Joint optimization of condition-based maintenance and production lot-sizing

被引:63
|
作者
Peng, Hao [1 ]
van Houtum, Geert-Jan [2 ]
机构
[1] Chinese Acad Sci, Acad Math & Syst Sci, Zhongguancun East 55, Beijing 100190, Peoples R China
[2] Eindhoven Univ Technol, Dept Ind Engn & Innovat Sci, Postbus 513, NL-5600 MB Eindhoven, Netherlands
关键词
-Condition-based maintenance; Economic manufacturing quantity; LINE EQUIPMENT CONDITION; PREVENTIVE MAINTENANCE; PRODUCTION CYCLE; PROCESS MODEL; POLICY; REPLACEMENT; MACHINE; INFORMATION; COMPONENTS; SCHEDULES;
D O I
10.1016/j.ejor.2016.02.027
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Due to the development of sensor technologies nowadays, condition-based maintenance (CBM) programs can be established and optimized based on the data collected through condition monitoring. The CBM activities can significantly increase the uptime of a machine. However, they should be conducted in a coordinated way with the production plan to reduce the interruptions. On the other hand, the production lot size should also be optimized by taking the CBM activities into account. Relatively fewer works have been done to investigate the impact of the CBM policy on production lot-sizing and to propose joint optimization models of both the economic manufacturing quantity (EMQ) and CBM policy. In this paper, we evaluate the average long-run cost rate of a degrading manufacturing system using renewal theory. The optimal EMQ and CBM policy can be obtained by minimizing the average long-run cost rate that includes setup cost, inventory holding cost, lost sales cost, predictive maintenance cost and corrective maintenance cost Unlike previous works on this topic, we allow the use of continuous time and continuous state degradation processes, which broadens the application area of this model. Numerical examples are provided to illustrate the utilization of our model. (C) 2016 Elsevier B.V. All rights reserved.
引用
下载
收藏
页码:94 / 107
页数:14
相关论文
共 50 条
  • [41] A Collaborative Lot-Sizing Problem with Production Limitations
    Ziebuhr, Mario
    Buer, Tobias
    Kopfer, Herbert
    2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2015, : 1005 - 1012
  • [42] Joint optimization of condition-based maintenance and EPQ based on the random coefficient growth model
    Liu X.
    Feng Z.
    Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2019, 39 (01): : 251 - 258
  • [43] Joint optimization decision of equipment condition-based maintenance and spare parts inventory
    Lu C.
    Xu T.
    Wang H.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2019, 41 (07): : 1560 - 1567
  • [44] Economic production lot-sizing for an unreliable machine under imperfect age-based maintenance policy
    El-Ferik, Sami
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2008, 186 (01) : 150 - 163
  • [45] Lot-sizing with production and delivery time windows
    Laurence A. Wolsey
    Mathematical Programming, 2006, 107 : 471 - 489
  • [46] Economic Lot-Sizing for Integrated Production and Transportation
    Hwang, Hark-Chin
    OPERATIONS RESEARCH, 2010, 58 (02) : 428 - 444
  • [47] OPTIMAL LOT-SIZING IN MULTIITEM PRODUCTION INVENTORIES
    YOUNIS, MA
    MAHMOUD, MS
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 1990, 21 (04) : 789 - 798
  • [48] Deep reinforce learning for joint optimization of condition-based maintenance and spare ordering
    Hao, Shengang
    Zheng, Jun
    Yang, Jie
    Sun, Haipeng
    Zhang, Quanxin
    Zhang, Li
    Jiang, Nan
    Li, Yuanzhang
    INFORMATION SCIENCES, 2023, 634 : 85 - 100
  • [49] Optimization of Joint Policy of Condition-Based Maintenance and Spare Part Ordering Considering Imperfect Maintenance
    Zhao F.
    Li X.
    Guo M.
    Dongbei Daxue Xuebao/Journal of Northeastern University, 2022, 43 (10): : 1413 - 1421
  • [50] Condition-based Maintenance Optimization of Degradable Systems
    Wei, Shuaichong
    Nourelfath, Mustapha
    Nahas, Nabil
    INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES, 2022, 7 (01) : 1 - 15