Joint optimization of spare parts inventory and maintenance policies using genetic algorithms

Ilgın, Mehmet Ali; Tunalı, Semra

doi:10.1007/s00170-006-0618-z

Cited by 89 publications

(46 citation statements)

References 32 publications

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“…The fitness function was defined as a multi-objective function of profit and system availability. Ilgin and Tunali [89] proposed a GA for the joint optimization of periodic preventive maintenance and spare provisioning policies of a manufacturing system operating in the automotive sector. A 5% reduction in total annual cost and increase in throughput was obtained by their GA based approach.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Celen

Djurdjanović

2012

ASME 2012 International Manufacturing Science and Engineering Conference

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In highly flexible and integrated manufacturing systems such as those in semiconductor manufacturing, there exist strong dynamic interactions between the equipment condition, operations executed on the equipment and the resulting product quality. These interactions necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper we aim to devise an integrated decision making policy for maintenance scheduling and production sequencing with the objective of maximizing an adaptive profit function, while taking into account operation-dependent degradation models and a production target. In order to obtain the optimal decision policy, a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system is used. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

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Section: Metaheuristic Approachesmentioning

confidence: 99%

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Celen

Djurdjanović

2012

ASME 2012 International Manufacturing Science and Engineering Conference

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“…When the two costs are considered together, production may improve. In one example addressing a motor block manufacturing line, joint optimization brought a 53% reduction in total annual maintenance cost and 6% improvement in average monthly production [7]. Van Horenbeek et al reviewed relevant work and classified them based on the combination of maintenance policy (block-based, age-based, and condition-based) and inventory policy (periodic review and continuous review) [8].…”

Section: Introductionmentioning

confidence: 99%

“…The former have been used in joint models about block replacement and continuous review inventory policies [7], age-based maintenance and continuous review inventory policies [9], and condition-based maintenance related joint policies for multi-unit systems [10]. It is a good choice when mathematical models cannot be established.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory

Jiang

Chen

Zhou

2015

Journal of Manufacturing Systems

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“…The task of placing spare parts at different levels of supply is to achieve maximum integrated benefits and guarantee high efficiency of technical training of the serviced systems. Despite numerous studies of this problem (see, for example, [1][2][3]), in most papers only one criterion has been optimized. In fact, the task of placing spare parts for maintenance companies should be formulated simultaneously for several target functions, as well as the required probability of sufficient volume of the order of spare parts for the planned period and minimization of total costs.…”

Section: Introductionmentioning

confidence: 99%

Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles

Ashraf¹,

Aleksandr²,

Kamal³

2017

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Abstract:On the example of the two-criterion problem with the objective functions of the maximum, the confidence probabilities of the demand and the minimum of the total costs show the applicability of the method of Vector Optimization of Particle Swarm Optimization (VEPSO). Compared with genetic algorithms and other methods of evolutionary modeling, this method is easy to implement and has high efficiency, as well as the accelerated cost of an approximate solution of the problem from the external archive of the no dominant best solutions to the Pareto front, which is the boundary of the Pareto-optimal Compromise) solutions.

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Joint optimization of spare parts inventory and maintenance policies using genetic algorithms

Cited by 89 publications

References 32 publications

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems

Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory

Two-Level Multi-criteria Model for Calculating Multinomenclature Spare Parts of an Auto Service Enterprise Based on the Rougher Algorithm for Optimizing the Behavior of Their Particles

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