Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using evolutionary computation and local search to solve multi-objective flexible job shop problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Solving multiobjective flexible job-shop scheduling using an adaptive representation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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The flexible job-shop scheduling problem (FJSP) is one of the most popular scheduling problems, due to its relative simplicity, compared to real-world scheduling problems, and its NP-hardness. There are two types of decisions involved in solving FJSP problems: (1) assigning machines to operations, or routing, and (2) assigning a time slot in the assigned machines to the operations, or scheduling. Most approaches commit all routing (machine assignment) decisions before the scheduling process. Our proposed representation, AdRep, encodes simple heuristics, such as routing rules, instead of explicit machine assignments, delaying the explicit routing decision until absolutely necessary. This allows the scheduler to take the current state of the system into account before assigning a machine to an operation. We also utilize similar paradigms in the scheduling process. Experimental results show that our method is as capable, makespan-wise, as other published methods. Also, our method performs adequately against disruption.