On merging sequencing and scheduling theory with genetic algorithms to solve stochastic job shops
On merging sequencing and scheduling theory with genetic algorithms to solve stochastic job shops
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Generating robust and flexible job shop schedules using genetic algorithms
IEEE Transactions on Evolutionary Computation
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The traditional focus of scheduling research is finding schedules with a low implementation cost. However, in many real world scheduling applications finding a robust schedule (a quality schedule expected to still be acceptable if something unforeseen happens) or a flexible schedule (a quality schedule expected to be easy to change) is just as important. In this paper the robustness and flexibility of schedules produced by minimizing neighbourhood based robustness measures are investigated. The basic idea is to minimize not only the implementation cost of a single schedule, but the implementation costs of a set of schedules located around a centre schedule. The problems used in the experiments are worst tardiness, summed tardiness and total flow time job shop problems. It is found that the robustness measures increase robustness and to some degree flexibility for worst tardiness and loose summed tardiness problems, while they do not perform well for tight summed tardiness problems and total flow time problems. It is conjectured that neighbourhood based robustness can be expected to work well on problems with few critical points and not well on problems with many critical points.