A biased random-key genetic algorithm for the time-invariant berth allocation and quay crane assignment problem
| Autor: | Juan Francisco Correcher, Ramón Alvarez-Valdés |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
021103 operations research Operations research Heuristic (computer science) Computer science Heuristic business.industry 0211 other engineering and technologies General Engineering 02 engineering and technology Computer Science Applications Artificial Intelligence Container (abstract data type) Genetic algorithm 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Local search (optimization) business Assignment problem Metaheuristic Local search (constraint satisfaction) |
| Zdroj: | Expert Systems with Applications. 89:112-128 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2017.07.028 |
| Popis: | We address Berth Allocation and Quay Crane Assignment Problems in a heuristic wayWe propose a Biased Random-Key Genetic Algorithm for BACAP and its extension BACASPSolutions of the Genetic Algorithm are improved by a Local SearchThe complete procedure obtains high-quality solutions for large instances Maritime transportation plays a crucial role in the international economy. Port container terminals around the world compete to attract more traffic and are forced to offer better quality of service. This entails reducing operating costs and vessel service times. In doing so, one of the most important problems they face is the Berth Allocation and quay Crane Assignment Problem (BACAP). This problem consists of assigning a number of cranes and a berthing time and position to each calling vessel, aiming to minimize the total cost. An extension of this problem, known as the BACAP Specific (BACASP), also involves determining which specific cranes are to serve each vessel. In this paper, we address the variant of both BACAP and BACASP consisting of a continuous quay, with dynamic arrivals and time-invariant crane-to-vessel assignments. We propose a metaheuristic approach based on a Biased Random-key Genetic Algorithm with memetic characteristics and several Local Search procedures. The performance of this method, in terms of both time and quality of the solutions obtained, was tested in several computational experiments. The results show that our approach is able to find optimal solutions for some instances of up to 40 vessels and good solutions for instances of up to 100 vessels. |
| Databáze: | OpenAIRE |
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