We consider a novel variant of the perishable inventory profit management problem faced by a firm that sells a fixed inventory over a finite horizon in the presence of price-adjustment costs. In economics literature, such price-adjustment costs are widely studied and are typically assumed to include a fixed component (e.g., advertising costs), an inventory-dependent component (e.g., inventory relabeling costs), as well as a component that depends on the magnitude of the price adjustment (e.g., cognitive and coordination managerial costs).We formulate the firm's profit management problem as a finite-horizon dynamic program in which the state of the system is described by the inventory level as well as the current price level. We derive first-order properties of the optimal value function and give a complete characterization of optimal policies for the case of ample inventory. Through a set of examples we demonstrate the complex and counterintuitive nature of optimal price-adjustment policies. Consequently, we focus on developing easily computable and implementable heuristics with demonstrably good performance. To this end, we develop and solve a fluid model based on the original stochastic dynamics and propose three fluid-based heuristic policies. We derive expressions for the expected profit generated by each one of these heuristics when applied to the stochastic problem and derive sufficient conditions for the asymptotic optimality of the policies when the initial inventory levels and planning horizons are proportionally scaled up. We test the performance of the heuristics in a numerical study and demonstrate a robust, near-optimal performance of one of the heuristics (which we call the “Fluid Time” heuristic) for a wide range of problem parameters. Finally, we demonstrate the importance of proper accounting of price-adjustment costs in several alternative business settings.

This paper considers a profit-maximizing make-to-order manufacturer that offers multiple products to a market of price and delay sensitive users, using a model that captures three aspects of particular interest: first, the joint use of dynamic pricing and lead-time quotation controls to manage demand; second, the presence of a dual sourcing mode that can expedite orders at a cost; and third, the interaction of the aforementioned demand controls with the operational decisions of sequencing and expediting that the firm must employ to optimize revenues and satisfy the quoted lead times. Using an approximating diffusion control problem we derive near-optimal dynamic pricing, lead-time quotation, sequencing, and expediting policies that provide structural insights and lead to practically implementable recommendations. A set of numerical results illustrates the value of joint pricing and lead-time control policies.

We provide a thorough analysis of the effectiveness of different Variance Reduction Techniques (VRTs). We consider both stand-alone and combined applications of two input techniques, Antithetic Variates (AV) and Latin Hypercube Sampling (LHS), and two output techniques.. Control Variates (CV) and Poststratified Sampling (PS). Previous research in the area mainly focuses on asymptotic variance reduction. In this experimental study, we measure the performance of VRTs under finite simulation run lengths and analyze their effects. Our findings show that the asymptotic variance reduction results do not readily apply to finite-length simulations. We consider three different types of systems (M/M/1, serial production line and (s, S) inventory control systems) and compare the VRTs under various experimental conditions. We observe that a variance reduction cannot be guaranteed for every instance a VRT is applied. Our results also indicate that the output VRTs (CV, PS) are better than input VRTs (AV, LHS) on the average for the single systems considered in this study. More interestingly, the less-sophisticated techniques (AV, CV) often perform better than the relatively more-complex techniques (LHS, PS). A comprehensive bibliography is also provided.

Consider a make-to-order manufacturer that offers multiple products to a market of price and delay sensitive users. This paper studies the problem of maximizing its long-run average expected profits for a model that captures three aspects of particular interest: first, the joint use of dynamic pricing and leadtime quotation controls to manage customer demand; second, the presence of a dual sourcing mode that can be used to expedite orders at a cost; and third, the interaction of the aforementioned demand controls with the operational decisions of sequencing and expediting that the firm must employ to optimize revenues and satisfy the quoted leadtimes. Using an approximating diffusion control problem we derive near-optimal dynamic pricing, lead- time quotation, sequencing, and expediting policies that provide structural insights and lead to practically implementable recommendations. A set of numerical results illustrates the value of joint pricing and leadtime control, as well as the performance of the proposed set of policies.