The shifting bottleneck procedure for job shop scheduling
Management Science
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
A fast taboo search algorithm for the job shop problem
Management Science
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
A Hybrid Genetic Algorithm for the Single Machine Scheduling Problem
Journal of Heuristics
An Evolutionary Algorithm for Controlling Chaos: The Use of Multi-objective Fitness Functions
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A hybrid genetic algorithm for the job shop scheduling problems
Computers and Industrial Engineering
A genetic algorithm for the optimisation of assembly sequences
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
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Real world job shops have to contend with jobs due on different days, material ready times that vary, reentrant workflows and sequence-dependent setup times. The problem is even more complex because businesses often judge solution goodness according to multiple competing criteria. Producing an optimal solution would be time consuming to the point of rendering the result meaningless. Commonly used heuristics such as shortest processing time (SPT) and earliest due date (EDD) can be used to calculate a feasible schedule quickly, but usually do not produce schedules that are close to optimal in these job shop environments. We demonstrate that genetic algorithms (GA) can be used to produce solutions in times comparable to common heuristics but closer to optimal. Changing criteria or their relative weights does not affect the running time, nor does it require programming changes. Therefore, a GA can be easily applied and modified for a variety of production optimization criteria in a job shop environment that includes sequence-dependent setup times.