The shifting bottleneck procedure for job shop scheduling
Management Science
Job shop scheduling by simulated annealing
Operations Research
A fast taboo search algorithm for the job shop problem
Management Science
A heuristic for job shop scheduling to minimize total weighted tardiness
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Journal of Intelligent Manufacturing
Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm
Applied Soft Computing
Journal of Intelligent Manufacturing
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This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.