Mustafa Akan

Revenue management by sequential screening

Using a mechanism design approach, we consider a firms optimal pricing policy when consumers are heterogeneous and learn their valuations at different times. We show that by offering a menu of advance-purchase contracts that differ in when, and for how much, the product can be returned, a firm can more easily price discriminate between privately-informed consumers. In particular, we show that screening on when the return option can be exercised increases firm profits, relative to screening on the size of the refund alone, only if the expected gains from trade are higher for consumers who learn later. We show that in some settings (mean-preserving spread) the firm can achieve the complete-information profits and analyze the optimal contract in other settings (first-order stochastic dominance) in which the first-best allocation is not always feasible.

Asymmetric Information and Economies of Scale in Service Contracting

We consider outsourcing in two important service settings: call center and order fulfillment operations. An important factor in both is the inherent economies of scale. Therefore, we advance a unifying model covering both applications and study the associated contracting problem under information asymmetry. At the time of contracting, the outsourcing firm, “the originator,” faces uncertainty regarding the demand volume but has private information about its probability distribution. The true demand is quickly observed once the service commences. The service provider invests in capacity before the start of the operation and offers a menu of contracts to screen different types of the originator. Adopting a mechanism design approach, we prove that a menu of two-part tariffs achieves the full-information solution. Hence, it is optimal among all possible contracts (in both settings) because of economies of scale and contractibility of realized demand.

A Broader View of Designing the Liver Allocation System

We consider the problem of designing an efficient system for allocating donated livers to patients waiting for transplantation. The trade-off between medical urgency and efficiency is at the heart of the liver allocation problem. We model the transplant waiting list as a multiclass fluid model of overloaded queues, which captures the disease evolution by allowing the patients to switch between classes, i.e., health levels. We consider the bicriteria objective of minimizing total number of patient deaths while waiting for transplantation (NPDWT) and maximizing total quality-adjusted life years (QALYs) through a weighted combination. On one hand, under the objective of minimizing NPDWT, the current policy of United Network for Organ Sharing (UNOS) emerges as the optimal policy, providing a theoretical justification for the current practice. On the other hand, under the metric of maximizing QALYs, the optimal policy is an intuitive dynamic index policy that ranks patients based on their marginal benefit from transplantation, i.e., the difference in benefit with versus without transplantation. Finally, we perform a detailed simulation study to compare the performances of our proposed policies and the current UNOS policy along the following metrics: total QALYs, NPDWT, number of patient deaths after transplantation, number of total patient deaths, and number of wasted livers. Numerical experiments show that our proposed policy for maximizing QALYs outperforms the current UNOS policy along all metrics except the NPDWT.

Technical Note---Optimal Structural Results for Assemble-to-Order Generalized M -Systems

We consider an assemble-to-order generalized M-system with multiple components and multiple products, batch ordering of components, random lead times, and lost sales. We model the system as an infinite-horizon Markov decision process and seek an optimal policy that specifies when a batch of components should be produced (i.e., inventory replenishment) and whether an arriving demand for each product should be satisfied (i.e., inventory allocation). We characterize optimal inventory replenishment and allocation policies under a mild condition on component batch sizes via a new type of policy: lattice-dependent base stock and lattice-dependent rationing.

Bid-Price Controls for Network Revenue Management: Martingale Characterization of Optimal Bid Prices

We consider a continuous-time, rate-based model of network revenue management. Under mild assumptions, we construct a simple e-optimal bid-price control, which can be viewed as a perturbation of a bid-price control in the classical sense [Williamson, E. L. 1992. Airline network seat control. Ph.D. thesis, MIT, Cambridge, MA]. We show that the associated bid-price process forms a martingale and the corresponding booking controls converge in an appropriate sense to an optimal control as e tends to 0. Moreover, we show that there exists an optimal generalized bid-price control, where the bid-price process forms a martingale and is used in conjunction with a capacity usage limit process. We also discuss its connection to the bid-price controls in the classical sense and sufficient conditions for the (near) optimality of the latter.

Dynamic pricing of remanufacturable products under demand substitution: a product life cycle model

We consider a manufacturer who sells both the new and remanufactured versions of a product over its life cycle. The manufacturer’s profit depends crucially on her ability to synchronize product returns with the sales of the remanufactured product. This gives rise to a challenging dynamic optimization problem where the size of both the market and the user pool are dynamic and their current values depend on the entire history. We provide an analytical characterization of the manufacturer’s optimal pricing, production, and inventory policies which lead to a practical threshold policy with a small optimality gap. In addition, our analysis offers a number of interesting insights. First, the timing of remanufacturing activity and its co-occurrence with new product manufacturing critically depends on remanufacturing cost benefits, attractiveness of the remanufactured product and product return rate. Second, there is a small upward jump in the price of the new product when remanufacturing is introduced. Third, the manufacturer keeps the new product longer on the market as the cost of remanufacturing decreases. Fourth, partially satisfying demand for the remanufactured item is never optimal, i.e., it is satisfied either fully or not at all. Finally, user pool and inventory of returned products are substitutes in ensuring the supply for future remanufacturing.

GENOTYPE GUIDED THERAPEUTIC DOSING OF WARFARIN IN GERIATRIC PATIENTS

More than 2 million patients(pts) are prescribed warfarin for anticoagulation in the US annually and the annual cost associated with warfarin complications is estimated to be d1.1 billion. Pharmacogenomic studies have suggested that CYP2C9 and VKORC1 genetic variants may help predict

On bid-price controls for network revenue management

We consider a network revenue management problem and advance its dual formulation. The dual formulation reveals that the (optimal) shadow price of capacity forms a nonnegative martingale. This result is proved under minimal assumptions on network topology and stochastic nature of demand, allowing an arbitrary statistical dependence structure across time and products. Next, we consider a quadratic perturbation of the network revenue management problem and show that a simple (perturbed) bid-price control is optimal for the perturbed problem; and it is ɛ-optimal for the original network revenue management problem. Finally, we consider a predictable version of this control, where the bid prices used in the current period are last updated in the previous period, and provide an upper bound on its optimality gap in terms of the (quadratic) variation of demand. Using this upper bound we show that there exists a near-optimal such control in the usual case when periods are small compared to the planning horizon pro...

The benefits of state aggregation with extreme-point weighting for assemble-to-order systems

We provide a new method for solving a very general model of an assemble-to-order system: multiple products, multiple components which may be demanded in different quantities by different products, batch production, random lead times, and lost sales, modeled as a Markov decision process under the discounted cost criterion. A control policy specifies when a batch of components should be produced and whether an arriving demand for each product should be satisfied. As optimal solutions for our model are computationally intractable for even moderate sized systems, we approximate the optimal cost function by reformulating it on an aggregate state space and restricting each aggregate state to be represented by its extreme original states. Our aggregation drastically reduces the value iteration computational burden. For systems in which there is a product that has fulfillment priority over all other products at optimality, we derive error bound for our aggregate solution. This guarantees that the value iteration algorithm for the original problem initialized with the aggregate solution converges to the optimal solution. We also establish the optimality of a lattice-dependent base-stock and rationing policy in the aggregate problem when certain product and component characteristics are incorporated into the aggregation/disaggregation schemes. This enables us to further alleviate the value iteration computational burden in the aggregate problem by eliminating suboptimal actions. Leveraging all of our results, we are able to solve the aggregate problem for systems of up to 22 components, with an average distance of 11.09% from the optimal cost in systems of up to 4 components (for which we could solve the original problem to optimality).

The Pennsylvania Adoption Exchange Improves Its Matching Process

The Pennsylvania Adoption Exchange (PAE) helps caseworkers who represent children in Pennsylvania’s child welfare system by recommending prospective families for adoption. We describe PAE’s operational challenges using caseworker surveys, and analyze child outcomes through a regression analysis of data collected over multiple years. A match recommendation spreadsheet tool implemented by PAE incorporates insights from this analysis and allows PAE managers to better utilize available information. Using a discrete-event simulation of PAE, we justify the value of a statewide adoption network, and demonstrate the importance of generating better information about family preferences for increasing the percentage of children who are successfully adopted. Finally, we detail a series of simple improvements that PAE achieved by collecting more valuable information and aligning incentives for families to provide useful preference information.