Enhanced Berth Allocation Using the Cuckoo Search Algorithm

Abstract

Berth allocation is one of the most important optimization problems in container terminals at ports worldwide. From both the port operator’s and the shipping lines’ point of view, minimizing the time a vessel spends at berth and minimizing the total cost of berth operations are considered fundamental objectives with respect to terminal operations. In this study, we focus on the berth allocation problem (BAP), where berth positions are assigned to arriving ships with the objective of reducing the total service cost, which includes waiting cost, handling cost, and several penalties, such as a penalty for late departure and a penalty for non-optimal berth allocation. First, the BAP is formulated as a mixed-integer linear programming (MILP) model. Since BAP is an NP-hard problem and cannot be solved by mathematical approaches in a reasonable time, a metaheuristic approach, namely, a cuckoo search algorithm (CSA), is proposed in this study to solve the BAP. To validate the performance of the proposed CSA-based method, we use two benchmark approaches, namely, the genetic algorithm (GA) and the optimal MILP solution. Next, we conduct several experiments using a benchmark data set as well as a randomly-generated larger data set. Simulation results show that the proposed CSA algorithm has higher efficiency in allocating berths within a reasonable computation time than its counterparts.