An integrated bi-objective optimization model and improved genetic algorithm for vehicle routing problems with temporal and spatial constraints

• Published in: Journal of industrial and management optimization Vol. 16; no. 3; pp. 1203 - 1220
• Main Authors: Li, Jiao-Yan, Hu, Xiao, Wan, Zhong
• Format: Journal Article
• Language: English
• Published: Springfield American Institute of Mathematical Sciences 01.05.2020
• Subjects: Genetic algorithms; Optimization; Optimization algorithms; Routes; Vehicles; optimization model; bi-objective optimization; algorithms; carbon emissions; Vehicle routing problems
• ISSN: 1553-166X; 1547-5816; 1553-166X
• DOI: 10.3934/jimo.2018200

Vehicle routing problem (VRP) is a typical and important combinatorial optimization problem, and is often involved with complicated temporal and spatial constraints in practice. In this paper, the VRP is formulated as an optimization model for minimizing the number of vehicles and the total transportation cost subject to constraints on loading plan, service time and weight capacity. The transportation cost consists of the rent charge of vehicles, fuel cost, and carbon tax. Owing to complexity of the built model, it is divided into two subproblems by a two-stage optimization approach: at the first stage, the number of vehicles is minimized, then the routing plan is optimized at the second stage. For solving the sequential subproblems, two correlated genetic algorithms are developed, which share the same initial population to reduce their computational costs. Numerical results indicate that the developed algorithms are efficient, and a number of important managerial insights are revealed from the model.