| Title |
The bi-objective periodic closed loop network design problem |
| ID_Doc |
12633 |
| Authors |
Mamaghani, EJ; Davari, S |
| Title |
The bi-objective periodic closed loop network design problem |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.eswa.2019.113068 |
| 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. (C) 2019 Elsevier Ltd. All rights reserved. |
| Author Keywords |
Network design; Closed loop supply chain; Periodic location-routing problem; Simultaneous pickup and delivery; Time window; Bi-objective |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000514218700011 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science |
| Research Area |
Computer Science; Engineering; Operations Research & Management Science |
| PDF |
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