Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure process

Kuo, Yarlin; Chang, Zi-Ann

doi:10.1002/nav.20232

Cited by 35 publications

(8 citation statements)

References 18 publications

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“…In particular, they formulate integrated planning models to simultaneously face production and maintenance planning (Kuo andChang 2007, Cassady andKutanoglu 2005). These contributions are not based on the reliability of parts and components involved and are not suitable to strategically design a task plan tailored to a selected production system subject to failure.…”

Section: Literature Reviewmentioning

confidence: 99%

The scheduling of maintenance. A resource-constraints mixed integer linear programming model

Manzini

Accorsi

Cennerazzo

et al. 2015

Computers & Industrial Engineering

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Section: Literature Reviewmentioning

confidence: 99%

The scheduling of maintenance. A resource-constraints mixed integer linear programming model

Manzini

Accorsi

Cennerazzo

et al. 2015

Computers & Industrial Engineering

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“…While the majority of this literature deals with deterministic problems where limited availabilities of machines only result from planned maintenance, a number of authors have studied a single-machine scheduling problem assuming that the machine is not continuously available due to both planned maintenance and random machine breakdowns Kutanoglu 2003, 2005;Kuo and Chang 2007;Hadidi et al 2011Hadidi et al , 2012b. The problem of integrated maintenance planning and production scheduling of the first category has also been extended to flowshop setting that is similar to the manufacturing configuration of the fabrication process in the semiconductor industry (Allaoui and Artiba 2004).…”

Section: Integrated Maintenance Planning and Production Schedulingmentioning

confidence: 99%

A two-stage coupled algorithm for an integrated maintenance planning and flowshop scheduling problem with deteriorating machines

Bajestani

Beck

2015

J Sched

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We address a novel integrated maintenance and production scheduling problem in a multi-machine and multi-period production system, considering maintenance as a long-term decision. Deterioration of machines over time decreases production capacity. Since maintenance activities not only improve machine conditions, increasing production capacity, but also take time that cannot be used for production, the challenge is to assign maintenance to periods and to schedule maintenance and production activities within each period to minimize the combined cost of maintenance and lost production over the planning horizon. Motivated by logic-based Benders decomposition, we design an integrated two-stage algorithm to solve the problem. The first stage assigns maintenance to machines and time periods, abstracting the scheduling problem, while the second stage creates a schedule for the current time period. The first stage is then re-solved using feedback from the schedule. This iteration between maintenance planning and scheduling continues until the solution costs in two stages converge. The integrated approach models the interdependencies between maintenance and scheduling decisions in highly coupled processes such as wafer fabrication in the semiconductor manufacturing. Our results demonstrate that the benefit of integrated decision making increases when maintenance is less expensive relative to lost production cost and that a longer horizon for maintenance planning is beneficial when maintenance cost increases.

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“…For a single machine manufacturing system, Kuo and Chang (2007) developed an integrated maintenance scheduling and production planning optimization model with the objective of minimizing the total tardiness. Aramon Bajestani et al (2014) formulated a Markov decision process model for the problem of integrated maintenance and production scheduling in a deteriorating multi-machine production system.…”

Section: Introductionmentioning

confidence: 99%

“…Reineke et al (1999) addressed the problem of determining the appropriate age PM policy in order to improve the availability and cost performance for a system that has high availability requirements. For a single machine manufacturing system, Kuo and Chang (2007) developed an integrated maintenance scheduling and production planning optimization model with the objective of minimizing the total tardiness. Aramon Bajestani et al (2014) formulated a Markov decision process model for the problem of integrated maintenance and production scheduling in a deteriorating multi-machine production system.…”

Section: Introductionmentioning

confidence: 99%

Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems

Assid

Gharbi

Hajji

2015

International Journal of Production Research

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This article addresses the problem of joint optimization of production, setup and maintenance activities of unreliable manufacturing system producing two products. Given the complexity of the problem in a dynamic and stochastic environment, the literature has treated the problem separately by considering each axis individually (setup, production and maintenance) or by combining two axes simultaneously (production-setup; production-maintenance). Following the trend of scientific research advances that supports the fact that an integrated control leads to best performances, the main objective of this paper is to provide a control policy that will simultaneously combine the production, the setup and the preventive maintenance activities.To tackle the problem, an experimental resolution approach using combined continuous/discrete event simulation models is considered. The aim is to accurately imitate the production system behavior and to optimize the control policy parameters which minimize the total cost incurred. An in-depth study of the effects of the system parameters variation on the performance of the studied policies is performed in order to draw meaningful conclusions and to illustrate the robustness of the proposed resolution approach.Keywords: Production / setup control, preventive maintenance, inflexible and unreliable manufacturing system, simulation modeling, optimization, response surface methodology. IntroductionIn the context of unreliable manufacturing system, one of the most effective strategies is the hedging point policy (HPP) concept (Akella and Kumar, 1986 Accepted in International Journal of Production Research, Vol. 53, no 15, 2015 2 appropriate safety stock while the machine is operational to respond to customer demands when the machine is down. For failure and repair times described by homogeneous Markov processes, the optimality of the HPP is proved in the case of one-machine one-product type manufacturing system (M 1 P 1 ) with constant demand rate. Integrating preventive maintenance (PM) in the production planning has also attracted many researchers since machine breakdowns may disturb production process and cause delay in schedules. The PM function, in the overall manufacturing sector, is a priority and is central to the concerns of manufacturers. Its role is crucial and is manifested in the failures prevention and the maintaining of production tools in service. Several integrated models that coordinate PM planning decisions with the production scheduling were proposed (Cassady and Kutanoglu, 2005;Wang and Liu, 2013). The main objective was to minimize the total expected weighted completion time of jobs. Other studies argued the interests to integrate PM and planning for production as PM helps maintain the production tool in service and improve the system performances (Wang, 2002). Wee and Widyadana (2013) explain that PM can result in savings due to an increase of the effective service life of the system. Reineke et al.(1999) addressed the problem of determining the appropri...

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Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure process

Cited by 35 publications

References 18 publications

The scheduling of maintenance. A resource-constraints mixed integer linear programming model

The scheduling of maintenance. A resource-constraints mixed integer linear programming model

A two-stage coupled algorithm for an integrated maintenance planning and flowshop scheduling problem with deteriorating machines

Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems

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