|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|93179||2017||32 صفحه PDF||سفارش دهید||11137 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers & Industrial Engineering, Volume 109, July 2017, Pages 113-129
This paper considers a location-inventory problem in the three-level supply chain where demand across the retailers is assumed to be correlated and inventory shortages are allowed. For better monitoring the stock status, a periodic review of inventory level is taken into account. In order to overcome the joint location-inventory problem, this paper proposes an optimization model based on a mixed integer non-linear programming (MINLP) whose objective function is the minimization of the total supply chain costs. To solve the designed MINLP model, two meta-heuristic algorithms are presented, including genetic algorithm (GA) and simulated annealing (SA) with an appropriate decoding scheme. Since the performance of meta-heuristic algorithms depends on setting the parameters; therefore, the Taguchi method is used to set parameters of the developed solving algorithms. Finally, the proposed algorithms have been used to several numerical test problems that indicate the higher performance of the GA compared with the SA in terms of objective function.