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In recent years, nature-inspired metaheuristic algorithms have shown an outstanding performance in solving optimization problems. However, very few studies have given attention to the adoption of these algorithms to solve the container retrieval problem (CRP). The CRP involves finding an optimal sequence of operations for the crane, enabling the retrieval of all the containers from the bay following a predefined order. This problem addresses the operational efficiency in a container terminal system since it aims to minimize the crane’s operating time. This study adopts a more accurate computation of the crane’s operating time than computation used in previous studies for the CRP. The adopted calculation is more accurate because it considers more crane’s operational aspects, such as twistlock times and different speeds and accelerations. Also, a novel optimization algorithm is proposed, which is inspired by the coronavirus behavior (responsible for COVID-19), to address the CRP. This bio-inspired approach simulates how, from an initial individual, the coronavirus infects new individuals, creating newly infected populations. The proposed algorithm employs an intelligent selection procedure for determining the initial individual and an efficient spread process for infecting new individuals using a quick constructive heuristic for the CRP. The computational results, performed on an extensive instance set, reveal that the proposed bio-inspired algorithm manages to outperform leading algorithms from the recent literature examined. |
