本研究提出一個混合式粒子群演算法（hybrid particle swarm optimization, HPSO）以同時求解貨櫃碼頭動態船席指派問題（dynamic and discrete berth allocation problem, DDBAP）及橋式起重機指派問題（quay crane assignment problem, QCAP）。粒子群演算法（particle swarm optimization, PSO）是一種新的演進方法可以用來求解各種的組合最佳化問題。不過，粒子群演算法過去並未應用在此兩問題之同時求解上。因此，本研究中整合了粒子群演算法與啟發式法並應用了序值技巧（rank order values, ROVs）發展出此混合式粒子群演算法。實驗數據顯示此方法在考慮最小化整體完成時間（makespan）上可求得最佳／近似最佳解，而且，所需時間很少可應付實務問題。

This paper presents a novel hybrid particle swarm optimization (HPSO) approach to solve the dynamic and discrete berth allocation problem (DDBAP) and quay crane assignment problem (QCAP) simultaneously. The PSO approach is a new evolutionary technique capable of solving various combinational optimization problems (COPs). However, to our best knowledge, the PSO approach has never been employed for solving the dynamic DBAP and QCAP simultaneously for a container terminal (CT). Thus, a HPSO, combining PSO with heuristic, based on the rank order values (ROVs) is initiated in this study to solve the two problems. Experiments have been performed and the results show that the proposed HPSO approach able to find the optimal/near-optimal solution in terms of the makspan within short time.