| Summary: | 碩士 === 國立虎尾科技大學 === 工業管理系工業工程與管理碩士班 === 105 === The goal of logistics is to deliver goods to customers efficiently. This thesis explored the heterogeneous fleet vehicle routing problem (HFVRP). This problem is also an extension of the vehicle routing problem. The main difference is that this study assumes different types of goods and demand quantity for each point. Additionally, a delivery problem with two types of different goods demand is studied, including single type goods demand or mixed type goods demand,. Moreover, three types of demand quantity for demand points are assumed for each demand point, namely, (1) general goods demand=frozen goods demand for points, (2) general goods demand > frozen goods demand, (3) general goods demand < frozen goods demand. Applications of this considered problem include 7-11 delivery service, flower delivery and 3C delivery.
In this thesis, we explored an example in Taipei City in which three types of vehicle are assumed, namely, (1) general truck, (2) frozen truck, (3) composite truck (which can deliver both general goods and frozen goods simultaneously). In addition, we propose a new encoding method to solve the considered problem under various combinations including different demands for points, different vehicle capacities, different weights of objective etc. In this thesis, we applied genetic algorithm (GA), immune algorithm (IA), particle swarm algorithm (PSO) to solve this problem. Numerical results show that these three algorithms can schedule the demand points and the routes effectively such that the total routing distance (objective 1) is minimized and the gap of routing distance among the three types vehicles (objective 2) is minimized. Numerical results also show that immune algorithm and genetic algorithm are superior to particle swarm algorithm for most of test instances.
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