Natasa Kovac

Evolutionary algorithm for the minimum cost hybrid berth allocation problem

A new optimization method based on the Evolutionary Algorithm (EA) is developed for solving the Minimum Cost Hybrid Berth Allocation Problem (MCHBAP) with fixed handling times of vessels. The goal of the MCHBAP is to minimize the total costs of waiting and handling, as well as earliness or tardiness of completion, for all vessels. It is well known that this kind of problem is NP hard. The main problem one faces when dealing with the MCHBAP is a large number of infeasible solutions. In order to overcome this problem, we propose an EA implementation adapted to the problem that involves four types of mutation operator and two additional improvement strategies, but no crossover operator. The proposed EA implementation is benchmarked on real life test instances. Our computational results show that the proposed EA method is able to find optimal solutions for real life test instances within relatively short running time, having in mind the nature of the considered problem.

Variable Neighborhood Search Methods for the Dynamic Minimum Cost Hybrid Berth Allocation Problem

This study considers the Dynamic Minimum Cost Hybrid BerthAllocation Problem (DMCHBAP) with fixed handling times of vessels.The objective function to be minimized consists of threecomponents: the costs of positioning, waiting, and tardiness ofcompletion for all vessels. Having in mind that the speed offinding high-quality solutions is of crucial importance fordesigning an efficient and reliable decision support system incontainer terminal, metaheuristic methods represent the naturalchoice to deal with DMCHBAP. Four variants of VariableNeighborhood Search (VNS) metaheuristic are designed for DMCHBAP.All four proposed VNS methods are evaluated on three classesof randomly generated instances with respect to solution quality and running times.The conducted computationalanalysis indicates that all four VNS-based methods representpromising solution approaches to DMCHBAP and similar problems inmaritime transportation. DOI: http://dx.doi.org/10.5755/j01.itc.47.3.20420

Metaheuristic Approaches for the Minimum Cost Hybrid Berth Allocation Problem

The Minimum Cost Hybrid Berth Allocation problem is defined as follows: for a given list of vessels with fixed handling times, the appropriate intervals in berth and time coordinates have to be determined in such a way that the total cost is minimized. The costs are influenced by positioning of vessels, time of berthing, and time of completion for all vessels. Having in mind that the speed of finding high-quality solutions is of crucial importance for designing an efficient and reliable decision support system in container terminal, metaheuristic methods are the obvious choice for solving MCHBAP. In this chapter, we survey Evolutionary Algorithm (EA), Bee Colony Optimization (BCO), and Variable Neighborhood Descent (VND) metaheuristics, and propose General Variable Neighborhood Search (GVNS) approach for MCHBAP. All four metaheuristics are evaluated and compared against each other and against exact solver on real-life and randomly generated instances from the literature. The analysis of the obtained results shows that on instances reflecting real-life situations, all four metaheuristics were able to find optimal solutions in short execution times. The newly proposed GVNS showed to be superior over the remaining three metaheuristics in the sense of running times. Randomly generated instances were out of reach for exact solver, while EA, BCO, VND, and GVNS easily provided high-quality solutions in each run. The results obtained on generated data set show that the newly proposed GVNS outperformed EA, BCO, and VND regarding the running times while preserving the high quality of solutions. The computational analysis indicates that MCHBAP can be successfully addressed by GVNS and we believe that it is applicable to related problems in maritime transportation.