SEMINAR:Asymptotically optimal energy consumption and inventory...

Guest: Erhun Özkan, Koç University

Title: Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system (IE, ME, MFE, CS)

Date/Time: November 13, 2024, 13:40

Location: FENS G032

Abstract: We study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the server’s energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments.

Bio: Erhun Özkan is an assistant professor of Operations Management at the College of Administrative Sciences and Economics at Koç University. He received his BSc and MSc degrees from the Industrial Engineering department of the Middle East Technical University. He received his Ph.D. degree from the Data Sciences and Operations Department of the University of Southern California. His research interests include control of stochastic systems within the application domains such as production systems, maintenance systems, healthcare, revenue management, and online platforms. He was a finalist in the 2017 INFORMS George Nicholson Student Paper Competition. He received the BAGEP award in 2024.