Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems

Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling pr...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 32; no. 1; pp. 1 - 13
Main Authors: Kacem, I., Hammadi, S., Borne, P.
Format: Journal Article
Language:English
Published: New-York, NY IEEE 01.02.2002
Institute of Electrical and Electronics Engineers
Subjects:
Applied sciences
Computer Science
Construction
Costs
Decisions
Diversity reception
Encoding
Evolutionary
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Genetics
Job shop scheduling
Localization
Mathematical models
Miscellaneous
Operational research and scientific management
Operational research. Management science
Position (location)
Processor scheduling
Production management
Resource management
Robustness
Scheduling
Evolutionary algorithm
Ideal
Resource allocation
Job shop
Multiobjective programming
Scheduling
Modeling
Optimization
Workload
Assignment
Production management
Genetic algorithm
Coding
Manipulation
Genetics
Localization
ISSN:1094-6977
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Summary:Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies.
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ISSN:1094-6977
DOI:10.1109/TSMCC.2002.1009117