| Summary: | 碩士 === 國立清華大學 === 工業工程與工程管理學系 === 103 === Ship routing problem (SRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The SRP has several variants depending on some restrictions such as time window, multiple vessels and so on. In this research, considering the ship routing problem with multi-product, heterogeneous vessel and having loading and port constraints The problem is to find an optimal assignment of the ship routing and loading volume of demand simultaneously in order to minimize the total cost satisfy capacity of ships.
Since SRP is an NP-hard problem, we propose a hybrid particle swarm optimization (HPSO) to solve ship routing problem. HPSO is an improved algorithm based on particle swarm optimization (PSO) incorporated with crossover and mutation operators can provide better solving quality. The performance of the proposed method is compared with genetic algorithm (GA) and particle swarm optimization (HPSO). The experimental results show that the proposed algorithm exhibits good performance and solving effectiveness for the test problem.
In the real situation, the demand of customers are not constant. It would change by temporary or seasonal demand. Therefore, we consider stochastic demands of customers. We apply optimal computing budget allocation (OCBA) to allocate simulation resource. It can allocate simulation resource efficiently and reduce solving time.
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