Browsing by Author "Karabag, Oktay"

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Citation - WoS: 14
Citation - Scopus: 12
Integrated optimisation of pricing- manufacturing- and procurement decisions of a make-to-stock system operating in a fluctuating environment
(TAYLOR & FRANCIS LTD, 2023) Oktay Karabag; Burak Gokgur; Gokgur, Burak; Karabag, Oktay
Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations they typically utilise operational hedging strategies in terms of pricing manufacturing and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically we study an integrated problem of procurement manufacturing and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain and they directly affect the system's procurement price raw material flow rate and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We particularly investigate how production rate holding cost procurement price and demand variabilities customers' price sensitivity and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular the profit enhancement being achieved with dynamic pricing can reach up to 15% depending on the problem parameters.
Citation - WoS: 2
Citation - Scopus: 3
Markovian Decision Process Modeling Approach for Intervention Planning of Partially Observable Systems Prone to Failures
(Springer Science and Business Media Deutschland GmbH, 2022) Oktay Karabağ; Önder Bulut; Ayhan Özgür Toy; Toy, Ayhan Ozgur; Karabag, Oktay; Bulut, Onder; C. Kahraman , S. Cevik Onar , B. Oztaysi , I.U. Sari , A.C. Tolga , S. Cebi
In this work we consider a system which gradually deteriorates over time. The system is fully functional in the beginning. Over time the system eventually becomes malfunctional. Once malfunctional the system must be replaced with a (new) fully functional system. There is a cost associated with this system replacement. However there is an option of repair/correction of partially deteriorated system at a lower cost. Once replaced or repaired/corrected the system is as good as new. The information about the deterioration level of the system is monitored through signals which provide only partial information. These signals are based on classification of intelligent sensors for deterioration monitoring. Signals are received as green yellow or red. The green signal indicates a system in a condition from fully functional to a predefined level of partially deteriorated system, the yellow signal indicates a system in a condition from the predefined level of partially deteriorated system to malfunctional system, finally the red signal indicates a malfunctional system. We model this system as a discrete time Markovian decision process and solve it through Linear Programming. Our work herein comprises model development and extensive numerical studies for impact of system parameters on the maintenance decisions and costs. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 16
Production and energy mode control of a production-inventory system
(ELSEVIER, 2023) Baris Tan; Oktay Karabag; Siamak Khayyati; Khayyati, Siamak; Tan, Baris; Karabag, Oktay
Energy efficiency in manufacturing can be improved by controlling energy modes and production dy-namically. We examine a production-inventory system that can operate in Working Idle and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival production and warm-up times we formulate the problem of de-termining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint pro-duction and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )