Applying tabu search to the job-shop scheduling problem
Annals of Operations Research - Special issue on Tabu search
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
The hybrid heuristic genetic algorithm for job shop scheduling
Computers and Industrial Engineering
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A hybrid genetic algorithm for the job shop scheduling problems
Computers and Industrial Engineering
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
A hybrid immune simulated annealing algorithm for the job shop scheduling problem
Applied Soft Computing
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
A tag machine based performance evaluation method for job-shop schedules
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A new hybrid GA/SA algorithm for the job shop scheduling problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm
Applied Soft Computing
An integrated search heuristic for large-scale flexible job shop scheduling problems
Computers and Operations Research
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Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, some genetic operators were designed in this paper. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm.