A brain storm optimization approach for the cumulative capacitated vehicle routing problem

• Published in: Memetic computing Vol. 10; no. 4; pp. 411 - 421
• Main Author: Ke, Liangjun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2018; Springer Nature B.V
• Subjects: Algorithms; Applications of Mathematics; Artificial Intelligence; Bioinformatics; Combinatorial analysis; Complex Systems; Control; Convergence; Engineering; Mathematical and Computational Engineering; Mechatronics; Optimization; Regular Research Paper; Robotics; Route planning; Routing; Vehicle routing; Cumulative time; Vehicle routing problem; Brain storm optimization
• ISSN: 1865-9284; 1865-9292
• DOI: 10.1007/s12293-018-0250-0

The cumulative capacitated vehicle routing problem (CCVRP) is a combinatorial optimization problem which aims to minimize the sum of arrival times at customers. This paper presents a brain storm optimization algorithm to solve the CCVRP. Based on the characteristics of the CCVRP, we design new convergent and divergent operations. The convergent operation picks up and perturbs the best-so-far solution. It decomposes the resulting solution into a set of independent partial solutions and then determines a set of subproblems which are smaller CCVRPs. Instead of directly generating solutions for the original problem, the divergent operation selects one of three operators to generate new solutions for subproblems and then assembles a solution to the original problem by using those new solutions to the subproblems. The proposed algorithm was tested on benchmark instances, some of which have more than 560 nodes. The results show that our algorithm is very effective in contrast to the existing algorithms. Most notably, the proposed algorithm can find new best solutions for 8 medium instances and 7 large instances within short time.