Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
An ant colony system for permutation flow-shop sequencing
Computers and Operations Research
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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In this study, a bi-objective multi-start simulated-annealing algorithm (BMSA) is presented for permutation flowshop scheduling problems with the objectives of minimizing the makespan and total flowtime of jobs. To evaluate the performance of the BMSA, computational experiments were conducted on the well-known benchmark problem set provided by Taillard. The non-dominated sets obtained from each of the existing benchmark algorithms and the BMSA were compared, and then combined to form a net non-dominated front. The computational results show that more than 64% of the solutions in the net non-dominated front are contributed by the proposed BMSA. It is believed that these solutions can serve as new benchmarks for future research.