The Study of Algorithmic Strategies for Integrating Machine Scheduling and Vehicle Routing

• Main Authors: Yan-Hong Chen, 陳彥宏
• Other Authors: Gen-Han Wu
• Format: Others
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/88034971297561313762

碩士 === 國立東華大學 === 運籌管理研究所 === 103 === In this study, we focus on solving the integrating problem of parallel machine scheduling and vehicle routing with the objective of minimizing the total weighted tardiness time. We coordinate the production sequence in identical parallel machines and delivery routes in identical vehicles simultaneously after accepting the customers’ order requests. Six dispatching rule are used as the initial soultions. Both of the nested and non-nested neighborhood structures in variable neighborhood search are developed to find the optimal solution. In order to intensify the capability of neighborhood search, we further embed particle swarm optimization algorithm into nested and non-nested variable neighborhood search and compare their solving effects. In experimental analysis, two variable neighborhood searches including nested and non-nested neighborhood structures and two particle swarm optimization algorithms embedded with the proposed nested or non-nested variable neighborhood searches are implemented to obtain their solving effects in different sizes of problems. The numerical results show that the embedded particle swarm optimization algorithm can obtain better objective values. However, it seems to be no difference in the solving effects between the nested and non-nested neighborhood structure.