Abstract
Berth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method.
Highlights
During the last century, global trade and freight growth impose new challenges and requirements for efficient management of transport processes
In order to assess the performance of the model, we introduced a new set of instances based on Livorno port real data
We compared the exact solutions obtained for the proposed Mixed Integer Linear Programming (MILP) formulation by using CPLEX with near-optimal solutions obtained by the proposed Bee Colony Optimisation (BCO) algorithm
Summary
Global trade and freight growth impose new challenges and requirements for efficient management of transport processes. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model In the literature, this problem has been studied by several researchers using different approaches. Various models aim at minimising the sum of the ships’ waiting time or the total staying time for completing the activities, increasing the attractiveness and efficiency of container terminal ports Another classification of BAP is formulated including the synchronisation between the BAP and the Quay Cranes Assignment Problem (QCAP), and it has been treated by several authors in the literature ([21, 40, 58, 60, 61]). We applied the proposed model to a real case study (Livorno port in Italy), and we compared the results obtained by the BCO with the Ant Colony Optimisation (ACO) metaheuristic, another wellknown Swarm Intelligence algorithm.
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