Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. ,  consider a bi-criteria permutation flow shop scheduling problem, in which the ighted mean completion time and the ighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective immune algorithm (MOIA) is proposed for searching locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOIA performs better than the above genetic algorithm, especially for large-sized problems
['Reza Tavakkoli-Moghaddam', 'Alireza Rahimi-Vahed', 'Ali Hossein Mirzaei']
I am writing a research paper on a idea Solving a Bi-Criteria Permutation Flow Shop Problem Using Immune Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Solving a Bi-Criteria Permutation Flow Shop Problem Using Immune Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. ,  consider a bi-criteria permutation flow shop scheduling problem, in which the ighted mean completion time and the ighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective immune algorithm (MOIA) is proposed for searching locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOIA performs better than the above genetic algorithm, especially for large-sized problems"}]