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
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
Computers and Operations Research
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
Adaptive representation for flexible job-shop scheduling and rescheduling
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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
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In this paper, we present an alternative representation for solving multiobjective Flexible Job-shop Scheduling Problems (FJSP). In FJSP, there may be a choice of machines that can perform any given operation. In order to schedule an operation, it needs to be assigned a machine first, a process known as routing. Most previous approaches to solving FJSP assigned machines to all schedules before beginning any scheduling. In our approach, Adaptive Representation (AdRep), we assign a machine to an operation just at the time it is ready to be scheduled, allowing the routing process to incorporate information from the scheduling environment. Experimental results show that although AdRep performance does not scale as well with problem size as some other approaches that are not simultaneously searching for machine assignments, it is able to find all best published solutions on a three-objective Pareto front including makespan, total workload, and maximum workload on any machine, while its simultaneous routing search opens up new possibilities for optimality of rescheduling in response to machine failure.