Joint Optimization of Preventive Maintenance and Spare Parts Logistics for Multi-echelon Geographically Dispersed Systems

2018 ◽  
pp. 643-653 ◽  
Author(s):  
Keren Wang ◽  
Dragan Djurdjanovic
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Joint Optimization ◽  
Dispersed Systems ◽  
Geographically Dispersed

scholarly journals Joint Optimization of Preventive Maintenance, Spare Parts Inventory and Transportation Options for Systems of Geographically Distributed Assets

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 55 ◽  
Author(s):  
Keren Wang ◽  
Dragan Djurdjanovic
Keyword(s):  
Decision Making ◽  
Preventive Maintenance ◽  
Spare Part ◽  
Spare Parts ◽  
Joint Optimization ◽  
Joint Decision ◽  
Geographically Dispersed ◽  
Simulation Based ◽  
The Right ◽  
Maintenance Schedules

Maintenance scheduling for geographically dispersed assets intricately and closely depends on the availability of maintenance resources. The need to have the right spare parts at the right place and at the right time inevitably calls for joint optimization of maintenance schedules and logistics of maintenance resources. The joint decision-making problem becomes particularly challenging if one considers multiple options for preventive maintenance operations and multiple delivery methods for the necessary spare parts. In this paper, we propose an integrated decision-making policy that jointly considers scheduling of preventive maintenance for geographically dispersed multi-part assets, managing inventories for spare parts being stocked in maintenance facilities, and choosing the proper delivery options for the spare part inventory flows. A discrete-event, simulation-based meta-heuristic was used to optimize the expected operating costs, which reward the availability of assets and penalizes the consumption of maintenance/logistic resources. The benefits of joint decision-making and the incorporation of multiple options for maintenance and logistic operations into the decision-making framework are illustrated through a series of simulations. Additionally, sensitivity studies were conducted through a design-of-experiment (DOE)-based analysis of simulation results. In summary, considerations of concurrent optimization of maintenance schedules and spare part logistic operations in an environment in which multiple maintenance and transpiration options are available are a major contribution of this paper. This large optimization problem was solved through a novel simulation-based meta-heuristic optimization, and the benefits of such a joint optimization are studied via a unique and novel DOE-based sensitivity analysis.

scholarly journals On the joint optimization of the periodic preventive maintenance and the spare parts inventory problem

2016 ◽  
Vol 49 (12) ◽  
pp. 881-885 ◽  
Author(s):  
Anis Mjirda ◽  
Rachid Benmansour ◽  
Hamid Allaoui ◽  
Gilles Goncalves
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Joint Optimization ◽  
Inventory Problem ◽  
Spare Parts Inventory ◽  
Periodic Preventive Maintenance

scholarly journals Ecological and joint optimization of preventive maintenance and spare parts inventories for an optimal production plan

2015 ◽  
Vol 48 (3) ◽  
pp. 2139-2144 ◽  
Author(s):  
Ba Kader ◽  
Dellagi Sofiene ◽  
Rezg Nidhal ◽  
Erray Walid
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Joint Optimization ◽  
Production Plan ◽  
Optimal Production

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

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Cai ◽  
Yibing Yin ◽  
Li Zhang ◽  
Xi Chen
Keyword(s):  
Inventory Management ◽  
Preventive Maintenance ◽  
Spare Parts ◽  
Combination Method ◽  
Joint Optimization ◽  
Inventory Level ◽  
Cost Rate ◽  
Proposed Model ◽  
Spare Parts Inventory ◽  
The Cost

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.

scholarly journals Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jianfei Ye ◽  
Huimin Ma
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Preventive Maintenance ◽  
Production Scheduling ◽  
Job Shop ◽  
Joint Optimization ◽  
Planning Problem ◽  
Flexible Job Shop ◽  
Maintenance Planning ◽  
Joint Decision

In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

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