Lyes Benyoucef

A Simulation Optimization Methodology for Supplier Selection Problem

Strategic sourcing plays a critical role in supply chain planning. Supplier selection is one of the decisions that determine the long-term viability of a company. In this paper, a new simulation optimization methodology is presented to make decisions on supplier selection. The methodology is composed of three basic modules: A genetic algorithm (GA) optimizer, a discrete-event simulator and a supply chain modelling framework. The GA optimizer continuously search different supplier portfolio and related operation parameters. Corresponding simulation models are automatically created through an object-oriented process. After simulation runs, the fitness value of candidate supplier portfolio is derived from the estimations of key performance indicators (KPI). The fitness is returned to the GA to be utilized in searching the next prominent direction. By using the proposed methodology, the supply chain planner is able to optimize the supplier portfolio with taking uncertainties into consideration. Finally, a real-life case study is presented to illustrate the applicability of the proposed methodology. Experimental results are presented and analysed.

A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization

Abstract Nowadays, in a hotly competitive environment, companies are continuously trying to provide products and/or services to customers faster, cheaper, and better than the competitors do. Managers have learned that they cannot do it alone; rather, they must work on a cooperative basis with other organizations in order to succeed. Although the resulting enterprise networks are more competitive, the tasks for planning, management and optimization are much more difficult and complex. In this paper, we present a newly developed toolbox “ONE” to support decision makers for the assessment, design and improvement of such supply chain networks. The toolbox comprises innovative and user-friendly concepts related to the modeling, simulation and optimization of modern enterprise networks. Two case studies, proposed by partners from automotive and textile industries, are presented and computational results analysed.

Stochastic multi-objective production-distribution network design using simulation-based optimization

This paper addresses the design of production-distribution networks including both supply chain configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimization methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimize not only the structure of the production-distribution network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the production-distribution network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed method is applied to a real life case study from automotive industry.

A new approach for evaluating agility in supply chains using Fuzzy Association Rules Mining

Besides its effectiveness, supply chain management (SCM) is a complex process because of the stochastic and dynamic nature, multi-criterion and ever-increasing complexity of supply chains. Furthermore, companies have realized that agility is essential for their survival and competitiveness. Consequently, there is no generally accepted method by researchers and practitioners for designing, operating and evaluating agile supply chains. Moreover, the ability to build agile supply chain has developed more slowly than anticipated, because technology for managing agile supply chain is still being developed. Therefore, in this paper, we develop a new approach based on Fuzzy Association Rule Mining to support the decision makers by enhancing the flexibility in making decisions for evaluating agility with both tangibles and intangibles attributes/criteria such as Flexibility, Profitability, Quality, Innovativeness, Pro-activity, Speed of response, Cost and Robustness. Also, by checking the fuzzy classification rules, the goal of knowledge acquisition can be achieved in a framework in which evaluation of agility could be established without constraints, and consequently checked and compared in several details. Efficacy and intricacy of the proposed approach for finding fuzzy association rules from the database for evaluating agility is demonstrated with the help of a numerical example.

What's the buzz about moving from ‘lean’ to ‘agile’ integrated supply chains? A fuzzy intelligent agent-based approach

The ability to build lean and agile supply chains has not developed as rapidly as anticipated, because the development of technology to manage such concepts of lean/agile for integrated supply chains is still under way. Also, due to ill-defined and vague indicators, which exist within leanness/agility assessment, many measures are described subjectively by linguistic terms, which are characterized by vagueness and multi-possibility, and the conventional assessment approaches cannot suitably or effectively handle such dynamic situations. In this paper, we propose a novel approach to model agility (which includes leanness) and introduce dynamic agility level index (DALi) through fuzzy intelligent agents. Generally, it is difficult to emulate human decision making if the recommendations of the agents are provided as crisp, numerical values. The multiple intelligent agents used in this study communicate their recommendation as fuzzy numbers to accommodate ambiguity in the opinion and the data used for modelling agility attributes for integrated supply chains. Moreover, when agents operate based on different criteria pertaining to agility like flexibility, profitability, quality, innovativeness, pro-activity, speed of response, cost, robustness, etc., for integrated supply chains, the ranking and aggregation of these fuzzy opinions to arrive at a consensus is complex. The proposed fuzzy intelligent agents approach provides a unique and unprecedented attempt to determine consensus in these fuzzy opinions and effectively model dynamic agility. The efficacy of the proposed approach is demonstrated with the help of an illustrative example.

A fuzzy TOPSIS based approach for e-sourcing

This study presents a methodology for solving the sealed bid, multi-attribute reverse auction problem of e-sourcing in which the sales item is defined by several attributes, the buyer is auctioneer, and the suppliers are the bidders. There is only one buyer and a number of suppliers. Both qualitative and quantitative attributes of benefit and cost types are considered for solving the winner determination (WD) problem of reverse auction. Here, the WD problem is considered as multi-criteria decision making problem (MCDM). In order to address the imprecision of suppliers or decision makers in formulating the preference value of various attributes in MCDM, a fuzzy TOPSIS based methodology along with a mechanism for determination of fuzzy linguistic value of each attribute is proposed in this article. Entropy method is utilised to enumerate the weights of various attributes automatically without involvement of decision makers. An illustrative example is presented to demonstrate the applicability of the proposed methodology.

An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system

With burgeoning global markets and increasing customer demand, it is imperative for companies to respond quickly and cost effectively to be present and to take the lead among the competitors. Overall, this requires a changeable structure of the organization to cater to a wide product variety. It can be attained through adoption of the concept of reconfigurable manufacturing system (RMS) that comprises of reconfigurable machines, controllers and software support systems. In this paper, 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. With the help of a numerical example, the efficacy of the proposed approach is demonstrated.

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.

Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem

Two new fuzzy hybrid approaches for the strategic supplier selection problem are developed.The first approach combines the fuzzy consensus-based possibility measure and TOPSIS method.The second approach combines the fuzzy consensus-based neat OWA and goal programming model.The CCSD model is used to compute the criteria weights.Comparison between individual solutions and collective solution using the Levenshtein distance. The current complexity of supply chains (SC) activities requires the need for coordination between supply chains partners to maximize the efficiency. Considered by practitioners as one of the main SC coordination problems, this paper considers the strategic supplier selection problem. Fuzzy set is used in order to address the imprecision of supply chain partners in formulating the preferences values of various selection criteria. The problem is formulated as a multi-stakeholder multi-criteria (MSMC) decision making problem and solved using two novel approaches. The first hybrid approach combines the fuzzy consensus-based possibility measure and fuzzy TOPSIS method. The second hybrid approach combines the fuzzy consensus-based neat OWA and goal programming model where, the inclusion and participation of stakeholders in the decision-making process is explicit. For each approach, the correlation coefficient and standard deviation (CCSD) based objective weight determination model is used to compute the criteria weights. To demonstrate the applicability of the proposed approaches, a simple example of strategic supplier selection problem is presented and the numerical results analyzed. Moreover, for each approach, the deviations between individual solutions and collective solution are evaluated using the Levenshtein distance. Finally, the advantages and disadvantages of each approach are listed.

Managing long supply chain networks: some emerging issues and challenges

Purpose – The emergence of new manufacturing technologies, spurred by intense competition, will lead to dramatically new products and processes. New management systems, organizational structures, and decision‐making methods will also emerge as complements to new products and processes. This paper attempts to investigate technologies, systems and paradigms for the effective management of networked enterprise (supply chain networks), especially long supply chains. In doing so, the paper presents not only an exhaustive literature review to identify the complexities, gaps and challenges associated with long supply chains but also the emerging enabling technologies to support these gaps and challenges.Design/methodology/approach – The approach takes the form of an interview of industrials, researchers and a literature review.Findings – “Competition in the future will not be between individual enterprises but between competing supply chains.” Business opportunities are captured by groups of enterprises in the s...