The shifting bottleneck procedure for job shop scheduling
Management Science
An algorithm for solving the job-shop problem
Management Science
A practical use of Jackson's preemptive schedule for solving the job shop problem
Annals of Operations Research
Job shop scheduling by simulated annealing
Operations Research
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A fast taboo search algorithm for the job shop problem
Management Science
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
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Direct Chromosome Representation and Advanced Genetic Operators for Production Scheduling
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
An Efficient Genetic Algorithm for Job Shop Scheduling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers & Mathematics with Applications
A variable neighborhood search for job shop scheduling with set-up times to minimize makespan
Future Generation Computer Systems
Research on job shop scheduling under uncertainty
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A high performing metaheuristic for job shop scheduling with sequence-dependent setup times
Applied Soft Computing
Search intensity versus search diversity: a false trade off?
Applied Intelligence
Production scheduling with a memetic algorithm
International Journal of Innovative Computing and Applications
Expert Systems with Applications: An International Journal
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
A new hybrid genetic algorithm for job shop scheduling problem
Computers and Operations Research
Managing dynamic flows in production chains through self-organization
Engineering Self-Organising Systems
Scheduling and control modeling of HVLV systems using max-plus algebra
VECoS'11 Proceedings of the Fifth international conference on Verification and Evaluation of Computer and Communication Systems
An agent-based parallel approach for the job shop scheduling problem with genetic algorithms
Mathematical and Computer Modelling: An International Journal
A memetic algorithm for the capacitated m-ring-star problem
Applied Intelligence
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In previous work, we developed three deadlock removal strategies for the job shop scheduling problem (JSSP) and proposed a hybridized genetic algorithm for it. While the genetic algorithm (GA) gave promising results, its performance depended greatly on the choice of deadlock removal strategies employed. This paper introduces a genetic algorithm based scheduling scheme that is deadlock free. This is achieved through the choice of chromosome representation and genetic operators. We propose an efficient solution representation for the JSSP in which the job task ordering constraints are easily encoded. Furthermore, a problem specific crossover operator that ensures solutions generated through genetic evolution are all feasible is also proposed. Hence, both checking of the constraints and repair mechanism can be avoided, thus resulting in increased efficiency. A mutation-like operator geared towards local search is also proposed which further improves the solution quality. Lastly, a hybrid strategy using the genetic algorithm reinforced with a tabu search is developed. An empirical study is carried out to test the proposed strategies.