A Particle Swarm Optimization Solution Approach and an Iterated Local Search Heuristic for the Multi-Compartment Vehicle Routing Problem

• Main Authors: Ou, Jhe-Yu, 歐哲瑜
• Other Authors: 韓復華
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/h59p72

碩士 === 國立交通大學 === 運輸與物流管理學系 === 105 === Multi-compartment vehicle routing problem (MCVRP) is an extension of the classical Vehicle Routing Problem (VRP) with multiple products that must be stored in the given compartments in the vehicle. The main difference between MCVRP and VRP is the compartment capacity limit for various products. Depending on if the different products of a customer can be shipped by different vehicles, the MCVRP can be classified into the split and non-split type of problems respectively. In this thesis, we apply the particle swarm optimization (PSO) method and a multi-start iterated local search (MS_ILS) approach for solving the MCVRP respectively. In the PSO part, we propose an n-dimension decoding method for solving both the non-split and split type of the MCVRP. In our MS_ILS, we first use the n-dimension decoding method to construct multiple initial solutions. Then, we apply a randomized variable neighborhood decent (RVND) module with the local search operators of 2-opt, Or-opt, λ-interchanges and 2-opt* to improve the solution. A p-point perturbation is also applied in the MS_ILS to increase the diversification of search. Our proposed algorithms were tested on four problem instance sets proposed by Fallahi et al. [9] and Reed et al. [24]. The results show that the MS_ ILS performs better than PSO. On the other hand, our proposed n-dimension decoding method of PSO can save about half computer time than the original 2n-dimension decoding method in solving the MCVRP for the split type of problems. Out of 136 instances tested, our MS_ILS has found 23 best known solutions (BKS) and 38 new best solutions. The average deviation from BKS is 0.18%. Key Words: Multi-Compartment Vehicle Routing Problem (MCVRP), Particle Swarm Optimization (PSO), Iterated Local Search (ILS), Multi-start