Joint optimization of preventive maintenance and inventory management for standby systems with hybrid-deteriorating spare parts

2021 ◽  
pp. 107686
Author(s):  
Jian-Xun Zhang ◽  
Dang-Bo Du ◽  
Xiao-Sheng Si ◽  
Chang-Hua Hu ◽  
Han-Wen Zhang
Keyword(s):  
Inventory Management ◽  
Preventive Maintenance ◽  
Spare Parts ◽  
Joint Optimization

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 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 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.

Preventive Maintenance Scheduling

2013 ◽  
Vol 4 (2) ◽  
pp. 69-94
Author(s):  
S.A. Oke ◽  
O.E. Charles-Owaba ◽  
A.E. Oluleye
Keyword(s):  
Cost Function ◽  
Inventory Management ◽  
Preventive Maintenance ◽  
Unit Cost ◽  
Spare Parts ◽  
Maintenance Scheduling ◽  
Maintenance Cost ◽  
Test Problems ◽  
Idle Period ◽  
Cost Parameter

In this work, the effectiveness of preventive maintenance scheduling (PMS) decisions was reported based on a techno-economic model that reflects cost objective function for ship maintenance activities. With a potential to impact on both transportation businesses and users of transportation services, the model provides an alternative to the combined classical literature problems of spare-parts inventory management and control, failure prediction and reliability. The PMS model developed incorporates separate and combined functions of indirect, direct and factor maintenance costs. Idleness period for various formulated schedules are evaluated and compared. First, a general form of the preventive maintenance cost function incorporating unit cost of maintaining ships, a set of cost function parameters and variables was developed. The operations research framework for the problem is then applied to obtain test cases in which cost parameter(s) was/were used for scheduling decisions. Monte Carlo simulation is applied to generate additional test problems. Practical data were used to validate the model. For each problem, optimal schedules based on one to four cost parameters were determined. For each schedule, the total maintenance cost, cost of idleness, total ship idle period and total ship operation period were computed under inflation, opportunity and combined opportunity and inflation and compared with the values corresponding to maintenance cost parameter using t-test (p<0.05). Thus, the use of combined data from maintenance, opportunity and inflation for preventive maintenance scheduling of a fleet of ships is more effective than direct maintenance cost approach.

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 Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

2021 ◽  
Vol 11 (15) ◽  
pp. 7088
Author(s):  
Ke Yang ◽  
Yongjian Wang ◽  
Shidong Fan ◽  
Ali Mosleh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Inventory Management ◽  
Hierarchical Classification ◽  
Critical Issue ◽  
Spare Parts ◽  
Deep Convolutional Neural Network ◽  
Learning Ability ◽  
Second Phase ◽  
Good Decision

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

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