Abstract
The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.
Highlights
This article focuses on the use of estimation of distribution algorithms (EDAs) as a section of the classification of evolutionary algorithms
The support method used to improve the performance of EDAs differs between authors, but the most common are heuristics or other evolutionary algorithms
The comparison mentioned helps to determine the importance of hybridization for the performance of the proposed EDA
Summary
90% of products that are globally commercialized are transported by sea, and in one decade, the average capacity of cargo ships has doubled, with ports being the ones that allow the execution of exchanges. The authors consider the distinct fuel efficiencies, cost structures of the ships, and capacities These conditions determine the number of containers transported, the bunker fuel consumption, and the operating cost of a shipping route. [4] concerns the increase in seaport operations in the last thirty years, and argues that berth scheduling can improve the throughput of marine container terminals. The author develops a novel memetic algorithm to help the marine container terminal operators to build proper schedules, and to tackle congestion issues caused by the increasing number of large size vessels. This study does not account for uncertainty in vessel arrivals, the proposed algorithm serves as an efficient planning tool for marine container terminal operators and assists with efficient the berth scheduling
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