Michael Quade
email: michael.quade@siemens.com

David Jentsch
email: david.jentsch@mb.tu-chemnitz.de

Egon Mueller
email: egon.mueller@mb.tu-chemnitz.de

Outline of a Methodic Realization of Construction Kits for Changeable Production Systems

Keywords: Production equipment, reconfigurability, flexibility, change enablers, construction kit

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

Introduction

Due to increased market competition, globalisation combined with changes in customers' requirements towards higher product variance and uncertain market prediction increase the pressure for changeability of production environments. This topic is discussed on different levels of factory and production system since years (e.g. [START_REF] Wiendahl | Changeable Manufacturing -Classification, Design and Operation[END_REF], [START_REF] Elmaraghy | Changeability -An Introduction[END_REF]. Nevertheless, on various levels sufficient changeability from industries perspective is yet not achieved. Uncertainties regarding the change criteria as well as the unspecific implementation of changing enabling measures often negate the positive impact of changeability. This results in increase in systems cost hindering dissemination of the changeability paradigms. This paper outlines an approach for a methodical design process for construction kit systems. A construction kit establishes building blocks (or modules) suitable to set-up a large (sometimes infinite) number of final products by combining these elements.

Assembly and manufacturing systems consisting of building blocks structured by the method fulfil requirements for flexibility and reconfiguration regarding defined change criteria. This is intended to provide systems with a fit of cost and changeability.

Realizing changeability on production system level is subject of a variety of publication (e.g. [START_REF] Koren | The rapid responsiveness of RMS[END_REF]- [START_REF] Ackermann | Approaches for planning and operation of adaptable factories[END_REF]). Its huge complexity combined with its impact on productivity makes this level highly attractive for investigation. Various system suppliers developed modular assembly systems, which are accepted by industry and provide limited changeability by rather fixed solutions. Nevertheless, applicable methods to guarantee deliberately designed changeability by considering the requirements for changeability in the design process of the construction kit system for realized machinery are yet not available to industry. In this initial publication, the fundamental flow when using the methodic approach from the perspective of production equipment manufacturers is given. Future work will further elaborate on details.

Overall goal

The approach for setting-up equipment with a construction kit designed by the presented methodology is depicted in Fig. 1: Optimally, for a new production process a system consists of building blocks from the construction kit, enriched by specific sub-assemblies. This allows setting up the system quickly and efficiently, while addressing special production processes accordingly. On the one hand, requirements for changeability (e.g. change criteria) define the design of specific sub-assemblies. On the other hand, they also lead to adequate structuring of the construction kit and defining measures in the blocks. Whereas in some blocks measures for e.g. high mobility are required, others may require a different set of enabling measures depending on their functionality. Implementing a change enabler in a particular building block is beneficial only in case that it is relevant for the criteria in conjunction with the function realized in the building block. Which measures need to be implemented in a specific building block depends on two facts:

1. The dependencies of the functions realized by the building block on relevant change criteria 2. The dependencies of the change enabler on the relevant change criteria 3

Review of relevant engineering domains

In this section, an overview shall be given regarding the relevant engineering domains being part of the presented approach.

Domain Design Methodologies

Different methodologies for product design had been established in the past years: besides traditional product development processes [START_REF] Grote | Entwicklung und Konstruktion[END_REF], [START_REF] Ehrlenspiel | Kostengünstig Entwickeln und Konstruieren[END_REF], special processes for modular structures and construction kits emerged [START_REF] Stone | Towards a Theory of Modular Design[END_REF]- [START_REF] Göpfert | Modulare Produktentwicklung[END_REF]. Generally, those processes include a functional analysis, being the basic step in defining the product. Components, modules or in the case of construction kits building blocks are allocated to one or several functions being its function carrier. This combination of the functional and physical description is the product architecture.

As construction kits are explicitly relevant in the approach presented in this publication, this specialisation of product design will be presented in more detail. A thorough overview including advantages, risks and opportunities is presented in [START_REF] Ehrlenspiel | Kostengünstig Entwickeln und Konstruieren[END_REF], [START_REF] Pahl | [END_REF].

Combining building blocks according to requested functions for a product allows fulfilling many different customers' requirements cost efficiently with a limited amount of block entities. Building blocks may exist in different variations, increasing the amount of final products even further. The nature of the analysed functions map to the building blocks accordingly: distinguishing mandatory, eventually mandatory and optional functions structures the building blocks analogously. Due to the compatibility of their building blocks, construction kits allow adapting the product and its functionality easily during the usage phase of the product.

Domain Production Systems

Basically, the term production includes all aspects for transforming raw material into final products. Auxiliary material as well as energy could be required for this transformation, which will take place on production equipment. Relevant publications considering production systems (the actual utilization of the construction kit) include [START_REF] Schenk | Fabrikplanung und Fabrikbetrieb[END_REF], [START_REF] Spur | Produktion[END_REF].

Two major areas being part of a production system include manufacturing and assembly systems. Manufacturing as defined in [START_REF] Din | Einteilung der Fertigungsverfahren[END_REF] includes all processes changing the material composition or spatial relationships of products. Although joining processes are part of this definition, assembly as defined in [START_REF] Din | Fertigungsverfahren Fügen[END_REF] is often examined separately.

A functional analysis of systems in assembly and manufacturing may indicate large similarities; nevertheless, the detail requirements differ tremendously (e.g. in the case of mechanical requirements on manufacturing systems to prevent vibration). These differences clearly influence the design of adequate building blocks and their relationships. For the method presented below, both production areas are considered.

Another fundamental aspect for the method presented is the production level, which is object of matter. According to [START_REF] Wiendahl | Wandlungsfähigkeit, Schlüsselbegriff der zukunftsfähigen Fabrik[END_REF], those levels range from a distributed factory network level, via site, segment, system, cell to a single station, realizing one particular process. As the presented method reflects on the realization of production equipment from building blocks, the relevant level is on cell and system level.

Domain Changeability

In a variety of publications, the concepts of change processes are subject of matter [START_REF] Pachow-Frauenhofer | Planung veränderungsfähiger Montagesysteme[END_REF], [START_REF] Elmaraghy | Flexible and reconfigurable manufacturing systems paradigms[END_REF]- [START_REF] Nyhuis | Wandlungsfähige Produktionssysteme[END_REF]. Although differences exist in the details, many aspects are identical:

• Change drivers, such as demand volatility and company strategy, trigger the need for a change process • Change criteria, such as product variant or volume (which often are defined vaguely), are the objective of the change process • Change enablers, such as mobility or compatibility (whose relevant measures according to a given change object are often vague), allow a system to be changed easily according to the change criteria Some authors employ the hierarchical view on production systems (levels as presented above) in order to discern different kinds of change. A frequently cited source [START_REF] Zäh | Symbiosis of Changeable and Virtual Production[END_REF] draws attention to different modes of adaption: flexibility and reconfiguration. The difference of either of these modes is depicted in Fig. 2.

Fig. 2. Comparison of flexibility and reconfiguration (according to [21])

Following this logic, the definition is as follows:

• Flexibility: Ability of a system to economically adapt to changes of requirements in a given corridor • Reconfigurability: Ability of a system to easily change the flexibility corridor

Outline of basic approach

The three major domains briefly reviewed above will be combined to describe a methodic approach for realizing a construction kit for assembly and manufacturing systems, which fulfills the requirements for changeability in an optimal fashion. The basic idea is depicted in Fig. 3:

Fig. 3

. Realization of changeable production equipment from building blocks

It needs to be highlighted, that detailing the methodic approach divides in two parts:

1. Required analysis for developing the method 2. Required analysis when using the method The content of this publication focuses on the second part, i.e. using the method from the perspective of a machine supplier or end user (e.g. production management). At this stage, only a general overview will be given. Simplified, the method consists of the following steps:

1. Assessing relevant change criteria 2. Defining change enablers in building blocks 3. Realizing change enabling measures in building blocks

Assessing relevant change criteria

As described above, relevant change criteria to consider in assembly or manufacturing systems realized by building blocks need to be captured. This is a tremendously important step, as only structuring a kit which addresses valid change criteria will lead to a system useful during its usage period.

If optimal reconfiguration of a system addresses an unnecessary change criteria, instead of increasing efficiency the opposite effect will occur (more expensive systems and change processes).1

Defining change enablers in building block

Based on the previous step of the method, identification of change enablers to be realized in certain building blocks is required. It is inefficient to realize the whole set of change enablers in each building block. Instead, enablers to implement in a specific building block are defined by evaluating the functions, which are fulfilled by the building block.

As outlined above, two facts need to be taken into account:

• The dependencies of the functions realized by the building block on relevant change criteria • The dependencies of the change enabler on the relevant change criteria 2 Exemplarily, a function Move and orientate product (intra-system handling) is heavily impacted by the change criteria product variant. Additionally, regarding handling functions, the enabler universality strongly relates with the criteria. Thus, the building block realizing the function shall include measure to implement universality 3 .

Realising change enabling measures in building blocks

As in the previous step a set of change enablers were defined for each building block, finally measures to be implemented during the design of the construction kit system will be chosen. This can be done in conjunction with the set of functions realized by the building block, and a measures catalogue is envisaged to guide designers during developing the kit.

To fulfill the enablers in the designated kit modules appropriately, this catalogue includes

• Specific design elements for building block (e.g. wheels to address mobility)

• Non-functional requirements for the building block (e.g. minimal weight of building block to address mobility) • Solution component for function (e.g. jointed-arm robot for pick and place process to address universality) • Description of interfaces between building blocks and environment (to address compatibility)

Implementing change enablers in building blocks at different extend may be relevant, allowing realizing a manufacturing or assembly system optimally according to the specific requirements of the application case. Although some generic catalogue en-2 Defining the relationship between change criteria and enablers depending on the system functions is amongst the major developments to be conducted in the future. 3 In case that weighting the change criteria will take place, clearly different change enablers in building blocks are rated, too. In case of contradicting enabling measures in one building block, this allows guidance in the design process.

tries will be given, its content largely depends on the domain, in which the system suppliers or end users business takes place.

Conclusion and outlook

In this paper an overview was given regarding the development of a method to support the developing of a construction kit system. A construction kit realized by this methodic approach, allows setting up manufacturing and assembly systems, which address crucial change criteria in an optimal, cost efficient way. This will strengthen the dissemination of changeable systems in industrial application.

Although the basic steps of the method had been defined, further work is required: Decomposing the functions of manufacturing and assembly systems is the first crucial step in construction kit design. One major difference to other products is the high degree of uncertainty on the requirements of a realised system. Major reason for this lies in an unknown assembly and manufacturing process and the characteristics of the processed parts. Generally, when structuring the kit, these products are unknown and only general constrains could be assumed.

A catalogue for implementing specific measures according to defined change enablers need to be defined. This includes the structure of the catalogue as well as the initial content. It is envisaged to perform an analysis of available catalogues presented in a number of publications. The relationship between crucial change criteria and relevant change enablers and measures for certain function carriers needs investigation. The linkage currently is rather vague and based on expert knowledge of industrial and equipment engineers. A more structured approach is required to formalize this aspect.

Fig. 1 .

 1 Fig. 1. Setting-up of changeable, kit-based production equipment

In further development of the method itself, weighting of change criteria against each other could be implemented, allowing emphasizing one relevant criterion over another one.


Matthias Heinicke
email: matthias.heinicke@ovgu.de

Framework for Resilient Production Systems

Keywords: resilience, functional map, production control, configuration

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

Introduction

Various influencing factors work on an existing production system. Accordingly, their impact is currently represented mainly by the effort of increased agility in recent years. This concept includes increased responsiveness to changing environmental conditions (customer demand, pricing pressures, and supplier development). Consequently, an anticipation and adaptation to the turbulence of the environment is imperative. However, the ideal measures to meet these requirements in various fields are discussed in literature intensively as regards resilience [START_REF] Bhamra | Resilience: The concept, a literature review and future directions[END_REF]. Resilience serves as means in order to cope with turbulence [START_REF] Wieland | The influence of relational competencies on supply chain resilience[END_REF][START_REF] Ivanov | Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaption of performance under uncertainty[END_REF]. However, its impact and effects require thorough empirical evidence.

In productions systems it is aimed at avoiding or at least reduction of dissipation. In this context, dissipation describes all actions or events in terms of perturbations, which affect efficiency and lower productivity. To overcome the consequent issues resulting from turbulence, various efforts are made within the scope of production planning and control. Particularly production control aims at reliable and robust production processes ensuring a rapidly response to changes and the achievement of production targets despite turbulent environmental influences. However, an accurate execution of control principles often fails due to a lack of understanding of the interdependencies and effects [START_REF] Schuh | Design of Production Control's Behavior[END_REF].

Therefore, this paper introduces an approach providing a framework for analysing the interactions of the individual tasks of production planning and control. Thus, transparency is increased and operational users are enabled to track the information flow in this regard. The approach additionally intends to reveal interactions between environmental conditions and organizational aspects of production systems in the context of ensuring a reliable operation of organizational processes. For this purpose, a hierarchical pattern is deduced to specify the roles and the corresponding tasks of production planning and control. The focus of the following approach is the optimization of the transformation process as regards the avoidance of operational and organizational errors.

2

Definition of resilient production processes

Agility and robustness in an organizational context

Robustness is the property of a system to resist change or external influences without adapting its initial stable configuration and continuously provide a desired output [START_REF] Ivanov | Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaption of performance under uncertainty[END_REF][START_REF] Wieland | Dealing with supply chain risks[END_REF]. Accordingly, a robust system is insensitive to disturbances of the system during its operation [START_REF] Zhang | Toward a resilient manufacturing system[END_REF]. In general, robustness refers to a proactive strategy, which prevents supplier-related volatility from restricting the functionality of a supply chain [START_REF] Wieland | Dealing with supply chain risks[END_REF] and thus production processes also. This concept implies resistance to anticipated changes [START_REF] Wieland | The influence of relational competencies on supply chain resilience[END_REF].

The possibilities to design a robust system configuration are limited to ongoing internal and external changes. In this context, path dependency (Codes and Hülsmann, 2013) is a crucial factor in terms of an appropriate determination of a specific agility level. In addition to the robustness requirement, agility enables a reaction to severe disturbances that cannot be endured by the robustness of a production system [START_REF] Ivanov | Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaption of performance under uncertainty[END_REF]. Identifying the correct manner and time of response to this kind of influence is crucial for the sustainability of production. The concept of speed is inherent to agility [START_REF] Wieland | Dealing with supply chain risks[END_REF][START_REF] Bhamra | Resilience: The concept, a literature review and future directions[END_REF]. Agility refers to a quickly and adequate response to unexpected changes and thus is similar to flexibility [START_REF] Ivanov | Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaption of performance under uncertainty[END_REF]. Therefore, a perception of current changes is imperative for a fast reaction to perceived issues [START_REF] Wieland | The influence of relational competencies on supply chain resilience[END_REF].

Resilience in an organizational context

The concept of resilience contains ecological, social, individual/psychological and organizational/socio-technical aspects [START_REF] Bhamra | Resilience: The concept, a literature review and future directions[END_REF]. Resilience always is the ability of a system to cope with change [START_REF] Wieland | The influence of relational competencies on supply chain resilience[END_REF]. In the context of production systems, the resilience domain combines two dimensions: agility, which expresses reactive strategies, and robustness including proactive strategies [START_REF] Wieland | The influence of relational competencies on supply chain resilience[END_REF]; thus it is similar to changeability [START_REF] Wiendahl | Wandlungsfähigkeit: Schlüsselbegriff der zukunftsfähigen Fabrik[END_REF].

The first one meets environmental changes with corresponding organizational action by re-configuring operating states of a prodution system rapidly and flexibly. The latter is based on forecasting and prevention. Thus, robust systems endure rather than respond to changes due to the preservation of a stable system configuration. As a result, resilience implies self-regulation and resistance to disturbances of a system [START_REF] Bhamra | Resilience: The concept, a literature review and future directions[END_REF].

In summary, operational and organizational robustness expresses the ability of a system obtaining its functionality even under fluctuating environmental conditions. With respect to the resilience definition, both aspects, robustness and agility, have to be considered in terms of a closed-loop control system as regards production systems. Minor perturbations endured by the robustness of the system differ from grave disturbances that require a rapid re-configuration of the production system based on its agility property.

3

Resilience by the means of production control

Influencing production systems by production control

In addition to production planning, the main factor to influence existing production systems represents production control. Production planning produces an image of the desired target state by the generation of default values for manufacturing and assembly. Empirically, the target state will not occur due to uncertainties as well as disturbances during production. In this context, production control ensures compliance with the desired management objectives (lead time, capacity utilization, inventory, on-time delivery) by intervening in current production processes. Based on the production program, results of the respective upstream planning level within the operational volume planning, scheduling and capacity levelling represent the input data for the next item. This is called cascaded loops [START_REF] Nyhuis | Produktionsregelung mit logistischen Kennlinien[END_REF]. Figure 1 illustrates a cascaded loop in production, which represents a hierarchical pattern of individual functions. For this purpose, the respective input and output variables of the individual viewing areas are represented and analyzed in their dependency and interaction (cf. Section 4.2). Unforeseen dissipation in a production system necessarily effect that its targets cannot be met. Consequently, in these cases an intervention at an operational level is required to ensure compliance with deadlines and other objectives. It is assumed that certain interactions between the various functional areas exist within production systems [START_REF] Zülch | Systematisierung von Strategien der Fertigungssteuerung[END_REF]. In this context, the kind of order release, the sequence of produc- tion orders and the batch size (order generation) are crucial parameters [START_REF] Nyhuis | Konfiguration der Fertigungssteuerung[END_REF]. The combination of these parameters enables specific control methods (e.g. kanban, load-dependent order release, etc.). According to the presented task groups, following strategies in the production control can be distinguished:

• strategies for determining batch size, • strategies for resource allocation (selection and scheduling), • strategies for sequencing and • strategies for order release.

A reasonable combination of these strategies is crucial. In this context, for employees both informal and formal degrees of freedom apply in exercising their functions.

Deficits in production control

In contrast to the deterministic effects assumed by existing approaches in terms of production control configuration, the influence of socio-technical aspects on the performance of control principles is nearly neglected [START_REF] Schuh | Design of Production Control's Behavior[END_REF]. In this context, various problems in the accurate implementation of control principles result from the diversity of choice, a lack of communication and a deficient comprehension of the effects of decisions. Therefore, degrees of freedom as a function of the applied control method, the specific task within the production control as well as in consideration of the respective groups of people (production planners, foremen, and workers) necessitate a clear definition in terms of the hierarchical structure of control processes. It is assumed that within production systems people adapt processes and its control. Regardless of the existing configuration of a production system in terms of robustness and agility, all the persons involved affect the level of its resilience.

Production planners primarily control the flow of information as regards production program. Task of foremen is the organization of the production processes. Workers will perform the actual manufacturing techniques and processes. In this context, it is assumed that the formal rules of the production control in terms of their effectiveness are limited in multistage production systems. This entails other organizational rules for trapping occurring negative effects that cannot be confined by common control principles. Therefore, identifying an appropriate operating point regarding the balancing between the degree of freedom of the employees and the deterministic execution of control principles is imperative depending on factors of influence and disturbances.

4

Approach for resilient production systems

Closed loop of the tasks of production control

Production control serves to determine or rather anticipate derivations and to make the necessary adjustments accordingly. A proper balance between stability in terms of robust processes and dynamic adaption based on the agility of the system can be realized by appropriate principles of production control [START_REF] Schuh | Design of Production Control's Behavior[END_REF]. Thus, pro-duction control and adequate decisions of people implement the concept of resilience within production systems. For an expedient adaption by decision, it is essential to identify the cause-effect relationships for the individual operational targets in advance. Thus, this enables a positioning of the production in the tension of operational objectives.

A robust order processing in compliance with the logistic objectives therefore requires the mapping and analysis of all relevant processes and their interdependencies (Schuh and Stich 2, 2012). The efficiency of production is determined by a variety of factors. In this context, order generation and order release represent possibilities to influence the manufacturing system directly. In contrast, organization and availability of resources are two aspects that individual persons hardly can work on. Those can be characterized as random and thus are not directly affected by the decisions of the production control. The production resources include equipment, personnel and material. A lack of availability leads to a decline of the efficiency and the intrinsic flexibility of production. A reduction of availability even by disturbing environmental factors is possible. The organization provides a restrictive framework for the objectives, which exists before the implementation of production control. Predominantly, the workplace layout, the legal guidelines, the process principle of the production (operational organization) as well as the flow of information are determined as the most important factors that affect the attainable targets in advance.

Functional map of production control tasks

During a production process, variations and disturbances that complicate the control of production typically occur. Plan deviations appearing at different points of the production processes can be of various kinds and entail different consequences. In this context, robustness is measured in form of deviations as regards the four logistic objectives mentioned above. In order to protect against deviations, a high transparency in the planning and control processes is necessary. The functional map (Figure 2) represents such a tool depicting the individual tasks of production planning and control dependent on the hierarchy levels. The concatenating representation of the entire planning and controlling activities includes the appropriate dissemination of information. This tool depicts a self-contained and interactive structure. Thus, effects of a re-configuration of the flow of information and the scope for decision-making are assessable. Thereby, the functional map allows for the determination of the causes of problems as regards occurring deviations or faults. In addition, this facilitates coping with such disturbances by adopting effective measures quickly. The feedback of the results obtained in terms of data is very important to initiate improvements in a timely manner. Moreover, it is crucial that an adequate communication between the hierarchical levels proceeds (Schuh and Stich 1, 2012). As a result, the functional map systematically reveals both potentials and capabilities regarding the ideal interaction of the tasks of production planning and control. Thus, it supports the operational process of decision making.

Implementation of robust processes

A robust configuration of manufacturing systems needs to meet several requirements. In this context, it is aimed at an accurate and controllable execution of tasks as regards fault prevention by anticipation (occurrence of errors) as well as reducing the effects of disturbances due to preparedness (coping with errors). Moreover, robustness implies various operational objectives. Hereby, the focus is on a comparably qualitative and quantitative yield of repetitive production processes. This requires a precise definition of all operations and a reliable understanding of one´s role within the hierarchical structure of the production system. In addition, operational key figures should be insensitive to disturbances of a production based on robust processes. In this context, robustness of production systems is assessable by the degree of change in the achievement of objectives in terms of production-related indicators as a function of environmental factors [START_REF] Jodlbauer | [END_REF]. This implies that the production program is fulfilled in spite of unforeseen variations reliably.

Furthermore, a clear understanding of the course of processes within in the production system is imperative. Apart from the production control activities, all inputoutput-relations of the execution of orders have to be considered. For that purpose, the functional map reduces the complexity of the entire production planning and control by a classification of individual tasks with respect to hierarchy levels. Thus, the effects of respective measures can be assessed easily. To realize insensitiveness to disturbances, potential fault causes has to be determined first. Subsequently, an identification of potential effects of disturbances allows a definition of the scope and manner of measures for each hierarchy level. At the backdrop of the implementation of robust processes, this contribution offers an approach managing all issues related to the identification and elimination of the effects of disturbances. In consideration of each operational objective, the following steps have to be passed:

• Identification of influences on the production system, • Classification in disturbance variable and control variable, and • Prioritization as regards controllability as well as relevance.

A cause and effect diagram supports the identification of influences. Hence, all potential influences are collected and well-structured. Due to the interdependencies of management goals, the analysis in terms of influence on production systems has to be conducted for each objective separately. After collecting all influences, identified issues should undergo a classification. In consideration of the amount of influences, it is differentiated between control and disturbance variables. The crucial difference is the potential exertion of influence. Disturbance variables represent external or environmental aspects. In contrast, some issues can be controlled within a production system. Lastly, every detected influence is assessed as regards controllability and relevance. The first dimension represents the differentiation between control and disturbance variable. The relevance aspect is an indicator for the effects of corresponding influences. It is assumed that the total time an influence factor impedes production processes represents an adequate key figure for relevance. Prioritizing influences enables operational users to take the ideal measures. With respect to different hierarchy levels, the individual scope of action is crucial in this regard.

Beyond robust production systems

In consideration of organizationally robust processes, a limitation of the scope of action and decision of the individual process owners is required regarding the sequence and priority of individual orders. This consequently affects the departmental job control in terms of self-organization and self-optimization [START_REF] Hartmann | Merkmale zur Wandlungsfähigkeit von Produktionssystemen bei turbulenten Aufgaben[END_REF]. Thus, if the robustness is no longer sufficient, the agility properties are claimed. Moreover, an establishment of a defined corridor of flexibility is necessary [START_REF] Terkaj | Focused Flexibility in Production Systems[END_REF]. In this context, various aspects of changeability come into consideration [START_REF] Wiendahl | Wandlungsfähigkeit: Schlüsselbegriff der zukunftsfähigen Fabrik[END_REF]. To meet the requirements of a turbulent environment, an examination of the effects of structural and organizational aspects of agility is imperative. Furthermore, the focus is on recognizing the need for changes of production systems in terms of structure (elements and resources) or organization (organizational structure and operational organization).

Conclusion and future work

In summary, this paper introduces an approach that combines socio-technical aspects and deterministic interactions of the tasks of production control. Due to the fact that several operators are unable to cope with complex control principles, their in-volvement is imperative in order to estimate the effects of the freedom of choice as regards the individual tasks of production control. The functional map enables an analysis of key levers influencing production systems. Based on the hierarchical pattern possibilities of intervention are drawn systematically, so that operational users will be in a position to act independently within coordinated control loops. Production control executed by operational users enables an adaptive balancing between robustness and agility and thus represents the implementation of the concept of resilience within production systems. However, the more intricate the production system and its organization are, the more difficult the application of this approach seems to be. In this regard, additional research is necessary.

Fig. 1 .

 1 Fig. 1. Cascaded closed-loop production control.

Fig. 2 .

 2 Fig. 2. Functional map of the basic tasks of production control.


Geovanny Osorio

Lorraine Trilling

Thibaud Monteiro

Frédéric Albert

Pierre-Alain Millet
email: pierre-alain.millet@insa-lyon.frfrederic.albert@cerclh.com

A method to create a process reference model adapted to heterogeneous organizations

Keywords: process reference model, value chain, BPMN, process approach

In some cases there is a need to compare similarities between different process models. For example, given two organizations that offer products or services with similarities, we would like to know how much they differ from each other. This paper aims at defining a method to create a process reference model, which can be adapted to heterogeneous organizations. In order to construct this, we propose a method using BPMN language and based on the value chain approach. This process reference model issued from heterogeneous structures could enable managers to achieve a more comprehensive and unified vision of their organizations, and promote a coherent monitoring of activities. This work presents a unified method of communication between managers and organizations as well as throughout the structures comprising the organization. It provides a basis to establish an alignment of different structures in term of goals and business process following two axes.

Introduction

The constant evolution of business world lead organizations to change their perimeter (acquisition, separation, reorganization, etc.). Those changes require an aggregation of heterogeneous business process with a consistent management approach. So that, companies need to react by improving their knowledge about the process they performed as well as the new process to integrate. The fact of knowing the process and capitalize them by using a Process Reference Model (PRM) allows managers to cope with frequent and rapid changes in the business environment [START_REF] Van Der Aalst | Process-aware information systems: Lessons to be learned from process mining[END_REF]. In this way companies need spontaneous solutions which can hardly be derived from wavering development [START_REF] Mintzberg | In a Dynamic Business World, Phrases Such as 'Strategic Planning[END_REF]. Furthermore industrial and service sectors are pushed to the creation of process, which can respond to a customer demanding more and more customization of products and services [START_REF] Hau | Getting ahead of your competition through design for mass customization[END_REF].

Managers witnessed many attempts to improve the process-awareness in order to achieve a more comprehensive and unified vision of their sector, and to promote performance management organizations internally. A PRM should come from a button-up approach around the different types of organizations, in order to identify the variables that can affect the elements of the system as well as the specific activities of each type of organization [START_REF] Ulrich | Evaluation of reference models[END_REF]. If operational process models are unknown, structured top-down approaches, as conceptual methods, such as CIMOSA [START_REF] Vernadat | Enterprise modelling and integration: principles and applications[END_REF] or GRAI [START_REF] Kromm | Reference decision models in the medico-social service sector[END_REF] cannot be useful.

The construction of a PRM comes from a collaborative and multidisciplinary work that seeks to develop a common language for operational, tactical and decision-makers at all levels. The combination of a PRM with management tools like balanced scorecards can play a crucial role in the development and structuring of the common vision, as experience shows [START_REF] Kaplan | Transforming the Balanced Scorecard from Performance Measurement to Strategic Management: Part I[END_REF][START_REF] Martello | Implementing a Balanced Scorecard in a not-forprofit organization[END_REF]. The aim of this paper is to propose a method to obtain a PRM. This method is illustrated by an application in the socio-medical sector, but could be used in other fields. This article is divided into 5 parts. Section 2 highlights the necessity for the construction of a PRM, supported by a literature review. Section 3 explains the proposed method for the construction of a PRM. Section 4 gives the application of the proposed approach in socio-medical sector, which requires such reference model before being able to monitor activities. Finally, section 5 provides a conclusion and gives some perspectives on this research.

Related works

The emergence of computer technology and the increase in data flows in the last two decades leads the organizations to improve its knowledge about the processes they perform using information systems. Early examples of these, seek to organize a process as a defined sequence of tasks. Such systems were called Workflow management systems (WFM) [START_REF] Van Der Aalst | Workflow Management: Models, Methods, and Systems[END_REF][START_REF] Jablonski | Workflow Management: Modeling Concepts, Architecture, and Implementation[END_REF]. At the present, these systems have evolved with a greater range allowing the processes simulation and monitoring of the activities [START_REF] Wohed | On the Suitability of BPMN for Business Process Modelling[END_REF][START_REF] Wynn | Business process simulation for operational decision support[END_REF][START_REF] Rozinat | Workflow simulation for operational decision support using design, historic and state information[END_REF]. These ones are now called Business Process Management (BPM). Either the WFM or BPM, both systems aim to support operational processes. Process modeling allows structuring and visually representing activities of an organization. It facilitates communication around the processes between different actors and enhance the understanding of the functioning of the company [START_REF] Ulrich | Evaluation of reference models[END_REF]. The creation of a PRM for an organization raises several issues. It involves (1) the gathering and analysis of currents models, (2) a field study in a sample of different structures to complete the analyzed information, (3) a modeling of performed activities in each structure using a common language, (4) a comparison of the modelled activities with good professional practices and the respect of legal standards, (5) an identification of the main similarities and differences to retain the models or model's fragments [START_REF] Rosa | Business process model merging: an approach to business process consolidation[END_REF]. Thus, using these fragments, we must create a PRM in which all the fragments can fit while avoiding duplication [START_REF] Fiorèse | Découvrir et comprendre l'ingénierie système[END_REF]. Indeed, there are several invariants in the set of structures that led to the institutionalization of successful process to be re-used. In this context, experts have developed generic models allowing a logical progression for resolution of problems. Our aim is to create a model that subsumes a collection of process models from each structure (representing variants of the same underlying process) with the aim of replacing the variants with the merged model. The challenge here is not the modeling of processes from each structure, but the creation of a method to integrate different activities from a set of heterogeneous structures which is not specifically proposed in literature.

Method for construction of the process reference model

In order to achieve one PRM which can be transposable to different kind of organizations, we proposed a method divided in three main steps Fig 1 . The first step aims to collect information in the field in order to identify activities carried out in different organizations and the manner in which these are performed. The second step seeks to establish the working perimeter conditioned by good professional practice and the respect of legal standards in the field of the application, which should be integrated in the PRM. Finally, the goal of the third step is to compare and combine different existing models using a set of aggregation and transformation operators, which would lead to construct an activities reference model matching with all types of organizations and taking into account existing references.

Fig. 1. Proposed method to create a process reference model

Current state modelling

To gather the necessary information enhancing the knowledge of production flows and activities carried out by the organizations, it is necessary (i) to use reliable sources describing the process, as existing official procedures (flowcharts respecting the legal framework and referential quality) and (ii) to collect information from people in the field (information extracted from interviews with several professionals in different structures of the organization). This set of information should be formalized for each structure using a simple and universal language allowing the modeling of business processes easily readable and understandable for all staff involved. We use a process map inspired by the Value Chain developed by Michael Porter [START_REF] Porter | Competitive advantage: Creating and sustaining superior performance[END_REF] and a process approach [START_REF] Brandenburg | L'approche processus[END_REF] structuring activities around the three types of processes: Value-added process, Control process, and Support process.

To detail the processes as a flowchart of activities, we used the BPMN [START_REF] Jablonski | Workflow Management: Modeling Concepts, Architecture, and Implementation[END_REF]. This language provides an easy notation that is intuitive for both technical users and business users. The Fig . 2 illustrates the structure of the map and the principle of the processes decomposition (level 0) in an ordered organization of activities (level 1).

Fig. 2. Map of business processes and process detail with BPMN.

Contextual exigencies for a process reference model

Even if professionals consider that modeled processes match to the processes performed in their structures, the modeling of these must drive to an assessment of activities. This is necessary to evaluate whether these activities actually correspond to good professional practice and assess if they are in accordance with the regulations of the sector. The formalization of existing practices helps to highlight deviations from best practices and conducts to the creation of a shared model.

To perform this step, we used a working group (composed by professionals of each modeled structure, and the deputy general director) with the aim to present the different practices carried out. This allows reaching a consensus on the practices to be used in the PRM as well as the way to name each of them.

3.3

Operators for the construction of a process reference model

The characteristic of a reference model is that it enables an entity or structure to recognize itself within a common framework which is not however specifically designed for this entity. The PRM is issued from current state models compared to the contextual exigencies. This construction has to minimize the gap between starting models and the final one. A particular attention is paid to integrate the constraints imposed by the consensus around contextual exigencies (best practices, legal standards…). Furthermore, it is necessary to define the granularity level of the PRM; this one would be able to fit the different structures, while including specificities at a finer level of detail. It has to be able to report a board spectrum of activities. The PRM has to be precise enough to clearly describe the activities carried out in each structure which are inherently heterogeneous.

The construction of a PRM allows revealing similarities in the activities of each structure. However, the knowledge of best practices to improve is not acquired. So that, any improvement in the performance necessarily involves a practice's identification [START_REF] Jacques | Amélioration de la performance industrielle à partir d'un processus Référent[END_REF]. Models must be built by the same team, in order to have the same interpretation. Hence, process models should be built one after another. Generally, the designer of a PRM might not foresee all configuration options that are required in actual practice. Hence, additional model adaptations might become necessary when configuring a particular process variant [START_REF] Manfred | Enabling flexibility in process-aware information systems: challenges, methods, technologies[END_REF]. In order to construct the PRM we propose to use specific operators. Those operators translate an analysis of the similarities and differences between starting model (Fig.

Field of application

In order to validate the proposed method, we conducted a work in an important medicosocial foundation from Rhône-Alpes Region named OVE. Medico-social sector witnessed many attempts to achieve a more comprehensive and unified vision, and to promote performance management structures internally. Furthermore, on a work carried out by the National agency to support the performance (ANAP) express that "the medico-social structures produce little or no data about his performed activities" [START_REF] Anap | Piloter la performance dans le secteur médico-social: Expérimentation d'un tableau de bord de pilotage[END_REF]. This sector is a whole wide range dedicated to the support of vulnerable people who are the users of the system (people in precarious situations, children or troubled teens, disabled persons and the elderly). Due to the heterogeneity of the sector and the large number of structures (around 8200 in France) this research must lead to a PRM, which takes into account several structures for centralized decisions.

Results

Using seven type of structures, we constructed seven different models taking into account the specific characteristics of activities carried out in each type of structure, afterwards, we classified these activities according to the three type processes: valueadded, support and control processes. This model "all terrain" has allowed to adopt a common language, shared from top management to professionals, for naming all the activities performed by the structures.

Using the models process divided in two levels (Fig. 2) and the specific operators (Fig. 3), the construction of a PRM is a gradual process of comparison between the different starting models (Fig. 4).

The first reference model serves to identify the first activities as well as a first "sketch" of a process model. With the next models, we created a model that subsumes a collection of process models (typically representing variants of the same underlying process) with the aim of replacing the variants with the merged model. Then activities were reorganized into a common process that can be adaptable to the set of organizations using the operators proposed. Besides serving as a state of the art and show a first fieldwork of the functioning of the organizations, this process allows to identify the specific activities performed, as well as activities that do not correspond to good professional practice or that are not supported in the policies of the foundation. Making this comparison Fig. 4, we can observe that after the fifth modeled structure, specific processes found begin to decrease.

Fig. 4. Gradual process of comparison between the different starting models

This means that the model begins to converge and all the identified processes can describe almost entirely the process of the last two structures. This is however not sufficient for the construction of a PRM, which requires the continuous support of the general direction. This last is necessary to arbitrate the processes to be integrated into the reference model, as well as the flow charts of process information and samples of good practices. This PRM represents the starting point for activity monitoring and performance assessment while providing a unified method of communication throughout the structures of the foundation in order to establish an alignment in the set structures goals.

4.3

Validation of process reference model using a software for activities monitoring: Gestactiv

In order to check whether the reference model is adapted to a set of heterogeneous structures we proposed a software to collect information related to the activities (formalized in the process reference model) that are performed in all structures. This survey information provided by this software enables us to confront the selected PRM with the activities performed by the set of structures. The evaluation of the PRM was based in a real size experimentation. Nine "pilot" structures with almost 150 professionals and 350 users over a period of three months have entered their activities in the system according to the reference model. This has led to validate the ability of constructed PRM to reflect the day-to-day of professional activities. With the prospect of a generalization in 2015 to 65 structures from OVE Foundation, the PRM has proved robustness with respect to the reality of the implementation in the field activities. This is reflected by the significant integration of the tool in professional routines. The main indicators were the capture rate of foresight activities (43% of activities scheduled at least 3 days in advance), the degree of validation following the completion of them (95%) and the rate of implication of professionals to capture the positive returns.

Conclusions

The bottom up approach facing with contextual exigencies meets the need for a better knowledge about how an organization works. This approach provides a structured representation of the activities carried out within structures on which an activity monitoring tool is based. An experiment in the medico-social sector has validated the proposed approach.

For future works, the measures collected from the combination of process reference activities and monitoring tool will offer the possibility to answer questions at the operational level related to the user assistance and the consumption of resources. These measures establish a basis on which improvements can be developed: Balanced Scorecard (BSC), tools and methods to assist in the management organizations works. This approach combined with a tool allowing the lift up of the activities will translate strategy into operational action plans at all levels of the organization by providing information on the performance of each entity, useful to control them. These examples could open paths to numerous development opportunities in this sector.

Fig. 3 .

 3 Fig. 3. Operators for the construction of the process reference model


Eric Innocenti
email: eric.innocenti@univ-corse.fr

Paul-Antoine Bisgambiglia
email: bisgambiglia@univ-corse.fr

Dominique Urbani
email: durbani@univ-corse.fr

Activity-Based Discrete Event Simulation of Spatial Production Systems: Application to Fisheries

Keywords: Spatial Production Systems, Cellular Automata Model, DEVS. 1

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

Introduction

Application of long-term effective policies in natural production systems needs relevant decision-making indicators and quantitative assessments. This issue is critical in fisheries management facing the decline of world productivity due to pressures from overfishing, habitat change, pollution, and climate change [START_REF] Klemas | Fisheries applications of remote sensing: An overview[END_REF]. It is possible to develop software tools dedicated to the election of strategies for sustainable fisheries management (fishing gear regulation, stock rebuilding projects, etc.) [START_REF] Chen | Modeling the effects of fishery management and marine protected areas on the Beibu Gulf using spatial ecosystem simulation[END_REF] - [START_REF] Johnson | Applying Computer Simulation Models as Learning Tools in Fishery Management[END_REF]. This work remains part of a computer tool designed to help fishery managers. The main goal is to assist in the definition of a responsible fishing policy in Corsica. We discuss the opportunity to develop a modular framework using the Discrete EVent system Specification formalism (DEVS) and the activity concept. We plan to develop software to simulate coastal fishery policies to improve the management of resources. We use a Cellular Automata Model (CAM) for spatial representation and taking into account the activity concept [START_REF] Innocenti | Randomizing activity in fire spreading simulations[END_REF]- [START_REF] Zeigler | Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems[END_REF]. CAM are usually considered as an aggregation of discrete components with local interactions. The model of fish population growth is inspired by the literature [START_REF] Fox | An exponential surplus-yield model for optimizing exploited fish populations[END_REF], [START_REF] Pella | A generalized stock production model[END_REF], and spatially explained with a CAM. The techniques developed are discussed, including challenges, perspectives and limitations. Section 2 reminds the theoretical concepts and section 3 details the software framework before presenting an illustrative application (section 4). The last section before the discussion and concluding remarks gives our simulation results, we want to show that when CAM are jointly used with the DEVS formalism [START_REF] Muzy | What is new with the activity world view in modeling and simulation? using activity as a unifying guide for modeling and simulation[END_REF] and the activity concept, both the model and the simulator objects efficiently exploit the numerous spatial components.

Backgrounds

Cellular automata modelling

Cellular Automata Model (CAM) are an evolved form of von Neumman's Cellular Automata (CA) [START_REF] Zeigler | Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems[END_REF], [START_REF] Muzy | What is new with the activity world view in modeling and simulation? using activity as a unifying guide for modeling and simulation[END_REF]. These computational models are well suited to capture essential features of spatial systems in which large-scale behaviour arises from the collective effect of a great number of locally interacting simple components. As CAM inherit their basic characteristics from CA, they get the benefit of emerging phenomena.

From a simple and deterministic local rule, CAM can generate a surprisingly complex global behaviour. The spatial interactions between cells are key elements of such models. At each time step, a cell changes state according to the local rule linked to its neighbourhood. The successive states of the cells follow a state trajectory, in the discrete time base. The state of the CAM is the aggregation of the cell states. Over the last years, the study of IT implementation of CAM considerably increased [START_REF] Podrouzek | Stochastic Cellular Automata in Dynamic Environmental Modeling: Practical Applications[END_REF][START_REF] Guinot | Modelling using stochastic, finite state cellular automata: rule inference from continuum models[END_REF][START_REF] Krougly | A stochastic model for generating disturbance patterns within landscapes[END_REF][START_REF] Zeigler | Continuity and Change (Activity) Are Fundamentally Related in DEVS Simulation of Continuous Systems[END_REF][START_REF] Qiu | Spatial activity-based modeling for pedestrian crowd simulation[END_REF]. The determination of active components in this kind of model is also an essential aspect to improve the computing model elegance and to guarantee acceptable simulation times. In the next subsection we give more information on the activity concept. .

Activity concept

The activity concept is usually formulated as a measure of change in system behaviour [START_REF] Muzy | What is new with the activity world view in modeling and simulation? using activity as a unifying guide for modeling and simulation[END_REF], and is used to concentrate computations on the high rates of change [START_REF] Zeigler | Continuity and Change (Activity) Are Fundamentally Related in DEVS Simulation of Continuous Systems[END_REF], [START_REF] Qiu | Spatial activity-based modeling for pedestrian crowd simulation[END_REF].

Framework

In this section, we give a brief description of our simulation software. Our approach is not based on Cell-DEVS model aggregates the entire

Activity-based Discrete Event Simulation

The use of the activity lation procedure based on events. described, it is appropriate to use a discrete event system th through the triggering of events based on message established on the basis of gregated in an AM. It interacts with the simulator with ports and me rimeter of the active area is updated when an internal event occurs (

Object implementation

Objects ensure modularity, genericity, and reusability and comes with well established and the interconnections are presented in figure 1. tion part, it is conventional in DEVS. The right part shows the encapsulation mech nism of CAM in an atomic model.

the external state transition function, where Q = {(s,e) | s in S, 0 ≤ is the elapsed time since the last state transition; δ int S⟶S the internal state tra Y the output function and t a :S⟶R + 0⟶∞ the time advance fun describes the behaviour of the system. A CM describes the composition models, i.e AM or CM. We have developed our DEVS simulator inspired by pythonDEVS [START_REF] Bolduc | A modeling and simulation package for classic hierarchical DEVS[END_REF] and DEVS-Ruby [START_REF] Franceschini | DEVS-Ruby: a Domain Specific Language for DEVS Modeling and Simulation (WIP)[END_REF], and in this work we use ing into account the activity concept previously described.

In this section, we give a brief description of our simulation software. Our approach is DEVS [START_REF] Wainer | Application of the Cell-DEVS Paradigm for Cell Spaces Modelling and Simulation[END_REF], but on the multi-component concepts [START_REF] Zeigler | Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems[END_REF]. One atomic the entire environment.

based Discrete Event Simulation concept implies the prediction of next active cells from a sim lation procedure based on events. While applying these criteria to the CAM previously described, it is appropriate to use a discrete event system that cause transitions through the triggering of events based on message exchanges. For that, the model is established on the basis of DEVS formalism. The CAM used for the simulation is a . It interacts with the simulator with ports and messages. The p rimeter of the active area is updated when an internal event occurs (δ int ).

Object implementation

Objects ensure modularity, genericity, and reusability and the DEVS formalism comes with well established discrete event simulation algorithms. The main objects and the interconnections are presented in figure 1. The left part describes the simul tion part, it is conventional in DEVS. The right part shows the encapsulation mech nism of CAM in an atomic model. ≤ e ≤ t a (s) the internal state tranthe time advance funcdescribes the composition . We have developed our DEVS simulator we use the concept previously described.

In this section, we give a brief description of our simulation software. Our approach is . One atomic active cells from a simupreviously at cause transitions exchanges. For that, the model is used for the simulation is agsages. The pe-

DEVS formalism

The main objects The left part describes the simulation part, it is conventional in DEVS. The right part shows the encapsulation mecha-

Illustrative application: Fox model adaptation

To study the effect of the fishery on biomass and to estimate the Maximum Sustainable Yield (MSY) in each cell, we implement a Fox Surplus-Production Model (SPM) [START_REF] Fox | An exponential surplus-yield model for optimizing exploited fish populations[END_REF], [START_REF] Pella | A generalized stock production model[END_REF]. The MSY corresponds to the maximum capture that does not exhaust the fish population. SPM models are advantageous as they allow to simply estimate both fish stock and fish rates. Moreover, they collect in the same production model the evolution rules of the biomass, the mortality and population growth. Thus, they only require temporal series of stock abundance indexes that eases their use. The Fox SPM [START_REF] Wainer | Application of the Cell-DEVS Paradigm for Cell Spaces Modelling and Simulation[END_REF] is a particular case of the Pella and Tomlison model [START_REF] Pella | A generalized stock production model[END_REF]. It allows characterising the fish population x according to a set of coefficients, as:

( ) = ( ) × × × (1 - ) (1)
( ) = × (1 - - * ( / )) × (2)
Where is the fish rate, the environmental variability, r the population rate, K the maximum capacity of the environment, p an asymmetric coefficient.

In the Fox's model, p is near 0; this allows getting an asymmetric fish stock evolution. The model grows up to a K maximum constant, and it decreases keeping an asymptotic course. These models are relatively simple and well documented in literature. This model is implemented in each cell of the CAM with K=90.

SPM Model settings

The environmental map is composed by reef and fishing areas: the reef areas model the life spaces of the fish. They have a strong attraction capacity as they feed the fish. The attraction coefficient aCff allows setting the attraction capacity of the cell. The value of aCff of each cell depends on the distance between the cell and the location of the reef area. The attraction capacity of the cells around the reef areas is randomly calculated at the initialization. The fishing areas are space with fishing production determined by the fishing rate . In the original Fox's model, the fish rate is applied to all the cells of the space. In order to clearly study the effect of fishing, we only apply it to the cells set as fishing areas. During simulation, we apply the transition rule in charge of the fish population evolution, and the propagation rule in charge of the movement of fishes in space (Fig. 2).

1: c: current cell, nCell: neighbouring cell, x: fish, K: max cell capacity, aCff: attraction coefficient 2: If c.nbFish > 0 3: For each n in nCell { 4:

. If c.aCff < n.aCff Or c.nbFish > (K-20) Then 5:

#Different attraction da 6:

da <-n.aCff -c.aCff 7:

#Moving potential 8:

pd <-int(K -c.nbFish) 9:

. #Small attraction difference, little migration Fig. 2. CAM Propagation phase algorithm.

Simulation example

We compare the fish population evolution in fishing areas and in an area where fishing is prohibited. Thanks to the activity concept we can simulate a domain of 350x250 cells. We experiment a reef area with maximal attraction (1.0). The attraction around the reef area is randomly determined between 0.5 and 0.9. These coefficients have been empirically defined. The number of active cells evolves during the simulation process. The main values of the model's coefficients are detailed in table 1.

Ocean Reef-Crown Fishing area (10 cells) Fishing area t=40 (83 cells)

Population growth r 0.2 0.9-0.75 0.9 0.9

Cell capacity K 90 90 90 90

Fish rate 0.1 0.4-0.45 1 2.5

Attraction aCff 0 0.5-1 0.5-1 0.5-1

Table 1.

Model's setting.

The simulation results point out the fish quantities evolution in the simulated time.

Results

The curve presented in Fig. 3 shows the activity of cells. It decreases slightly with a stronger tendency to t = 40, corresponding to the beginning of overfishing. Trawlers have emptied cells and they have become inactive. Figure 4a represents the evolution of the activity, at t=0, 41, greatly influenced by the reef areas and their attraction. Figure 4b shows the popul tion fish at 0, 41, 120, and 180. After initialization, the homogenization of the fish Fish quantity and activity evolution. The blue curve represents the evolution of the fish population. The red curve describes the maximum capacity of the environment accor K parameter and the number of active cells.

The fish population evolves as the activity level. The activity curve is a good indicator Figure 4a represents the evolution of the activity, at t=0, 41, 120, 180. Active cells are greatly influenced by the reef areas and their attraction. Figure 4b shows the popul tion fish at 0, 41, 120, and 180. After initialization, the homogenization of the fish represents the evolution of the fish population. The red curve describes the maximum capacity of the environment according of the good indicator 120, 180. Active cells are greatly influenced by the reef areas and their attraction. Figure 4b shows the population fish at 0, 41, 120, and 180. After initialization, the homogenization of the fish population is due to the propagation rule. The fish population comes on the outskirts of the reefs. The results highlight the influence of attractions areas in the development of biomass, and the impact of fishing and overfishing. The activity does not directly increases with the increase of fish quantity, it is quite stable, and clearly shows to t=40 the increase in fishing. A targeted and intensive fishing activity quickly reduces the fish population per cell. To improve the model, we will substitute the fishing areas by agents following the fish population.

Conclusion

In this paper we described a research approach using the activity concept and the Discrete EVent system Simulation Specification formalism to model the management and harvesting of resources for fisheries. The DEVS formalism is coupled with a Cellular Automata Model to efficiently exploit numerous spatial components. With this approach we simulate a complex fishery production system with strong interactions between components evolving in huge spatial areas. Spatial data are generated in a modular and hierarchical way in reasonable computation times. We use our framework to explore the maximum sustainable yield (MSY) and to fix the max sustainable catch in a large fishing area. Using our software framework, decision makers can explore various policies and make choices to improve the management and harvesting of fisheries resources. Using a traditional population growth model from literature [START_REF] Fox | An exponential surplus-yield model for optimizing exploited fish populations[END_REF], we show how our theoretical approach can be used to integrate spatial dynamics, in spite of a huge number of components. Future works will require the integration of the findings and insights of experimental studies. As in [START_REF] Duboz | Specification of dynamic structure discrete event multiagent systems[END_REF], we plan also to add an economic model that takes into account the sales of fish and the cost of fuel. We can thus add a propagation rule for trawlers. At the environmental level, we want to integrate the dynamics of biomass (phyto/zooplankton) and currents and finally implement a propagation rule of biomass based on currents.

Fig. 1 .

 1 Fig. 1. The CAM and its DEVS objects.

Fig. 3 .

 3 Fig. 3. : Fish quantity and activity evolution population. The red curve describes the maximum capacity of the environment accor

  Fig.

Fig. 4 .

 4 Fig. 4. Activity (a) and population (b) evolutions.

determine

	The activity in a CAM is determined by the measure of the active cells, i.e. by
	the set of cells that can change state between two global state transitions. The activity
	is measured at the highest level of the hierarchy, i.e., at the global level of the CAM in
	a set of active cells. The local level informs the global level on the model activity: (1)
	at time t, the simulator browses the active cells and executes their transition functions
	δ int (transition phase); (2) at the same time, the neighbouring inactive cells are tested
	in order to

if they can become active at t+1(propagation phase). This step is called and relies on the propagation rule. The inactive neighbouring cells of active cells are placed as active if they can become active at t+1. Whatever the size of the CAM considered, computations only depend on activity, thus the formulation of the propagation rule is a key element to enhance simulation times.

2.3 DEVS formalism

DEVS formalism was introduced in

[

8] as a rigorous basis for the compositional modelling and simulation of discrete event systems. A DEVS model is either an Atomic (AM) or a Coupled (CM) model. An AM is a structure: <X, Y, S, δ ext , δ int , λ, t a > with X the set of external events, Y the set of output events, S the set of sequential states, δ ext : Q×X⟶S the external state transition function, where and e is the elapsed time since the last state transition; sition function; λ:S⟶Y tion. The AM describes the behaviour of the system. A of several DEVS sub-models, i.e inspired by pythonDEVS [ DEVS simulator taking into account the


A M N D B Seneviratne
email: dammika.seneviratne@uis.no

R M Chandima Ratnayake
email: chandima.ratnayake@uis.no

Use of MCDM & AI Techniques for Mechanization of In-Service Inspection Planning Process

Keywords: In-service inspection planning,, AI techniques,, MCDM techniques,, Inspection programs,, Aging O&G P&PFs

The in-service inspection planning process for topside piping equipment of aging oil and gas (O&G) production and process facilities (P&PFs) involves personnel with different kinds of expertise, experience, and knowledge as well as a vast amount of data and information. To simplify the inspection planning process and increase the quality of an inspection program, various industrial organizations as well as researchers have been developing numerous techniques in an isolated fashion to address the challenges pertaining to different activities involved in the inspection planning process. In order to mechanize the overall inspection process, suitable techniques need to be identified for the different activities carried out in a generic inspection planning process. This manuscript discusses the potential use of multi-criteria decision analysis (MCDM) and artificial intelligence (AI) techniques. It also provides evidence about the suitability of AI techniques in relation to fuzzy logic and artificial neural networks for the mechanization of the inspection planning process in a dynamic manner.

Introduction

In offshore P&PFs, the topside piping equipment plays a vital role in the production of hydrocarbons (O&G). In this process, performing equipment maintenance at a level anticipated to comply with the standards and guidelines imposed by regulatory authorities (i.e. Petroleum Safety Authority (PSA), Norway) is a mandatory task for the aging O&G P&PFs operating in the North Sea. Therefore, the owner/operator companies of the P&PFs conduct inspections to identify the equipment's fitness for service and the level of required maintenance and modifications (Ratnayake, 2012a). In this context, the in-service inspection planning process, which consists of a series of subprocesses, for instance, preparation of inspection programs, carrying out inspections at plant level, evaluating inspection results and updating the risk level of equipment, feedback for operation and maintenance and evaluation of resource allocation for the next inspection cycle, is of major significance.

Researchers have identified the importance of inspection planning in various industrial settings, and reliability-based and risk-based approaches were developed from 1963 onwards, gradually establishing risk-based inspection (RBI) concepts for the planning of inspections over the last 25-30 years [START_REF] Ratnayake | Maintenance Integrity: Managing Flange Inspections on Aging Offshore Production Facilities[END_REF][START_REF] Straub | Computational Aspects of Risk-Based Inspection Planning[END_REF]. The application of RBI planning has been limited in the past due to the significant numerical effort required by these methods. The MCDM methods, such as the analytic network process (ANP), and the analytic hierarchy process (AHP) developed by [START_REF] Satty | The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation[END_REF], were applied by many researchers in different levels of the inspection planning process. Ratnayake (2012a[START_REF] Ratnayake | A Decision Model for Executing Plant Strategy: Maintaining the Technical Integrity of Petroleum Flowlines[END_REF][START_REF] Ratnayake | Utilization of Piping Inspection Data for Continuous Improvement: A Methodology to Visualize Coverage and Finding Rates[END_REF] and [START_REF] Ratnayake | Maintaining Technical Integrity of Petroleum Flow Lines on Offshore Installations: A Decision Support System for Inspection Planning[END_REF] used AHP in planning inspections for topside mechanical equipment. The views of the industry's professionals were considered in developing these AHP models to recommend critical thickness measurement locations (TMLs) (Ratnayake, 2012a[START_REF] Ratnayake | A Decision Model for Executing Plant Strategy: Maintaining the Technical Integrity of Petroleum Flowlines[END_REF]. The models were based on prioritizing the critical TMLs and optimising the cost for the inspections. [START_REF] Dey | Decision Support System for Inspection and Maintenance: A Case Study of Oil Pipelines[END_REF]) also used the AHP in hydrocarbon pipeline inspection planning, which was illustrated by case studies. The AI techniquebased models were also developed to identify critical TMLs, considering the degradation mechanisms. The techniques, for instance fuzzy logic and artificial neural network (ANN), are used by [START_REF] Nesic | An Open Source Mechanistic Model for CO2 / H2S Corrosion of Carbon Steel[END_REF], [START_REF] Singh | A Methodology for Risk-Based Inspection Planning of Oil and Gas Pipes Based on Fuzzy Logic Framework[END_REF], [START_REF] Zio | Prognostics and Health Management of Industrial Equipment[END_REF] in developing the models. Most of these models are based on empirical methods, in which planning personnel use these models together with their expertise for the planning purposes. The models which developed utilizing AI techniques are more able to incorporate expertise than the empirical models. This manuscript discusses the advantages and disadvantages of the MCDM and AI approaches in the inspection planning process. The goal of this paper is to identify the sub-processes in the inspection planning process where it is possible to use the AI techniques for the mechanisation of the inspection planning process.

Background

The inspection planning work process is defined in several standards, for instance DNV RP G101 (2010) and API RP 581 (2008), and illustrates the sub-processes and flow of sub-processes. A generic inspection planning work process consisting of subprocesses that have been employed in the industry is illustrated in Fig. 1. The main part of the inspection planning work process is the preparation of the inspection program. In planning inspection programs, the recommendation of critical TMLs for inspection is a primary task. A huge amount of data is gathered, and various techniques are used to identify TMLs by prioritization of criticality. In P&PFs, the topside piping equipment inspection planning personnel's primary concern is corrosion and erosion trends. However, there are other degradation trends, for instance, fatigue degradation, crack propagation due to fatigue, stress-induced cracking, slug effects, flow turbulence effects, stress generation, thermal effects, etc. In general, chemical and mechanical corrosion have been taken into consideration (i.e. CO 2 corrosion, H 2 S corrosion, microbially-induced corrosion and sand erosion) during inspection planning. NORSOK M-506 ( 2005) and DNV RP O501 ( 2007) are examples of the models developed in standards to assist the inspection planning. The prioritization of identified critical TMLs is based on the thresholds provided by the governing documents, for instance plant strategy, RBI guidelines, piping standards (e.g. ASME B31.3), etc.

In executing inspections, resource allocation is another major part of the planning process. The TMLs' accessibility, methods for inspection, manpower and working hours needed to be calculated. The feedback process performs by comparing the current measurements and historical data to identify the current risk level of TMLs. Furthermore, the annual inspection budget, the annual activity plan and the feedback PM plan are assigned according to the feedback provided by the risk level of the TMLs. Therefore, the inspection planning sub-processes consist of data analysis, forecasting, optimizing and prioritizing. Currently this is performed by using basic calculations and primary software tools.

Static and Dynamic Modelling for Inspection Planning

The models developed in the condition based maintenance and monitoring paradigm, in particular to identify the inspection intervals and the prioritization of the inspections can be divided into static and dynamic models [START_REF] Wang | Modelling Condition Monitoring Intervals: A Hybrid of Simulation and Analytical Approaches[END_REF]. Static models are driven by the fixed condition monitoring interval data, while dynamic models are driven by both fixed condition monitoring interval data and real-time condition monitoring data. Therefore, the dynamic model overrides the static model with real-time condition monitoring data, which keeps the equipment under close attention before an appropriate mitigation action takes place when it reaches a critical level [START_REF] Wang | Modelling Condition Monitoring Intervals: A Hybrid of Simulation and Analytical Approaches[END_REF]. The currently developed static and dynamic models are less likely to connect to the real-time condition monitoring data [START_REF] Jardine | A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance[END_REF]. However, the aging P&PFs are constantly affected by the changing production and process conditions where the deterioration and degradation trends are dramatically changing over time. Therefore, the dynamic behaviour of the models which can be connected to the condition monitoring data will be useful for the aging P&PFs to monitor the critical equipment regularly.

The Decision Support Modules in Inspection Planning

Different decision support modules (DSM) are developed based on the static and dynamic models to support the decision-making process of the sub-processes in inspection planning. DSMs are developed in an isolated fashion to address the different scenarios, such as the identification of different degradation trends for TMLs, the prioritization of TMLs, inspection methods, etc. However, a number of researchers have observed the difficulty in making proper decisions on the above analysing process and have suggested different approaches. The main model development is fo-cused on degradation monitoring followed by the identification of critical TMLs. The techniques, for instance AHP, ANP, outranking, fuzzy logic, artificial neural networks and genetic algorithms, are used in developing the DSMs.

The main contribution of the DSM in inspection planning is through degradation models. [START_REF] Ratnayake | Maintaining Technical Integrity of Petroleum Flow Lines on Offshore Installations: A Decision Support System for Inspection Planning[END_REF] and [START_REF] Singh | A Methodology for Risk-Based Inspection Planning of Oil and Gas Pipes Based on Fuzzy Logic Framework[END_REF] have reviewed the degradations and degradation models for O&G topside piping and hydrocarbon transportation pipelines. The O&G piping equipment is frequently subject to different degradation mechanisms, internally and externally. A number of manuscripts address specific types of corrosion behaviour: [START_REF] Ramsamooj | Modeling of Corrosion Fatigue in Metals in an Aggressive Environment[END_REF] offer a detailed description of corrosion and fatigue modelling in unfavourable conditions; NORSOK M-506 (2005), [START_REF] Singh | A Methodology for Risk-Based Inspection Planning of Oil and Gas Pipes Based on Fuzzy Logic Framework[END_REF] and [START_REF] Valor | Stochastic Approach to Pitting-Corrosion-Extreme Modeling in Low-Carbon Steel[END_REF] address C0 2 -driven corrosion and the problems of pitting corrosion; [START_REF] Walton | A Numerical Model of Crevice Corrosion for Passive and Active Metals[END_REF] deal with the problems of modelling marine corrosion damage, and DNV RP O501 (2007) illustrates the erosion behaviour of the equipment subjected to produced sand. A large amount of degradation modelling causes the problem to converge into one variable situation, by treating individual items of equipment independently. For large systems, such as those found in the O&G industry, this is a practical assumption that allows the modeller to handle potentially intractable modelling problems. However, this is not usually a realistic assumption since the systems are undergoing different degradation mechanisms in different locations at different times. Therefore, the application of different degradation models (i.e. based on different degradation mechanisms) in one system will enable the identification of different degradation tends in the TMLs. The results from the different models can be used for cumulative prioritization to identify the most critical TMLs in a system.

Use of MCDM and AI Methods in Inspection Planning

The MCDM and AI techniques are used in static as well as dynamic modelling of the stages of the inspection planning process. The main functions of the exploited models are optimization, forecasting and prioritization. In MCDM, there are two different approaches for problem solving. The first is to evaluate problems which consist of a known number of alternatives in the solution domain. The second approach is where the number of alternatives for the problem is unknown. However, in this situation, the problem is solved by mathematical modelling to identify the solution [START_REF] Triantaphyllou | Multi-Criteria Decision Making: A Comparative Study[END_REF]. Therefore, in the inspection planning process of O&G topside piping equipment, the alternatives are limited and the first approach in MCDM is used by many researchers.

Outranking methods, ANP and AHP are the three most frequently used MCDM techniques for prioritization [START_REF] Bozbura | Prioritization of Organizational Capital Measurement Indicators Using Fuzzy AHP[END_REF]. The outranking methods determine which alternatives are being preferred to the others by systematically comparing each criterion instead of building complex utility functions [START_REF] Brans | PROMETHEE Methods[END_REF]. In AHP, pair-wise comparisons are made in between elements at each level of the hierarchy by means of a nominal scale to establish a comparison matrix. The eigenvector of the matrix is derived as the weights' vector of elements at the hierarchy. Finally, overall priority can be obtained by synthesizing local and global weights. The fuzzy version of AHP is preferred in the prioritization of problems for the following reasons: no measurement scale needs to be explicitly defined for each criteri-on/attribute in pair-wise comparison; representation of uncertainties such as vagueness; non-specificity and discord can be incorporated in the models [START_REF] Klir | Fuzzy Sets and Fuzzy Logic: Theory and Applications[END_REF]. However, there are limitations in using fuzzy-AHP, and these are some of the problems: a high number of computational requirements; only triangular membership functions can be used;, difficulties with criteria and attribute addition and deletion and the number of pair-wise comparisons are increased with the number of criteria [START_REF] Buyukozkan | A Fuzzy Multicriteria Decision Approach for Software Development Strategy Selection[END_REF].

In recent years artificial intelligence techniques have also been used successfully in condition based maintenance planning. The earliest works made use of expert systems [START_REF] Medsker | Design and Development of Hybrid Neural Network and Expert Systems[END_REF]; then came a number of studies using ANN [START_REF] Caputo | An Inverse Approach for Piping Network Monitoring[END_REF]. [START_REF] Jang | ANFIS: Adaptive-Network-Based Fuzzy Inference System[END_REF] proposed the adaptive network-based fuzzy inference system (ANFIS), a hybrid learning algorithm extensively used in forecasting problems. The ANN model is also used with a back propagation algorithm for predicting failure rates [START_REF] Al-Garni | Neural Network-Based Failure Rate Prediction for De Havilland Dash-8 Ttires[END_REF]. [START_REF] Ciarapica | Managing the Condition-Based Maintenance of a Combined-Cycle Power Plant: An Approach Using Soft Computing Techniques[END_REF] experimentally used ANN and neuro-fuzzy systems to forecast activities in the rotating machinery preventive maintenance cycles. Genetic algorithms are used in optimizing the maintenance schedule tasks in production environments by Cavory et al. (2001). These are also used by [START_REF] Sortrakul | Genetic Algorithms for Integrated Preventive Maintenance Planning and Production Scheduling for a Single Machine[END_REF] to solve an integrated optimization model for production scheduling and preventive maintenance planning. More recently, several works have employed fuzzy logic systems in the identification of critical TMLs (Ratnayake, 2014a[START_REF] Ratnayake | KBE Development for Criticality Classification of Mechanical Equipment: A Fuzzy Expert System[END_REF][START_REF] Seneviratne | In-Service Inspection of Static Mechanical Equipment: Use of a Fuzzy Inference System for Maintaining the Quality of an Inspection Program[END_REF]. However, the neuro-fuzzy approach integrates the neural networks and fuzzy logic to create powerful expert decision systems. Many authors have proposed various neuro-fuzzy models and complex training algorithms in inspection and maintenance planning [START_REF] Zhang | Extended Neuro-Fuzzy Models of Multilayer Perceptrons[END_REF]. Table 1 illustrates the usage of different methods for inspection planning process modelling in various industries with their advantages and disadvantages. Although different models have been developed using different techniques, full mechanization of the inspection planning process has not yet been achieved. Researchers address the mechanization of some of the sub-processes illustrated in Fig. 1.

To achieve total mechanization, as short-term goals, correct techniques for modelling the sub-processes needed to be identified. As middle-term goals, the knowledge bases for the models needed to be created. For long-term goals, full mechanization can be achieved by the integration of models, developed knowledge bases and the connection of condition monitoring sensors to the models.

Discussions and Conclusions

The planning process consists of several stages with different levels of data analysis.

In this analysis the experience, knowledge and intuition of the field experts is extensively used. In the mechanization of the inspection planning process, prioritization techniques which can incorporate human-like abilities need to be used. The techniques, for instance fuzzy logic and ANN, demonstrate human-like reasoning abilities. However, referring to the advantages and disadvantages illustrated in Table 1, individual AI techniques show only a limited number of reasoning behaviours. Therefore, the use of multiple AI techniques in a model as hybrid AI techniques will enable human-like reasoning abilities to be embedded into the DSMs.

In the O&G P&PFs, the product and process condition variations cause the arbitrary degradation trends of aged equipment which are reaching critical levels. However, the financial productivity is less in aged P&PFs. Therefore, owner/operator companies face difficulties in performing inspections regularly to monitor the critical equipment. Hence, the dynamic modelling approach is necessary for the aged O&G P&PF inspection planning, where the condition monitoring data can be linked to the DSMs to identify the critical TMLs in a dynamic manner.

Fig. 1 .

 1 Fig. 1. Inspection planning work process

Table 1 .

 1 Comparison of usage of MCDM/AI techniques in inspection planning

	Method	Usage in inspection planning process modelling	Advantages	Disadvantages
	MCDM	Extensively used in inspection planning.	Easy interpretation of the	Static behaviour of the models.
	methods	Static modelling behaviour due to the fixed	criteria and alternatives.	Reflects the designer's perspec-
	(Brans and	designed methods by designer.	Easy for designing the models.	tives.
	Mareschal,	Decision selection is from fixed alternative	Flexibility in defining the	Fixed boundaries in generating
	2005)	domain.	threshold limits.	decisions.
	Fuzzy logic	Relatively well used in the inspection	Interpretation of expert	More toward static behaviour in
	(Buyukoz-	planning.	knowledge, intuition and expe-	the modelling.
	kan et al.,	Represents the process uncertainty pa-	rience.	Limitations in interpreting hu-
	2004)	rameters.	Interprets the vague, noise and	man-like reasoning behaviour.
		Modelling is static. However, by incorpo-	imprecise data and infor-	Inability of self-learning capabili-
		rating other AI techniques, dynamic be-	mation.	ties.
		haviour can be obtained.	Rule base is used to mimic
		Utilizes vagueness and impression of	human-like reasoning by using
		parameters to design noise-tolerant mod-	suitable inferencing methods.
		els.
		Rule base inferencing.
	Artificial	Relatively well used in inspection planning.	Dynamic and good in model-	Selection and designing of ANN
	neural	Dynamic behaviour is observed in the	ling nonlinear and unstable	for solving a problem situation is
	network	neural network models.	processes.	complex.
	(Zhang et	Equipped with the self-learning abilities to	Trained for complex input	Identification of proper network
	al., 2004;	adapt the problem-solving capabilities.	output modelling.	design needs extensive effort

Acknowledgements

The authors would like to thank Roy Martin Zydeman and the in-service inspection planning team in Aker Solutions Offshore Partner, Norway, for their support in this research study.