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Currently Funded Projects
Tubitak 1001 Grant # 111M533: Coordinated Pricing and Inventory Management of Perishable Products
Papers under Review
Kaya, O., Urek, B., Facility Location and Pricing in Closed Loop Supply Chains, submitted for publication
Bilgin, A., Kaya, O., Karaesmen, F., Dynamic Pricing of Inventories
under Exponential Utility Maximization, submitted for publication
Bilgin, A., Kaya, O., Karaesmen, F., Dynamic Pricing of Inventories under Exponential Utility Maximization, submitted for publication
Tuncel, Ö, Aksen, D., Kaya, O., Salman, S., An Adaptive Large Neighborhood Search Algorithm for a Selective and Periodic Inventory Routing Problem, submitted for publication
Kaya, O., Ozkok, D., A Network Design Problem for Blood Banks with Inventory, Location and Routing Considerations, in preparation
Kaya,O., Polat, A.L., Coordinated Pricing and Inventory Decisions for Perishable Products, in preparation
Kaya, O., Ghahroodi, S.R., Dynamic Pricing and Inventory Control for Perishable Products under Uncertain and Time Dependent Demand
Publications in Conference Proceedings
CONFERENCES AND INVITED PRESENTATIONS
PROFESSIONAL ACTIVITIES AND AFFILIATIONS
My research mainly focuses on developing effective models and solution techniques in operations management. My main research interests include revenue management in various areas, effective management of perishable products, supply chain coordination and contracting including closed loop supply chains and remanufacturing operations, healthcare management, and risk averse decision making. I am particularly interested in identifying operational problems with high environmental and social impact, and modeling and analyzing these problems with appropriate operations management techniques such as dynamic programming, game theory, integer programming, queueing theory and stochastic modeling techniques. I also work on advancing operational tools that can be used by other academicians or industry practitioners.
First, I present one of my current works in joint pricing and inventory management of perishable products. I work on this subject as a part of Tubitak 1001 project for which I am the sole PI. Effective management of perishable products is an important issue for many companies and management of these products is especially difficult for the managers because of the perishability risks of these products in a very short time. Different than durable products, as the perishable products age, the demand for these products start to decline and in a short time these products can become completely obsolete. Thus, not only the amount of these products but also their status or age effect the inventory and pricing decisions of these products making the problem much harder to solve. There are many studies in literature considering the inventory and pricing decisions for perishable products, however most of these studies consider the inventory and pricing decisions separate from each other and coordinated inventory and pricing decisions are not studied as much. In addition, in most of the studies in literature, single product systems are considered and it is assumed that the products are sold according to first-come-first-served order and demand is time-independent. However, in reality, due to customer choices, the actual demand depends on the age or status of the products at hand. When the products are new or fresh, the demand rate is high, however as the products age, they will be demanded less and the customers can move to other types of products. Since the customers prefer newer products, the first-come-first-served assumption may not be valid and other types of sale orders, such as last-come-first-served, needs to be considered. In addition, in systems with multiple types of products, substitution of customer demand across products can affect the system significantly and lead to much different results than the single product systems. In this project, we aim to fill the gaps in literature by considering the coordinated inventory and pricing decisions in systems where the demand is a function of the status of the products at hand, considering different orders of sales. We already prepared a paper in this area and it is currently under review for publication. We also continue working on this subject and plan to submit two more papers in the following year.
Kaya (2013) is
another study of mine that is in the area of revenue management. I focus
on the dynamic pricing of durable products
considering different types of customers in the market and demand
interactions over time. In this study,
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. To decide on the optimal prices at
every period, we model this problem using an infinite horizon 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.
In another study that is currently under review for publication, we consider risk considerations and risk sensitivity of the managers and focus on the dynamic pricing of inventories under exponential utility maximization. In this paper, we consider the dynamic pricing problem of inventories with a risk-averse approach. Risk aversion manifests itself in our problem as a utility maximization formulation. In the classical dynamic pricing problem, the objective of the seller is purely to maximize expected total profit obtained by selling its inventories. In our problem, the objective of the seller is to maximize the expected utility. We use an exponential utility function where the risk tolerance constant reflects the risk aversion degree of the seller. For this formulation, we establish some structural properties of the optimal solution. A numerical study investigating the influence of risk aversion on pricing decisions is also presented.
My second research focus is in the area of remanufacturing and closed loop supply chains. In a recent study, we consider a biodiesel production company that collects waste vegetable oil from source points that generate waste in large amounts. The company uses the collected waste as raw material for biodiesel production. The manager of this company needs to decide which of the present source points to include in the collection program, which of them to visit on each day, which periodic routing schedule to repeat over an infinite horizon and how many vehicles to operate such that the total collection, inventory and purchasing costs are minimized while the production requirements and operational constraints are met. For this selective and periodic inventory routing problem, we propose two different formulations, compare them and apply the better performing one on a real-world problem with 36 scenarios. We generate lower bounds using a partial linear relaxation model, and observe that the solutions obtained through our model are within 3.28% of optimality on the average. Several insights regarding the customer selection, routing and purchasing decisions are acquired with sensitivity analysis. Please see Aksen et. al. (2012) for our studies in this area. We continue working on this area and submitted another paper that focuses on developing effective heuristics for this problem.
We also study about the production planning in remanufacturing systems under uncertainty. We focus on the capacity planning, inventory and production planning decisions under uncertainty using stochastic and robust optimization techniques. There is growing interest over closed loop supply chain management due to the cost and the legislation issues. In this paper, we are interested in disassembly, remanufacturing and refurbishing operations of closed loop supply chains. We employ the stochastic and robust optimization techniques in this study and develop a generic mathematical model using a mixed integer model. However, due to the large scale of the model, the solution of the problem becomes quite complex. In addition, there are two uncertain factors in our model, which are the distribution of demand and returns. We use a two-stage model; in the first stage we decide on the tactical decisions such as the capacities of the disassembly and refurbishing sites and in the second stage we decide on the operational decisions such as the production quantities. Considering the randomness in the system, we use robust optimization and stochastic optimization approaches to decide on the capacity and production decisions and we analyze the expected costs and the deviations from the optimum values. We analyze the mean and variance of the costs under different scenarios. Please see Kaya et. al. (2013a) for our studies about this subject.
In another study related to remanufacturing, 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. Please see Kaya (2010) about my paper on this subject.
I also have another paper in the area of closed loop supply chain management and it is currently under review for publication. We address the inventory-location and pricingproblem in a closed-loop supply chain in which the collection of the used products and the distribution of the newly produced ones are done simultaneously. We focus on the price and incentive determinations for the sale and collection activities as well as the determination of the best facility locations to open. We develop a mixed integer nonlinear facility location allocation model with inventory considerations to decide both optimal location of these collection and distribution centers and the optimal price and incentive values for the sale of new and collection of used products, respectively, in order to maximize the total profit. To solve this NP-hard problem, we propose three different hybridized heuristic approaches which correspond to applications of simulated annealing, tabu search and genetic algorithms, all of which are hybridized with variable neighborhood search (VNS) algorithm. Different neighborhood structures are embedded in these heuristics to obtain better results.
I also worked on the classical supply chain management area in the previous years. In the papers Kaya et. al. (2013b and 2013c), we focus on the coordinated production and shipment problem in a supply chain considering both deterministic and stochastic demand cases. In Kaya et. al. (2013b), we consider a model in which 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 in 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.
In a related study to the above paper, in Kaya et. al. (2013c), we consider a similar model in a stochastic environment. In this stochastic model, the supplier controls the production, holds inventory and ships the products to the retailer to satisfy the external demand. We model this system as a Markov decision process and show that the optimal production and transportation decisions are complex and non-monotonic. Therefore, we analyze twowidely-used shipment policies in the industry as well, namely time-based and quantity-based policies. Using the optimal shipment frequencies and shipment quantities according to these two policies, we numerically compare the performances of thesepolicies with respect to the optimal policy. We show that the quantity-based policy is superior to the time-based policy and gives very close results to the optimal solution.
In addition, I study on supply chain coordination and contracting in Kaya (2011) in order to increase the efficiency of the system through various contracts. I 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. I 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. I analyze and compare several contracts for decentralized supply chain models and I aim to find which contracts are best to use in different cases. I find the optimal contract parameters in each case and perform extensive computational testing to compare the efficiencies of these contracts. I 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.
As a follow-up to this study, we consider a similar model in our second
paper, however, in this second paper, both the manufacturer and the
supplier do not have to follow a make-to-order policy and can stock some
level of inventory in order to increase system performance. In this
second paper, we find the optimal levels of inventory that needs to be
stocked at both facilities in addition to developing an algorithm for
scheduling and due-date quotation in this system. In addition to our
analytical results for the optimal inventory levels, we perform
extensive computational testing of our heuristics to show their close-to-optimal
Finally, in our third paper, we consider a more general model using a supply chain network 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. Please see the papers Kaminsky and Kaya (2008a, 2008b and 2009) about our studies in this area.