Browsing by Author "Tan, Baris"

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Optimal Purchasing and Production Control for a Circular Production System
(Taylor & Francis Inc, 2026) Song, Yun-Lei; Frigerio, Nicla; Karabag, Oktay; Wang, Jun-Qiang; Tan, Baris
The paper investigates the optimal purchasing and production control policy within a circular production system that processes two returned material types through a shared manufacturing center. Materials are purchased or rejected upon arrival, with differences in procurement cost. At each decision epoch, the system must decide whether to purchase incoming materials and which type to prioritize for production. The problem is formulated as a Markov decision process, and a linear programming approach is developed to compute the optimal purchasing and production policy for the system under both non-preemption and preemption rules. Theoretically, the structure of the optimal policy is analytically characterized under the preemption rule in the case of equal production rates. Numerical experiments reveal that this policy remains optimal or near-optimal for the system under non-preemption and preemption rules with varying production rates. Additionally, comparisons of the optimal policies between non-preemption and preemption rules demonstrate that the system under the preemption rule yields higher average reward than that under the non-preemption rule, to varying degrees depending on system parameters, such as production rate, holding cost, and arrival rate.
Citation - WoS: 1
Citation - Scopus: 1
Optimizing Production and Remanufacturing Decisions for Make-to-Stock Hybrid Manufacturing Systems Using Real-Time Information on Products in Use
(Taylor & Francis Ltd, 2025) Karabag, Oktay; Karaesmen, Fikri; Tan, Baris
This study presents an analytical framework for make-to-stock hybrid manufacturing systems that use both virgin and returned materials to produce a single product. The system is modelled as a Markov Decision Process, and a linear programming approach is used to determine optimal decisions on when to produce, which material to use, and whether to remanufacture or dispose of returned items. A key feature of the model is the inclusion of real-time tracking information on products in use within such a circular system, which supports informed and timely decision-making. We conduct a comparative numerical analysis between the optimal policy and a simplified one that omits orbit information. Our results show that excluding orbit information leads to an average profit loss of 2.21%, with losses reaching up to 11.18% in some settings, quantifying the economic impact of tracking capabilities in closed-loop systems. We also examine the effect of regulatory minimums that require a portion of production to use returned materials. While such policies support sustainability goals, they may reduce profitability when return rates are low or holding costs are high. We believe these findings offer practical guidance for firms seeking to balance profitability, regulatory compliance, and operational simplicity in circular manufacturing environments.
Citation - WoS: 4
Citation - Scopus: 4
Purchasing and Remanufacturing Decisions With Different Quality Returned Material and Finished Goods
(Taylor & Francis Ltd, 2025) Karabag, Oktay; Karaesmen, Fikri; Tan, Baris
To transition from a linear to a circular value chain, an effective refurbishment policy is crucial to preserve material value and functionality at the end of a product's life-cycle. This study examines a refurbisher that processes first- and second-quality returned materials to produce first- and second-quality products in a make-to-stock system. The refurbisher makes purchasing decisions for the returned materials and determines whether to refurbish or remain idle, and if refurbishing, how to convert them into finished goods of varying quality. There are five refurbishment decisions (converting first-quality to first- or second-quality, second-quality to first- or second-quality, or no production) and two purchasing decisions for the materials. With production and arrival times modelled as exponential random variables, the optimal control problem is formulated as a Markovian Decision Process, using a long-run average profit criterion to identify optimal decisions. A linear programming approach is employed for numerical optimisation. Results show that the most profitable option based solely on sales prices, purchasing, and conversion costs may not be optimal. Instead, the optimal policy is influenced by per-unit profit differences, returned material availability, demand rates, and production times across various refurbishment scenarios.