Joint optimization of preventive maintenance and spare parts inventory for an optimal production plan with consideration of CO2 emission

2016 ◽  
Vol 149 ◽  
pp. 172-186 ◽  
Author(s):  
Kader Ba ◽  
Sofiene Dellagi ◽  
Nidhal Rezg ◽  
Walid Erray
Keyword(s):  
Preventive Maintenance ◽  
Co2 Emission ◽  
Spare Parts ◽  
Joint Optimization ◽  
Production Plan ◽  
Optimal Production ◽  
Spare Parts Inventory

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 Preventive Maintenance Interval Prediction: a Spare Parts Inventory Cost and Lost Earning Based Model

2015 ◽  
Vol 5 (3) ◽  
pp. 811-817
Author(s):  
O. A. Adebimpe ◽  
V. Oladokun ◽  
O. E. Charles-Owaba
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Manufacturing Firms ◽  
Inventory Cost ◽  
Manufacturing Firm ◽  
Interval Prediction ◽  
Spare Parts Inventory ◽  
Optimal Maintenance

In this paper, some preventive maintenance parameters in manufacturing firms were identified and used to develop cost based functions in terms of machine preventive maintenance. The proposed cost based model considers system’s reliability, cost of keeping spare parts inventory and lost earnings in deriving optimal maintenance interval. A case of a manufacturing firm in Nigeria was observed and the data was used to evaluate the model.

Joint Optimization of Transportation and Spare Parts Inventory Policies in a Reverse Logistics Network Using OptQuest

2008 ◽  
Author(s):  
Mehmet A. Ilgin ◽  
Surendra M. Gupta
Keyword(s):  
Reverse Logistics ◽  
Printed Circuit Boards ◽  
Discrete Event ◽  
Spare Parts ◽  
Simulation Software ◽  
Joint Optimization ◽  
Optimum Number ◽  
Inventory Policies ◽  
Logistics Network ◽  
Spare Parts Inventory

The aim of this study is the joint optimization of the transportation and spare parts inventory policies in a reverse logistics (RL) network designed for End of Life (EOL) television (TV) recyling. Besides recycling, Printed Circuit Boards (PCBs) recovered from EOL TVs are used to meet the spare PCB demand. In order to model this RL network with its disassembly, transportation and spare parts inventory related aspects, a discrete event simulation (DES) model has been developed in detail using Arena simulation software. Next, Arena OptQuest has been used to propose optimum number and size of trucks together with the optimum reorder (s) and order quantity (Q) levels for the spare PCBs based on the minimization of total cost which includes inventory holding, PCB recovery, new PCB acquisition, truck amortization and operating costs.

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