| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 98 === This thesis studies the problem of vehicle minimization for periodic deliveries (VMPD). The problem aims to minimize the total number of vehicles with balance consideration. As known, vehicle periodic delivery problem is an NP-hard. Therefore, the studied vehicle periodic delivery problem with balance consideration is also NP-hard. In this thesis, we will apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm and partical swarm optimization, for solveing the problem. The objective of the problem is to minimize (i) the summation of total number of vehicles, (ii) the deviation of total delivery quantities for each vehicle, (iii)the deviation of total delivery quantities for each day.
In this thesis, a new two-phase approach is proposed to solve for the vehicle periodic delivery problem with balance consideration. In the first phase, we aim to minimize the (i) the summation of total number of vehicles, (ii) the deviation of total delivery quantities for each vehicle, (iii) the deviation of total delivery quantities for each day. In the second, we aim to adjust the deviation of total delivery quantities for each day. In this thesis, we apply the three algorithms for solving the problem under various combinations of parameters. Numerical results show that the immune algorithm performs better than the other two approaches, especially when the problem size is larger.
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