(373ar) Simultaneous Berth and Yard Allocation Planning of Container Vessels for Port Throughput Maximization
Most of the customers (i.e., vessel owners) expect prompt berthing upon arrival and early leaving upon finish of their vessels. To minimize the total transporting cost (in the former problem), the berthing location should ideally be selected close to the storage location of the containers and that most of the vessels will be allocated berthing space close to their preferred locations within the terminal. However, to minimize the total handling time of all vessels, all the available quay cranes should be utilized, the berthing location should ideally be selected according to the quay cranes capacity and availability at the time. Meanwhile, transportation cost and equipment operating cost at the terminal will be dramatically increased compared to the former problem. Correspondingly, the port throughput will also be increased to compensate.
In this study, a new systematic methodology together with a mixed integer nonlinear programming (MINLP) model for simultaneous planning of berth and yard allocation for container vessels have been developed. We consider the simultaneous planning problem at the tactical level. This implies that certain decisions have already been made at a strategic level. The objective of this problem is to simultaneously optimizes spatial schedules for both vessels and containers to minimize the total handling time of all vessels, which is equivalent to maximizing the port throughput, subject to various port resource limits and operating constraints. It incorporats proactive robustness into the nominal berth and yard planning by introducing predetermined flexible vessel arrival and departure time windows. The well-known McCormick relaxation is employed to transform the MINLP model into a MILP model. CPLEX has been employed to obtain the optimal solutions of the developed MILP model. And detailed case study will be followed to demonstrate the efficacy of the developed methodology and simultaneous planning model.