Genetic Algorithm for Integrated Models of Continuous Berth Allocation Problem and Quay Crane Scheduling with Non Crossing Constraint

Abstract

Background/Objectives: This paper focused on integrated Continuous Berth Allocation Problem (BAPC) and Quay Crane Scheduling Problem (QCSP) by considering non-crossing constraint to make it more realistic. Methods/Statistical analysis: Genetic Algorithm (GA) is a metaheuristic method that has been used extensively in Berth Allocation Problem (BAP). Crossover and mutation are selected as operators in this paper. Findings: The integrated model is formulated as a Mix Integer Problem (MIP) with the objective to minimize the sum of the processing times. A vessel's processing time is measured between arrival and departure includingwaiting time to be berthed and servicing time.The new algorithm of GA arecompatible with the integrated model and useful for finding near-optimal solutions. Three phase new algorithms of GA are proposed and provide a wider search to the solution space. Application/Improvements: Three phase of GA is another significant and promising variant of genetic algorithms in BAPC and QCSP. The probabilities of crossover and mutation determine the degree of solution accuracy and the convergence speed that GA canobtain. By using fixed values of crossover and mutation, the algorithm utilize the population information in each generation and adaptively adjust the crossover and mutation. So, the population diversity and sustain the convergence capacity is maintained.