نوع مقاله : پژوهشی
نویسندگان
1 دانشکده مهندسی صنایع و مدیریت-دانشگاه صنعتی غیرانتفاعی سجاد
2 دانشکدهی مهندسی صنایع دانشگاه صنعتی سجاد
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Nowadays, increasing the quality level in production systems is one of the significant goals of manufacturers as well as reducing costs. The more manufacturer pays for more qualitative raw materials, more skilled labor, and more advanced and accurate machines, the more waste is reduced. Increasing quality levels and decreasing costs become more complex when some parameters are under uncertainty. One of the methods to encounter uncertainties is robust optimization, where uncertainty’s probability distribution is unknown. As a consequence, the robust scenario-based approach, which is presented by Mulvey, is applied. In this paper, we present a bi-objective scenario-based supply chain model. In this model three echelons including suppliers, manufacturers, and customers are considered. Also, we consider uncertainty in backorder, demand, and cost values. The first objective function aims to minimize supply chain costs including production, raw material purchasing, production inventory holding, raw material inventory holding, transportation, and backorder. The second objective function aims to minimize the total number of raw materials wastes in the production line and supplier batch. The proposed model has been defined as a multi-product, multi-period, multiple suppliers, multiple customers, and multiple transportation modes mixed-integer linear programming model. Also, in this model workforce efficiency, storage and transportation capacities, and inventory planning are considered. The parameters of the model are considered randomly distributed. The Epsilon constraint method, NSGA-II, and SPEA2 algorithms are applied to solving the proposed model. Also, the Taguchi method is applied to tune the parameters of the algorithms. Then, a comparison between the quality of results and the CPU time of these methods is provided. This comparison demonstrated that using evolutionary algorithms provides close results with the exact method in less CPU time. Afterward, the Mean Ideal Distance (MID) and Analytic Hierarchy Process (AHP) methods are respectively applied to evaluate Pareto fronts performance and make a decision about selecting the best cost and quality level policy.
کلیدواژهها [English]
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