An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem. (October 2019)

• Record Type: Journal Article
• Authors: Kavoosi, Masoud; Dulebenets, Maxim A.; Abioye, Olumide F.; Pasha, Junayed; Wang, Hui; Chi, Hongmei
• Is Part Of: Advanced engineering informatics. Volume 42(2019)
• Publication Date: 2019-10
• Subjects: Marine transportation -- Marine container terminals -- Berth scheduling -- Optimization -- Evolutionary algorithms -- Parameter control -- Parameter tuning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2019.100972
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A mathematical model is presented for the berth scheduling problem. The objective aims to minimize the total vessel service cost. An Evolutionary Algorithm is designed to solve the problem. The algorithm relies on the augmented self-adaptive parameter control strategy. The computational experiments showcase efficiency of the algorithm. Abstract: The demand for international seaborne trade has substantially increased over the last three decades and is predicted to continue increasing during the upcoming years. A marine container terminal, as an important node in supply chains, should be able to successfully cope with increasing demand volumes. Berth scheduling can significantly influence the general throughput of marine container terminals. In this study, a mixed-integer linear programming mathematical model is proposed for the berth scheduling problem, aiming to minimize the summation of waiting costs, handling costs, and late departure costs of the vessels that are to be served at a marine container terminal. An innovative Evolutionary Algorithm is designed to solve the developed mathematical model. The proposed solution algorithm relies on the augmented self-adaptive parameter control strategy, which is developed in order to effectively change the algorithmic parameters throughout the search process. Performance of the designed algorithm is evaluated against nine alternative state-of-the-art metaheuristic-based algorithms, which have been frequently used for berth scheduling in the marine container terminal operations literature. The results demonstrate that all the developed algorithms have a high level of stability and return competitive solutions at convergence. The computational experiments also prove superiority of the designed augmented self-adaptive Evolutionary Algorithm over the alternative algorithms in terms of different performance indicators.