Joint optimization of spare parts ordering and age-based preventive replacement

2019 ◽  
Vol 58 (20) ◽  
pp. 6283-6299 ◽  
Author(s):  
Sofia Panagiotidou
Keyword(s):  
Spare Parts ◽  
Joint Optimization ◽  
Preventive Replacement

scholarly journals Joint optimization strategy of condition-based preventive replacement and spare parts ordering for multi-unit systems

2021 ◽  
Vol 1983 (1) ◽  
pp. 012120
Author(s):  
Hongqing Ye ◽  
Haochen Wang ◽  
Huade Su ◽  
Jie Lin ◽  
Wei Weng ◽  
...  
Keyword(s):  
Spare Parts ◽  
Joint Optimization ◽  
Optimization Strategy ◽  
Preventive Replacement

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.

scholarly journals Joint Optimization of Condition-based Maintenance and Inventory Ordering Based on Status Monitoring for Multi-unit system

2021 ◽  
Vol 37 (4) ◽  
Author(s):  
H. Deng ◽  
Q. Shi ◽  
Y. Wang
Keyword(s):  
Spare Parts ◽  
Condition Based Maintenance ◽  
Data Driven ◽  
Joint Optimization ◽  
Equipment Failure ◽  
Analysis Method ◽  
Monitoring Methods ◽  
State Monitoring ◽  
Status Monitoring ◽  
Unit System

In the modern industry, in order to reduce the inventory pressure, a variety of parts began to use unified kind spare parts for maintenance. However, highly integrated equipment is more difficult to use traditional RCM models, and researchers begin to steering based on state monitoring methods. Deepen a prediction of equipment failure. This paper mainly discussed the data-driven analysis method based on the Wiener process to predict the fault law of the same type. The joint model innovatively adopts the (s-1, s) policy considering the industrial characteristic and multi-period resupply. In the end, we analyze (s-1, s) policy in joint optimization by comparison to draw the optimal policy combination.

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