supply adjust capacity by utilizing extra staff, approving overtime, and using sub-suppliers and cross training. Demand is adjusted by building queues, prioritizing patients according to medical condition and re-scheduling patients. Moving capacity between points of use (e.g., relocating surgery equipment between operating rooms) is rarely used as a measure of adjusting capacity because each department's equipment is typically stationary and requires specialized staffing. However, relocating resources does occur with some nurses and physicians when the required competence is the same. The admission planning and scheduling rules are not used due to the large number of emergency patients.

Output:

The output of the existing planning process is not a production plan, but rather a set of recommendations regarding what to produce. Existing planning activities provide feedback to the strategic level regarding the consumption of the budget and what the production process has achieved to deliver.

Urology

The Urology department treats approximately 22,000 patients each year and performs about 1,300 inpatient and 5,000 polyclinic operations, with the largest patient group being cancer patients. The department comprises two ward units and one outpatient clinic, which consists of eight nurse receptions, each one specializing in distinct specialties, such as prostate cancer, colostomy, kidney cancer or kidney stones.

Structure and Activities:

Planning process activities include calculation of available and required capacity, comparison of available capacity and required capacity, establishing measures to adjust the two, adaption of a production plan and establishing the production plan. The activities are treated at the monthly planning meetings. The planning process is heavily dependent on the department manager and a spreadsheet-based planning tool supports the planning. The established plan is locked for changes 3-5 weeks ahead, with built-in slack for emergency patients. The planning process is divided into two separate parts: clinic planning and surgery and wards (inpatient) planning. The reason for combining the surgery and wards planning is the strong dependency between surgery and wards. However, the separation between clinic and inpatient planning make the planning of the inpatient treatment process fragmented by decoupling one patient's clinical treatment from the inpatient part of the treatment. The planning meetings include department manager, 6 unit managers 7 and administrator 8 . The planning horizon is a 12-month rolling period that bridges the budget year. The planning objective is to form patient groups for the two separate planning processes that are quantified differently. At the surgery unit, the resource requirements of the patient groups are measured in multiples of required surgery time (minutes), while the requirements at the ward unit are measured in days (or sometimes in beds). Clinical planning focuses on patient groups and the required number of visits (one patient one visit, and days include a number of visits), allowing adjusting resources according to the varying requirements of a specific patient group.

Input:

The department makes use of available data from the IT systems regarding patient demand. Demand is forecast by factoring the amount of demand met during the previous year (hospital days, number of visits and discharges) and patient waiting lists. The forecast is adjusted with tacit knowledge, such as experience and other relevant information. The number of referrals is not directly used in the planning due to the department's ability to admit all referred patients at the clinic within a reasonable time.

Available capacity is estimated based on previously used capacity (i.e., the volume of capacity that each treatment unit (e.g., lab or surgery) provided last year. This method is preferable due to generally small changes in staff, facil-6 See footnote 2 7 See footnote 3 8 Staff with insight into the historic data of the department ities and equipment. Capacity is measured in the same way as demand (i.e., number of patients per patient group, days at the ward or surgery unit, visits). Restrictions on the planning process are derived from strategic decisions made by hospital management and factor in the allocated budget and the maximum patient waiting time for new and return visits. The strategic decisions are also used to formulate targets for the production system, such as patient waiting time for new and return visits and the yearly number of surgeries. Other targets are to prioritize patients according to medical priority and to treat critical conditions within an adequate timeframe.

Measures: Measures taken for capacity adjustments include the following: overtime and moving workforce and ambulatory equipment. In general, there is a lack of urologists in Sweden; therefore, overtime at evening clinics is frequently used. The department manager has limited authority to make changes outside of the budget and, when required changes are outside his authority, they are passed higher up in the hierarchy. Measures taken to adjust the demand to better fit the capacity abide by scheduling rules. The surgery unit is perceived as the bottleneck of the production system, and the purpose of using scheduling rules is to make better use of available capacity at the surgery unit. Medical priority is not articulated as a measure to adjust demand. However, dedicating slack time to treating emergency patients can indirectly prioritize urgent medical conditions. While queues and admissions planning are not intentionally used, queue length varies as a natural consequence of patient inflow.

Output:

The output of the planning process is actively used to create production support on both a weekly and monthly basis. The feedback the planning process provides is regarding insufficient capacity and how well the system produces according to targets.

Psychiatry

The studied Psychiatry department specializes in affective disorders and is comprised of nine outpatient clinics at different geographic locations with varying specializations. The department also consists of five ward units for inpatient care and one unit for research and teaching.

Structure and Activities:

Planning activities include estimating the required and available capacity, comparing the two types of capacity and choosing suitable measures that will adjust the balance, thereby establishing a production plan. First, the forecast from the previous planning period is evaluated and compared with the outcome (i.e., number of referrals, accepted referrals, first visits, return visits and discharges). The meeting also includes follow-ups on tasks discussed during the previous meeting. Second, production plans for each clinic are established, and then combined into an overall plan for the whole department. Third, capacity and demand adjustments are made within or between clinics, if needed. Fourth, the plan is frozen one month prior to the planning meeting, with changes allowed only when forecasted patient waiting times exceed three months. The department manager, unit managers, administrator, 9 and operational developer 10 take part in the monthly meetings. The planning objective is patient visits over a rolling 12-month planning horizon. The majority of the treatment process is based on visits, with the exception of an inpatient resource demand of beds and the rare need for radiology equipment. New visits are given priority over return visits because this is the politically stated performance measure used. Spreadsheets are used to support planning and to control production.

Input: Demand input consists of data from the patient process at an aggregated level: total number of incoming patients, total number of visits and discharges. The available capacity is the number of employees and their competence. According to hospital management, each treating staff member (e.g., psychiatrist or therapist) is expected to provide 800 hours of patient treatment time per year, which is less than 50% of full-time employment. How the number of patients in the production system translates into capacity requirements is not known and is thereby not used as input when planning.

The expressed restrictions are the allocated budget; however, there are also capacity adjustment restrictions (i.e., the possibility of hiring additional staff members). Targets of the capacity planning process are the length of patient waiting time, the number of new visits and the number of discharged patients. All of the targets are related to performance measures by which the production is evaluated. The tolerance level and targets are formulated according to the number of new visits and maximum patient waiting time.

Measures: Measures used for adjusting the capacity of the production system include overtime and moving capacity. The movement of capacity is usually in the form of physicians and psychiatrists, while nursing staff is seldom moved. Measures for adjusting demand include admissions planning and rescheduling. Admissions planning is accomplished by redirecting referrals between units that have equal supplies of competence, with a goal of balancing the numbers of new and return visits. To adjust the demand to meet capacity, the frequency of visits is decreased from four visits per month 9 Staff with insight into the historic data of the department 10 Hospital support function for improving the production performance of the hospital to three visits. However, when the treatment process becomes ineffective due to scarce visits with the therapist, a frequency limit must be considered when scheduling patients' return visits. Patients may also be rescheduled to alternative, similar treatment resources. Lastly, the department also adjusts the demand for resources by reducing the number of patients in the production system.

Output:

The output of the planning process is a monthly production plan for all eight clinics, providing planning support on two levels: the individual clinic production and the total amount of psychiatric care production for the hospital region. The output of the planning process also provides feedback regarding the forecast accuracy, which is invaluable for strategic planning by hospital management. Feedback is made by signaling when production is about to violate the tolerance levels for maximum patient waiting time. In this case, tolerance levels are formulated according to how many visits are required to reach the target of maximum patient waiting time, rather than being based on patient throughput time or the number of patients that must be re-booked due to lack of capacity.

Analysis

Table 2 Cross--case analysis of planning processes at Cardiology, Urology and Psychiatry departments

Structure and Activities

Cardiology Urology Psychiatry

Cross-Case Analysis

Structured capacity planning process

No Yes Yes There is no hospital wide standardized planning when it comes to structure and activities, which forces the department managers to form the process themselves. In the Cardiology department the capacity planning process is under development and, therefore, not used as a tool for pro-active actions. The Psychiatry department is starting to make use of the planning process for better balance between supply and demand. However, lack of relevant data (e.g., proper patient care plans) complicates the planning and evaluation of production outcome. The Urology planning process is actively used to plan and control department operations; however, there are still potential for improvements when bureaucracy delays the timing of proper measures.

Frequency of meetings

Yearly (Quarterly)

Monthly

Monthly

The frequency of Urology and Psychiatry department meetings is similar to the frequency of theory, but not for Cardiology, for which there are fewer opportunities to react to changes due to infrequent planning meetings. The scheduling of staff is made quarterly.

Meeting participants

Crossfunctional

Crossfunctional

Crossfunctional

All three departments use a cross-functional process in which meeting participants include a department manager, unit and section (when applicable) managers, and a representative providing data. Since the Cardiology department planning meeting is mainly a budget meeting, the human resource representative and the economist are also present. The Cardiology and Psychiatry departments are both developing their capacity planning processes and, therefore, include a planning support function at the meetings.

Planning horizon

Year

Rolling year

Rolling year

All three departments have a planning horizon of 12 months. However, the Urology and Psychiatry departments utilize a rolling horizon, which results in static planning perspective, while the Cardiology department's planning horizon decreases during the fiscal year.

Planning object

Some pat groups

Pat groups Total amount of visits

The focus on patient groups makes it possible for the Urology department to adjust resources according to the varying requirements. However, in the Psychiatry department, all patients are aggregated and the focus lies on the total number of new and return visits, which has led to the inability of capacity planning to provide improvements for specific patients groups. The Cardiology department has been better able to provide appropriate care since it began to focus on the number of visits to patient groups. Planning based on patient groups is not currently utilized for all patient groups but is a first step in creating a capacity planning process.

Activities No Yes Yes

Since Cardiology has only one yearly meeting with random check-ups during the year, it has little knowledge of the performance of the production system during the year. Urology is the most updated of current state among the departments and controls the production through its planning activities and adjustments of activities according to capacity and demand. Psychiatry has a functioning system of activities but requires better input to improve planning performance.

Analytical methods

Partly

Yes Yes For all three departments, simple comparisons of numbers in Excel are the only analytical method used; there is no use of optimization calculations The use of spreadsheets is probably suitable for its purpose. Generally, the available data has a low level of detail, which reduces the potential for the exploitation of a more sophisticated planning tool.

Input

Future demand Historical data

Historical data and tacit knowledge

Historical data

All three departments use the historical data of treated patients as forecasts, but use the data differently. Since the department treats all referred patients, Urology forecasts demand using historical data and adding tacit adjustments when it is needed. Psychiatry forecasts demand using historical data, but does not know how to interpret the number of patients as resource requirements due to undefined patient care processes. Cardiology uses the data as forecasts but is unaware how it corresponds to the actual patient demand and whether it has changed.

Available capacity

Yes

Yes No

All three departments estimate capacity based on the previous year. This method is consciously chosen for Urology since there is little variation in staff, equipment and facilities between years. Cardiology strictly uses the data from previous year out of tradition. Psychiatry has no active way of measuring capacity and uses the performance of the previous year as guidance, without making adjustments. Psychiatry uses capacity levels garnered from hospital management, which is based on negotiations between management and staff representatives rather than time measures and treatment plans.

Structure and Activities

Cardiology Urology Psychiatry

Cross-Case Analysis

Restrictions Yes Yes Yes Budget is a restriction used by all three departments. In addition, Cardiology and Urology use patient waiting time as a restriction. In the Psychiatry department, the patient waiting time is classified as a target. For the departments that use patient waiting time as restriction, the waiting time is also used as target.

Targets Yes Yes Yes Department targets (e.g., patient waiting time, number of visits or number of discharges) are generally formulated according to the KPIs (key performance indexes) by which the production is evaluated. The targets are also formulated in efficiency terms (i.e., number of patients delivered for a certain amount of money and number of surgeries per year).

Tolerance levels Yes Yes Yes At both Cardiology and Urology, the department manager oversees that production delivers within tolerance levels and according to targets. In the case of Psychiatry, tolerance levels are set by and supervised by hospital management. Thereafter, hospital management provides feedback on production performance.

Measures

Capacity adjustments

Yes Yes, focus Yes, limited Capacity measures made by Cardiology focus on using extra resources (e.g., sub-suppliers and extra staff), and adjustments made within current resources are by via cross-training and overtime usage. Urology focuses on adjusting capacity according to demand. The adjustments are made within current resources with no addition of sub-suppliers or extra staff. Psychiatry adjusts both demand and capacity whenever possible. Capacity adjustments for Psychiatry are, however, limited by the supply of qualified staff and regulations of hiring temporary psychiatrists.

Demand adjustments

Yes

Yes, minor Yes Urology has created measures on the demand side to schedule surgeries and better utilize the operation theatre. While the Urology department focuses on capacity measures, the Cardiology department makes increased use of demand measures as well as Psychiatry, which focuses on making adjustments at the demand side.

Output

Feasible production plan

Recommendation MP MP Cardiology does not use a planning process to obtain a feasible production plan. The scarce output of planning is a recommendation for next year's production. The planning processes of both Urology and Psychiatry provide a feasible master production plan. For the Psychiatry department, the plan is given on two levels.

Feedback Yes Yes Yes

The feedback that Cardiology is utilizing for planning is the status on consumed budget. Urology frequently uses the planning process for continuous feedback on production performance, which is used in the next round of planning. The feedback of the Psychiatry planning process is based on patient waiting time and delivered to upper hospital management. The planning process also provides the manager with feedback on forecast accuracy.

Discussion and conclusions

The purpose of this paper is to describe the essential components and output of the tactical planning process and to explore context-related variations in relation to the applicability of the planning process for hospital departments. The components and the output that this study has identified are summarized in Table 1. Regarding the context related variation, presented in Table 2, the devotion of the manager was superior for the performance of the planning process. It was foremost decisive of the activities performed, the frequency of meetings, the planning targets and tolerance levels, the choice of measures and the use of the planning process output. The planning process at the Urology department showed to be the most developed process, followed by the Psychiatry department and Cardiology. Urology shows a pro-active planning process, which focuses on providing the most care with the given capacity. The planning object is patient-groups but complements with an overall target of total number of visits and surgeries. Compared to Psychiatry, which also measure total number of visits, the Urology planning process are able to proactively provide measures for patients groups when required while the Psychiatry planning process is dull in its measures with its overall number of visits. The planning at the Cardiology department has improved by introducing patient groups as planning object.

Context related variations regarding the process input were evident in the available data at the Psychiatry department. There were no care plans specified for patient groups and the IT systems were lacking relevant data. Deficient input on required capacity and limited options for adjusting capacity resulted in a re-active capacity planning, with focus on demand adjustments. The cost intense production system of Cardiology along with the immature planning process has lead to a planning process that is more of a budget tool than a support system, where the output is a budget consumption indicator. The way Psychiatry uses the process output enlarge the utility planning process output, from providing production plans and feedback on tolerance levels to include evaluation of previously made forecasts.

This study illustrates the possibilities that an active tactical planning process can provide regarding the number of possible measures to adjust capacity. An active tactical planning process enables the potential to move away from the current short-term, costly fire-fighting measures to a more proactive manner of capacity adjustments within hospital departments that allow the department to maintain the budget while keeping waiting times and queues within limits and, perhaps, shortened.

Table 1 Summary of analytical framework based on theory Structure and Activities Input Measures Output Meetings

 1

		Future demand	Capacity adjustments
	Frequency	Production plan based on:	Overtime
	Participants	Unconstrained and consensus-based forecast	Extra staff
	Planning horizon	Downstream demand	Sub-suppliers, i.e. Buy care from
	Planning object	Backlog/waiting lists	other health care provider
			Moving capacity
	Activities	Available capacity	Cross-training
	Calculate available and required	RCCP -including anticipated capacity cut
	capacity	downs (e.g. further training of staff)	Demand adjustments
	Compare available capacity with		Medical priority
	required capacity	Restrictions	Re-scheduling
	Choose suitable measure/-s consider-	Budget (available funding)	Building queues
	ing targets	Strategic planning	Admissions planning
	Adapt the delivery plan and/or the	Operational constrains	Scheduling rules
	production plan
	Establish delivery plan, production plan and actions taken at the tolerance levels	Targets Through-put (time and volume) Waiting time
	Analytical methods	Length of waiting lists Resources
	Spreadsheet	Costs (change in budget)
	IT system support
	Mathematical models	Tolerance levels

The department manager is responsible for the provision of hospital specialty care. In this paper, the relevant individuals manage the Urology, Cardiology and Psychiatry (affectionate disorders) departments.

The department manager is responsible for the provision of hospital specialty care (e.g., Cardiology, Urology and Psychiatry. Psychiatry, which in this case includes affectionate disorders.)

The unit being an organisational part of the production system, such as a ward unit or a clinic

The section is a subdivision according to speciality of care within the department, defined by the competence of the physi-cians

[START_REF] Willcox | Measuring and reducing waiting times: a cross--national comparison of strategies[END_REF] The hospital's support function for improving the production performance of the hospital


Takeshi Shimamura
email: t-shimmura@aist.go.jp

Yoshihiro Hisano

Syuichi Oura

Tomoyuki Asakawa

Toshiya Kaihara

Nobutada Fujii

Tomomi Nonaka

Using a Cooking Operation Simulator to Improve Cooking Speed in a Multiproduct Japanese Cuisine Restaurant

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Until the 1960s, dining out was a luxury enjoyed by wealthy people. Restaurants provided fine dishes to satisfy wealthy customers. For such occasions, they hired skillful chefs at high salaries to compete with other restaurants by offering fine dishes. Consequently, fine dining was expensive. The restaurant market remained small.

In the 1970s, some innovative Japanese restaurant companies introduced chain store operation systems to reduce restaurant costs and therefore prices. The system introduced factory production systems with a so-called "central kitchen" to reduce the number of kitchen staff members. In addition, the central kitchen was able to reduce the dependence on skillful chefs because demanding cooking processes were done at a central kitchen. In addition, chain store restaurants reduced the total menu items to simplify cooking processes [START_REF] Mariani | America Eats Out, An Illustrated History of Restaurants, Taverns, Coffee Shops, Speakeasies, and Other Establishments That Have Fed Us for 350 Years[END_REF] [START_REF] Chase | A history of research in service operations: What's the big idea?[END_REF]. Especially, fast food and family restaurants made strong efforts to introduce systems providing ordered items quickly because the cooking speed strongly affects customer satisfaction and productivity [START_REF] Davis | An analysis of customer satisfaction with waiting times in a two-stage service process[END_REF] [START_REF] Luo | Impact of process change on customer perception of waiting time: a field study[END_REF].

In the 1980s, Japanese restaurants introduced information systems to improve cooking speeds further. For instance, point of sale (POS) systems that had been introduced into the CVS industry were introduced in restaurants [START_REF] Swart | A microcomputer network to enhance restaurant productivity[END_REF] [START_REF] Stein | Point-of-sales systems for foodservice[END_REF]. Until the introduction of POS systems, order information was written down on order sheets by service staff. Sheets were handed to the kitchen staff. A worker had to input each ordered item and a price when a customer checked out. In contrast, the POS system records order information, stores it on a POS server using a small order-entry device, and transmits it to the kitchen. A kitchen printer immediately issues an order sheet and a kitchen staff member cooks after referring to it. In addition, the POS cashier sums up the total of price using order information. Therefore, a service staff member need not convey an order sheet when receiving an order, and need not input a price when a customer checks out [5] [6].

The restaurant industry introduced cooking machines to simplify cooking operations. Sushi companies developed sushi makers. Before introduction of such machines, sushi was prepared by an experienced sushi chef. For that reason, sushi was expensive and preparation was slow. By contrast, a sushi maker prepares sushi automatically (a part time employee merely inserts raw fish), and serves it on a belt conveyer. The price of sushi dropped to that of fast food. Today, sushi has become a popular food for restaurant diners [START_REF] Ngai | Development of an RFID-based sushi management system: the case of a conveyor-belt sushi restaurant[END_REF].

Dining out had become not a luxury but light leisure for Japanese customers by the introduction of new systems and technologies. As a result, the restaurant industry market size expanded rapidly. By the early 1990s, the restaurant industry had become a key industry in the Japanese economy. The Japanese restaurant market was approximately 30 trillion yen (300 billion US dollars), employing approx. 4 million staff workers [START_REF] Takenaka | Process management in restaurant service -a case study of Japanese restaurant chain[END_REF].

However, as the market has expanded, the gap separating production systems and customer requirements has widened. As customers have experienced various dining experiences, their requirements have become diverse [START_REF] Takenaka | Practical and Interactive Demand Forecasting Method for Retail and Restaurant Services[END_REF]. The chain store system has simplified menus and cooking operations. Restaurant companies understand the gap, but they cannot change or diversify menus because changing production systems and menus entails abandonment of the chain store system and production system.

Instead, Japanese restaurant companies have introduced low-price strategies to achieve breakthroughs. They accelerated investigation of restaurants to realize economies of scale, not introducing new production systems and menus to adapt to customer needs. That strategy saturated the Japanese restaurant market with many competitors. Many restaurant companies were bankrupted during the 1990s.

In the 2000s, the Japanese restaurant industry changed its policy from a "low price strategy" to "menu diversification". For instance, the largest providers increase menus from 3 to 21 categories [10]. That strategy satisfied customer requirements, but productivity declined because increased cooking speeds aggravated the sheet turnover ratio and increased the number of cooking staff. The restaurant industry must introduce new production systems.

The authors have introduced a cell production system to reduce labor costs and to reduce food production amounts at multiproduct Japanese restaurants [11]. The system concept combines machine production and manual work. In addition, the system is intended to reduce labor costs by enhancing labor elasticity. When a restaurant is rushed, many staff members work in the kitchen to prepare numerous and diverse orders. When the order number is reduced, a multi-skilled worker prepares dishes using cell equipment.

However, previous studies have revealed some problems. For instance, the study did not change the basic kitchen layout. The cell was placed in a vacant area to improve cooking operations but optimization of the kitchen layout was not considered. The entire kitchen layout is regarded as improving cooking operations. Moreover, the study did not measure cooking speed, although it is necessary for customer satisfaction, turnover ratio of table, and labor input.

To resolve the problems, a cooking operation simulator was developed to optimize the kitchen layout based on a food factory simulator [START_REF] Fujii | Facility Layout Planning of Central Kitchen in Food Service Industry -Application to the real-scale problem[END_REF]. The system simulates the cooking time of individual orders. Simulations are run several times using order data, labor input data, cooking-operation database, and a kitchen-layout database. Some kitchen layouts are designed based on the simulation results. A method of kitchen layout design using the simulator is demonstrated and discussed.

Simulating Cooking Operation

Cooking operation simulator

The cooking simulator system is developed to simulate the cooking time of orders using actual order information (Fig. 1). The system comprises a POS system, an attendance management system, a cooking-operation database, and a kitchen-layout database. Order information (Demand) is recorded by the POS system; the labor input (Supply) is measured by the attendance management system. The cooking time of the multiproduct Japanese cuisine restaurant A (6 floors, 500 sheets, 2 washoku kitchens on the first floor and sixth floor, 1 sushi kitchen on the first floor, and 210 menu items) managed by restaurant company B (operating 100 Japanese cuisine restaurants in Osaka, Japan) is simulated. The average cooking time of orders at each cooking position (T p ) and total orders (T a ) are simulated as KPI of [START_REF] Davis | An analysis of customer satisfaction with waiting times in a two-stage service process[END_REF][4] [START_REF] Shimmura | Real-time process management system in a restaurant by sharing food order information[END_REF].

Cooking times of 210 menus are measured by 22 cooking staff members and scorers to create a cooking-operation database of restaurant A. To create the present kitchen layout database of restaurant A, the location, size, front, and production capacity of the cooking machine are measured using a drawing sheet of restaurant A. Customer orders (484 orders), working hours (128 hours), and places of staff members (20 staff members) of restaurant A on 12, 8, 2013 are recorded by the POS system and the attendance management system to simulate the cooking time at restaurant A. Based on these results, problems of the present kitchen layout are discussed by an experienced store manager (41 years old, 19 years of service at B), a grand chef (52 years old, 34 years of service at B) of restaurant A, and a kitchen planner (43 years old, 15 years of service at B). Two ideas for kitchen layout improvement at restaurant A were planned based on the results (plan A, plan B). Plan A maintains the present kitchens (2 washoku kitchens, 1 sushi kitchen). Plan B consolidates the 2 washoku kitchens to 1 kitchen on the first floor. T p and T a of plan A and B are simulated by the system to confirm the efficacy of plans A and B.

Results and discussion

Results

Table 1 and Table 2 present results of simulations. T a of the present layout was 4.22 min (SD=3.51 min). Plan A was min (SD=3.18 min). Plan B was 5.31 min (SD=3.28 min). Plan C was 3.75 min (SD= 2.67 min).

Discussions

First, the respective problems and efficacies of plan A and plan B are discussed. Both plans A and B were insufficient to improve T a , although the plans were designed by experienced staff members. Long experience and diverse skills are important factors supporting layout design, but both plans based on their heuristics were insufficient to enhance cooking time.

Three reasons explain why T a of plans A and B worsened compared to the present layout: 1) reduction of cooking positions, 2) increasing work-load ratio because of changing work plans, and 3) increasing work-load ratio because of changing cooking equipment. 1) T p of sushi and sashimi worsened because of the reduction of cooking positions.

Both plans A and B reduced 1 sushi and sashimi cooking position because both plans were aimed at consolidating the washoku kitchen located at the first floor.

The main washoku kitchen of restaurant A is located on the sixth floor. The sub washoku kitchen is located at the first floor, although the main floor of restaurant A is located on the first floor. 2) T p of the tray service worsened because of increasing work load. Both plan A and B added noodle-cooking operation to tray service staff because T p of noodles was sluggish compared to tray service, and tray service of sub washoku kitchen was placed next to noodle boiler. Instead, T p of noodle of both plans A and B was improved compared to the present layout because of the work plan redesign. However, T p will worsen if staff members of tray service do not help with noodle cooking. An alternative solution should be introduced to improve T p of the position. 3) T p of both simmered and fried foods worsened because of cooking machine replacement. Both plan A and B moved the fryer and stove burner from the sixth floor to the first floor to save conveyance operations from the sixth floor to the first floor by lift. Although the conveyance process was eliminated, the work load of staff members of both positions worsened because the work loads of staff members at simmered food and fried food areas increased. However, T p of salads and small dishes of both plans A and B were improved. Both plans A and B moved the position to the first floor to save conveyance operations from the sixth floor to the first floor by lift. Different from simmered and fried foods, salads and small dishes can cook and stock using idle time. Therefore, they need not cook when they receive orders.

Second, the efficacy and problems of plan C are discussed based on the problem categories. Plan C improved T a compared to the present layout, and plans A and B. Plan C was designed based on the simulation results. Therefore, plan C was redeemed to resolve problems of plans A and B. 1) T p of sushi and sashimi improved compared to present layout, plan A, and plan B.

Plan C did not reduce sushi and sashimi cooking positions to maintain T p of sushi and sashimi. Moreover, Plan C moved the sashimi cooking position from the washoku kitchen at the sixth floor to sushi kitchen to improve its T p . Results show that staff members of sushi and sashimi cooking positions increased from 3 to 4; their work load was reduced. 2) T p of noodles maintained improvement compared to present layout, and plans A and B. Plan C replaced the work plan of tray service staff, and changed the kind of noodle boiler to enhance T p of noodles. Results show that improvement of T p was retained, although plan C stopped noodle-cooking support by tray service staff. Nevertheless, T p of tray service was not improved despite work plan replacements. The work load of tray service seemed to increase because T p of sushi, sashimi, noodles, simmered foods, baked foods, salads, and small dishes were improved, and dishes flooded to the position of plan C did not increase the total number of staff members. 3) T p of simmered foods improved compared to the present layout. Plan C increased the number of stove burners compared to the present layout to enhance cooking capacity. In addition, Plan C cleared the wall located between the sushi and washoku kitchen on the first floor to enhance cooperation between sushi and washoku staff members to assist one another when they had free time. Changing the layout, work plans, and increasing the cooking capacity are measures that boost the T p of both positions. However, T p of fried foods was not improved even though plan C increased the number of fryers, and cleared the wall separating the sushi and washoku kitchen areas. Simmered foods are cooked dish-by-dish using a pan. Therefore, the cooking time depends strongly on the total number of stove boilers and staff members. Different from simmered foods, many fried foods are cooked simultaneously using fryers. Therefore, the cooking time has nothing to do with the total number of staff members. Moreover, the cooking speed depends strongly on ingredients. For instance, the cooking time of sliced vegetables such as pumpkin for fried foods is 3 min, that of eggplant is 2 min, and that of shrimp is 1.5 min. Extension of fryers does not necessarily improve the cooking speed of fried foods. As discussed above, kitchen design based on simulator results can improve the cooking time of multiproduct Japanese cuisine restaurant, but some critical problems remain.

First, Japanese cuisine chefs shun mutual cooperation because of their pride and habits. They believe that if a chef is supported by another chef, then the former must be regarded as "less-skilled" [START_REF] Fujii | Facility Layout Planning of Central Kitchen in Food Service Industry -Application to the real-scale problem[END_REF]. Moreover, sushi and washoku chefs dislike working together because they belong to different guilds. In Japan, sushi and washoku guilds have existed for more than 300 years. They have avoided copying and sharing their respective techniques. Such a historical reason prevents them from working together. They should be educated to change from a pride-based working system to a CS-based working system. Restaurant companies should produce a new training system for staff members, instead of a guild-based training system.

Second, results of simulation should be applied to actual restaurants. Although plan C improves T a , and T p of sushi, sashimi, noodles, simmered foods, baked foods, salads, and small dishes, the results are only reflective of the simulation. Other factors affect the cooking speed such as the total number of available kitchenware such as dishes and pots, and operational impediments such as complaints and mistakes, and fatigue of staff members. Appropriate methods such as IE methods, behavior measures, and QC activities must be introduced to resolve those problems [START_REF] Ueoka | Introduction of Computer Supported Quality Control Circle in Japanese Cuisine Restaurant[END_REF], [START_REF] Tenmoku | Service-Operation Estimation in a Japanese Restaurant Using Multi-Sensor and POS Data[END_REF].

Third, the redesign of work plans and work scheduling should be conducted each time the menu is changed. Multiproduct restaurants frequently change menus to tune the line up for customer requirements. For instance, the Japanese cuisine course "Kaiseki" changes the menu every month. In addition, the operation change should be discussed at turn of the year because contents of orders differ among seasons.

Conclusions

This study was undertaken to improve cooking times at multiproduct Japanese cuisine restaurants by introducing a cooking operation simulator. The cooking times of dishes at restaurant A were simulated using order data recorded by a POS system of restaurant A, with labor input recorded by input attendance management system of restaurant A. Results show that the cooking times of dishes can improve the work plan redesign, cooking machine placement, and changing kitchen layout. To realize simulation results at an actual restaurant, 1) working habits of chefs should be changed, 2) some other methodology such as IE and QC should be introduced to resolve some other problems, and 3) simulations should be conducted frequently to adapt to changing customer needs and changing order contents.

Fig. 1 :

 1 Fig. 1: Layout of the restaurant A's kitchen. Based on those results, they again discussed effects and problems of the respective plans and designed a more efficient kitchen layout (plan C). The T p and T a of plan C are simulated to confirm the efficacy of plan C. Then they were compared to the present layout, plan A, and plan B.

Table 1 :

 1 Average cooking time of simulations (min)

		Cooking Position	Present	Plan A	Plan B	Plan C
		Sushi, Sashimi	3.73	5.06	5.08	3.47
		Noodles	4.79	4.47	4.36	4.11
		Tray Service	2.08	2.79	3.68	2.18
	T p	Fried Foods	4.76	5.93	6.51	5.00
		Simmered Foods	4.93	5.48	6.38	4.88
		Baked Foods	5.31	5.13	5.96	4.78
		Salads, Small Dishes	6.50	4.34	6.37	3.74
	T a		4.22	4.81	5.31	3.75

Table 2 :

 2 Standard deviation of the simulation (min)

		Cooking Position	Present	Plan A	Plan B	Plan C
		Sushi, Sashimi	2.28	3.47	3.03	1.85
		Noodles	1.80	1.39	1.05	1.35
		Tray Service	1.60	2.33	3.19	1.75
	T p	Fried Foods Simmered Foods	3.30 2.35	2.24 2.53	2.74 3.37	2.34 2.80
		Baked Foods	0.35	0.58	1.25	0.56
		Salads, Small Dish-es	7.33	4.35	4.90	5.22
	T a		3.51	3.18	3.28	2.67

Acknowledgements

This study was partially supported by the Project of Service Science from JST/RISTEX for Service Science in 2013, and as a joint study with the Center for Service Research, National institute of Advanced Industrial Science and Technology (AIST).


Tomomi Nonaka
email: nonaka@ise.aoyama.ac.jp

Mitsuru Igarashi

Hajime Mizuyama

A Customer Satisfaction Model for Effective Fast Fashion Store Service

Keywords: customer satisfaction, multiple regression analysis, multi-agent, perceived time

This paper proposed a customer satisfaction model to effectively manage staff priorities and service at fast fashion stores. An evaluation model of customer satisfaction was developed through multiple regression analysis of two measures. The first measure considered the difference between actual times and perceived times of customers' behavioural processes while shopping to determine whether or not customers felt dissatisfied. The second measure identified factors which led to customer frustration through a multiple choice questionnaire. The proposed model was applied to multi-agent simulation to compare customer satisfaction levels.

Introduction

The service sector plays a key role in economic activity and the creation of employment opportunities in developed as well as developing countries. The growth of the fashion retail industry, which is part of the service sector, has been increasing as new industry players called 'fast fashion retailers' enter the scene. Fast fashion is used to refer to retailers with strategies to adopt current and emerging trends quickly and effectively [START_REF] Fernie | Retail Logistics[END_REF]. They sell clothes and provide related services at their stores, which is categorised as a business to customer (B to C) service. The importance of customer satisfaction in B to C service has been widely recognised, as it has been found to be a significant determinant of repeat sales, positive word of mouth, and consumer loyalty [START_REF] Bearden | Selected Determinants of Consumer Satisfaction and Complaint Reports[END_REF]. Many studies have investigated the relationship between service quality and customer satisfaction [START_REF] Iacobucci | Distinguishing Service Quality and Customer Satisfaction: The Voice of the Consumer[END_REF][4][5] [START_REF] Spreng | An Empirical Examination of a Model of Perceived Service Quality and Satisfaction[END_REF], where it has been demonstrated [START_REF] Sureshchandar | The Relationship between Service Quality and Customer Satisfaction -A Factor Specific Approach[END_REF] that these variables are independent but closely related.

To increase customer satisfaction and, at the same time, be profitable, it is necessary to provide quality service with limited human resources. Therefore, strict task prioritisations and staff assignments are often employed in fast fashion stores. The rules, which are often founded according to customer-first philosophies and prior experience, may make it difficult to adapt to fluctuating customer demands. Furthermore, the effects of prioritisation on customer satisfaction have not yet been revealed. Thus, a model to evaluate customer satisfaction should be applied to effectively manage task priorities. Customer satisfaction has been evaluated by measuring the customer wait experience [START_REF] Mcguire | A Framework for Evaluating the Customer Wait Experience[END_REF]- [START_REF] Chebat | The Impact of Waiting in Line on Consumers[END_REF], which is described as a pervasive and often unavoidable experience that appears to be a strong determinant of overall satisfaction [START_REF] Pruyn | Effects of Waiting on the Satisfaction with the Service: Beyond Objective Time Measures[END_REF] [START_REF] Taylor | Waiting for Service: The Relationship between Delays and Evaluations of Service[END_REF]. A negative correlation between wait times and overall customer satisfaction has been shown [START_REF] Chebat | The Impact of Waiting in Line on Consumers[END_REF] [START_REF] Murdick | Service Operations Management[END_REF]; when time spent waiting increases, satisfaction in general decreases [START_REF] Taylor | Waiting for Service: The Relationship between Delays and Evaluations of Service[END_REF] [START_REF] Davis | A Framework for Relating Waiting Time and Customer Satisfaction in a Service Operation[END_REF]. Following these findings, actual wait times compared with perceived wait times have been introduced as indicators of customer satisfaction [8][11][12] [START_REF] Chebat | The Impact of Waiting in Line on Consumers[END_REF]. An earlier study [START_REF] Luo | Impact of Process Change on Customer Perception of Waiting Time: A Field Study[END_REF] measured both actual and perceived customer wait times, and then compared these before and after a process change. The effects of a process change on customers' perceptions of wait times and customer satisfaction have also been examined. An earlier study [START_REF] Shimmura | Improving Customer's Subjective Waiting Time Introducing Digital Signage[END_REF] measured the actual and perceived wait times at a restaurant, and found that perceived wait times became shorter with the introduction of a digital signage system that provided interesting menu-related information to customers while waiting for their orders to arrive.

In the aforementioned studies, customer satisfaction has only been evaluated in select situations. However, assessing overall customer satisfaction is needed in order to control staff task prioritisation and improve service quality. Thus, this study proposes a customer satisfaction model using multiple regression analyses of two measures. The first measure was the difference between actual and perceived times spent at each stage of the customer's behavioural process, which indicated the degree of customer dissatisfaction throughout the shopping experience. The second measure aimed to identify key factors which led to customer frustration through a multiple choice questionnaire. The proposed model was then incorporated into a multi-agent simulation to examine customer satisfaction potentials related to different managerial methods under various conditions.

The remainder of this paper will propose an evaluation model and introduce a method to measure customer satisfaction. The paper will then test this proposed model on customer satisfaction levels through a multi-agent simulation analysis. Following the results, the discussion and conclusions are provided.

Customer Satisfaction Model

The proposed customer satisfaction model considers the difference between actual and perceived times [START_REF] Luo | Impact of Process Change on Customer Perception of Waiting Time: A Field Study[END_REF][11] [START_REF] Gail | A Field Study Investigating the Effect of Waiting Time on Customer Satisfaction[END_REF][15] during the behavioural process of customers at a store as an evaluation method of customer satisfaction. Evaluating customer satisfaction throughout the entire service process rather than at specific stages is required to improve overall service quality.

Measuring perceived and actual times

Customers' perceived and actual time spent at specific stages during their shopping experience were measured. First, a customer visited a fast fashion store accompanied by an investigator. The customer was required to decide on a product to purchase before entering the shop. The investigator observed the customer's buying behaviour and measured the actual time spent at each stage of the shopping process. Investigators then surveyed customers about the perceived amount of time spent at each stage. The stages were classified as follows:

Stage A:

The time it took to enter the shop and find the display cabinet where the predetermined product was kept Stage B:

The time it took to reach the display cabinet and select an item Stage C:

The time it took to use a fitting room Stage D:

The time it took to decide on an item and reach a cash register Stage E:

The time spent waiting in line Stage F:

The time it took to complete the transaction Next, the differences between the actual measured times and customers' perceived times spent at each stage were evaluated as a dissatisfaction indicator. The indicator was calculated as the proportion of perceived time (T1) to actual time (T2). Dissatisfaction levels were evaluated as either 'very dissatisfied':

𝑇 1 𝑇 2 ⁄ ≥ 1.5, 'dissatisfied': 1.5 > 𝑇 1 𝑇 2
⁄ ≥ 1.0, or 'slightly dissatisfied or no dissatisfaction': 1.0 > 𝑇 1 𝑇 2 ⁄ . Depending on the second measure described in the next subsection, the answer could also be judged as 'no dissatisfaction' if a customer did not select a frustration factor at a stage.

Multiple choice questionnaire

A multiple choice questionnaire was completed by customers to identify factors which led to frustration during the shopping experience. As shown in Table 1, possible dissatisfaction factors are listed for each stage of the shopping experience, as described in section 2.1. The number of choices for each question ranges from five to eight, and allows for customers to make multiple selections. The answers to the questionnaire were then quantified and converted to either the number 1, which meant selected, or 0, which meant not selected by dummy variables.

Developing the customer satisfaction model

Multiple regression analysis was used to integrate the results of the two measures discussed in sections 2.1 and 2.2. First, a regression model that linked dissatisfaction levels with dissatisfaction factors was obtained. The model considered each stage separately. The customer satisfaction model is shown in Eq. 1.

𝑌 = 𝛽 0 + 𝛽 1 𝑥 1 + 𝛽 2 𝑥 2 + ⋯ + 𝛽 𝑛 𝑥 𝑛 + 𝜀 (1)
The explained variable 𝑌 represents the proportion of perceived time (𝑇 1 ) to actual time (𝑇 2 ): 𝑇 1 𝑇 2 ⁄ . The independent variables 𝑥 1 , 𝑥 2 , … , 𝑥 𝑛 are known constants that represent the dissatisfaction factor derived from the answers to the questionnaire. The variables are quantified and converted to 1, which means selected, or 0, which means not selected by dummy variables as shown in section 2.2. In this model, the intercept coefficient 𝛽 0 Table 1. Items from the Multiple Choice Questionnaire is adopted as the value '1'. Therefore, the explained variable 𝑌 is '1' when all independent variables are '0', which means customers do not feel any dissatisfaction, as represented by 𝑇 1 𝑇 2 ⁄ = 1.

In addition, this paper set the parameters of 𝛽 1 , 𝛽 2 , … , 𝛽 𝑛 as positive values in order to prevent a factor from having unnatural effect of reducing the degree of dissatisfaction. When a parameter was estimated as a negative number, the corresponding dummy variable was removed from the model and the analysis was repeated. The estimation was repeated until all parameters became positive values.

Stage

Items from the multiple choice questionnaire

Stage A:

The time it took to enter the shop and find the display cabinet where the predetermined product was kept 1. difficulty browsing the store due to crowds (𝑥 1 ) 2. could not find the predetermined item (𝑥 2 ) 3. there were no staff around to ask where the predetermined item was located (𝑥 3 ) 4. could not find the predetermined item due to confusing instructions from staff (𝑥 4 ) 5. the store was dusty and dirty with many unfolded clothes (𝑥 5 ) 6. other reasons (𝑥 6 )

Stage B:

The time it took to reach the display cabinet and select an item 1. could not buy the predetermined item because it was out of stock (𝑥 1 ) 2. the desired colour, pattern, or size was not readily visible (𝑥 2 ) 3. could not find the desired design (𝑥 3 ) 4. more expensive than expected (𝑥 4 ) 5. unable to decide on an item due to sizing uncertainty (𝑥 5 ) 6. could not reach the desired item due to crowds (𝑥 6 ) 7. the store was dusty and dirty with many unfolded clothes (𝑥 7 ) 8. other reasons (𝑥 8 )

Stage C:

The time it took to use a fitting room 1. long wait time due to full fitting rooms (𝑥 1 ) 2. could not judge whether or not one was allowed to use the fitting rooms (𝑥 2 ) 3. there were no staff around to consult (𝑥 3 ) 4. there was trash or clothes hangers inside the fitting room (𝑥 4 ) 5. felt the fitting room was small and uncomfortable (𝑥 5 ) 6. other reasons (𝑥 6 )

Stage D:

The time it took to decide on an item and reach a cash register 1. difficulty reaching a cash register due to crowds (𝑥 1 ) 2. could not find where the cash registers were located (𝑥 2 ) 3. there were no staff around to ask where the cash registers were located (𝑥 3 ) 4. could not find the cash registers due to confusing instructions from staff (𝑥 4 ) 5. the store was dusty and dirty with many unfolded clothes (𝑥 5 ) 6. other reasons (𝑥 6 )

Stage E:

The time spent waiting in line 1. the line was long and processed slowly (𝑥 1 ) 2. could not find the end of the line (𝑥 2 ) 3. another customer cut the line (𝑥 3 ) 4. lack of attention to customer care, with no words such as, 'sorry to have kept you waiting'(𝑥 4 ) 5. the cashier worked slowly (𝑥 5 ) 6. the counter was dusty and dirty (𝑥 6 ) 7. too few cash registers (𝑥 7 ) 8. other reasons (𝑥 8 )

Stage F:

The time it took to complete the transaction 1. staff were hard to hear (𝑥 1 ) 2. staff spoke too fast (𝑥 2 ) 3. artificial service (𝑥 3 ) 4. the cashier took a long time to pack the bought items (𝑥 4 ) 5. treated items roughly (𝑥 5 ) 6. the counter was dirty (𝑥 6 ) 7. the counter space was small (𝑥 7 ) 8. other reasons (𝑥 8 ) Fig. 1. Dissatisfaction levels at each stage of shopping 3 Field Experiment

Implementation

This section discusses how the proposed model was tested in an actual experiment at a fast fashion store. The experiment was conducted on weekdays during the busy shopping season between November and December in order to test the model in crowded store situations. A sample size of 20 students aged in their twenties was used. A limited age range was included in the study in order to control for the influence of ageing in the perception of time [START_REF] Wada | Duration Judgments in Elderly-Hospitalized Persons: Aging and Subjective Time Passages[END_REF]. First, actual and perceived times were measured. Figure 1 shows the dissatisfaction levels at each stage of shopping, with the vertical axis representing the total number of people. In this experiment, Stage C is removed from further analysis because there was only one consumer who used a fitting room. The results show that Stage D has the most 'very dissatisfied' ratings, although it is also observed in Stages A, B, E, and F. Furthermore, the stages all had similar numbers of 'very dissatisfied' and 'dissatisfied' ratings, the sum of these two values being 14, 11, 12, and 13 in Stages A, B, D, and E, respectively. These results indicate that customers feel dissatisfaction at multiples stages during shopping.

Next, a multiple choice questionnaire was distributed to the target customers.

Developing the customer satisfaction model

Using the results of the investigation shown in the previous section, a customer satisfaction model was developed using multiple regression analysis. The developed customer satisfaction models derived by Eq. 1 are shown in Eq. 2 to Eq. 6. The model considers each stage separately, as mentioned in section 2.2. The independent variables 𝑥 1 , 𝑥 2 , … , 𝑥 𝑛 correspond with the dissatisfaction factors shown in Table 1.

1. Stage A: The time it took to enter the shop and find the display cabinet where the predetermined product was kept. As a result, it was estimated that 𝑥 1 (difficulty browsing the store due to crowds) was the most significant factor, followed by 𝑥 3 (there were no staff around to ask where the predetermined product was located), and 𝑥 2 (could not find the predetermined product).

𝑦 = 1.00 + 0.31𝑥 1 + 0.23𝑥 2 + 0.26𝑥 3 + 0.04𝑥 5 (2)

Stage B:

The time it took to reach the display cabinet and select an item. 𝑦 = 1.00 + 0.14𝑥 1 + 0.64𝑥 6 [START_REF] Iacobucci | Distinguishing Service Quality and Customer Satisfaction: The Voice of the Consumer[END_REF] The equation estimated that 𝑥 6 (could not reach the desired item due to crowds) was the most significant factor, followed by 𝑥 1 (could not buy the predetermined item because it was out of stock).

Stage C: The time it took to use a fitting room.

There was only one consumer who used a fitting room during the experiment, therefore Stage C was removed from further analysis.

Stage D:

The time it took to decide on an item and reach a cash register.

𝑦 = 1.00 + 0.52𝑥 1 (4)
The most significant factor was estimated as 𝑥 1 (difficulty reaching a cash register due to crowds).

Stage E:

The time spent waiting in line. 𝑦 = 1.00 + 0.29𝑥 1 + 0.02𝑥 2 + 0.13𝑥 5 + 0.01𝑥 7 [START_REF] Sureshchandar | The Relationship between Service Quality and Customer Satisfaction -A Factor Specific Approach[END_REF] It was estimated that 𝑥 1 (the line was long and processed slowly) was the most significant factor, followed by 𝑥 5 (the cashier worked slowly).

6. Stage F: The time it took to complete the transaction. 𝑦 = 1.00 + 0.23𝑥 3 + 0.13𝑥 4 [START_REF] Spreng | An Empirical Examination of a Model of Perceived Service Quality and Satisfaction[END_REF] The variable 𝑥 3 (artificial service) was the most significant factor, followed by 𝑥 4 (the cashier took a long time to pack the purchased items).

The findings regarding Stage E (the time spent waiting in line) supported the findings by [START_REF] Pruyn | Effects of Waiting on the Satisfaction with the Service: Beyond Objective Time Measures[END_REF][13] [START_REF] Davis | A Framework for Relating Waiting Time and Customer Satisfaction in a Service Operation[END_REF]. In addition, the independent factors with the highest parameters were all related to crowded situations, as demonstrated by 𝑥 1 (difficulty browsing the store due to crowds) in Stage A, 𝑥 6 (could not reach the desired item due to crowds) in Stage B, and 𝑥 1 (difficulty reaching a cash register due to crowds) in Stage D. The results indicated that dissatisfaction factors were not only related to waiting, but also other situations encountered while shopping.

Multi-agent simulation

A simulation model was developed on a software named artisoc and the proposed custo-Fig. 2. Store layout and item location implemented by artisoc mer satisfaction model was incorporated into a multi-agent simulation model. Figure 2 shows the implemented store layout and item locations. In this paper, customers, store staff, and cabinet agents were implemented and the store space was about 500 squire meters. The action model for customers was determined using the following rules: (1) avoid passing through aisles, (2) exploring desired product categories, and (3) waiting in line at cash registers. Each rule was implemented by the program using the results derived from section 3. This paper examined two conditions. The first considered staff assignments and task priorities as usual. The second changed staff assignments by moving one staff member from the sales floor to the cash register. Additionally, the rule regarding the limitation of working area was removed. The results were then evaluated and compared with the proportion of perceived time (𝑇 1 ) to actual time (𝑇 2 ): 𝑇 1 𝑇 2 ⁄ . The simulation was repeated 50 times.

The results showed that the value of 𝑇 1 𝑇 2 ⁄ was 1.11 for the first condition and 1.07

for the second condition. This confirmed that changing the priority and assignment rules derived from the proposed model improved customer satisfaction.

Conclusion

This paper proposed a customer satisfaction model to effectively manage staff task priorities in fast fashion service. The experiment was conducted at an actual store and validated the model. The results indicated the stages during shopping where customers experienced the greatest dissatisfaction, and which factors caused this dissatisfaction. Furthermore, multi-agent simulation was implemented using the results of the experiment, and confirmed that customer satisfaction levels increased after introducing task prioritisation and staff assignment changes. Applying the proposed evaluation model to other cases and testing it through further simulation are the next steps of this study.

Acknowledgements

This work was partially supported by JSPS KAKENHI Grant-in-Aid for Young Scien-Knitwear for ladies Innerwear for ladies Outerwear for ladies Outerwear for men Knitwear for men Innerwear for men Entrance Entrance tists (B) Number 26730159.


Morteza Zangeneh
email: mzangeeh@ut.ac.ir

Peter Nielsen

Asadolah Akram
email: aakram@ut.ac.ir

Alireza Keyhani
email: akeyhani@ut.ac.ir

Agricultural Service Center Location Problem: Concept and a MCDM Solution Approach

Keywords: Attribute, Objective, Service, Location, Agriculture

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

1.1 Agricultural services Agriculture is the only major sector that uses the land surface as an essential input into its production function. This wide geographical dispersion of agricultural production has the important economic consequence; transportation becomes essential. Output must be transported for consumption by others and inputs; such as modern seeds, fertilizer, pesticides, or machinery, must be transported to the farm to raise output (Timmer, Falcon et al. 1983). Locating and sizing facilities to serve customers is an aspect of supply chain design that presents a number of challenges.

Customers are sensitive to the total cost of interacting with a firm's service, including time and access costs, in addition to price [START_REF] Pangburn | Service Facility Location and Design with Pricing and Waiting-Time Considerations[END_REF]. Several services can be provided to the whole agricultural supply chain (ASCH) includes: Input supply, mechanization, advisory, financial, inspection, business and etc. Unfortunately, the majority of supplier companies remains concentrated in urban areas or rural zones with large concentrations of commercial farmers. Therefore millions of poor farmers in rural areas without large commercial farmers do not have access to affordable agricultural inputs such as improved seeds, chemical fertilizers and other agro chemicals needed to raise farm productivity [START_REF] Dorward | The Malawi agricultural input subsidy programme: 2005/06 to 2008/09[END_REF]. The manufacture, distribution, repair, maintenance, management and utilization of agricultural tools, implements and machines are covered under the discipline of mechanization services [START_REF] Lak | An analytical review of parameters and indices affecting decision making in agricultural mechanization[END_REF]. The basic indicators for the success of a demand-driven advisory service system in agriculture are: farmers have access to and use agricultural advisory services [START_REF] Chipeta | Demand driven agricultural advisory services[END_REF]).

Agricultural service center location problem

Location problems (LP) in agriculture exhibit several features, such as their large scope and size, or the consideration of multiple and often conflicting objectives and, thus, implies high levels of complexity [START_REF] Lucas | Applications of location analysis in agriculture: a survey[END_REF]. Agricultural services have different characteristics than other public services such as health, police or etc., resulting in somehow different location selection procedure. The details of the current LP are shown in Table 1. There are three types of customers in this LP: Customer type A; The number of farms is very large. Any demand point has different types of service requirements. Any demand point may need several services at any given time. Candidate locations and demand points are the same (both are village points). For transport of any product it is enough to reach the demand point and the specific destination is not important in this LP.

Customers type B and C; they want access to the services at their location. This type may be available or not in some rural regions. The layout of the agricultural service center location problem (ASCLP) is shown in Fig. 1. Since there are several services, so more distance functions should be considered. In this paper, initially a Delphi Fuzzy Analytic Hierarchy Process (Delphi-FAHP) survey has been completed to extract the objectives and attributes of ASCLP. After that, using FAHP the local weight of location attributes for the LP has been computed. In this paper a stepwise approach to solve the ASCLP has been introduced. So the main aim of this study is to develop a multi-attribute decision making approach considering all aspects of this LP. The ideal solution for this problem should satisfy the main objectives, including simultaneously: maximizing service quality, service speed, and service centers profit and minimizing service cost. Widespread demand and the short available time to respond to them is the main distinguishing feature of current LP in compare to classical production systems.

Material and methods

Generally in this research, the FAHP has been used to assess the location indicators (both objectives and attributes) of agricultural service centers (ASC) [START_REF] Cho | Development of a new technology product evaluation model for assessing commercialization opportunities using Delphi method and fuzzy AHP approach[END_REF]. Using the FAHP method, the ASC location attributes has been prioritized based on objectives which have no differences. Another phase is however added to the framework to address the possible differences between objectives using Multi Choice Goal Programming, i.e. in the case that the priority of objectives is not the same. So this research has five main phases for assessing the location attributes (see Fig. 2). To select the best candidate location for ASC, some sub-attribute for each location attribute which can easily be measured has been defined (see Table 2). Distance to center of district (maximin): Proximity to more population and cultivated lands:

The number of families (maxi-max):

Total cultivated area (maxi-max):

Easy access: Total distance to other villages (maxi-min): Proximity to less tractor and machinery availability:

Number of tractors (maxi-min):

The value of mechanization efficiency (maxi-min): Proximity to more service demand:

The number of crop type cultivated (maxi-max):

The ratio of irrigated cultivated land to dried cultivated land (maxi-max):

Conclusion

In this paper a concept is presented and a fast and intelligible approach to solve an important agricultural facility location problem is developed. Agricultural services due to their dispersed customers' need to be optimally distributed and be able to supply all required services. The ASCLP also plays an important role in the efficiency of the ASCH. As can be understood from the results of this research, establishing an ASC network can lead to high quality services, low price and cost, fast deliver services, and sustainable service centers able continue their activity due to good estimated customer availability for their services. In this paper all aspects of the ASCLP have been considered, i.e. from the viewpoint of customers and service suppliers, and also technical suitability of candidate location for locating the service center on them. 5

Fig. 1 .

 1 Fig. 1. Layout of the agricultural service network

Table 1

 1

					. -Services description
	Characteristics				Service type
			Pre-		Production			Post Production
			Production
			Input	Mechanizat	Consulting	Financial	Consulting	Inspection	Business
			supply	ion	for farms
	Times for each	Single	Multiple	Multiple	Single	Multiple	Single	Multiple
	season
	Customer for	A	A	A	A	B,C	B,C	A,B,C
	each service
	Location	of	On farm	On farm	On service	On service	On site	On site	On site
	service				center	center
	demand
	Capacity		Unlimited	Limited	Unlimited	Limited	Unlimited	Unlimited	Limited
	Demand		Weighted	Weighted	Un-	Weighted	Un-	Un-	Weighte
	weight				weighted		weighted	weighted	d
	Time criticality	Yes	Yes	No	Yes	No	No	Yes

Table 2 .

 2 The attribute and sub-attribute for ASCLP

	Attribute	Sub-attribute
	Easy support:	Distance to center of county (maxi-
		min):

Fig. 2. Summary of proposed MCDM approach

The Eq. 1 can be used to calculate the value of each attribute for each candidate location:

Where; is the index of sub-attribute and is the normalized value of subattribute .

Since the unit of sub-attributes is different, to use them in formulations, they are converted to a normal range between one and zero before calculation of decision parameters using Eq. ( 2):

There are two type of attributes viewpoint of their preferred values, for example lower values of distances (e.g. ) are preferred, while higher values of total cultivated area is outranked to select such a candidate location for establishing an ASC. So, two separate formulations for maxi-min and maximax attributes (see Eq. 3 and 4) are defined. These equations will be used to compute the suitability of candidate locations for service center establishment.

Where; is the index of candidate locations and demand points, is the index of attributes of ASCLP, ∇ is the final score of candidate location for attributes where lower values of them are preferred (maxi-min), ∆ is the final score of candidate location for attributes where greater values of them are preferred (maxi-max), is the importance weight of attribute obtained from FAHP and is the value of attribute for candidate location .

After calculating the final scores of each candidate location, they will be ranked based on their score values. Then candidate locations which their score of maxi-min ( ∇ ) is lower than a threshold ( % of the selected range which identified by decision maker) and also for maxi-max scores ( ∆ ) which is higher than a threshold ( % of the selected range which identified by decision maker) will be selected and categorized in two set of and for each region of study (see Eq. 5 and 6).

In the last step of this approach, locations are selected which satisfy both selection conditions described previously. The aim of this study is to select at least one location for each region to open a service center, locations will be selected which is in the subset of and (see Eq. 7):

Where; is the index of region = (1, … ,7), is the set of candidate locations selected by using the minimization selection criteria, is the set of candidate locations selected by using the maximization selection criteria, is the final selected locations for region .

After finding the set of , one must propose the best location, because potentially more than one location satisfies the selection conditions. In classic location models the distance function is always used to select the best location. However in this case due to the characteristics of agricultural services, other attributes (technical aspects of the locations) supplementing the distance function should be imported.

3

Results and discussion

The approach proposed in this paper can quickly lead to the best nondominated solution, as it utilizes simple calculations and uses measurable attributes. To prove the capability of proposed approach, a case study of Iran has been done.

Using the defined set conditions, candidate location which satisfied the selection condition in each region has been selected and the numbers of selected candidate locations are illustrated in Table 3. ,3,4,5,7,8,9,10,14,17} {1,5,6,7,10,13,15,17,20 = 50% , = 50%

Usually in location decisions, distance and transportation play a very important role. So in this paper the role of maxi-min attributes is highlighted, which contains different distance functions, in selecting the best solutions.

As can be seen in fig. 3, in Boqrati regions, one of studied regions, there is no unique non-dominated solution. In Boqrati region, ∇ is lower than ∇ while ∆ is higher than ∆ . In this situation no one can dominate the other one. In such cases the decision maker has to propose all non-dominated solutions and this is the main disadvantage of proposed approach, while it has several advantages such as simplicity of calculations, the ability to consider inherent specification of agricultural activities.