Department of Industrial Engineering & Decision Analytics [Joint IEDA/ISOM] seminar - Exploiting Random Lead Times in Inventory Systems
Supporting the below United Nations Sustainable Development Goals:支持以下聯合國可持續發展目標：支持以下联合国可持续发展目标：
First, we discuss a proof-of-concept theoretical result, showing that a policy exploiting randomness of a lead time can provide a potentially infinite inventory cost reduction compared to the standard Constant Base Stock (CBS) policy. Specifically, we consider the classical single-item inventory system with backlogs, random replenishment lead times and order crossovers, operating in continuous time. We propose a new policy that, instead of focusing on the inventory position, uses the net inventory level to set a dynamic target for inventory in-transit, and places orders to follow that target. We prove that in the case of exponentially distributed lead times, as the demand rate becomes large, the expected (absolute) inventory level under our policy vanishes relatively to that under CBS policy.
In addition to this proof-of-concept, we introduce and study new policies exploiting lead time randomness in a more realistic -- discrete-time -- setting. For these policies we obtain stochastic stability and average inventory finiteness results. Simulations show that, under a variety of lead time distributions, new policies do provide very substantial inventory cost reduction over CBS.
Based on joint works with Q.Wang (UIUC) and A. Taneja (UIUC)
Alexander Stolyar is a Professor in the ISE Department and Coordinated Science Lab at UIUC since 2017. His research interests are in stochastic processes, queueing theory, and stochastic modeling of information, communication, service and supply chain systems. He received Ph.D. in Mathematics from the Institute of Control Science, Moscow, in 1989, and was a research scientist at the Institute of Control Science in 1989-1991. In 1992-1998 he was working on stochastic models in telecommunications at Motorola and AT&T Research. From 1998 to 2014 he was with the Bell Labs Mathematical Sciences Research, Murray Hill, New Jersey, working on stochastic networks and resource allocation problems in a variety of applications, including wireless and wireline communications, service systems, network clouds. In 2014-2016 he was a Professor in the ISE Department at Lehigh University. He is an INFORMS Fellow, and received INFORMS Applied Probability Society 2004 Best Publication award and SIGMETRICS'1996 Best Paper award.