| Summary: | 碩士 === 國立成功大學 === 電機工程學系專班 === 100 === The vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with finite capacity from a depot to a set of geographically scattered nodes with known demands and predefined time windows. This problem is solved by optimizing routes for the vehicles so as to meet all given constraints as well as to minimize the objectives of number of vehicles and delivering distance. This thesis proposes a hybrid evolutionary search strategy algorithm (HESSA) that incorporates heuristics solution of the local exploration in the evolutionary search with specialized genetic operators. The fitness function and mutation of the sequence-oriented optimization in VRPTW are realized by the inverse of the total distance and the roulette wheel, respectively. Different existing VRPTW researches often aggregate multiple criteria and constraints into a compromise function, the proposed HESSA simultaneously optimizes all routing constraints and objectives, and improves the routing solutions in many appearances, such as lower routing cost, better cluster trace, and wider scattering area search. The HESSA can obtain the optimal solution by balancing the exploring and developing ability. Finally, the proposed HESSA is applied to solve the benchmark problem, Solomon’s 56 VRPTW 100-customer instances. Simulation results demonstrate that 17 routing solutions are better than or competitive as compared to the best solutions published in the literature.
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