Researcher:
Kaya, Onur

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Faculty Member

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Onur

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Kaya

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Kaya, Onur

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2008 - 200932010 - 20147

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Researcher Of Publications: search.filters.isAuthorOfPublication.d66a4be2-d62a-4d8a-821a-d716918a9aac

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    An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem
    (Elsevier, 2014) Department of Business Administration; Department of Industrial Engineering; Department of Industrial Engineering; N/A; Aksen, Deniz; Kaya, Onur; Salman, Fatma Sibel; Tüncel, Özge; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Sciences; College of Engineering; Graduate School of Sciences and Engineering; 40308; 28405; 178838; N/A
    We study a selective and periodic inventory routing problem (SPIRP) and develop an Adaptive Large Neighborhood Search (ALNS) algorithm for its solution. The problem concerns a biodiesel production facility collecting used vegetable oil from sources, such as restaurants, catering companies and hotels that produce waste vegetable oil in considerable amounts. The facility reuses the collected waste oil as raw material to produce biodiesel. It has to meet certain raw material requirements either from daily collection, or from its inventory, or by purchasing virgin oil. SPIRP involves decisions about which of the present source nodes to include in the collection program, and which periodic (weekly) routing schedule to repeat over an infinite planning horizon. The objective is to minimize the total collection, inventory and purchasing costs while meeting the raw material requirements and operational constraints. A single-commodity flow-based mixed integer linear programming (MILP) model was proposed for this problem in an earlier study. The model was solved with 25 source nodes on a 7-day cyclic planning horizon. In order to tackle larger instances, we develop an ALNS algorithm that is based on a rich neighborhood structure with 11 distinct moves tailored to this problem. We demonstrate the performance of the ALNS, and compare it with the MILP model on test instances containing up to 100 source nodes.
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    Stochastic models for the coordinated production and shipment problem in a supply chain
    (Pergamon-Elsevier Science Ltd, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863
    In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a stochastic environment. The supplier controls the production, holds inventory and ships the products to the retailer to satisfy the external demand. We model the system as a Markov decision process, and show that the optimal production and transportation decisions are complex and non-monotonic. Therefore, we analyze two widely-used shipment policies in the industry as well, namely time-based and quantity-based shipment policies in addition to a hybrid time-and-quantity based shipment policy. We numerically compare the performances of these policies with respect to the optimal policy and analyze the effects of the parameters in the system.
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    A coordinated production and shipment model in a supply chain
    (Elsevier Science Bv, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863
    In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a deterministic inventory system. In this supply chain, the customers are willing to wait at the expense of a waiting cost. Accordingly, the retailer does not hold inventory but accumulates the customer orders and satisfies them at a later time. The supplier produces the items, holds the inventory and ships the products to the retailer to satisfy the external demand. We investigate both a coordinated production/transportation model and a decentralized model. In the decentralized model, the retailer manages his own system and sends orders to the supplier, while the supplier determines her own production process and the amount to produce in an inventory replenishment cycle according to the order quantity of the retailer. However, in the coordinated model, the supplier makes all the decisions, so that she determines the length of the replenishment and transportation cycles as well as the shipment quantities to the retailer. We determine the structure of the optimal replenishment and transportation cycles hi both coordinated and decentralized models and the corresponding costs. Our computational results compare the optimal costs under the coordinated and decentralized models. We also numerically investigate the effects of several parameters on the optimal solutions.
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    Inventory positioning, scheduling and lead-time quotation in supply chains
    (Elsevier Science Bv, 2008) Kaminsky, Philip; Department of Industrial Engineering; Kaya, Onur; Faculty Member; Department of Industrial Engineering; College of Engineering; 28405
    We consider supply chain networks composed of several centrally managed production facilities as well as external suppliers. We design effective heuristics for inventory positioning, order sequencing, and short and reliable due-date quotation for this supply chain. We perform extensive computational testing to assess the effectiveness of our algorithms, and we explore the impact of supply chain topology on inventory costs and effective due-date quotation.
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    Outsourcing vs. in-house production: a comparison of supply chain contracts with effort dependent demand
    (Pergamon-Elsevier Science Ltd, 2011) N/A; Department of Industrial Engineering; Kaya, Onur; Faculty Member; Department of Industrial Engineering; College of Engineering; 28405
    We analyze the effort and pricing decisions in a two facility supply chain in which one of the parties can exert costly effort to increase demand. We consider an outsourcing model in which the supplier makes the effort decision and an in-house production model in which the manufacturer decides on the effort level and we compare these two models with each other. We analyze and compare several contracts for decentralized supply chain models and we aim to find which contracts are best to use in different cases. We find the optimal contract parameters in each case and perform extensive computational testing to compare the efficiencies of these contracts. We also analyze the effect of the powers of the parties in the system and the effect of system parameters on the performances of the contracts and on the optimal values of the model variables such as price, effort and demand.
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    Dynamic pricing of durable products with heterogeneous customers and demand interactions over time
    (Pergamon-Elsevier Science Ltd, 2013) N/A; Department of Industrial Engineering; Kaya, Onur; Faculty Member; Department of Industrial Engineering; College of Engineering; 28405
    In this study, we analyze a dynamic pricing problem in which the demand is interdependent over time and the customers are heterogeneous in their purchasing decisions. The customers are grouped into different classes depending on their purchase probabilities and the customer classes evolve over time depending on the demand realizations at every period, which are a function of the prices set by the company. To decide on the optimal prices at every period, we model this problem using a stochastic dynamic program (SDP) and we develop several approximation algorithms to solve this SDP since the size of the state space of the SDP makes the optimal solution almost impossible to find. We present the efficiencies of the heuristics and provide managerial insights through a computational study in which we compare the revenues obtained with each heuristic with an upper bound value that we find on the optimal revenues.
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    Combined make-to-order/make-to-stock supply chains
    (Taylor & Francis Inc, 2009) Kaminsky, Philip; Department of Industrial Engineering; Kaya, Onur; Faculty Member; Department of Industrial Engineering; College of Engineering; 28405
    A multi-item manufacturer served by a single supplier in a stochastic environment is considered. The manufacturer and the supplier have to decide which items to produce to stock and which to produce to order. The manufacturer also has to quote due dates to arriving customers for make-to-order products. The manufacturer is penalized for long lead times, missing the quoted lead times and high inventory levels. Several variations of this problem are considered and effective heuristics for the make-to-order/make-to stock decision are designed to find the appropriate inventory levels for make-to-stock items. Scheduling and lead time quotation algorithms for centralized and decentralized versions of the model are also developed. Extensive computational testing is performed to assess the effectiveness of the proposed algorithms, and the centralized and decentralized models are compared in order to quantify the value of centralized control in this supply chain. As centralized control is not always practical or cost-effective, the value of limited information exchange for this system is explored. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource(s): Online appendix including additional computational analysis and proofs.].
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    Incentive and production decisions for remanufacturing operations
    (Elsevier Science Bv, 2010) N/A; Department of Industrial Engineering; Kaya, Onur; Faculty Member; Department of Industrial Engineering; College of Engineering; 28405
    We consider a manufacturer producing original products using virgin materials and remanufactured products using returns from the market where the amount of returns depend on the incentive offered by the manufacturer. We determine the optimal value of this incentive and the optimal production quantities in a stochastic demand setting with partial substitution. We analyze 3 different models in centralized and decentralized settings where the collection process of the returns is managed by a collection agency in the decentralized setting. We also analyze contracts to coordinate the decentralized systems and determine the optimal contract parameters. Finally, we present our computational study to observe the effect of different parameters on the system performance. (C) 2009 Elsevier B.V. .
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    Scheduling and due-date quotation in a make-to-order supply chain
    (John Wiley & Sons Inc, 2008) Kaminsky, Philip; Department of Industrial Engineering; Kaya, Onur; Faculty Member; Department of Industrial Engineering; College of Engineering; 28405
    We consider a manufacturer, served by a single supplier, who has to quote due dates to arriving customers in a maketo-order production environment. The manufacturer is penalized for long lead times and for missing due dates. To meet due dates, the manufacturer has to obtain components from a supplier. We model this manufacturer and supplier as a two-machine flow shop, consider several variations of this problem, and design effective due-date quotation and scheduling algorithms for centralized and decentralized versions of the model. We perform extensive computational testing to assess the effectiveness of our algorithms and to compare the centralized and decentralized models to quantify the value of centralized control in a make-to-order supply chain. Since complete information exchange and centralized control is not always practical or cost-effective, we explore the value of partial information exchange for this system.
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    Planning of capacity, production and inventory decisions in a generic reverse supply chain under uncertain demand and returns
    (Taylor & Francis, 2014) Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Bağcı, Fatih; Türkay, Metin; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 24956
    There is a growing interest for the design and operation of reverse supply chain systems due to the cost and the legislation issues. In this paper, we address the disassembly, refurbishing and production operations in a reverse supply chain setting for modular products such as computers and mobile phones considering the uncertainties in this system, which are the return amounts of the used products and demand for final products. We develop a large-scale mixed integer programming model in order to capture all characteristics of this system, and use two-stage stochastic optimisation and robust optimisation approaches to analyse the system behaviour. In the first stage, we focus on the strategic decisions about the capacities at disassembly and refurbishing sites considering different scenarios regarding the uncertainties in the system. In the second stage, we analyse the operational decisions such as production, inventory and disposal rates. We observe through our extensive numerical analysis that the randomness of demand and return values effect the performance of the system substantially and the uncertainty of the return amounts of used products is much more important than the uncertainty of demand in this system.