Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem

This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the main...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2009-08, Vol.20 (4), p.389-400
Main Authors: Berrichi, A., Amodeo, L., Yalaoui, F., Châtelet, E., Mezghiche, M.
Format: Article
Language:English
Subjects:
Algorithms
Business and Management
Comparative studies
Computer Science
Control
Genetic algorithms
Job shops
Literature reviews
Machines
Maintenance management
Manufacturing
Mechatronics
Operations Research
Optimization
Optimization algorithms
Pareto optimum
Planning
Preventive maintenance
Process engineering
Processes
Production
Robotics
Scheduling
Scheduling algorithms
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Summary:This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the maintenance aspect of the problem. The aim is to simultaneously optimize two criteria: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance side. Two decisions are taken at the same time: finding the best assignment of n jobs to m machines in order to minimize the makespan and deciding when to carry out the preventive maintenance actions in order to minimize the system unavailability. The maintenance actions numbers as well as the maintenance intervals are not fixed in advance. Two evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front in the parallel machine case. On a large number of test instances (more than 5000), the obtained results are promising.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-008-0113-5