Matchup scheduling with multiple resources, release dates and disruptions
Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
An indirect genetic algorithm for a nurse-scheduling problem
Computers and Operations Research
Filtered-beam-search-based algorithm for dynamic rescheduling in FMS
Robotics and Computer-Integrated Manufacturing
Computers and Operations Research
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Match-Up Strategies for Job Shop Rescheduling
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
FMS scheduling with knowledge based genetic algorithm approach
Expert Systems with Applications: An International Journal
A genetic algorithm for job shop scheduling with load balancing
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Generating robust and flexible job shop schedules using genetic algorithms
IEEE Transactions on Evolutionary Computation
On the identical parallel-machine rescheduling with job rework disruption
Computers and Industrial Engineering
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Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.