مقاله مهندسی صنایع به زبان انگلیسی |Supply chain optimization under new product development and emergence of risks with meta-heuristic approach

Supply chain optimization under new product development and emergence of risks with meta-heuristic approach

Abstract.

In this paper a comprehensive multi-objective model has proposed to optimize a supply chain when a new product launches and risks emerges. In doing so, primarily, a set of risks and needed mitigation strategies are identified, secondly, new product development (NPD) most important criteria are introduced, these criteria are weighted by AHP approach, then based on the calculated weights, manufacturers, which are the second echelon of the model are being ranked by fuzzy-TOPSIS method. Since the model is of NP-hard type due to its complexity, two different meta-heuristic algorithms, multi-objective Particle swarm optimization (MOPSO) based on Pareto archive and non-dominated sorting genetic algorithm II (NSGA-II), are employed to solve the model and finally the outputs of these algorithms are compared from the aspects of quality index, dispersion index and uniformity index. The data to solve the problem are utilized from the Iranian UPVC profile industry.

The outputs illustrate that amongst the most important NPD criteria, production cost is of the highest rating; moreover, fuzzy-TOPSIS method which utilized for the ranking of manufacturers shows that “manufacturer A” has the highest rank based on the identified NPD criteria; and finally, the performance comparison of the both aforementioned meta-heuristic algorithms reveals that MOPSO has the higher capability to explore the feasible region and solve the problem, in comparison with NSGA-II. Whereas, from the aspect of run-time efficiency, NSGA-II has a faster execution time, as compared with MOPSO.

Keywords.

                         Supply Chain, risk, NPD, MOPSO, NSGA-II

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