| Summary: | 碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 103 === This thesis explored the New Museum Routing Problem. In this problem, there are two types of exhibition rooms for visitors. First type is the must-visit exhibition rooms, and it means that all visitor groups have to visit. Second type is the select-visit exhibition rooms, and it means that each visitor group may visit or not visit. In this thesis, we assume that there are four sizes of visitor groups, namely small, medium, large and very large, we also assume that larger visitor groups will have longer visiting time for exhibition rooms. This considered problem in this thesis is an extended problem of Open Shop Scheduling Problem (OSSP), and it is also an NP-hard problem.
In this thesis, we apply three artificial intelligence algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Immune Algorithm (IA), to solve the New Museum Routing Problem with the objective of minimizing makespan of visitor groups and the total waiting times of visitor groups. Two examples of Chimei Museum (Tainan) and Taipei Fine Arts Museum (Taipei) are solved and analyzed based upon different problem parameters. In addition, in this thesis we propose a new encoding method to convert a sequence of integers into a feasible solution of visitor group visiting sequence in different exhibition rooms. Numerical results of this thesis show that Immune Algorithm and Genetic Algorithm perform better than Particle Swarm Optimization. However, Particle Swarm Optimization is faster than both Immune Algorithm and Genetic Algorithm.
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