Joint optimization of ordering and maintenance with condition monitoring data

Moghaddass R., Ertekin Bolelli Ş.

ANNALS OF OPERATIONS RESEARCH, cilt.263, ss.271-310, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 263
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s10479-017-2745-3
  • Dergi Adı: ANNALS OF OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.271-310
  • Anahtar Kelimeler: Real-time analytics, Partially observable semi-Markov decision process, Condition monitoring, Deteriorating systems, SPARE-PROVISIONING POLICY, MULTISTATE DETERIORATING SYSTEMS, OPTIMAL REPLACEMENT POLICIES, PROPORTIONAL HAZARDS MODEL, MARKOVIAN DETERIORATION, SILENT FAILURES, INVENTORY, INFORMATION, EQUIPMENT, TIME
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

We study a single-unit deteriorating system under condition monitoring for which collected signals are only stochastically related to the actual level of degradation. Failure replacement is costlier than preventive replacement and there is a delay (lead time) between the initiation of the maintenance setup and the actual maintenance, which is closely related to the process of spare parts inventory and/or maintenance setup activities. We develop a dynamic control policy with a two-dimensional decision space, referred to as a warning-replacement policy, which jointly optimizes the replacement time and replacement setup initiation point (maintenance ordering time) using online condition monitoring data. The optimization criterion is the long-run expected average cost per unit of operation time. We develop the optimal structure of such a dynamic policy using a partially observable semi-Markov decision process and provide some important results with respect to optimality and monotone properties of the optimal policy. We also discuss how to find the optimal values of observation/inspection interval and lead time using historical condition monitoring data. Illustrative numerical examples are provided to show thatour joint policy outperforms conventional suboptimal policies commonly used in theliterature.