Volume 21, Issue 2 (6-2024)
jor 2024, 21(2): 35-55 |
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Hasanpour Jesri Z S, Hassannayebi E, Abolghasemian M. Optimization Models for Vehicle Routing Problems with Simultaneous Delivery and Pickup under Time Window Constraints. jor 2024; 21 (2) :35-55
URL: http://jamlu.liau.ac.ir/article-1-2220-en.html
URL: http://jamlu.liau.ac.ir/article-1-2220-en.html
Industrial Engineering Department, Sharif University of Technology, Tehran, Iran , Hassannayebi@sharif.edu
Abstract: (997 Views)
This paper investigates the problem of heterogeneous vehicle routing with a time window to simultaneously pick up and deliver products to customers. In this method, vehicles with different speeds and capacities have been used to deliver products. In this research, mathematical programming models have been developed to minimize the routing costs of heterogeneous vehicles by considering the time windows of the collection cost until the delivery of the product from its authorized centers to the customer. Due to the NP-hard nature of the routing problem on a large scale, heuristic and meta-heuristic algorithms have been used to solve this problem. Based on the obtained results, three meta-heuristic methods of iterative local search ILS, combined genetic algorithm, and automatic moving grid search algorithm (AGS) have been used. The calculation results show that the lowest cost has been achieved by sending 67 different vehicles to pick up and deliver multiple products to the customer by dividing the city of Tehran into 16 districts. The results of the research show that with the dispatch and optimal routing of this number of vehicles, the cost of the logistics system will decrease by 38.58% compared to the current situation. Based on the computational results, the proposed algorithm can optimally route heterogeneous vehicles under the time window constraint for simultaneous delivery of products.
Keywords: Vehicle Routing, Mathematical Optimization, Simultaneous Pickup and Delivery, Genetic Algorithm
Type of Study: Research |
Subject:
Special
Received: 2023/09/16 | Accepted: 2024/02/14 | Published: 2024/06/21
Received: 2023/09/16 | Accepted: 2024/02/14 | Published: 2024/06/21
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