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Publication Metadata only A lab-scale manufacturing system environment to investigate data-driven production control approaches(Elsevier Sci Ltd, 2021) N/A; N/A; Department of Business Administration; Khayyati, Siamak; Tan, Barış; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 28600Controlling production and release of material into a manufacturing system effectively can lower work-inprogress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a datadriven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the datadriven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.Publication Open Access A machine learning approach for implementing data-driven production control policies(Taylor _ Francis, 2021) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AGiven the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.Publication Open Access A method for estimating stock-out-based substitution rates by using point-of-sale data(Taylor _ Francis, 2009) Öztürk, Ömer Cem; Department of Business Administration; Tan, Barış; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600; 38819Empirical studies in retailing suggest that stock-out rates are quite high in many product categories. Stock-outs result in demand spillover, or substitution, among items within a product category. Product assortment and inventory management decisions can be improved when the substitution rates are known. In this paper, a method is presented to estimate product substitution rates by using only Point-Of-Sale (POS) data. The approach clusters POS intervals into states where each state corresponds to a specific substitution scenario. Then available POS data for each state is consolidated and the substitution rates are estimated using the consolidated information. An extensive computational analysis of the proposed substitution rate estimation method is provided. The computational analysis and comparisons with an estimation method from the literature show that the proposed estimation method performs satisfactorily with limited information.Publication Metadata only A min-sum-max resource allocation problem(Kluwer Academic Publ, 2000) Kouvelis, P.; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819In this paper we describe a class of resource allocation problems with a min–sum–max objective function. We first discuss practical applications of the problem. We then present a result on the computational complexity of the problem. We propose an implicit enumeration procedure for solving the general case of the problem, and report on our computational experience with the solution procedure.Publication Metadata only A near-optimal order-based inventory allocation rule in an assemble-to-order system and its applications to resource allocation problems(Springer, 2005) Xu, Susan Hong; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400Assemble-to-order (ATO) manufacturing strategy has taken over the more traditional make-to-stock (MTS) strategy in many high-tech firms. ATO strategy has enabled these firms to deliver customized demand timely and to benefit from risk pooling due to component commonality. However, multi-component, multi-product ATO systems pose challenging inventory management problems. In this chapter, we study the component allocation problem given a specific replenishment policy and realized customer demands. We model the problem as a general multidimensional knapsack problem (MDKP) and propose the primal effective capacity heuristic (PECH) as an effective and simple approximate solution procedure for this NP-hard problem. Although the heuristic is primarily designed for the component allocation problem in an ATO system, we suggest that it is a general solution method for a wide range of resource allocation problems. We demonstrate the effectiveness of the heuristic through an extensive computational study which covers problems from the literature as well as randomly generated instances of the general and 0-1 MDKP. In our study, we compare the performance of the heuristic with other approximate solution procedures from the ATO system and integer programming literature.Publication Open Access A preference-based, multi-unit auction for pricing and capacity allocation(Elsevier, 2018) Lessan, Javad; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint.Publication Metadata only An auction mechanism for pricing and capacity allocation with multiple products(Wiley-Blackwell, 2014) N/A; Department of Business Administration; N/A; Karabatı, Selçuk; Yalçın, Zehra Bilgintürk; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 38819; N/AWe consider a pricing and short-term capacity allocation problem in the presence of buyers with orders for bundles of products. The supplier's objective is to maximize her net profit, computed as the difference between the revenue generated through sales of products and the production and inventory holding costs. The objective of each buyer is similarly profit maximization, where a buyer's profit is computed as the difference between the time-dependent utility of the product bundle he plans to buy, expressed in monetary terms, and the price of the bundle. We assume that bundles' utilities are buyers' private information and address the problem of allocating the facility's output. We directly consider the products that constitute the supplier's output as market goods. We study the case where the supplier follows an anonymous and linear pricing strategy, with extensions that include quantity discounts and time-dependent product and delivery prices. In this setting, the winner determination problem integrates the capacity allocation and scheduling decisions. We propose an iterative auction mechanism with non-decreasing prices to solve this complex problem, and present a computational analysis to investigate the efficiency of the proposed method under supplier's different pricing strategies. Our analysis shows that the problem with private information can be effectively solved with the proposed auction mechanism. Furthermore, the results indicate that the auction mechanism achieves more than 80% of the system's profit, and the supplier receives a higher percentage of profit especially when the ratio of demand to available capacity is high.Publication Open Access An empirical analysis of the main drivers affecting the buyer surplus in E-auctions(Taylor _ Francis, 2018) Department of Business Administration; Department of Industrial Engineering; Karabağ, Oktay; Tan, Barış; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; N/A; 28600We empirically examine the impacts of the product category, the auction format, the 2008 global financial crisis, the group purchasing, the contract type, the platform ownership, and the number of participating suppliers on the buyer surplus obtained from e-auctions. To this end, we collect a unique dataset from a purchasing organisation that offers e-auction solutions to its corporate customers. By using a standard Tobit model, we show that the product categories, the auction type, and the number of participating suppliers have significant effects on the decrease in the procurement prices with respect to the minimum of the initial submitted bids. It is observed that the 2008 global financial crisis led to an increase in the buyer surplus. We classify the product categories into three groups based on their impacts on the average of the decrease in the procurement prices. We show that the average decrease in procurement prices is higher for the group purchasing option than for the individual buying option. It is concluded that the types of contract between buyers and auctioneer and the platform ownership have no statistically significant effects on the average decrease in procurement prices.Publication Open Access Analysis of a general Markovian two-stage continuous-flow production system with a finite buffer(Elsevier, 2009) Gershwin, Stanley B.; Department of Business Administration; Tan, Barış; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600Fluid flow models are used in the performance evaluation of production, computer, and telecommunication systems. In order to develop a methodology to analyze general Markovian continuous material flow production systems with two processing stages with an intermediate finite buffer, a general single-buffer fluid flow system is modelled as a continuous time, continuous-discrete state space stochastic process and the steady-state distribution is determined. Various performance measures such as the production rate and the expected buffer level are determined from the steady-state distributions. The flexibility of this methodology allows analysis of a wide range of models by specifying only the transition rates and the flow rates associated with the discrete states of each stage. Therefore, the method is proposed as a tool for performance evaluation of general Markovian continuous-flow systems with a finite buffer. The solution methodology is illustrated by analyzing a production system where each machine has multiple up and down states associated with their quality characteristics.Publication Open Access Analysis of a group purchasing organization under demand and price uncertainty(Springer, 2018) Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Karabağ, Oktay; Faculty Member; Resercher; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600; N/ABased on an industrial case study, we present a stochastic model of a supply chain consisting of a set of buyers and suppliers and a group purchasing organization (GPO). The GPO combines orders from buyers in a two-period model. Demand and price in the second period are random. An advance selling opportunity is available to all suppliers and buyers in the first-period market. Buyers decide how much to buy through the GPO in the first period and how much to procure from the market at a lower or higher price in the second period. Suppliers determine the amount of capacity to sell through the GPO in the first period and to hold in reserve in order to meet demand in the second period. The GPO conducts a uniform-price reverse auction to select suppliers and decides on the price that will be offered to buyers to maximize its profit. By determining the optimal decisions of buyers, suppliers, and the GPO, we answer the following questions: Do suppliers and buyers benefit from working with a GPO? How do the uncertainty in demand, the share of GPO orders in the advance sales market, and the uncertainty in price influence the players' decisions and profits? What are the characteristics of an environment that would encourage suppliers and buyers to work with a GPO? We show that a GPO helps buyers and suppliers to mitigate demand and price risks effectively while collecting a premium by serving as an intermediary between them.