Han Jingti

Y U Mingliang
email: yumingliang@163.com

Liu Lin

Research into Inventory Optimization of a Two-echelon Distribution System based on Particle Swarm Optimization (PSO)

Keywords: Different replenishment policies, Two-echelon distribution system, Particle Swarm Optimization (PSO), Inventory model

Based on the assumption that distributor and retailers adopt different replenishment policies, this article gives consideration to the relationship between the inventory strategies of the two echelon. The authors construct an inventory model of a two-echelon distribution system under stochastic demand, and solve the problem based on algorithm of Particle Swarm Optimization (PSO) to quantitatively get the optimal ordering strategies of distribution center and retailers.

Introduction

Inventory optimization of two-echelon distribution system is exactly the typical problem in multi-echelon inventory control of supply chain, as for the demand uncertainty of actual management, many experts make research on two-echelon inventory system based on random demand. [START_REF] Federgruen | Approximations of dynamic multi-location production and inventory problems[END_REF] established a two-echelon inventory system jointly operation model including a single supplier (or distributor) and many retailers. [START_REF] Axsäter | A simple decision rule for decentralized two-echelon inventory control[END_REF] proposed an inventory control model of a two-echelon distribution system based on decentralized control strategy. [START_REF] Olsson | A two-echelon base stock inventory model with Poisson demand and the sequential processing of orders at the upper echelon[J][END_REF] put forward that queuing theory can be used to simulate the process retailers waiting for the delivery under the assumption of Poisson distribution, thus a two-echelon inventory model based on (R,Q) inventory replenishment policy under stochastic demand was established. [START_REF] Thangam | A two-level distribution inventory system with stochastic lead time at the lower echelon[END_REF] put great emphasis on quantitative impact of retailers inventory strategy on demands faced by suppliers.

In this paper, we study a typical two-echelon distribution system, the distribution system consists a distributor and many retailers. Based on the assumption that distributor and retailers adopt different replenishment policies, we assume retailers with (R,Q) policy and distributor with (s,S) policy, the order quantity Q remains unchanged for each order. This article gives consideration to the relationship between the inventory strategies of the two echelon, aim to minimum the average total cost, establish an inventory model of a two-echelon distribution system and solve the problem based on algorithm of Particle Swarm Optimization (PSO) with a numerical example.

2

Assumptions and Symbols

Assumptions

a. In the two-echelon distribution system, distributor sends ordering information to suppliers and provides single species products. b. The final demands of customers faced by various retailers are mutually independent random variables, and demands per unit time follow a Poisson distribution with the same parameters. c. The retailers adopt a continuous review (R,Q) strategy. Assuming Q  R+1, so that order crossover phenomenon will not occur within two successive cycles. d. Distributor adopt a continuous stock-taking (s,S) strategy and s <S, Where s and S are integer multiple of order quantity Q. e. Backorders are allowed in each echelon of the two-echelon system and are delay in delivery, backorders are sale without loss, namely Backorder mode, delay in delivery has a corresponding cost .f. Lead time a random variable, the lead time is independent from unit time demand of retailers and it is composed of transport time and random delay of distributor. g. Assuming transportation time and cost are the same from distributor to different retailers.

Symbols

N -The total number of retailers,

3

An inventory model of a two-echelon distribution system

An inventory model of distribution center

A. Demand analysis on distribution center

The average arrival rate of distributor's demand is

r r N i i Q N Q       1 0 (3.1)
The orders received by distribution center 0 x obey Poisson distribution with parameter 0

 , that is ) ( ~0 0  P x
, As the batch of each order is r Q , the probability of the demand arrived at distribution center at moment

t is 0 )! ( 0      e r r Q j Q j j ,   N Q j r (3.2)
The orders arrive as Poisson flow, thus the probability that the number of orders arriving at distributor equal to

j within a time interval t  is       0 ! ) ( ) ( 0   e j j j ,  , 2 , 1 , 0  j (3.3)
As the batch of each order is , the probability of the demand equal to

d within a time interval t  is (3.4)
 presents for average demand per unit time of distribution center, then

r r N Q      0 (3.5) B.

Analysis of inventory level

Suppose the holding inventory at moment

t is } 0 ), ( {  t t IP and inventory level is } 0 ), ( {  t t IL ,So we can get the following properties, ) , ( ) ( ) ( 0 0 L t t D t IP L t IL     ) ( ) ( 0 L D t IP   (3.6
) is a random process with continuous parameters and discrete values, and the scope of )

(t IP 's value is } , , 2 , { 0 0 0 S Q s Q s r r   
. We can see from the assumption (5) that,

0 s and 0 S are integral multiples of r Q , assume r Q k s 0 0  , r Q K S 0 0 , 
, and

 N K k 0 0 ,
here. Suppose j  to be the probability that the holding inventory

) (t IP reach j , r mQ j  , 0 0 1 K m k   
. At the beginning of each order cycle, the orders of distribution centers ensure the holding inventory reach 0 S , so . In an order cycle, each time the arrival of the demand will cause transitions of holding inventory, and each holding inventory point can be reached at most once. Obviously, when

j f t IP   ) ( , if the next demand arriving at distribution center is j f  , then r Q        0 )! ( ) ( ) ( } ) ( { 0     e Q d d D P r Q d Q d r r } 0 ), ( {  t t IP t 1 0  S  the holding inventory j t IP  ) (
, so,there is

         0 1 ) ( K m i Q m i iQ j f j f f j r r      , that is      0 1 K Q j i j iQ iQ j r r r    (3.7)
Here,

0 0 0 , , 2 , S Q s Q s j r r    
. Only one solution can be get by solving

equation (3.7), denoted by ) , , 2 , ( 0 0 0 * S Q s Q s j r r j     
. In fact, * j  also can be seen as the expected frequencies that holding inventory reaches j in one order cycle. Therefore, the frequency of average total demand in one order cycle is

           0 0 0 0 0 1 * * * 2 * * K k i iQ S Q s Q s j r r r       .
As orders to distribution center obey compound Poisson process, and the average time intervals of demands to systems are always the same, therefore, under the stable state, the probability distribution of inventory location can be written as follow,

       0 0 1 * * * * } { K k i iQ y j y r y IP P     , 0 0 0 , , 2 , S Q s Q s y r r     (3.8)

C. Holding cost and backorder cost of anticipation inventory

For the given inventory level IL , inventory holding cost can be expressed as represents the sum of anticipation inventory cost and anticipation delayed delivery cost, so

    ) ( ) ( ) ( 0 0 IL E h IL E b y G (3.10) Because IL IL IL     ) ( ) ( , ) ( ) ( ) ) (( ) ( IL E IL E IL IL E IL E        (3.11) So,  ) ( y G ) ( 0 0 L y b     } ) ( { ) ( 0 1 0 0 r Q y i r iQ y L D P iQ b h r        (3.12)

D. Anticipation total cost per unit time of the distribution center

We can know from the analysis of (B) that the average number of holding inventories' changes in one order cycle is

 * j      0 0 1 * K k i iQ r
 , and every demand arrival can lead to changes of inventory levels, thus the average length of one order cycle is

        r r r r Q S Q s i iQ K k i iQ T 0 0 0 0 1 * 1 * 0   (3.13)
From the above analysis, we can know that anticipation total cost per unit time of the distribution center is

        N Q S Q s in kQ G kQ IP P T A S s ATC r r r Q S Q s k r r r 0 0 1 0 0 0 0 0 , ), ( } { ) , ( 0 0 (3.14)

2 An inventory model of retailers

A. The analysis on lead time of retailers' order

We can know from assumption(6) that the lead time of retailers' order consists of two parts, of which the fixed shipping time from distributor to different suppliers

r d N L        r r Q S Q s k r kQ IP P 0 0 1 } { } )] ( } ) ( ) ( { [ 1 0 0        k i r r r r L N kQ Q i k L D P iQ  (3.

B. Holding cost of inventory

From assumption (3), the demand during lead time r X is a discrete random variable taking positive integers, provided the cumulative distribution function is ) .

(x  , the probability distribution is ) ( ) ( x x X P r    , the expected value (average) is l . The average inventory in one cycle is ] [ 2 1 l R Q l R I r r r r      = l R Q r r   2 .
Therefore, the average inventory holding cost of the retailers in one cycle is,

) 2 ( l R Q Q h C r r r r r I r     (3.19)

C. Backorder cost

Backorders occur when demands in the lead time exceed a reorder point, so the average value of backorders in one cycle is, Here, ) (x  is the probability density function of demand r X in the lead time, and ) (x

 is a cumulative distribution function of r X . r  r Q ] ) ( 1 [ ) ( ) ( ) (           r r R x r r R x r R R x x x R x   
Thus the average total backorder cost in a cycle is , the e average total cost of retailers per unit time is

)]} ( 1 ( ) ( { r r R x r S r R R x x b C r         (3.
) 2 ( ) , ( l R Q h A Q Q R ATC r r r r r r r r r      )]} ( 1 [ ) ( { r r R x r r r R R x x Q b r          (3.21)
Retailers' demand per unit of time

     ! ) ( ) ( (3.22)    r R x r x R 0 ) ( ) (   (3.23) r r L l   (3.24) r
L can be get from (3.17) and (3.18). Refer to equation (3.14) and (3.21), the function of anticipation total cost of the overall two-echelon distribution system per unit time can be expressed as follow, ) , , ( ) , , , (

0 0 0 0 0 r r r Q S s ATC Q R S s ATC  )) , ( , , ( 0 0 S s L Q R ATC N d r r r   (3.25)
The goal of the model is to find the optimal

* * * 0 * 0 , , , r r Q R S s
, so that the anticipation total cost of the overall system per unit time to be minimum. So the problems to be solved by the model can be described as min

 ATC ) , , , ( 0 0 r r Q R S s ATC (3.26)                 ) 25 . 3 ( ) 1 . 3 ( , 1 0 , . . 0 0 0 0 Equation S s N Q R N Q R t s r r Q S Q s r r r r

4

Solution method for model based on Particle Swarm Optimization (PSO)

Coding and fitness function

Set particle size M=20, the maximum iteration K max =30, adopt real number coding on the respective components of each vector's position X i , the position vector ) ( ~r r r L P X  X i contains four dimensions, corresponding s 0 , S 0 , R r , Q r respectively, so the current position of each particle forms a solution vector, that is ) , , , (

i i i i i Q R S s X  , 20 , 2 , 1   i .
The goal of the two-echelon system joint inventory optimization is to optimize the total cost of distribution centers and the retailers, therefore, fitness function of algorithm can be defined as the sum of the cost of both, that is ) , , , ( ) , , (

0 i i i i r i i i Q R S s ATC N Q S s ATC fitness    4.2
The solving process of Particle Swarm Optimization (PSO)

Step1: Its scale 20  M , Maximum iteration 30 max  k .

Step2: Determine the initial position of each particle

M i Q R S s X i i i i i , , 2 , 1 ), , , , ( ) 0 (

  

, the initial velocity of each particle ) , , , (

) 0 ( Q i R i S i s i i v v v v v  .
Step3: Measure the fitness of each particle based on (4.1) ) 0 ( i f , the optimal position of the each particle

( 1 k i k i k i v X X    ) ( ) ( ) 1 ( 2 2 ) 1 ( 1 1 ) 1 ( ) (         k i g k i i k i k k i X p r c X p r c v w v , k k w w w w k     max min max max
Here, Step8: For each particle, compare its optimal position ) (k i p with the global optimal location of the whole group , place it as the optimal location of the whole group )

(k g p ) (k i p if it's better than ) (k g p , then } , , min{ ) ( ) ( 2 ) ( 1 ) ( k M k k k g p p p p   .
Step 9: Check the termination conditions, jump to step 6 if max k k  ; Otherwise, the maximum iteration, termination.

Examples

The day. Take various parameters in the above application examples into the two-echelon distribution system and solve the problem based on algorithm of Particle Swarm Optimization (PSO) with the software MATLAB (R2008a).We will record once the current optimal solution every three times of iterations, the results refer to Table 1.

From the table1 we can clearly see the convergence process of particle swarm, the difference of the optimal fitness between adjacent two iterations is gradually reduced, and the gap between the average fitness of each particle and the optimal value is gradually narrowing, indicating that each particle convergences to the optimal position, its visible that the algorithm has preferable convergence.

Table 1 Example 5.1 Iterative process records algorithm of PSO

0L--

  Fixed lead time of distributor's purchases from suppliers, 0 A -Fixed ordering cost per distributor order 0 h -Holding cost of per unit goods within per unit time of distributor, 0 b -Backorder cost of per unit goods within per unit time of distributor, t L -Fixed shipping time from distributor to different suppliers, r x -Retailers' demand per unit time, cost of per unit goods within per unit time of retailors, r A -Fixed ordering cost per retailor order, -Backorder cost of per unit goods within per unit time of retailors, 0  -The average arrival rate of distributor's demand, 0 x -The number of orders reaches distributor per unit time, Random demand of distributor from time period t to   t , -The probability of the demand arrived at distribution center at moment t , -The probability that the number of orders arriving at distributor is j in time period , d L -Random delay of retailers' orders at distribution center, lead time of retailers' orders.

.

  Assume inventory position of the distribution center is always y IP  , the following can be get from property(3.6).

  t L is constant while random delay of retailers' orders at distribution center d L is a random variable. Namely, Just because the uncertainty distribution of d L , its mean value is often used to represent the random variable in practice, we can get equation (3.16) from Little's formula in Queuing Theory, numerator represents the average backorder level of distribution center, the denominator represents the average demand per unit time of distribution center. Substitute equation (3.5) and equation (3.11) into equation (3.16), { 1

  17

Q

  , according to the characteristics of Poisson distribution, the average demand per unit time is, and the order quantity is , the average order times per unit time is r r , thus the average length of one order cycle is r r Q 

  20) D. The average total cost per unit time From equation (3.19) and (3.20), the average total cost of the retailers and since the average time of one ordering cycle is r r Q 



  , as convolution formula of Poisson distribution, demands during lead time meet ,so we have the followings in equation (3.21),

X

  iterations k=0 and k<-k+1, update particle velocity ) according to the following three equations.

)

Fund Program: 1.National Natural Science Foundation of China(71271126) ; 2.Research Fund for the Doctoral Program of Higher Education (20120078110002).

Summary

This paper selects an important part of the multi-echelon inventory systemdistribution system as the research object, the optimization of the two-echelon inventory system is studied under centralized control strategy. Distribution centers adopt (s,S) ordering strategy while retailers adopt (R,Q) ordering strategy, the lead time of distribution centers to retailers is a random variable while each retailer's demand is independent Poisson process, the authors construct an inventory model of a two-echelon distribution system under this assumption, and solve the problem based on algorithm of Particle Swarm Optimization (PSO) with inertia weights to achieve effective exploration on swarm intelligence technology on inventory optimization problems.

Number


Charalampos Apostolopoulos
email: charalampos.apostolopoulos.1@city.ac.uk

George Halikias
email: g.halikias@city.ac.uk

Krikor Maroukian
email: krikor.maroukian@kcl.ac.uk

Georgios Tsaramirsis
email: gtsaramirsis@kau.edu.sa

Measuring Change Risk for Organisational Decision Making through a Hierarchical Model Process Approach

Keywords: CRAM, Change Risk Assessment Model, Project Management, Change Management, Risk Management, Decision Analysis

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

Introduction

Nowadays, a mission critical necessity for an organisation seems to be the adaptation to specific customer requirements and concepts such as: strategic business planning, customer satisfaction, market adaptation and subsequently efficient and effective business change management. However, the transitional period of change is not only time consuming but also a risky process. In this context, risk can be regarded as an integral part for both, businesses and management.

Nonetheless, the processes of change management and risk assessment are usually regarded as separate business domains and ones which should be generally implemented during the whole life cycle of a project. Besides the generic need for change, implementing change is often perceived as an unsurpassable challenge due to several organisational barriers and behavioural aspects relating to human resources, who express considerable resistance to change which often hinders the overall process success.

Project management can have added strategic value, when the level of effectiveness and the efficiency with which a project is accomplished are interlinked and when project outcomes can provide overall business value. Furthermore, change management, also poses as a strategic and structured approach to transitioning individuals, teams and organisations from a current state to a desired future state.

The implementation of project management also requires changes, e.g. in the processes, tools, and methods used to fulfil organisational goals [START_REF] Martinuso | Project based management as an orgnanizational innovation: Drivers, changes and benefits of adopting project-based management[END_REF]. In effect, 'change' for project management can be seen as an integrated process which is related to controlling the project's requirements in an effort to change them so as to eventually place activities in order and conform to customers requirements.

This research paper, attempts to facilitate various organisational change risk factors which can influence project success by introducing a novel modelling approach named Change Risk Assessment Model (CRAM). The introduction of CRAM will allow the identification and definition of speculative relationships, between change risk events in the form of hierarchical risk model analysis.

Further to the introduction, this paper is organised as follows: section (2) presents existing literature findings. Section (3) discusses the methodology used to develop the model, section (4) provides an overview of the CRAM's processes. Research conclusions are presented in section (5) and finally, section [START_REF] Williams | Modelling complex project[END_REF] defines the next steps to future work.

Literature Review

There exist many different models and views for managing change, such as the three stage model (Unfreezing, Confusion, Refreezing), [START_REF] Lewin | Field Theory in Social Science[END_REF]; planned change phases (Exploration, Planning, Action, Integration), [START_REF] Bullock | It's just a phase we're going through: A review and synthesis of OD phase analysis[END_REF]; managing the transitional phases (Ending, Neutral and New Beginning), [START_REF] Bridges | Managing Transitions[END_REF]. Even though, this work is significant, it corresponds mainly to a narrative and multi-stage process description which excludes any change risk-assessment process. Change management, is often based on informal models with risk inconsistencies and generalisations that may lead to loss of meaning and semantic gaps. However, models depicting a simplification of reality withhold a significant advantage [START_REF] Pidd | Five simple principles of modelling[END_REF] and in most of the cases models can be used to deduce simplified conclusions about the real world [START_REF] Williams | Modelling complex project[END_REF].

Taking as example the two most established project management frameworks PRINCE2 ® (developed by UK Government) and PMBOK ® (developed by Project Management Institute); project success takes into account several significant environmental exogenous and endogenous project factors ( [START_REF]Managing Successful Projects with PRINCE2 Reference Manual[END_REF], [START_REF]A guide to the project management body of knowledge (PMBOK® guide)[END_REF]). It is clear that any changes in the project constraints as seen in Table 1, can influence the success or failure of the end result of a project or its deliverables. However, it is within the scope of this paper to examine different attributes far beyond the constraints which are extensively referenced in PMBOK ® and PRINCE2 ® , showing that the four major ones: time, cost, scope and quality are just the peak of the iceberg. If any one factor changes, at least one other factor is likely to be affected; effectively changing the project requirements or objectives may create in turn additional risks. Taking into account that project management and change management are integrated processes, an analogy can be found between the project and change influence factors. Critical success factors (CSFs) are requirements or deliverables that must have a satisfactory completion rate for the successful outcome of the project [START_REF] Heldman | Project Manager's Spotlight on Risk Management[END_REF].

In a relative research [START_REF] Bryde | Project management concepts, methods and application[END_REF] with sixty subjects it was indicated that 43.33% of the sample's respondents agreed that among other factors 'responsiveness to change' is a project success criterion. In a more practical approach [START_REF] Carnall | Managing change in organizations[END_REF], a change management toolkit was developed, which actually provides a numerical computation of change risk, irrespective of project management frameworks. The more identified risks before the initiation phase of the project, subject to project complexity, the better the expected outcome can be. Table 2, shows some indicative project risk categories: Changes and associated risks can occur during the whole life cycle of a project; however, it is vital to assess the possibility of success materialisation before the decision is made to proceed with the change or not. CRAM has the capacity to define the internal dynamics of change management within project management eliciting also risk cause-and-effect relationships. Effectively, stakeholders are allowed to describe a problem as they see it, refine the complexity and structure a hierarchy of attributes.

Methodology

The methodology in terms of scientific research used so as to develop the nodes and attributes of the prototype model, combined in depth literature review analysis and personal interviews in correlation with group meetings (Delphi technique). Interviews (high level executives from diversified industries) were proven more than assistive in coupling together not only professional experience, but also the personal reflection of the participants.

Taking into account that focused group discussions (Delphi Technique) was engaged as a further verification tool of the interviews results, it was more than obvious, that a group environment is beneficial for the respondent in gaining a deeper understating of the research questions. Professionals were able to discuss further their common opinions or disagreements; contribute more effectively either by listening to new ideas or even discussing in more depth with fellow participants.

The change risk categories that were identified are summarised in table 3. In turn, the attributes were categorised based on rationalism, testing theory and practice. The key idea of categorisation was to construct the prototype model in a sense that can be used repeatedly in various industries, minimising any bias as possible.

Change Risk Assessment Model Overview

Even if project managers, change managers or other stakeholders discuss about change and associated risks; still, there is a lot of room for research improvement in this area. Within the research scope, models are defined as the representation of a view of an interpreter about an entity or concept from the real world [START_REF] Seidewitz | What models mean[END_REF]. However, it is not uncommon to do business or perform business related activities without the use of models. Often, the modellers themselves have disappeared, and any knowledge that was not captured in the specialised models is inaccessible, forgotten, or written off [START_REF] Denno | Model-Driven Integration Using Existing Models[END_REF]. This leads to the inability to address key business environment factors. Nevertheless, business models which can be combined and configured with project business seem to be an exploited research area [START_REF] Wikström | Business models in project business[END_REF].

The significance of CRAM is that, it can be considered as a prototype change risk assessment modelling method that can be applied in various project types' or organisations irrespective of size or complexity as seen below. The method can be tailored to specific customer or project needs, taking into account significant environmental change risk factors (add or delete risk attributes on a per case basis).

Because project's scope and objectives differ, in the same sense it is difficult to identify all potential risks.

-Product and Strategy Management -Insurance -Software / Technology Solutions -Government -IT / Telecoms -Retail -Service Provisioning -Utilities -Consulting -Defence -Engineering -Banking

In addition, CRAM can be integrated with contemporary project management frameworks like for example PMBOK ® and PRINCE2 ® . The proposed model attempts to enforce and integrate currently prevailing descriptive risk analysis methodologies, by engaging a semi-quantitative and qualitative change management risk modelling approach.

CRAM Processes

CRAM is composed of three interrelated processes which are continually recorded and monitored. CRAM processes accomplish specific risk objectives, which are applied to projects or at a greater extent to business environments with a view to facilitate and control change risks. A schematic interpretation of CRAM processes can be seen in Fig. 1.

Fig. 1. Change Risk Assessment Model Processes

Risk Identification

Risk

Monitor and Control

Risk Assessment

Risk Identification

Risks can be found everywhere and in fact, the difficult part is not to identify but control them. The primary goal of Risk Identification is to recognise the threats and opportunities which may affect the project's objectives and consequently deliverables. Indicatively, risk(s) can be categorised as follows:

Known Risk: this kind of risk refers to an in-depth project analysis which has a considerably high probability of occurrence.

Predictable Risks: are those risks that past experience dictates one may face with high probability.

Unpredictable Risk: is the risk that could happen, but the probability of occurrence in terms, for example, of timing cannot be estimated accurately. The success of many projects is related to the level that this risk will be estimated [START_REF] Rescher | Risk[END_REF].

Irrespective of risk categorisation, CRAM proposes the following tools and techniques so as to identify change risks:

-SWOT analysis -Change/Risk surveys -Delphi technique -RACI diagrams -PERT diagrams -PESTEL analysis -Risk Breakdown Structure (RBS) -Interviews -Brainstorming sessions Of course, potential risks and required changes can be identified and decided during the entire lifecycle of the project. Nevertheless, they have to be assessed and monitored the sooner the possible.

Risk Assessment

The basic aim of the proposed model among other objectives is Risk Assessment (Estimation and Evaluation). Change, if uncontrolled can be associated with activities of uncertain outcome(s) which would be deemed unwanted deliverables from project stakeholders. However, when change management and risk management are coupled together, risk aftermath can be reduced; risk is estimated at the planning stage of a project and consequently, there is time to develop a risk mitigation plan and take all necessary preventive actions, acting proactively.

A simple definition of risk in terms of probability of occurrence and its related impact is given by the formula ( [START_REF] Heldman | Project Manager's Spotlight on Risk Management[END_REF], [START_REF] Kendrick | Identifying and Managing Project Risk[END_REF], [START_REF] Kerzner | Applied project management[END_REF]): Risk = Probability x Impact.

(

) 1
The majority of quantitative methodologies use extensively probabilities being rather less ambiguous and imprecise; meaning that they are more objective as far as the assessment of the information and data on identified risks is concerned. The narrative approach of risk estimation has an advantage of providing contextual information but on the other hand does not allow the level i.e. magnitude of risk, to be measured. Qualitative and quantitative scales do indicate levels or rating but lack the information content.

Estimation can facilitate project risks in terms of the probability of occurrence and impact. On the other hand, Evaluation assesses the overall effect of all identified risks aggregated together. Some kinds of risks, like for example financial risks, can be evaluated in numerical terms.

Risk Assessment can be accomplished with the aid of a variety of methods and techniques, such as for example: Simulations, Monte Carlo analysis, CPM (Critical Path Method), AHP (Analytic Hierarchy Process), risk maps, Bayesian probability and statistics, probability trees or even fault tree analysis.

Concerning evaluation, this can be indicatively accomplished for example by a means of benchmark questions, like for example:

-Were all implemented non-standard changes assessed? -Did the approved changes meet the intended goal? -Concerning result, does it satisfy stakeholders and more specifically conform to customer requirements? -Were there found any unplanned changes; which are the associated risks? -Concerning the implementation phase, did it exceed the project's constraints? -Are the results documented, for example, in the change risk log?

Risk Monitor and Control

The Risk Monitor and Control process mainly intends to: identify, analyse, control and track new risks. Risk monitor and control can be accomplished with the aid of a variety of methods and techniques, such as for example:

-Risk Reassessment -Meetings (consultation, status update) -Variance Analysis -Trend Analysis -Risk Auditing Alongside the aforementioned CRAM processes an 'Experts' Judgment' may be proven overall constructive. An expert might be, for instance, an individual (project manager, change manager) of a group of executives (Project Steering Committee, Change Advisory Board) which can influence and advice on CRAM results. The hierarchy of CRAM per levels is shown in table 4: Training Monitoring Flexibility Project Management Team An overall, schematic representation of the prototype risk tree is indicated in Fig. 2, which consists of one ( 1) core (root) node, eight (8) parent nodes, five (5) child nodes and its respective sixty-one (61) attributes.

Fig. 2. Change Risk Hierarchy Tree

Definitions of CRAM's Risk Attributes can be seen at: www.changemodel.net [START_REF]Change Risk Assessment (CRAM) Glossary[END_REF].

The 'project management team' is the only parent node possessing child attributes indicating a third level of analysis due to the overall significance in the process of structured project management frameworks. In case a project lacks project management team, then it would be expected to lack coordination and clarity on deliverables.

Depending on the scope and deliverables of a project, CRAM nodes and related risk attribute's hierarchy per level, can change so as to accommodate more of fewer criteria. The only restriction on the hierarchic arrangement of elements is that any element in one level must be capable of being related to some elements in the next higher level, which serves as a criterion for assessing the relative impact of elements in the level below.

Conclusions

Changes and the process of managing related risks differ among organisations and business cultures. As far as change risk assessment modelling is concerned, there is no one-size-fits-all or all-you-can-eat model. Each customer is different, but what stays the same is the expectation and demand for project success, delivery of services and overall customer's expectations conformance. Each project may require different changes and handling which may be reflected in culture, leadership, decision making, norms and directives and consequently in the general way of implementing and managing projects. CRAM attempts to take into account various environmental risk factors which influence project success. These risk factors are modeled, and can be assessed numerically in a top-down hierarchical model. Nevertheless, not all risks are the same or have the same priorities. Most of the objectives have to be measured to a certain degree.

On the other hand, there is no unique way to conform to project changes and estimate the relative risks, or to predefine the results of a project. This is because what may seem to be applicable on an individual basis or at a business level, might be inappropriate or insufficient for specific project environments where a number of vendors and clients coexist. Even though, CRAM may carry a degree of complexity on a per case basis, an aim of its scope is to collect data on a global basis, irrespective of specific structured project management framework approaches. In other words, an overall aim of CRAM would be its adoption in project business scenarios on a consistent and repetitive basis so as to identify patterns related to all parent nodes described previously.

Finally, CRAM does not actually dictate any direct or indirect walkthrough; rather it is regarded as a structured approach for facilitating change risk effectively. Even if for example, no project management framework is adopted, CRAM has exactly the same capabilities concerning change risk identification, assessment and monitor and control processes.

Future Work

Further to the key integration of Change Management, Project Management and Risk Management in terms of CRAM, future research efforts, will focus on the integration (numerical assessment) with quantitative analysis techniques. In addition, CRAM outcomes are characterised by environmental factors and attributes which can form the basis of an Environment-feature-driven model composition.

A framework capable of transforming such a model layer to subsequent models of lower level abstraction would require a pioneer approach to model-driven initiatives.

Such a framework, resides in the logic behind which the Model Driven Business Engineering (MDBE) framework was developed. In fact, it aims at the unification of model-driven aspects of software engineering and business process modeling, thus, attempting to reconcile the benefits of these two model-driven domains. The outcome could be a more attractive solution to model-driven initiatives for corporate entities whereas academia would have the opportunity to rethink an approach to the study of model based engineering and particularly, model-driven development.

Table 1 .

 1 Project

	constraints comparison
	PRINCE2 ® variables	PMBOK ® competing constraints
	Cost	Scope
	Timescale	Quality
	Quality	Schedule
	Scope	Budget
	Risk	Resources
	Benefits	Risks

Table 2 .

 2 Project Risk Categories

	Project Risk Categories
	Technical	Project
	Quality	Legal
	Performance	Environmental
	Change	Scope
	Organisational	Quality
	External / Internal	Schedule
	Business	Process
	Cultural	Requirements

Table 3 .

 3 Identified Change Categories

	Change Categories
	Individual	Rules / Regulations
	Organisational	Evolutionary
	Cost cutting	Revolutionary
	Process	Strategic
	Cultural	Transformational
	Technical	Proactive /Reactive
	Planned / Unplanned	Technological

Table 4 : CRAM Nodes Hierarchy

 4

	Level 1 (Root Node)	Level 2 (Parent Nodes)	Level 3 (Child Nodes)
	Change Risk	Leadership	Performance
		Communication	Motivation
		Culture	Appraisal
		Resistance	Rewards
		Requirements


Mazen Al-Ismail
email: malismai@myune.edu.au

A S M Sajeev
email: sajeev@une.edu.au

Usability Challenges in Smartphone Web Access: A Systematic Literature Review

Keywords: Mobile web, Systematic literature review, Web usability, User experience

Systematic literature reviews facilitate methodical understanding of current advances in a field. With the increasing popularity of smartphones, they have become an important means to access the web. Although the literature on this topic is growing in recent times, there has been no effort yet to systematically review it. This paper reports on a systematic literature review of primary studies from 2007 to 2012 that concern mobile web usability. We identify the usability dimensions tested and the testing procedures adopted in the literature. We anticipate that our work will not only help researchers understand the current state of usability testing of mobile web but also identify the areas where further research is needed in addressing the challenges identified.

Introduction

Web access through wireless devices has exploded in recent times with the increased popularity of smartphones and tablets that run on mobile operating systems such as Android [START_REF] Burnette | Hello, Android: introducing Google's mobile development platform: Pragmatic Bookshelf[END_REF] and IOS [START_REF] Grossman | Invention of the year: The iPhone[END_REF]. According to International Telecommunication Union (ITU), mobile phone subscribers have grown from around two billion in 2005 to close to seven billion by 2013. Mobile broadband access has grown from 268 million in 2007 to 2.1 billion in 2013 [START_REF]Key Global Telecom Indicators for the World Telecommunication Service Sector[END_REF]. While these developments put unprecedented pressure on designers to make web sites easy to access from mobile devices, growth in mobilefriendly websites has been slow. Google, in a survey of its largest advertisers in 2011, found that only 21% of them have mobile friendly websites [START_REF] Ha | Google Pushing Advertisers to Build for Mobile Search[END_REF].

In the mean time, the capabilities of mobile phones have increased from just being able to make phone calls and text messages, to become a sophisticated computing and communication device which can handle internet surfing, send and receive emails, play multimedia and run advanced apps in varying fields from social networking to health to commerce and education. As the number of users of these devices increase and the capabilities of the devices improve, there is a need to understand the challenges that both web developers and web users face in achieving high quality browsing experience on a mobile device. This paper provides a systematic review of the literature on mobile web usability. Systematic literature reviews (SLR) facilitate methodical understanding of current advances in a field. They help both in recording the achievements in well established fields such as medicine, and at the same time, offering in-depth understanding of developments in emerging fields such as in Information Technology. An example of the former is the SLR of effective methods of giving information to cancer patients by McPherson, et al. [START_REF] Mcpherson | Effective methods of giving information in cancer: a systematic literature review of randomized controlled trials[END_REF], and an example of the latter is the SLR of empirical work in global software engineering by Šmite, et al. [START_REF] Šmite | Empirical evidence in global software engineering: a systematic review[END_REF].

Holzinger [START_REF] Holzinger | Usability engineering methods for software developers[END_REF] identifies five characteristics of usability, namely, learnability, efficiency, memorability, low error rate and satisfaction. The International Standards Organisation, on the other hand, gives a more general definition of usability as "the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use" [START_REF]Ergonomic Requirements for Office Work with Visual Display Terminals -Part 11: Guidance on Usability[END_REF].

Therefore, it is safe to say that usability determines the relationship between a product and its user [START_REF] Amelung | Mobile Usability[END_REF]. From an engineering point of view, usability is reducing the complexity of the interface as much as possible so users can focus on their tasks rather than concentrating on the product. We explore how these dimensions are tested. Our research questions are: RQ1: What dimensions of usability are examined in mobile web testing? RQ2: What are the different purposes for which mobile web usability testing is conducted?

The main contributions of the paper are:  It explores the dimensions of usability that are identified in the literature and explain how these dimensions have been assessed with respect to mobile web usability.



It explains the current usability testing practices in mobile web and their limitations.



It applies the systematic review approach illustrated by Kitchenham, et al. [START_REF] Kitchenham | Systematic literature reviews in software engineering-a systematic literature review[END_REF], thus ensuring that papers reviewed are those meeting pre-specified search and quality criteria within the search period. The rest of the paper is organised as follows. In the next section, we explain the research method. In Section 3, we explore the different dimensions of usability testing and review how researchers conduct usability tests. In Section 4 we identify some of the limitations of this research, and finally, in Section 5 we give the conclusions.

Research Method

In order to conduct a systematic literature review, we used the research method explained by Kitchenham and Chatters [START_REF] Kitchenham | Guidelines for Performing Systematic Literature Reviews in Software Engineering[END_REF]. Accordingly, a protocol for literature search was formulated around our research questions. The protocol decided our search terms, choice of search engines, and inclusion and exclusion criteria for the selection of papers. This selection process is explained further in Section 2.1. The papers selected were then assessed based on a set of quality criteria as explained in Section 2.1.1; a score was given to each paper with a view of excluding those that scored less than a threshold value. Finally, the contents of the short-listed papers were compiled and analysed to formulate the answers to our research questions.

2.1

Search Process

The search engines we used were:  Google Scholar: http://scholar.google.com.au  ACM Digital Library: http://dl.acm.org  IEEE Digital Library: http://ieeexplore.ieee.org All search processes were conducted online using the above mentioned libraries. We used the search terms: ((Mobile AND Usability) AND (Web OR Website OR Site) in the title of the article, and year of publication between 2007 and 2012. Even though, we tried to search for literature using the keyword "smartphone", the term has not been widely used in the literature with respect to usability for our period of interest; hence our use of the word "mobile" instead.

We chose to include papers published from 2007 in our review for the following reason. The era of smartphones started largely with the release of iPhones in June 2007. Even though, there were mobile devices such as personal digital assistants (PDA) and WAP (Wireless Access Protocol) capable phones before then, they were not suitable for widespread mobile web access. Thus web usability has become a serious research issue with the advent of smartphones in 2007. The search resulted in 35 papers as shown in Table 1. The results were pruned to remove duplicate entries from different search engines. The following inclusion criteria for the SLR were then applied:


The paper is on the topic of mobile web usability 

The paper reports a primary study 

The paper is peer-reviewed and is written in English 

The paper was not published before 2007  If the paper is a book chapter, then the book includes at least one other paper on mobile web usability In the search results, there were eight duplicate entries, three non-English papers, two papers that were not peer-reviewed, and three papers unrelated to the topic; these were discarded. After this step, we ended up with 19 papers which are listed in Table 2.

Quality Assessment

Each paper's quality for inclusion was evaluated using the following criteria:

 [Q1]
Does the paper provide a clear method on usability testing or suggest an approach for designing a mobile web page to enhance the usability? 

[Q2] Does the paper test any usability dimensions?

Each paper was given a score based on a subjective assessment of how well it answered each of the questions (1 means very well, 0.5 means partly and means not at all) and the net quality rating was the sum of the three scores. The minimum required quality rating was set at 1; all the 19 papers met this rating as shown in Table 2.

Table 2. Papers selected for the study and their quality rating.

Authors, year and citation Title Total Q1 Q2 Total Rosario et al (2012) [START_REF] Rosario | A Study in Usability: Redesigning a Health Sciences Library's Mobile Site[END_REF] A Study in Usability: Redesigning a Health Sciences Library's Mobile Site 3 7

Yeh & Fontenelle (2012) [START_REF] Yeh | Usability study of a mobile website: the Health Sciences Library[END_REF] Usability Study of a Mobile Website: the Health Sciences Library, University of Colorado Anschutz Medical Campus, Experience 3 7

Hong and Kim (2011) [START_REF] Hong | Mobile Web Usability: Developing Guidelines for Mobile Web via Smart Phones[END_REF] Mobile Web Usability: Developing Guidelines for Mobile Web via Smart Phones 3 7

Tsiaousis & Giaglis (2010) [START_REF] Tsiaousis | An Empirical Assessment of Environmental Factors that Influence the Usability of a Mobile Website[END_REF] An Empirical Assessment of Environmental Factors that Influence the Usability of a Mobile Website 0 4

Shrestha (2007) [START_REF] Shrestha | Mobile web browsing: usability study[END_REF] Mobile Web Browsing: Usability Study 3 7

Schmiedl et al (2009) [START_REF] Schmiedl | Mobile phone web browsing: a study on usage and usability of the mobile web[END_REF] Mobile Phone Web Browsing: A Study on Usage and Usability of the Mobile Web 0 4

Jeong & Han (2011) [START_REF] Jeong | Usability study on mobile Web newspaper sites[END_REF] Usability Study on Mobile Web Newspaper Sites Frederick & Lal (2009) [START_REF] Frederick | Mobile Web Usability[END_REF] Mobile Web Usability 3 5

Brown et al (2010) [START_REF] Brown | The blind leading the blind: Web accessibility research leading mobile Web usability[END_REF] Blind Leading the Blind: Web Accessibility Research Leading Mobile Web Usability 3 5

Pendell & Bowman (2012) [START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF] Usability Study of a Library's Mobile Website: An Example from Portland State University 3 7

Kristjansdottir et al (2011) [START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF] Written Online Situational Feedback via Mobile Phone to Support Tsiaousis & Giaglis (2008) [START_REF] Tsiaousis | Evaluating the Effects of the Environmental Context-of-Use on Mobile Website Usability[END_REF] Evaluating the Effects of the Environmental Context-of-Use on Mobile Website Usability 3 6

Ivanc et al (2012) [START_REF] Ivanc | Usability Evaluation of a LMS Mobile Web Interface[END_REF] Usability Evaluation of a LMS Mobile Web Interface 3 5

3

Usability Design and Testing Practices

Summative Versus Formative Evaluation

Usability tests can be formative or summative [START_REF] Amelung | Mobile Usability[END_REF]. Formative evaluation occurs during the development process, whereas summative evaluation occurs after the mobile site is designed and constructed. For formative evaluation, since the system development is not complete, testing is conducted using a software emulator. Another common method of formative evaluation is to use website wireframes which are skeletal pagelayouts drawn on paper. When a participant "clicks" on a button, the sheet showing the page resulting from the click replaces the current sheet. While summative tests can use field tests or laboratory tests, formative testing needs a laboratory setup because prototype testing is difficult to perform in the field.

Testing Different Dimension of Usability

From the ISO definition [START_REF]Ergonomic Requirements for Office Work with Visual Display Terminals -Part 11: Guidance on Usability[END_REF] given in Section 1, we can deduce three dimensions of usability, namely, effectiveness, efficiency and user satisfaction. These three are the most common dimensions for which usability testing is conducted. Generally, as part of the test, participants are asked to use their mobile phones to perform a number of tasks (e.g., a task in [START_REF] Carta | Support for remote usability evaluation of web mobile applications[END_REF] is to check the United Airlines departure flights to Chicago). Effectiveness of a mobile web page was measured by the successful number of completed tasks and efficiency as the time taken for task completion. A high completion rate and a short period of time required to complete the tasks indicate that the examined system is effective and efficient, respectively. Satisfaction is the degree of accessibility and comfort perceived by the user of the measured system. It is measured by interviewing or surveying the participants to explore their user experience with the system. The normal practice is to use a post-test questionnaire of various satisfaction items with responses measured on a Likert-type scale. As exceptions, Kristjánsdóttir, et al. [START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF] used semi-structured interviews in addition to a questionnaire, and Yeh and Fontenelle [START_REF] Yeh | Usability study of a mobile website: the Health Sciences Library[END_REF] used product reaction cards [START_REF] Benedek | Measuring Desirability: New methods for evaluating desirability in a usability lab setting[END_REF] which are explained later in this section. In exploring the above three dimensions of usability, authors have used various usability terms which include error rates [START_REF] Hong | Mobile Web Usability: Developing Guidelines for Mobile Web via Smart Phones[END_REF][START_REF] Tsiaousis | An Empirical Assessment of Environmental Factors that Influence the Usability of a Mobile Website[END_REF][START_REF] Carta | Support for remote usability evaluation of web mobile applications[END_REF][START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF][START_REF] Tsiaousis | Evaluating the Effects of the Environmental Context-of-Use on Mobile Website Usability[END_REF], ease of use [START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF], user experience [START_REF] Shrestha | Mobile web browsing: usability study[END_REF][START_REF] Schmiedl | Mobile phone web browsing: a study on usage and usability of the mobile web[END_REF][START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF][START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF], usefulness [START_REF] Yeh | Usability study of a mobile website: the Health Sciences Library[END_REF][START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF][START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF], appearance [START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF], feasibility [START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF], accessibility [START_REF] Shrestha | Mobile web browsing: usability study[END_REF][START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF], effort [START_REF] Shrestha | Mobile web browsing: usability study[END_REF], understandability [START_REF] Yeh | Usability study of a mobile website: the Health Sciences Library[END_REF][START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF], clarity [START_REF] Shrestha | Mobile web browsing: usability study[END_REF][START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF] and relevance [START_REF] Yeh | Usability study of a mobile website: the Health Sciences Library[END_REF]. These terms are not all independent (for example, a low error rate is discussed in the context of effectiveness, and ease of use in the context of satisfaction), which leads to a classification of usability dimensions as shown in Fig. 1. Next, we provide the dimensions and the contexts of usability tests in the literature. The tests include investigating usability of websites on mobile phones vis-à-vis desktops, and mobile optimized sites vis-à-vis non-optimised sites; they also include studying the effect of environmental factors such as background noise on mobile web usability, and studying specific web applications related to library access and health intervention. Rosario et al. [START_REF] Rosario | A Study in Usability: Redesigning a Health Sciences Library's Mobile Site[END_REF] tested the effectiveness of redesigning a health science library's mobile site. Firstly they implemented open-ended questions where participants (library users) identified the most needed services, namely the need to search journal databases, access full-text articles, access databases, access e-books, search library catalogue, and find the library hours. After that, participants were invited to perform tasks using a prototype design on paper, simulating the intended mobile library web. While all participants could complete tasks such as finding the library hours, only 10% could complete the task of viewing full-text of an article on line; they found the search interface confusing and the screen size inadequate. Finally, the mobile library web was designed with basic HTML incorporating the results of the usability testing of the prototype.

A similar study done by Yeh and Fontenelle [START_REF] Yeh | Usability study of a mobile website: the Health Sciences Library[END_REF] measured the effectiveness, efficiency and satisfaction of a health sciences library by comparing optimized and non-optimized mobile websites. Effectiveness and efficiency were measured by asking participants to perform 10 information retrieval tasks. Yeh and Fontenelle used Microsoft's desirability toolkit with product reaction cards [START_REF] Benedek | Measuring Desirability: New methods for evaluating desirability in a usability lab setting[END_REF] to measure satisfaction; each card has a word that could describe a usability facet (examples are words like accessible, confusing, efficient, frustrating etc.) and participants are asked to select cards that reflect their user experience. As a further measure of satisfaction, participants are later interviewed to find reasons for their choice of reaction cards. Unlike a Likert-type questionnaire that tends to have mostly positive statements, which might result in participants unintentionally selecting positive answers, the product reaction cards are designed with 40% negative words and the remaining positive words. The authors found that optimized websites significantly improved the effectiveness (19% improvement) and efficiency (9% improvement) of information retrieval on smartphones. With respect to user satisfaction, there were less number of negative words (slow and unrefined) chosen by the participants in the case of the optimised site than in the case of non-optimised site (confusing, difficult, hard to use, ineffective, not valuable, slow, time consuming, and unrefined). In another usability experiment, Pendell and Bowman [START_REF] Pendell | Usability Study of a Library's Mobile Website: An Example from Portland State University[END_REF] tested an existing mobile-optimized library website by asking participants to perform five tasks to measure effectiveness, efficiency and satisfaction. Effectiveness was measured in terms of errors which they classified into three categories: fatal (for example, not being able to complete a task), major (for example, being able to complete a task but with delays) and minor (completing a task with errors easily corrected by the participant). Satisfaction was measured by asking participants to take notes of their experience and further through a post-test survey on the site's appearance and ease of use. As in the case of [START_REF] Rosario | A Study in Usability: Redesigning a Health Sciences Library's Mobile Site[END_REF], all participants completed the task of finding the library hours successfully. However, many users took a long time to search for a book; one of the reasons was that the developers included in the mobile site pull down menus and options to search different types of collections simply because they were available in the full site; this is an example of a challenge in mobile optimization where judicious decisions need to be made on what to exclude from the full site in order to improve usability.

Tsiaousis & Giaglis [START_REF] Tsiaousis | An Empirical Assessment of Environmental Factors that Influence the Usability of a Mobile Website[END_REF] reported a very different kind of study where they tested the impact of environmental contexts on effectiveness, efficiency, and satisfaction of general mobile web access. The study was conducted in a laboratory to control and simulate environmental contexts. For example, they studied the impact of meaningful sound (e.g. someone talking) versus background noise (e.g printer or fax sound) on participants who were browsing a mobile website; they found that sounds with 'semantics' significantly decreased effectiveness and efficiency compared to meaningless noise. This test was conducted in a laboratory since it is hard to control background sound in a field test; field factors can be easily controlled in a laboratory, for example, by turning on printers to create noise and asking volunteers to start dialogues with participants to create sound with semantics.

In another study, Schmiedl et al. [START_REF] Schmiedl | Mobile phone web browsing: a study on usage and usability of the mobile web[END_REF] measured efficiency by comparing the access on a mobile phone of a full website versus the corresponding mobile optimized site. They used five mobile optimised sites such as ebay and amazon and asked the participants to perform three to four tasks first on the full website and then on the mobile optimised site, and the time of accomplishing tasks were measured. They found 30 to 40% increase in efficiency in accessing mobile-optimized sites compared to the non-optimized versions. User satisfaction was also measured by seeking posttest comments from the participants, and interestingly, feature limitations were reported as annoying by the participants. This illustrates the dilemma between increasing efficiency by reducing features to make access faster, and achieving user satisfaction by providing a feature rich site. Schmiedl et al. also found that touch screen phones were preferred for surfing, with an additional keyboard found as an advantage whereas use of a pen for navigation was disliked.

Although many studies measured effectiveness by the number of successful tasks completed, Hong and Kim [START_REF] Hong | Mobile Web Usability: Developing Guidelines for Mobile Web via Smart Phones[END_REF] measured it by the number of errors that were made by participants. They conducted three studies. In the first study, participants were observed to find out when and where they made errors during web browsing on mobile. They used the think-aloud technique were participants are asked to speak their mind while performing the tasks and the session was video recorded. The common errors were related to (a) interaction where novice users had trouble with finding the right icons and hyperlinks on the screen and (b) navigation from one page to another. In the second study the participants were interviewed to collect qualitative data on their browsing experience. The most frequent complaint was the complexity of the websites accessed. Finally, 33 mobile sites were analysed for their efficiency of design layout which resulted in recommendations on good layout.

Shrestha [START_REF] Shrestha | Mobile web browsing: usability study[END_REF] measured effectiveness, efficiency, and satisfaction by asking participants to complete four tasks on a desktop browser and then on a mobile browser. The tasks on both the desktop and mobile browsers were similar in scope, and the websites were not optimized for mobile devices. The number of completed tasks on the mobile phone was compared with the number of completed tasks on the desktop, which reflected effectiveness. In order to measure efficiency, the tasks were timed to compare the completion time on mobile versus desktop. Finally, participants were asked to rate their level of satisfaction with each task performed. This is one of the earliest papers in our study literature when there was hardly any mobile optimized sites. Understandably, too much scrolling and getting visibly lost in the website were the highest reported usability problems. Of the total time spend on tasks 80% was on mobile device and only 20% on desktop thus indicating the difficulty of web tasks on mobile phones when the experiment was conducted.

Whereas all of the studies above measured effectiveness and/or efficiency, one study that did not use these "standard" dimensions of usability testing is by Kristjánsdóttir, et al. [START_REF] Kristjánsdóttir | Written online situational feedback via mobile phone to support self-management of chronic widespread pain: a usability study of a Web-based intervention[END_REF]. They tested a mobile-web solution for cognitive behavioural intervention for people with chronic pain; essentially, patients kept an online diary of their feelings and behaviour on the web using a mobile phone and received feedback from a therapist in the same way. The usability test involved testing feasibility and usefulness of the mobile web-based intervention. Feasibility was measured in terms of user experience and satisfaction which were assessed through questionnaires and semi-structured interviews. Usefulness of the feedback received was also measured through a daily two-item questionnaire. Most participants found the mobile web-based solution "supportive, inspiring and meaningful". Twothirds of the participants reported that the solution has increased their insights into their symptoms and taught them new methods to cope with their symptoms. However, negative issues included frustrations with filling out the diary and receiving a validation error because of poor internet connection, frustration with a patient feeling misunderstood and not being able to explain herself and the therapist sending feedback to the wrong patient by clicking the wrong button; problems that probably would not occur in a face-to-face situation.

Limitations

As in any research, this review has several limitations. We have chosen the research period from 2007 to 2012 which means that papers published outside this period are not included. Web access on mobile devices has been of interest in the days of PDAs and feature phones, and therefore, there will be usability studies on them which we have not covered. We did not include them in our research primarily because the issues identified in those days are probably of less interest with the paradigm shift that smartphones have achieved. We note that some of the early tests within our period of study also used feature phones and therefore their results may not be as relevant as later studies.

Even though, we believe that google-scholar is very comprehensive in its literature search capability, our choice of search engines and keywords may have caused some relevant papers not to be discovered.

Our search term used the word "mobile", however, all the papers resulted from the search used mobile phones for their usability testing; in future, other popular devices such as android tablets and iPads will also become part of usability test literature.

Discussion & Conclusion

As the capabilities of mobile devices increase, they are rapidly becoming popular for browsing the web. However, recent surveys have shown that even commercial companies, for which web as a medium for marketing and advertising is critical, are lacking in optimizing their web sites for mobile access [START_REF] Ha | Google Pushing Advertisers to Build for Mobile Search[END_REF][START_REF] Cookson | UK companies not ready for mobile internet[END_REF]. This adds to the usability challenges of browsing the web from a mobile device. A systematic literature review is often used to methodically identify the issues reported in the literature. We employed the same method to answer the research question: What dimensions of usability are examined in mobile web testing?

We identified the usability factors that are tested by different researchers. The three dimensions most studies used are effectiveness, efficiency and satisfaction. Effectiveness and efficiency are measured quantitatively whereas satisfaction is measured often through post-test surveys or interviews with the users; use of product reaction cards [START_REF] Benedek | Measuring Desirability: New methods for evaluating desirability in a usability lab setting[END_REF] was also found in one study. These dimensions were found to be subsuming a larger number of usability factors, the relationships between which we captured in Fig. 1. Our final research question was: What are the purposes for which mobile web usability testing is conducted? We found that researchers conducted usability tests to:  Compare user experience between mobile optimized sites and non-optimized sites  Compare user experience between websites accessed on desktops and accessed on mobile phones  Study the effect of environmental factors such as background noise on mobile web usability  Study the effectiveness of mobile web-based applications such as in health 

Test the usability of web sites such as University library sites after optimizing them for mobile devices. Both formative and summative evaluations are presented in the literature. We observed the following major limitations in the current usability testing practices:  Most studies use small samples which makes statistical analysis difficult. The absence of discussions on generalizability of the results in most of the studies is a consequence of this limitation.



The use of software tools to streamline the collection and analysis of data is also found to be lacking. A few researchers have used tools to draw paper prototypes for formative studies but not for the collection and analysis of data. One of the papers in the period of study described a software tool for collection of usability data [START_REF] Carta | Support for remote usability evaluation of web mobile applications[END_REF] which is promising; however, this or other tools are not used in the empirical papers reviewed. Tool support will assist in measuring, particularly, effectiveness and efficiency in web usability.  Some tests were not controlled for variability in the mobile devices used; this could particularly be a problem since device heterogeneity is one of the challenges in mobile web usage. In summary, the systematic literature review has identified and categorised the usability dimensions that are tested by mobile web researchers, and the purposes for which usability testing is currently employed. We believe, this study will assist researchers and practitioners in understanding the current challenges and limitations of mobile web usability, and encourage them to work on addressing them.

Fig. 1 .

 1 Fig. 1. Classifying usability dimensions.

Table 1 .

 1 Number of papers from initial search

	Digital Library Name	Number of Papers identified
	Google Scholar	27
	ACM Digital Library	4
	IEEE Digital Library	4

Acknowledgments. An earlier draft by the first author was edited with the help of Scribendi website.


Gewu Bu
email: gewu.bu@lip6.fr

Maria Potop-Butucaru
email: maria.potop-butucaru@lip6.fr

Total Order Reliable Convergecast in WBAN

Keywords: Wireless Body Area Networks, Total Order Reliable Convergecast, Networks Modelization

This paper is the first extensive work on total order reliable convergecast in multi-hop Wireless Body Area Networks (WBAN). Convergecast is a many-to-one cooperative scheme where each node of the network transmits data towards the same sink. Our contribution is threefold. First, we stress existing WBAN convergecast strategies with respect to their capacity to be reliable and to ensure the total order delivery at sink. That is, packets sent in a specific order should be received in the same order by the sink. When stressed with transmission rates up to 500 packets per second the performances of these strategies decrease dramatically (more than 90% of packets lost). Secondly, we propose a new posture-centric model for WBAN. This model offers a good characterization of the path availability which is further used to fine tune the retransmission rate thresholds. Third, based on our model we propose a new mechanism for reliability and a new converge-cast strategy that outperforms WBAN dedicated strategies but also strategies adapted from DTN and WSN areas. Our extensive performance evaluations use essential parameters for WBAN: packet lost, total order reliability (messages sent in a specific order should be delivered in that specific order) and various human body postures. In particular, our strategy ensures zero packet order inversions for various transmission rates and mobility postures. Interestingly, our strategy respects this property without the need of additional energy-guzzler mechanisms.

INTRODUCTION

Wireless Body Area Networks (WBAN) is a cross-area between Wireless Sensor Networks (WSN) and Delay Tolerant * This work has been supported by the SMARTBAN project, Labex SMART Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICDCN 2017 Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$15.00.

Networks (DTN) with as main objective the collection of physiological parameters from sensors deployed on a human body that undergo the human mobility. Designing efficient protocols for this area is a challenging task. One of the particularities of the WBAN area are the specific rates of the physiological parameters monitoring. In [1] the authors advocate that in the current applications of WBAN, the transmission rate is less than 10 packets per second. However, these rates may drastically increase in the future. Therefore, in order to meet the requirements of medical monitoring, WBAN is needed to withstand a continuous packets flows. In some critical applications such as monitoring patients during a surgery or post-surgery the flows carry vital alerts. Missing some of them or wrongly interpret the flow of data due to packets inversions may have a tremendous impact. In this context designing efficient total order reliable protocols is crucial for saving human lives.

In this paper we focus the convergecast communication primitive since this is one of the main building block in WBAN. Convergecast allows nodes to transmit data towards a sink. Convergecast has the total order property if packets are delivered at the sink in the same order as they have been transmitted.

Recently, [2] surveys the existing work on convergecast on various areas including Delay Tolerant Networks, DTN and Wireless Sensor Networks, WSN. They argue that most of the existing strategies are not directly implementable in WBANs due to their needs in computing capacities, memory or their energy consumption. Finally, building on top of [3,4,5,6,7], they propose and evaluate three classes of convergecast strategies taylored for WABN: 1) Multi-Paths based strategies, 2) Attenuation-based strategies and 3) Gossip-based strategies. Their evaluation does not target the total order delivery property of these strategies neither their resistance to increasing flows of messages. It merely focus on the resilience to the human body mobility and energy consumption. They measure the following parameters: percentage of received messages (under the hypothesis that each node sends a single message in each run), end-to-end delay and the number of transmission/receptions since this is a good indicator of the energy consumption.

In terms of reliability, to the best of our knowledge there are three main reliability mechanisms used in WBAN. The first mechanism computes and presets a static or dynamic overlay path for collecting data. Strategies proposed in [8,9] and attenuation-based strategies in [2] choose a reliable leader or a set of relay nodes as the next hop(s) to help in forwarding the packets. Strategies proposed in [10, 11,