An introduction of dominant genes in genetic algorithm for scheduling of FMS

This paper proposed a new idea named Dominant Genes (DGs) in Genetic Algoriths (GAs) to deal with FMS scheduling problem with alternative production routing. In traditional GAs approach, the crossover mechanism will randomly select a number of genes to undergo crossover. However, these selected genes may not contain or contain only part of the critical structure of its original chromosome. In addition, since the inherited complexity of the scheduling nature, the changes in the structure of the selected genes will further influence its strength. To tackle this problem, the proposed DGs in this paper are to identify and record the best genes in the chromosome. A new crossover mechanism is also designed to ensure the best genes will undergo crossover, and retain the originality of the structure of the crossover genes. The performance of the proposed DGs is testified by comparing it with other heuristic optimizations. The shows that DGs perform better than other approaches. ©2005 IEEE.published_or_final_versio

Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems

Multi-factory production networks have increased in recent years. With the factories located in different geographic areas, companies can benefit from various advantages, such as closeness to their customers, and can respond faster to market changes. Products (jobs) in the network can usually be produced in more than one factory. However, each factory has its operations efficiency, capacity, and utilization level. Allocation of jobs inappropriately in a factory will produce high cost, long lead time, overloading or idling resources, etc. This makes distributed scheduling more complicated than classical production scheduling problems because it has to determine how to allocate the jobs into suitable factories, and simultaneously determine the production scheduling in each factory as well. The problem is even more complicated when alternative production routing is allowed in the factories. This paper proposed a genetic algorithm with dominant genes to deal with distributed scheduling problems, especially in a flexible manufacturing system (FMS) environment. The idea of dominant genes is to identify and record the critical genes in the chromosome and to enhance the performance of genetic search. To testify and benchmark the optimization reliability, the proposed algorithm has been compared with other approaches on several distributed scheduling problems. These comparisons demonstrate the importance of distributed scheduling and indicate the optimization reliability of the proposed algorithm.link_to_subscribed_fulltex

An adaptive genetic algorithm with dominated genes for distributed scheduling problems

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. © 2005 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

Solving distributed scheduling problem with alternative routings by genetic algorithms

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