A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up. (13th August 2018)

• Record Type: Journal Article
• Authors: Rabbani, Masoud; Pourreza, Pooya; Farrokhi-Asl, Hamed; Nouri, Narjes
• Is Part Of: Journal of modelling in management. Volume 13:Number 3(2018)
• Page Start: 698
• Page End: 717
• Publication Date: 2018-08-13
• Subjects: Genetic algorithm -- Vehicle routing problem -- Hybrid genetic algorithm -- Multi-depot -- Time windows; Industrial management -- Mathematical models -- Periodicals; Industrial management -- Computer simulation -- Periodicals; Business -- Mathematical models -- Periodicals; Business -- Computer simulation -- Periodicals; 658.4033
• DOI: 10.1108/JM2-04-2017-0046
• Languages: English
• ISSNs: 1746-5664

Abstract

Abstract : Purpose: This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). Design/methodology/approach: The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Findings: A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA. Originality/value: This paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.