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The purpose of this paper is to propose a new approach for the supply chain management. This approach is based on the virtual enterprise paradigm and the usage of multi‐agent concept. The base component of our approach is a virtual enterprise node (VEN). The supply chain is viewed as a set of tiers (corresponding to the levels of production), in which each partner of the supply chain (VEN) is in relation with several customers and suppliers. Each VEN belongs to one tier. The main customer gives global objectives (quantity, cost and delay) to the supply chain. The mediator agent (MA) is in charge to manage the supply chain in order to respect those objectives as global level. Those objectives are taking over to negotiator agent at the tier level (NAT). This architecture allows supply chains management which is completely transparent seen from simple enterprise of the supply chain. The use of multi‐agent system allows physical distribution of the decisional system. Moreover, the hierarchical organizational structure with a decentralized control guarantees, at the same time, the autonomy of each entity and the whole flexibility.

In the decline phase of product lifecycle, industrials need to re-design their products to introduce new functions and/or customers’ new preferences. These changes may not only affect the product’s bill of material, but also its supply chain network. Consequently, new supply chain costs are generated. This paper addresses the problem of supply chain configuration considering new product re-design using a multi-agent system (MAS). The objective of the system is to ensure good collaboration between two different points of view, supply chain partners and product designers, to make better decisions. To model the proposed system, we select the multi-agent system engineering (MaSe) methodology. The MAS framework contains three types of agents, namely, “product design agent” and “supply chain agents” which are fitted with optimization tools. These tools allow costs’ optimization and selection of supply chain means (suppliers, technologies, etc.). Finally, the system contains a “communication agent” acting like a mediator; it facilitates data exchange between designers. To support distributed decision-making, two models of mixed integer linear programming are adopted and implemented within the framework for supply chain optimization. The overall MAS approach was tested in simulation with a case study. The objective of the simulation is to choose among three product alternatives the cheapest one based on its supplying and production costs, under capacity constraints. The MAS was able to find the best product alternative among three alternatives proposed by product design team and select optimal supply chain means. The optimal supply chain contains two suppliers: one machine and one subcontractor to satisfy customer’s demand.

The aim of this paper is to address the problem of supplier selection in a context of an integrated product design. Indeed, the product specificities and the suppliers’ constraints are both integrated into product design phase. We consider the case of improving the design of an existing product and study the selection of its suppliers adopting a bi-objective optimization approach. Considering multi-products, multi-suppliers and multi-periods, the mathematical model proposed aims to minimize supplying, transport and holding costs of product components as well as quality rejected items. To solve the bi-objective problem, an evolutionary algorithm namely, non-dominant sorting genetic algorithm (NSGA-II) is employed. The algorithm provides a set of Pareto front solutions optimizing the two objective functions at once. Since parameters values of genetic algorithms have a significant impact on their efficiency, we have proposed to study the impact of each parameter on the fitness functions in order to determine the optimal combination of these parameters. Thus, a number of simulations evaluating the effects of crossover rate, mutation rate and number of generations on Pareto fronts are presented. To evaluate performance of the algorithm, results are compared to those obtained by the weighted sum method through a numerical experiment. According to the computational results, the non-dominant sorting genetic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.