The bi-objective periodic closed loop network design problem. (15th April 2020)

• Record Type: Journal Article
• Authors: Mamaghani, Elham Jelodari; Davari, Soheil
• Is Part Of: Expert systems with applications. Volume 144(2020)
• Publication Date: 2020-04-15
• Subjects: Network design -- Closed loop supply chain -- Periodic location-routing problem -- Simultaneous pickup and delivery -- Time window -- Bi-objective; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113068
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The problem of designing a closed loop supply chain is addressed. Simultaneous pickup and delivery as well as time windows are considered. A bi-objective integer linear mathematical model is proposed. The performance of NSGA-II and NRGA are compared to solve the problem. Abstract: Reverse supply chains are becoming a crucial part of retail supply chains given the recent reforms in the consumers' rights and the regulations by governments. This has motivated companies around the world to adopt zero-landfill goals and move towards circular economy to retain the product's value during its whole life cycle. However, designing an efficient closed loop supply chain is a challenging undertaking as it presents a set of unique challenges, mainly owing to the need to handle pickups and deliveries at the same time and the necessity to meet the customer requirements within a certain time limit. In this paper, we model this problem as a bi-objective periodic location routing problem with simultaneous pickup and delivery as well as time windows and examine the performance of two procedures, namely NSGA-II and NRGA, to solve it. The goal is to find the best locations for a set of depots, allocation of customers to these depots, allocation of customers to service days and the optimal routes to be taken by a set of homogeneous vehicles to minimise the total cost and to minimise the overall violation from the customers' defined time limits. Our results show that while there is not a significant difference between the two algorithms in terms of diversity and number of solutions generated, NSGA-II outperforms NRGA when it comes to spacing and runtime.