The shifting bottleneck procedure for job shop scheduling
Management Science
An algorithm for solving the job-shop problem
Management Science
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 genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
A fast taboo search algorithm for the job shop problem
Management Science
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Developing multi-agent systems with a FIPA-compliant agent framework
Software—Practice & Experience
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Solving Multiprocessor Scheduling Problems
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
Ant colony optimization combined with taboo search for the job shop scheduling problem
Computers and Operations Research
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The job shop scheduling problem is one of the most important and complicated problems in machine scheduling. This problem is characterized as NP-hard. The high complexity of the problem makes it hard to find the optimal solution within reasonable time in most cases. Hence searching for approximate solutions in polynomial time instead of exact solutions at high cost is preferred for difficult instances of the problem. Meta-heuristic methods such as genetic algorithms are widely applied to find optimal or near-optimal solutions for the job shop scheduling problem. Parallelizing the genetic algorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel approach for the problem in which creating the initial population and parallelizing the genetic algorithm are carried out in an agent-based manner. Benchmark instances are used to investigate the performance of the proposed approach. The results show that this approach improves the efficiency.