The shifting bottleneck procedure for job shop scheduling
Management Science
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
Management Science
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Genetic Algorithms
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
GA-based discrete dynamic programming approach for scheduling inFMS environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiagent scheduling method with earliness and tardiness objectives in flexible job shops
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive representation for flexible job-shop scheduling and rescheduling
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A SOMO-based approach to the operating room scheduling problem
Expert Systems with Applications: An International Journal
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Flexible job shop scheduling problem (fJSP) is an extension of the classical job shop scheduling problem, which provides a closer approximation to real scheduling problems. We develop a new genetic algorithm hybridized with an innovative local search procedure (bottleneck shifting) for the fJSP problem. The genetic algorithm uses two representation methods to represent solutions of the fJSP problem. Advanced crossover and mutation operators are proposed to adapt to the special chromosome structures and the characteristics of the problem. The bottleneck shifting works over two kinds of effective neighborhood, which use interchange of operation sequences and assignment of new machines for operations on the critical path. In order to strengthen the search ability, the neighborhood structure can be adjusted dynamically in the local search procedure. The performance of the proposed method is validated by numerical experiments on several representative problems.