Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 ll as earliness or tardiness of completion, for all vessels. It is ll 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,  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.
['Natasa Kovac', 'Tatjana Davidović', 'Zorica Stanimirović']
I am writing a research paper on a idea Evolutionary algorithm for the minimum cost hybrid berth allocation problem
[{"from": "human", "value": "I am writing a research paper on a idea Evolutionary algorithm for the minimum cost hybrid berth allocation problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 ll as earliness or tardiness of completion, for all vessels. It is ll 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,  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."}]