Vahdani, H., Abrishami, S., Rezaee, B. (2019). An integrated lot-sizing model with supplier and carrier selection and quantity discounts considering multiple products. Scientia Iranica, (), -. doi: 10.24200/sci.2019.5155.1125
Hashem Vahdani; Salman Abrishami; Babak Rezaee. "An integrated lot-sizing model with supplier and carrier selection and quantity discounts considering multiple products". Scientia Iranica, , , 2019, -. doi: 10.24200/sci.2019.5155.1125
Vahdani, H., Abrishami, S., Rezaee, B. (2019). 'An integrated lot-sizing model with supplier and carrier selection and quantity discounts considering multiple products', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2019.5155.1125
Vahdani, H., Abrishami, S., Rezaee, B. An integrated lot-sizing model with supplier and carrier selection and quantity discounts considering multiple products. Scientia Iranica, 2019; (): -. doi: 10.24200/sci.2019.5155.1125
An integrated lot-sizing model with supplier and carrier selection and quantity discounts considering multiple products
1Department of Industrial Engineering, Faculty of Engineering, University of Bojnord, Bojnord, Iran
2Industrial Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
Because of growing competition in the global markets and the vital role of suppliers in business success, the subject of supplier selection has attracted many researchers and practitioners during the recent years. In addition to the supplier selection, the order quantity discount provided by the suppliers, is considered through a new mixed-integer linear programming (MILP) model involving a manufacturer with multiple products and multiple purchasing items over multiple periods. According to the proposed model, the manufacturer purchases different amount of raw materials from selected suppliers in order to produce different products. Customers’ demands are fulfilled by minimizing the total purchase, inventory, production, and transportation costs over a multi-period planning horizon. Since the problem is NP-hard, an efficient genetic algorithm (GA) is used to solve the large-scale real-world instances. The results are compared with results from the exact approach wherever possible in order to investigate the efficiency of the algorithm.
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