Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
Genetic Algorithms and Fuzzy Multiobjective Optimization
Genetic Algorithms and Fuzzy Multiobjective Optimization
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
An algorithm portfolio based solution methodology to solve a supply chain optimization problem
Expert Systems with Applications: An International Journal
Optimization of system reliability in multi-factory production networks by maintenance approach
Expert Systems with Applications: An International Journal
The distributed permutation flowshop scheduling problem
Computers and Operations Research
Computers and Industrial Engineering
A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop
Expert Systems with Applications: An International Journal
Assembly line balancing in garment industry
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A heuristic algorithm for the distributed and flexible job-shop scheduling problem
The Journal of Supercomputing
Minimizing the total completion time in a distributed two stage assembly system with setup times
Computers and Operations Research
Hi-index | 12.07 |
This paper proposes an adaptive genetic algorithm for distributed scheduling problems in multi-factory and multi-product environment. Distributed production strategy enables factories to be more focused on their core product types, to achieve better quality, to reduce production cost, and to reduce management risk. However, when comparing with single-factory production, scheduling problems involved in multi-factory one are more complicated, since different jobs distributed to different factories will have different production scheduling, consequently affect the performance of the supply chain. Distributed scheduling problems deal with the assignment of jobs to suitable factories and determine their production scheduling accordingly. In this paper, a new crossover mechanism named dominated gene crossover will be introduced to enhance the performance of genetic search, and eliminate the problem of determining optimal crossover rate. A number of experiments have been carried out. For the comparison purpose, five multi-factory models have been solved by different well known optimization approaches. The results indicate that significant improvement could be obtained by the proposed algorithm.