Ganesh Janakiraman

(s, S) Optimality in Joint Inventory-Pricing Control: An Alternate Approach

We study a stationary, single-stage inventory system, under periodic review, with fixed ordering costs and multiple sales levers (such as pricing, advertising, etc.). We show the optimality of (s, S)-type policies in these settings under both the backordering and lost-sales assumptions. Our analysis is constructive and is based on a condition that we identify as being key to proving the (s, S) structure. This condition is entirely based on the single-period profit function and the demand model. Our optimality results complement the existing results in this area.

New Policies for the Stochastic Inventory Control Problem with Two Supply Sources

We study an inventory system under periodic review in the presence of two suppliers (or delivery modes). The emergency supplier has a shorter lead-time than the regular supplier, but the unit price he offers is higher. Excess demand is backlogged. We generalize the recently studied class of dual index policies [Veeraraghavan, S., A. Scheller-Wolf. 2008. Now or later: Dual index policies for capacitated dual sourcing systems. Oper. Res.56(4) 850--864] by proposing two classes of policies. The first class consists of policies that have an order-up-to structure for the emergency supplier. We provide analytical results that are useful for determining optimal or near-optimal policies within this class. This analysis and the policies we propose leverage our observation that the classical “lost sales inventory problem” is a special case of this problem. The second class consists of policies that have an order-up-to structure for the regular supplier. Here, we derive bounds on the optimal order quantity from the emergency supplier, in any period, and use these bounds for finding effective policies within this class. Finally, we undertake an elaborate computational investigation to compare the performance of the policies we propose with that of dual index policies. One of our policies provides an average cost-saving of 1.1% over the best dual index policy and has the same computational requirements. Another policy that we propose has a cost performance similar to the best dual index policy, but its computational requirements are lower.

A 2-Approximation Algorithm for Stochastic Inventory Control Models with Lost Sales

In this paper, we describe the first computationally efficient policies for stochastic inventory models with lost sales and replenishment lead times that admit worst-case performance guarantees. In particular, we introduce dual-balancing policies for lost-sales models that are conceptually similar to dual-balancing policies recently introduced for a broad class of inventory models in which demand is backlogged rather than lost. That is, in each period, we balance two opposing costs: the expected marginal holding costs against the expected marginal lost-sales cost. Specifically, we show that the dual-balancing policies for the lost-sales models provide a worst-case performance guarantee of two under relatively general demand structures. In particular, the guarantee holds for independent (not necessarily identically distributed) demands and for models with correlated demands such as the AR(1) model and the multiplicative autoregressive demand model. The policies and the worst-case guarantee extend to models with capacity constraints on the size of the order and stochastic lead times. Our analysis has several novel elements beyond the balancing ideas for backorder models.

On the Optimal Policy Structure in Serial Inventory Systems with Lost Sales

We study a periodically reviewed, serial inventory system in which excess demand from external customers is lost. We derive elementary properties of the vector of optimal order quantities in this system. In particular, we derive bounds on the sensitivity (or, more mathematically, the derivative) of the optimal order quantity at each stage to the vector of the current inventory levels. Our analysis uses the concept of L-natural-convexity, which was studied in discrete convex analysis and recently used in the study of single-stage inventory systems with lost sales. We also remark on how our analysis extends to models with capacity constraints and/or backordering.

Lost-Sales Problems with Stochastic Lead Times: Convexity Results for Base-Stock Policies

We consider a single-location inventory system with periodic review and stochastic demand. It places replenishment orders to raise the inventory position-that is, inventory on hand plus inventory in transit-to exactly S at the beginning of every period. The lead time associated with each of these orders is random. However, the lead-time process is such that these orders do not cross. Demand that cannot be met with inventory available on hand is lost permanently. We state and prove some sample-path properties of lost sales, inventory on hand at the end of a period, and inventory position at the end of a period as functions of S. The main result is the convexity of the expected discounted sum of holding and lost-sales costs as a function of S. This result justifies the use of common search procedures or linear programming methods to determine optimal base-stock levels for inventory systems with lost sales and stochastic lead times. It should be noted that the class of base-stock policies is suboptimal for such systems, and we are primarily interested in them because of their widespread use.

Asymptotic Optimality of Order-Up-To Policies in Lost Sales Inventory Systems

We study a single-product single-location inventory system under periodic review, where excess demand is lost and the replenishment lead time is positive. The performance measure of interest is the long-run average holding cost and lost sales penalty cost. For a large class of demand distributions, we show that when the lost sales penalty becomes large compared to the holding cost, the relative difference between the cost of the optimal policy and the best order-up-to policy converges to zero. For any given cost parameters, we establish a bound on this relative difference. Numerical experiments show that the best order-up-to policy performs well, yielding an average cost that is within 1.5% of the optimal cost when the ratio between the lost sales penalty and the holding cost is 100. We also propose a heuristic order-up-to level using two newsvendor expressions; in our experiments, the cost of this order-up-to policy is 2.52% higher, on an average, than the best order-up-to policy.

A Decomposition Approach for a Class of Capacitated Serial Systems

We study a class of two-echelon serial systems with identical ordering/production capacities or limits for both echelons. Demands are assumed to be integer valued. For the case where the lead time to the upstream echelon is one period, the optimality of state-dependent modified echelon base-stock policies is proved using a decomposition approach. For the case where the upstream lead time is two periods, we introduce a new class of policies called “two-tier base-stock policies,” and prove their optimality. Some insight about the inventory control problem in N echelon serial systems with identical capacities at all stages and arbitrary lead times everywhere is also provided. We argue that a generalization of two-tier base-stock policies, which we call “multitier base-stock policies,” are optimal for these systems; we also provide a bound on the number of parameters required to specify the optimal policy.

Inventory Management with Auctions and Other Sales Channels: Optimality of (s, S) Policies

We study periodic-review inventory replenishment problems with fixed ordering costs, and show the optimality of (s, S) inventory replenishment policies. Inventory replenishment is instantaneous, i.e., the lead time is zero. We consider several sales mechanisms, e.g., auction mechanisms, name-your-own-price mechanisms, and multiple heterogeneous sales channels. We prove this result by showing that these models satisfy a recently-established sufficient condition for the optimality of (s, S) policies. Thus, this paper shows that the optimality of (s, S) policies extends well beyond the traditional sales environments studied so far in the inventory literature.

A Comparison of the Optimal Costs of Two Canonical Inventory Systems

We compare two inventory systems, one in which excess demand is lost and the other in which excess demand is backordered. Both systems are reviewed periodically. They experience the same sequence of identically and independently distributed random demands. Holding and shortage costs are considered. The holding cost parameter is identical; however, the cost of a lost sale could be different from the per-period cost of backlogging a unit sale. When these costs are equal, we prove that the optimal expected cost for managing the system with lost sales is lower. When the cost of a lost sale is greater, we establish a relationship between these parameters that ensures that the reverse inequality is true. These results are useful for designing inventory systems. We also introduce a new stochastic comparison technique in this paper.

Average Cost Single-Stage Inventory Models: An Analysis Using a Vanishing Discount Approach

An important problem in the theory of dynamic programming is that of characterizing sufficient conditions under which the optimal policies for Markov decision processes (MDPs) under the infinite-horizon discounted cost criterion converge to an optimal policy under the average cost criterion as the discount factor approaches 1. In this paper, we provide, for stochastic inventory models, a set of such sufficient conditions. These conditions, unlike many others in the dynamic programming literature, hold when the action space is noncompact and the underlying transition law is weakly continuous. Moreover, we verify that these conditions hold for almost all conceivable single-stage inventory models with few assumptions on cost and demand parameters. As a consequence of our analysis, we partially characterize, for the first time, optimal policies for the following inventory systems under the infinite-horizon average-cost criterion, which have thus far been a challenge: (a) capacitated systems with setup costs, (b) uncapacitated systems with convex ordering costs plus a setup cost, and (c) systems with lost sales and lead times.