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Akçay, Yalçın

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Yalçın

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Akçay

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2004 - 200962010 - 20188

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    Joint inventory replenishment and component allocation optimization in an assemble-to-order system
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2004) Xu, Susan H.; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    This paper considers a multicomponent, multiproduct periodic-review assemble-to-order (ATO) system that uses an independent base-stock policy for inventory replenishment. Product demands in each period are integer-valued correlated random variables, with each product being assembled from multiple units of a subset of components. The system quotes a prespecified response time window for each product and receives a reward if the demand for that product is filled within its response time window. We formulate a two-stage stochastic integer program with recourse to determine the optimal base-stock policy and the optimal component allocation policy for the ATO system. We show that the component allocation problem is a general multidimensional knapsack problem (MDKP) and is NP-hard. We propose a simple, order-based component allocation rule and show that it can be solved in either polynomial or pseudopolynomial time. We also use the sample average approximation method to determine the optimal base-stock levels and compare it with two variations of the equal fractile heuristic. Intensive testing indicates that our solution method for each stage of the stochastic program is robust, effective, and that it significantly outperforms existing methods. Finally, we discuss several managerial implications of our findings.
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    Dynamic assignment of flexible service resources
    (Wiley, 2010) Balakrishnan, Anant; Xu, Susan H.; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    Resource flexibility is an important tool for firms to better match capacity with demand so as to increase revenues and improve service levels. However, in service contexts that require dynamically deciding whether to accept incoming jobs and what resource to assign to each accepted job, harnessing the benefits of flexibility requires using effective methods for making these operational decisions. Motivated by the resource deployment decisions facing a professional service firm in the workplace training industry, we address the dynamic job acceptance and resource assignment problem for systems with general resource flexibility structure, i.e., with multiple resource types that can each perform different overlapping subsets of job types. We first show that, for systems containing specialized resources for individual job types and a versatile resource type that can perform all job types, the exact policy uses a threshold rule. With more general flexibility structures, since the associated stochastic dynamic program is intractable, we develop and test three optimization-based approximate policies. Our extensive computational tests show that one of the methods, which we call the Bottleneck Capacity Reservation policy, is remarkably effective in generating near-optimal solutions over a wide range of problem scenarios. We also consider a model variant that requires dynamic job acceptance decisions but permits deferring resource assignment decisions until the end of the horizon. For this model, we discuss an adaptation of our approximate policy, establish the effectiveness of this policy, and assess the value of postponing assignment decisions.
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    Revenue management for intermodal transportation: the role of dynamic forecasting
    (Wiley, 2016) Luo, Ting; Gao, Long; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    We study a joint capacity leasing and demand acceptance problem in intermodal transportation. The model features multiple sources of evolving supply and demand, and endogenizes the interplay of three leversforecasting, leasing, and demand acceptance. We characterize the optimal policy, and show how dynamic forecasting coordinates leasing and acceptance. We find (i) the value of dynamic forecasting depends critically on scarcity, stochasticity, and volatility; (ii)traditional mean-value equivalence approach performs poorly in volatile intermodal context; (iii) mean-value-based forecast may outperform stationary distribution-based forecast. Our work enriches revenue management models and applications. It advances our understanding on when and how to use dynamic forecasting in intermodal revenue management.
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    Scheduling of flexible resources in professional service firms
    (CRC Press, 2004) Balakrishnan, Anant; Xu, Susan H.; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    Flexibility is the ability of an organization to effectively cope with uncertainties and changes in the market by employing resources that can process different types of jobs. In today’s ever-changing business environment, driven by the challenges of quick response, increasing customization, shorter product life cycles, and intense competition, flexibility has become vital for both manufacturing and service operations to hedge these systems against uncertainty while controlling costs. Decision makers well understand the strategic significance of flexibility to compete effectively (Jones and Ostroy, 1984).However, these strategic decisions must be integrated with operational policies and resource usage decisions to fully realize the benefits of flexibility. At the strategic level, practitioners and researchers focus on the role of flexibility in handling uncertainties, translating flexibility requirements into capacity and performance objectives. This area has been a popular research field due to the emergence of related flexible manufacturing technologies (e.g., Fine and Freund, 1990; Jordan and Graves, 1995;Mieghem, 1998). Operational level issues, on the other hand, deal with designing specific methods for deployment of flexibility. Given the available level of flexibility in resources, the decision maker faces the problem of effectively allocating these flexible resources to various operations in order to achieve the strategic goals. In this chapter we investigate operational policies for deployment of flexible resources in professional service firms such as consulting, legal, or training firms. In particular, the work is motivated by resource assignment decisions within the workplace learning industry.
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    Pricing when customers have limited attention
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2018) Boyacı, Tamer; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    We study the optimal pricing problem of a monopolistic firm facing customers with limited attention and capability to process information about the value (quality) of a single offered product. We model customer choice based on the theory of rational inattention in the economics literature, which enables us to capture not only the impact of true quality and price, but also the intricate effects of customer's prior beliefs and cost of information acquisition and processing. We formulate the firm's price optimization problem assuming that the firm can also use the price to signal the quality of the product to customers. To delineate the economic incentives of the firm, we first characterize the pricing and revenue implications of customer's limited attention without signaling, and then use these results to explore perfect Bayesian equilibria of the strategic pricing signaling game. As an extension, we consider heterogeneous customers with different information costs as well as prior beliefs. We discuss the managerial implications of our key findings and prescribe insights regarding information provision and product positioning.
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    Greedy algorithm for the general multidimensional knapsack problem
    (Springer, 2007) Li, Haijun; Xu, Susan H.; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    In this paper, we propose a new greedy-like heuristic method, which is primarily intended for the general MDKP, but proves itself effective also for the 0-1 MDKP. Our heuristic differs from the existing greedy-like heuristics in two aspects. First, existing heuristics rely on each item's aggregate consumption of resources to make item selection decisions, whereas our heuristic uses the effective capacity, defined as the maximum number of copies of an item that can be accepted if the entire knapsack were to be used for that item alone, as the criterion to make item selection decisions. Second, other methods increment the value of each decision variable only by one unit, whereas our heuristic adds decision variables to the solution in batches and consequently improves computational efficiency significantly for large-scale problems. We demonstrate that the new heuristic significantly improves computational efficiency of the existing methods and generates robust and near-optimal solutions. The new heuristic proves especially efficient for high dimensional knapsack problems with small-to-moderate numbers of decision variables, usually considered as "hard" MDKP and no computationally efficient heuristic is available to treat such problems.
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    Selling with money-back guarantees: the impact on prices, quantities, and retail profitability
    (Wiley, 2013) Boyacı, Tamer; Zhang, Dan; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    In this paper, we consider a retailer adopting a money-back-guaranteed (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full or partial refund. The retailer either salvages returned products or resells them as open-box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open-box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open-box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered. We show that selling with MBGs increases retail sales and profit. Furthermore, the second-sale opportunity created by restocking returned products enables the retailer to generate additional revenues. Our analysis identifies the ideal conditions under which this practice is most beneficial to the retailer. Offering an MBG without restocking increases the new product price. We show that if the retailer decides to resell the returned items as open-box, the price of the new product further increases, while open-box items are sold at a discount. On the other hand, customers enjoy more generous refunds along with lower restocking fees. The opportunity to resell returned products also generally decreases the initial stocking levels of the retailer. Our extensive numerical study substantiates the analytical results and sharpens our insights into the drivers of performance of MBG policies and their impact on retail decisions.
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    On the structural properties of a discrete-time single product revenue management problem
    (Elsevier Science Bv, 2009) N/A; Department of Business Administration; Department of Industrial Engineering; Aydın, Seray; Akçay, Yalçın; Karaesmen, Fikri; Master Student; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; College of Engineering; N/A; 51400; 3579
    We consider a multi-period revenue management problem in which multiple classes of demand arrive over time for the common inventory. The demand classes are differentiated by their revenues and their arrival distributions. We investigate monotonicity properties of varying problem parameters on the optimal reward and the policy. (C) 2009 Elsevier B.V. All rights reserved.
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    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; 51400
    Assemble-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.
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    Joint dynamic pricing of multiple perishable products under consumer choice
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2010) Natarajan, Harihara Prasad; Xu, Susan H.; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400
    In response to competitive pressures, firms are increasingly adopting revenue management opportunities afforded by advances in information and communication technologies. Motivated by these revenue management initiatives in industry, we consider a dynamic pricing problem facing a firm that sells given initial inventories of multiple substitutable and perishable products over a finite selling horizon. Because the products are substitutable, individual product demands are linked through consumer choice processes. Hence, the seller must formulate a joint dynamic pricing strategy while explicitly incorporating consumer behavior. For an integrative model of consumer choice based on linear random consumer utilities, we model this multiproduct dynamic pricing problem as a stochastic dynamic program and analyze its optimal prices. The consumer choice model allows us to capture the linkage between product differentiation and consumer choice, and readily specializes to the cases of vertically and horizontally differentiated assortments. When products are vertically differentiated, our results show monotonicity properties (with respect to quality, inventory, and time) of the optimal prices and reveal that the optimal price of a product depends on higher quality product inventories only through their aggregate inventory rather than individual availabilities. Furthermore, we show that the price of a product can be decomposed into the price of its adjacent lower quality product and a markup over this price, with the markup depending solely on the aggregate inventory. We exploit these properties to develop a polynomial-time, exact algorithm for determining the optimal prices and the profit. For a horizontally differentiated assortment, we show that the profit function is unimodal in prices. We also show that individual, rather than aggregate, product inventory availability drives pricing. However, we find that monotonicity properties observed in vertically differentiated assortments do not hold.