ABSTRACT
The objectives of this study involve designing a multi stage closed loop supply chain (CLSC) optimization model that both minimize the total cost incurred in supply chain (SC) network, and addresses the problem of facility location, product allocation in the network forward flow and backward flow. A multi-stage mixed integer programming technique is adopted in formulating the closed loop supply chain mathematical model for a company that produces beverages, and uses Polyethylene Terephthalate (PET) to package their products. In addition, the study adopts the machine learning algorithm to predict and forecast sales. The results from the developed models show that, location of facilities and the material flows over the entire supply chain network is determined at lower costs. Results also show the optimized total cost of the network which included the supply, production, warehousing, distribution, retail, as well as the re-manufacturing, redistribution, disassembly, and disposal costs. Furthermore, findings show how many facilities should be utilized, which facilities should be opened, and the volume of products transferred between facilities for each period. A detailed sensitivity analysis, and scenario optimization is carried out to investigate the effects of changes in demands, capacity, and reverse rates on CLSC network’s total cost. Hence, adopting scientific methods to design and manage the CLSC is recommended, in-order to maintain coordination of activities, and sustainability in the SC network.
