Team costeam

Overall Objectives
Scientific Foundations
Application Domains
New Results

Section: New Results

Performance evaluation and systems sizing

Participants : Lyes Benyoucef, Sophie Hennequin, Nidhal Rezg, Nathalie Sauer, Alexandre Sava.

This part concerns the performance evaluation of critical problems. Setting up a new production system of goods or services, or modifying either the physical structure or the operation of an existing system, requires the system's performance to be evaluated and optimized.

Industrial systems and services systems

The first general subject concerns the scheduling of flexible systems (production or services) without storage capacity and with a specific blocking constraint encountered in many industrial processes. In classical blocking situations, a machine remains blocked by a job until this job starts the operation on the next machine in the routing. For the particular type of blocking constraints considered in our work, the machine remains blocked by a job until its operation on the downstream machine is finished (RCb constraint). Another new type of blocking constraint has been proposed and defined [48] . Heuristics and genetic algorithms for solving these Flow-shop problems are developed and different initial populations are tested to find the best adapted [19] , [39] . To solve the Job-Shop scheduling problem, possible conflicts situations are characterized and a heuristic method which avoids conflicting situations has been proposed [48] . In [35] , a necessary and sufficient condition to obtain this conflict is presented, a lower bound to solve this problem is given and a meta-heuristic method based on simulated annealing is presented to obtain a solution. Its performances are compared with the optimal solutions until certain size and lower bound for more complex problems.

The impact of delays such as transportation, production or lead-times, is also studied in another aspect of our research program. The basic idea of this study is to develop Perturbation Analysis (PA) for performance evaluation and optimization of failure-prone manufacturing systems. Indeed, in the domain of discrete event systems, it was discovered in the early 1980s that event-driven dynamics give rise to state trajectories (sample paths) from which one can very efficiently and nonintrusively extract sensitivities of various performance metrics with respect to at least certain types of design or control parameters. This has led to the development of a theory for perturbation analysis in discrete event systems. Using PA, one obtains unbiased gradient estimates of performance metrics that can be incorporated into standard gradient-based algorithms for performance evaluation and optimization purposes. Systems with delays hardly have begun to be investigated, and the few existing results indicate that the problem may become challenging, and the PA derivatives are more complicated than those that would be obtained for the system without delay. In our work we consider two models: discrete and continuous flow models, with inclusion of delivery times for more realistic performance evaluation and optimization of failure-prone manufacturing systems. While in most traditional continuous flow models the flow rates involved are treated as fixed parameters, a continuous flow model has the extra feature of treating flow rates as stochastic processes. Furthermore, continuous models have been shown to be very useful in simulating various kinds of high volume manufacturing systems and in this case are a good approximation of discrete settings. However, when the study should consider each part independently, discrete flow models are more appropriate. Unfortunately, PA estimates could become in this case biased (hence unreliable for control purposes) when significant discontinuities in sample functions appear. One salient feature of our work is the explicit modeling of delays without destroying the nature of pure continuous and discrete flow models. Thus, the main innovation of our research is to consider these both models with delays and to define PA estimates for performance evaluation and optimization. In a first step, discrete flow model for a simple manufacturing system composed by a failure-prone machine, a buffer and a customer is considered. The demand is stochastic and the delivery time between the buffer and the customer is supposed to be constant. The control policy applied to the machine is a hedging point policy which has been proved to be optimal for a system without delivery times and which ensures that the material does not exceed a given number of parts. A simulation based on perturbation analysis is then proposed for performance evaluation and optimization. The goal of the optimization is to evaluate the optimal buffer level (hedging point) to minimize the total cost function which is the sum of inventory cost, backlog cost, transportation cost [66] . The PA estimates are shown to be unbiased and comparison with discrete event system simulation proves that our results are very interesting and performing. In a second step of our work, the study of both continuous and discrete flow models with constant delivery times is pursued. By theoretical and numerical results it is shown that these models have the same behavior and for each model unbiased gradient estimates are obtained [50] .

Reconfigurable manufacturing systems (RMS) have been acknowledged as a promising means of providing manufacturing companies with the required production capacities and capabilities. This is accomplished through reconfiguring the system elements over the time for a diverse set of individualized products often required in small quantities and with short delivery lead time. In [7] , we propose a new approach to generate the dynamic process plan for reconfigurable manufacturing system. Initially, the requirements of the parts/products are assessed which are then compared with the functionality offered by machines comprising manufacturing system. If the production is feasible an optimal process plan is generated, otherwise the system shows an error message showing lack of functionality. Using an adapted NSGA-2 algorithm, a multi-objective scenario is considered with the aim of reducing the manufacturing cost and time. Moreover, to map the manufacturing system capabilities and other characteristics, RMS necessitate the developpment of a suitable index. As a new result, we developed an index to measure the reconfigurability of RMSs keeping in mind their various core characteristics such as modularity, scalability, convertibility and diagnosability. These characteristics are mapped together using Multi-Attribute Utility Theory (MAUT). We can easily use this index to find the reconfigurability of a system possessing different characteristics [11] .

Case of supply chains

This subject concerns supply chains. We begin by the more classic logistic systems, and then move on to enterprise networks which are characterized by an extensive use of information technology.

In the recent years, internet technologies have yielded a range of applications and decision support tools to manage the procurement process between industrial buyers and their suppliers. These tools aid in contract negotiation, cost analysis, supplier selection and optimal supplier performance. They provide the firms opportunities for entering new market, lowering their transaction cost, and improving their supply chain management capabilities while improvising their profitability over coming years. This process of identifying, evaluating and configuring the optimal grouping of buyers and suppliers in a supply chain using internet is often referred as e-sourcing. Traditionally e-sourcing are based on reverse auction or electronic auction which is an online, real-time dynamic auction between a industrial buyer and a group of its pre-qualified suppliers who compete against each other to win the business to supply goods or services that have clearly defined specifications for design, quantity, quality, delivery, and related terms and conditions. As results, we formulated the winner determination problem of multi-criteria reveres auction as multi criteria decision making problem and an extension of TOPSIS method based on fuzzy logic and interval arithmetic is proposed to solve the decision problem. In some cases, precise values are inadequate to model the criteria in real life. Therefore, we used fuzzy linguistic variables to map qualitative criteria in order to remove the vagueness of the decision makers and at the same time interval data is used for quantitative preferences which are difficult to be represented by exact numerical values. Correlation coefficient and standard deviation integrated (CCSD) methodology is proposed for automatic enumeration of the criteria weights and a mechanism is also developed for determination of the preferences of qualitative criteria using fuzzy linguistic variables [21] , [22] .

Supplier selection with order splitting represents one of the most important functions to be performed by the purchasing department that determines the long-term viability of dynamic supply chains. As a second result, a novel approach for automatic knowledge acquisition, which clubs supplier selection process with order splitting for dynamic supply chains based on the attained knowledge from the variations in the market is developed. Moreover, the suggested approach imitates the knowledge acquisition and manipulation in a manner similar to the human schedulers who have gathered considerable knowledge and expertise in a given domain. As per this concept, those decision criteria for supplier selection are considered first, which are qualitatively meaningful like performance, service quality, innovation, risk etc. and thereafter their application is quantitatively evaluated. State variables are derived from the decision criteria to match the factors (flexibility, responsiveness, agility, position etc) associated with local competitive situation of the candidate supplier. Therefore, logically it can be inferred that the developed approach can generate decision making knowledge as a result, the developed combination of rules for supplier selection can easily be interpreted, adopted and at the same time if necessary, modified by supply chain decision makers [55] , [54] .

Moreover, another research direction addresses the development of an intelligent simulator for complex supply chains analysis. It is well know that most of the simulation tools are able to map large size supply chains and can accommodate complex random phenomena. Nevertheless, they have significant weakness in the power of decision-making. Indeed, most of the problems of decision-making are typically determined by simplified rules. For this reason, involving optimization tools in decision-making will allow the simulation to explore the real performance of a supply chain. Motivated by the limitations of existing supply chains simulation and optimization tools, as a result we combined them in a single tool. More specifically, we developed an 'intelligent' simulation tool with an embedded optimization tool to solve various decision-making problems encountered during the simulation of a complex supply chain. Including an optimization tool in a simulation tool allows accurate assessment of supply chains performances and overcome the lack of powers of decision-making in traditional simulation tools [5] , [33] .

Another research area for our team concerns the traceability phenomenon implementation within the production organization, particularly in the field of raw materials management in the food industry. The objective is to minimize the raw material's dispersion in the manufactured products. We seek to solve the problem of raw materials allocation into finished products, in order to minimize its dispersion and moreover, the products recall if needed. Dispersion optimization has been obtained using a genetic algorithm. The dispersion criteria are afterwards used to determine production's criticality in terms of sanitary risk, from which it is possible to optimize the processes of picking and dispatching. In [47] , we explore an approach to the risk measurement problem of any multi-parameter production framework using precise decision making tools. The application model of artificial neural networks is presented in order to determine the global risk related to a given production. The proposed tool uses a feed-forward network able to treat any set of nonlinearly separable production markers. The risk values obtained with this approach are incorporated into the expedition management system in order to perform smarter deliveries and more accurate sanitary controls.


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