DETERMINISTIC AND METAHEURISTIC SOLUTIONS FOR CLOSED-LOOP SUPPLY CHAINS WITH CONTINUOUS PRICE DECREASE

In a global economy, an efficient supply chain as a main core competency empowers enterprises to provide products or services at the right time in the right quantity, at a low cost. This paper is to plan a single product, multi-echelon, multi-period closed loop supply chain for high-tech products (which have continuous price decrease). Ultimately, considering components rated to procurement, production, distribution, recycling and disposal, the final decisions are made. To solve the mixed integer linear programming (MILP) model for closed loop supply chain (CLSC) network plan of the paper, four heuristics-based methods including genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony (ABC) are proposed. Finally, the computational results of these four methods are compared with the solutions obtained by GAMS optimization software. The solution revealed that the ABC methodology performs comparatively better in terms of both quality of solutions and computational time.