Optimizing Joint Maintenance and Stock Provisioning Policy for a Multi-Echelon Spare Part Logistics Network

Chen, Mu-Chen; Hsu, Chih Ming; Chen, Shih Wei

doi:10.1080/10170660609509325

Cited by 20 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-identical Labor Simulation technique Optimization algorithm (Sarker and Haque 2000) No No Discrete event Design of experiment (Chen, Hsu, and Chen 2006) The aim of this paper is to explore specific gaps observed in the previous literature by optimizing preventive maintenance and spare provision policy under continuous review in a non-identical multicomponent manufacturing system through a combined discrete event and continuous simulation model coupled with an optimization engine. Three optimization algorithms are compared and examined, namely, Simulated Annealing, Random Solutions and Hill Climb.…”

Section: Papermentioning

confidence: 99%

“…Discrete event simulation was expected to dominate as it is the most popular technique in modeling manufacturing systems (Jahangirian Alrabghi, Tiwari, and Alabdulkarim et al 2010). Studies either used a single optimization algorithm (Chen, Hsu, and Chen 2006;Ilgin and Tunali 2007) or conducted design of experiments (Sarker and Haque 2000;Boulet, Gharbi, and Kenn 2009). Table 1 also shows how the present study complements the previous research by the inclusion of labor, modeling through combined discrete event and continuous simulation and examining three optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems

Alrabghi¹,

Tiwari²,

Alabdulkarim³

2013

2013 Winter Simulations Conference (WSC)

View full text Add to dashboard Cite

Maintenance and spare parts management are interrelated and the literature shows the significance of optimizing them jointly. Simulation is an efficient tool in modeling such a complex and stochastic problem. In this paper, we optimize preventive maintenance and spare provision policy under continuous review in a non-identical multi-component manufacturing system through a combined discrete event and continuous simulation model coupled with an optimization engine. The study shows that production dynamics and labor availability have a significant impact on maintenance performance. Optimization results of Simulated Annealing, Hill Climb and Random solutions are compared. The experiments show that Simulated annealing achieved the best results although the computation time was relatively high. Investigating multi-objective optimization might provide interesting results as well as more flexibility to the decision maker. INTRODUCTIONMaintenance plays a vital role in sustaining and improving asset availability, which in turn affects the total output production of the system. A large and growing body of literature has investigated the optimization of maintenance in manufacturing systems. Dekker (1996) conducted a comprehensive review of maintenance optimization models and applications. It is interesting to note that simulation optimization was not mentioned in his research, rather, the focus was on mathematical modeling only. As the complexity of maintenance systems has increased, the limitation of mathematical modeling became apparent as many maintenance policies are not analytically traceable (Garg and Deshmukh 2006, Nicolai andDekker 2008). In a recent review, Sharma and Yadava (2011) discussed the published maintenance optimization models. They observed that the use of simulation has been an emerging trend which changed the maintenance view. This is mainly because simulation allows experimentation and better understanding of complex systems (Sebastian 2006). Simulation based optimization (Rogers 2002) is an approach that consists of two main elements: a simulation model and an optimization engine. As shown in Figure 1, the optimization engine provides the simulation model with the decision variables. The simulation runs and provides the optimization engine with the objective function. Depending on the optimization algorithm the optimization engine will conduct an analysis and provide the simulation model with a new set of variables seeking to improve the objective function. This cycle will continue until a condition is met. For example a specific number of evaluations without improvement in the objective function. 1109 978-1-4799-3950-3/13/$31.00 ©2013 IEEE

show abstract

Section: Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems

Alrabghi¹,

Tiwari²,

Alabdulkarim³

2013

2013 Winter Simulations Conference (WSC)

View full text Add to dashboard Cite

show abstract

“…The joint optimization models are developing with the change of maintenance policies from age-based, periodic/block to condition-based maintenance (CBM). The agebased policy has been applied in the joint optimization models, such as [4][5][6][7][8][9]. And the periodic/block policy is widely adopted in the joint optimization models; for example, Acharva et al [10] found a jointly optimal block preventive replacement and spare provisioning policy for a system consisting of several like units.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

Cai

Yin

Zhang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

show abstract

“…-To the best of our knowledge, Chen et al [147] are the only authors that consider a multi-echelon spare part network in a joint maintenance and inventory decision problem. This topic can be further investigated in future research.…”

Section: Future Research Directionsmentioning

confidence: 99%

A survey of maintenance and service logistics management: Classification and research agenda from a maritime sector perspective

Eruguz

Tan

Houtum

2017

Computers & Operations Research

View full text Add to dashboard Cite

Optimizing Joint Maintenance and Stock Provisioning Policy for a Multi-Echelon Spare Part Logistics Network

Cited by 20 publications

References 27 publications

Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems

Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems

Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

A survey of maintenance and service logistics management: Classification and research agenda from a maritime sector perspective

Contact Info

Product

Resources

About