Supply Chain Optimization under New Product Development and Emergence of Risks with Meta-Heuristic Approach

In this paper, a comprehensive multi-objective model has been proposed to optimize a supply chain when new product launches and risks emerge. In doing so, primarily, a set of risks and needed mitigation strategies are identified; secondly, the essential criteria of new product development (NPD) are introduced. These criteria are then weighted by the analytic hierarchy process (AHP) approach. Based on the calculated weights, manufacturers, which are the model’s second echelon are being ranked by the fuzzy-TOPSIS method. Since the model is of non-deterministic polynomial-time hardness (np-hard), due to its complexity, two different meta-heuristic algorithms –multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II)- are conducted. Finally, the outputs of these algorithms are compared from the indices of quality, dispersion, and uniformity. The results illustrate that the production cost is of the highest rating among the most critical NPD criteria. The performance comparison of both aforementioned meta-heuristic algorithms reveals that MOPSO has the higher capability to explore the feasible region and solve the problem than NSGA-II. Whereas, from the aspect of run-time efficiency, NSGA-II has a faster execution time than MOPSO