Flow Shop Scheduling with Partial Resource Flexibility
Management Science
A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem
Expert Systems with Applications: An International Journal
Lagrangian Relaxation Algorithms for Re-entrant Hybrid Flowshop Scheduling
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 01
Genetic algorithm modeling for the inspection allocation in reentrant production systems
Expert Systems with Applications: An International Journal
IEEE Transactions on Evolutionary Computation
Solving the Register Allocation Problem for Embedded Systems Using a Hybrid Evolutionary Algorithm
IEEE Transactions on Evolutionary Computation
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The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem.