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Prognostics-Based Two-Operator Competition in Proactive Replacement and Service Parts Procurement

2014; Taylor & Francis; Volume: 59; Issue: 4 Linguagem: Inglês

10.1080/0013791x.2014.940563

1547-2701

Faranak Fathi Aghdam, Haitao Liao,

Statistical Distribution Estimation and Applications

AbstractEffective prognostics and timely maintenance of degrading components can improve the availability and economic efficiency of an engineering system. However, possible shortage of required service parts usually makes near-zero downtime difficult to achieve. To coordinate service parts availability with scheduled maintenance, it is necessary for the operator to decide when to order the service parts and how to compete with other operators in parts procurement. In this article, we consider a situation where two operators are to make prognostics-based replacement decisions and strategically procure the needed service parts. When a competition occurs, each of the operators has a bounded continuum of strategies. A one-shot sequential game (Stackelberg game) is formulated and a sequential, constrained maximin experimental design approach is proposed to facilitate searching for the equilibrium solution. This approach is quite useful in handling cases where the follower's best response to the leader's strategy, both chosen from continuums of strategy sets, is difficult to obtain analytically. Numerical studies on wind turbine operation are provided to demonstrate the use of the sequential decision-making method in solving such complex, yet realistic maintenance and service parts logistics problems. NomenclatureC(i)price paid by operator i to acquire a service partCoregular ordering cost for a part from the primary supplierC'oemergency ordering cost for a part from the secondary supplierCp(i)total preventive replacement cost of operator iCu(i)total corrective replacement cost of operator iCf(i)unit downtime cost of operator i due to a failureCh(i)unit holding cost of operator iCp(i)unit downtime cost of operator i due to preventive replacementfRUL(i)(u)pdf of RUL(i) with scale parameter ηi and shape parameter βiMextra charge for bidding on a part from the primary supplierNENash equilibriumpdfprobability density functionRULremaining useful lifeRUL(i)random RUL of the unit being used by operator iRRULω(u)reliability function of RUL(i)Titime to perform preventive replacement of operator iTo(i)time to order a service part of operator iTTIinspection intervalTw(i)actual waiting time of operator i for receiving a partia specific strategy [C(i),To(i),Ti] of operator iΠi( · )objective function of operator iτ1primary supplier's replenishment lead timeτ′1secondary supplier's replenishment lead timeτ2time needed to perform preventive replacementτ3time needed to perform corrective replacementΩithe strategy set of operator iAdditional informationNotes on contributorsFaranak Fathi AghdamFaranak Fathi Aghdam, M.S., is a Ph.D. student in the Systems and Industrial Engineering Department at The University of Arizona. She received her B.S. degree in industrial engineering from Iran University of Science and Technology, and her M.S. degree in industrial engineering from the University of Tennessee–Knoxville. Her research focuses on modeling of device reliability and nanowire growth process.Haitao LiaoHaitao Liao, Ph.D., is an associate professor in the Systems and Industrial Engineering Department at The University of Arizona (UofA), Tucson, Arizona. He is also the Director of Reliability & Intelligent Systems Engineering (RISE) Laboratory at UofA. He received his Ph.D. in industrial and systems engineering from Rutgers University, New Jersey. He also received his M.S. degrees in industrial engineering and statistics from Rutgers University and B.S. in electrical engineering from the Beijing Institute of Technology. His research interests include modeling of accelerated testing, maintenance models and optimization, service parts logistics, prognostics, and probabilistic risk assessment. He is a recipient of the National Science Foundation CAREER Award in 2010, the winner of 2010 and 2013 William A. J. Golomski Award, and 2013 IIE QCRE Track Best Paper Award. He is a member of IIE, INFORMS, IEEE, and SRE.