A Fluid Model of Dynamic Pricing and Inventory Management for Make-to-Stock Manufacturing Systems

In this paper, we introduce a fluid model of dynamic pricing and inventory management for make-to-stock manufacturing systems. Instead of considering a traditional model that is based on how price affects demand, we consider a model that relies on how price and level of inventory affect the time a unit of product remains in inventory. Our motivation is based on the observation that in inventory systems, a unit of product incurs a delay before being sold. This delay depends on the unit price of the product, prices of competitors, and the level of inventory of this product. Moreover, delay data is not hard to acquire and is internally controlled and monitored by the manufacturer. It is interesting to notice that this delay is similar to travel times incurred in a transportation network. The model of this paper includes joint pricing, production and inventory decisions in a competitive, capacitated multi-product dynamic environment. In particular, in this paper we (i) introduce a model for dynamic pricing and inventory control that uses delay rather then demand data and establish connections with traditional demand models, (ii) study analytical properties of this model, (iii) establish conditions under which the model has a solution and finally, (iv) establish an algorithm that solves efficiently a discretized version of the model

Efficiency Analysis of Cournot Competition in Service Industries with Congestion

We consider Cournot competition in the presence of congestion effects. Our model consists of several service providers with differentiated services, each competing for users who are sensitive to both price and congestion. We distinguish two types of congestion effects, depending on whether spillover costs exist, that is, where one service provider's congestion cost increases with the other providers' output level. We quantify the efficiency of an unregulated oligopoly with respect to the optimal social welfare with tight upper and lower bounds. We show that, when there is no spillover, the welfare loss in an unregulated oligopoly is limited to 25% of the social optimum, even in the presence of highly convex costs. On the other hand, when spillover cost is present, there does not exist a constant lower bound on the efficiency of an unregulated oligopoly, even with affine cost. We show that the efficiency depends on the relative magnitude between the marginal spillover cost and the marginal benefit to consumers

Modeling Travel Times in Dynamic Transportation Networks; A Fluid Dynamics Approach

In this paper, we take a fluid dynamics approach to determine the travel time in traversing a network's link. We propose a general model for travel time functions that utilizes fluid dynamics laws for compressible flow to capture a variety of flow patterns such as the formation and dissipation of queues, drivers' response to upstream congestion or decongestion and drivers' reaction time. We examine two variants of the model, in the case of separable velocity functions, which gives rise to two families of travel time functions for the problem; a polynomial and an exponential family. We analyze these travel time functions and examine several special cases. Our investigation also extends to the case of non-separable velocity functions starting with an analysis of the interaction between two links, and then extending it to the general case of acyclic networks

A Fluid Model of Spillback and Bottleneck Phenomena for Determining Dynamic Travel Times

In this paper we introduce travel time models that incorporate spillback and bottleneck phenomena. In particular, we study a model for determining the link travel times for drivers entering a link as well as drivers already in the link but whose travel times are affected by a significant change in traffic conditions (e.g. spillback or bottleneck phenomena). To achieve this goal, we extend the fluid dynamics travel time models proposed by Perakis [11] and subsequently by Kachani and Perakis [6], [7], to also incorporate such phenomena. These models utilize fluid dynamics laws for compressible flow to capture a variety of flow patterns such as the formation and dissipation of queues, drivers' response to upstream congestion or decongestion and drivers' reaction time. We propose variants of these models that explicitly account for spillback and bottleneck phenomena. Our investigation considers both separable and non-separable velocity functions

Dynamic Pricing through Sampling Based Optimization

In this paper we develop an approach to dynamic pricing that combines ideas from data-driven and robust optimization to address the uncertain and dynamic aspects of the problem. In our setting, a firm off ers multiple products to be sold over a fixed discrete time horizon. Each product sold consumes one or more resources, possibly sharing the same resources among di fferent products. The firm is given a fixed initial inventory of these resources and cannot replenish this inventory during the selling season. We assume there is uncertainty about the demand seen by the fi rm for each product and seek to determine a robust and dynamic pricing strategy that maximizes revenue over the time horizon. While the traditional robust optimization models are tractable, they give rise to static policies and are often too conservative. The main contribution of this paper is the exploration of closed-loop pricing policies for di fferent robust objectives, such as MaxMin, MinMax Regret and MaxMin Ratio. We introduce a sampling based optimization approach that can solve this problem in a tractable way, with a con fidence level and a robustness level based on the number of samples used. We will show how this methodology can be used for data-driven pricing or adapted for a random sampling optimization approach when limited information is known about the demand uncertainty. Finally, we compare the revenue performance of the di fferent models using numerical simulations, exploring the behavior of each model under diff erent sample sizes and sampling distributions.National Science Foundation (U.S.) (Grant 0556106-CMII)National Science Foundation (U.S.) (Grant 0824674-CMII)Singapore-MIT Allianc

Competitive Multi-period Pricing with Fixed Inventories

This paper studies the problem of multi-period pricing for perishable products in a competitive (oligopolistic) market. We study non cooperative Nash equilibrium policies for sellers. At the beginning of the time horizon, the total inventories are given and additional production is not an available option. The analysis for periodic production-review models, where production decisions can be made at the end of each period at some production cost after incurring holding or backorder costs, does not extend to this model. Using results from game theory and variational inequalities we study the existence and uniqueness of equilibrium policies. We also study convergence results for an algorithm that computes the equilibrium policies. The model in this paper can be used in a number of application areas including the airline, service and retail industries. We illustrate our results through some numerical examples.Singapore-MIT Alliance (SMA

Averaging Schemes for Solving Fived Point and Variational Inequality Problems

We develop and study averaging schemes for solving fixed point and variational inequality problems. Typically, researchers have established convergence results for solution methods for these problems by establishing contractive estimates for their algorithmic maps. In this paper, we establish global convergence results using nonexpansive estimates. After first establishing convergence for a general iterative scheme for computing fixed points, we consider applications to projection and relaxation algorithms for solving variational inequality problems and to a generalized steepest descent method for solving systems of equations. As part of our development, we also establish a new interpretation of a norm condition typically used for establishing convergence of linearization schemes, by associating it with a strong-f-monotonicity condition. We conclude by applying our results to transportation networks

Dynamic Pricing: A learning Approach

We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, but rather assume parametric families of demand functions that are learned over time. We first consider the noncompetitive case and present dynamic programming algorithms of increasing computational intensity with incomplete state information for jointly estimating the demand and setting prices as time evolves. Our computational results suggest that dynamic programming based methods outperform myopic policies often significantly. We then extend our analysis in a competitive environment with two firms. We introduce a more sophisticated model of demand learning, in which the price elasticities are slowly varying functions of time, and allows for increased flexibility in the modeling of the demand. We propose methods based on optimization for jointly estimating the Firm's own demand, its competitor's demand, and setting prices. In preliminary computational work, we found that optimization based pricing methods offer increased expected revenue for a firm independently of the policy the competitor firm is following

On the Convergence of Classical Variational Inequality Algorithms

In this paper, we establish global convergence results for projection and relaxation algorithms for solving variational inequality problems, and for the Frank-Wolfe algorithm for solving convex optimization problems defined over general convex sets. The analysis rests upon the condition of f-monotonicity,which we introduced in a previous paper, and which is weaker than the traditional strong monotonicity condition. As part of our development, we provide a new interpretation of a norm condition typically used for establishing convergence of linearization schemes. Applications of our results arize in uncongested as well as congested transportation networks