Efficient and effective berth allocation is essential to guarantee high container throughput in a container terminal. Modern mega-terminals are usually comprised of multiple disjointed berths. However, this type of Berth Allocation Problem (BAP) has not attracted a lot of attention from the academic world due to its great complexity. This research develops new methodologies for solving complex BAPs, in particular, BAPs involving quay crane scheduling in a multiple-berth environment. This research develops a mathematical model and a new Branch and Price algorithm (B&P) which hybridizes the column generation approach and the Branch and Bound method (B&B) to generate optimal multiple-berth plans (MBAP) within acceptable time limits. A new exact algorithm based on the label-correcting concept is designed to obtain all potential columns by defining a new label structure and dominance rules. To accelerate the generation of columns, two heuristics are proposed to distribute vessels among berths and to establish the handling sequence of the vessels allocated to each berth. An early termination condition is also developed to avoid the “tailing off effect” phenomenon during column generation process. The effectiveness and robustness of the proposed methodology are demonstrated by solving a set of randomly generated test problems. Since the Berth Allocation Problem (BAP) and the Quay Crane Scheduling Problem (QCSP) strongly interact, this research also studies the Simultaneous Berth Allocation and Quay Crane Scheduling Problem (BAQCSP). An advanced mathematical model and a new hybrid meta-heuristic GA-TS algorithm which is based on the concept of Genetic Algorithm (GA) are developed to solve the proposed BAQCSP effectively and efficiently. A new crossover operation inspired by the memory-based strategy of Tabu Search (TS) and the mutation operation are implemented to avoid premature convergence of the optimization process. The local search ability of TS is incorporated into the mutation operation to improve the exploitation of the solution space. Comparative experiments are also conducted to show the superiority of the performance of the proposed GA-TS Algorithm over the B&B and the canonical GA. Furthermore, this research extends the scope of BAQCSP to consider the Simultaneous Multiple-berth Allocation and Quay Crane Scheduling Problem (MBAQCSP). A MBAQCSP model is developed consisting of various operational constraints arising from a wide range of practical applications. Since MBAQCSP combines the structures of both MBAP and BAQCSP, the exact B&P proposed for solving MBAP can be modified to optimally solve MBAQCSP. However, the calculation time of B&P increases significantly as the V/B ratio (i.e., vessel number to berth number) grows. In order to eliminate this shortcoming, this research develops a GA-TS Aided Column Generation Algorithm which hybridizes the GA-TS Algorithm proposed for solving BAQCSP with the Column Generation Algorithm to locate the optimal or near optimal solutions of MBAQCSP. The computational results show that the proposed hybrid algorithm locates excellent near optimal solutions to all test problems within acceptable time limits, even problems with high V/B ratios. Finally, this research also shows that the proposed GA-TS Aided Column Generation Algorithm can be easily modified to solve MBAP efficiently. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Ports and container terminals have very complex and dynamic operations. Effective and efficient berth schedules are essential for profitable and sustainable operations. This research studies berth scheduling under uncertainties in arrival and handling times. Port operators usually face challenges in the development of berth schedules. This thesis presents a model formulation of discrete berth space that accounts for uncertainty in arrival and handling times. The problem is reformulated as a biobjective bi-level optimization berth scheduling problem. A solution approach is presented using evolutionary algorithms and heuristics. The objective of the formulation is to provide a robust berth schedule by minimizing the average and the range of the total service times for all vessels served at the terminal. Simulation is utilized to evaluate the proposed berth scheduling policy and compare it to two first-come-first-served policies. Results showed that the proposed berth schedules outperform under high congestion. / by Maria Isabel Portal Palomo. / Vita. / Thesis (M.S.C.S.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.