A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
| Hi-index | 0.00 |
Job shop scheduling problem has been always a hardest task in the combinatorial research. Keeping in view the strong computational power of MATrix LABoratory (MATLAB) and robustness of GA, we have used a different novel approach for solving difficult shop floor scheduling problems. In this paper, parallel genetic algorithm based solution methodology has been presented and the algorithm is implemented using powerful MATrix LABoratory (MATLAB) environment to solve practical problems of job shop. The special coded mutation and crossover operators were designed to avoid any infeasible formulation of children. The solution result reveals that this methodology can be used to solve the complex optimization problems. In this work, job shop scheduling problem has been formulated and subsequently solved with the parallel genetic algorithm approach. The robustness and flexibility of GA offers a lot to tackle the stochastic solution of nondeterministic polynomial (NP) hard problems. The work is supported by the experimental and simulation results. The make span minimisation performance criteria were chosen in the experimental analysis.