Soheil Sibdari

Dynamic price competition with discrete customer choices

For many years, dynamic pricing has proven to be an effective tool to increase revenue in the airline and other service industries. Most studies, however, focused on monopolistic models and ignored the fact that nowadays consumers can easily compare prices on the Internet. In this paper, we develop a game-theoretic model to describe real-time dynamic price competition between firms that sell substitutable products. By assuming the real-time inventory levels of all firms are public information, we show the existence of Nash equilibrium. We then discuss how a firm can adapt if it knows only the initial - but not the real-time - inventory levels of its competitors. We compare a firms expected revenue under different information structures through numerical experiments.

Dynamic pricing with uncertain production cost: An alternating-move approach

This article studies a two-firm dynamic pricing model with random production costs. The firms produce the same perishable products over an infinite time horizon when production (or operation) costs are random. In each period, each firm determines its price and production levels based on its current production cost and its opponent’s previous price level. We use an alternating-move game to model this problem and show that there exists a unique subgame perfect Nash equilibrium in production and pricing decisions. We provide a closed-form solution for the firm’s pricing policy. Finally, we study the game in the case of incomplete information, when both or one of the firms do not have access to the current prices charged by their opponents.

A dynamic programming approach for agent's bidding strategy in TAC-SCM game

Intelligent agents have been developed for a number of e-commerce applications including supply chain management. In trading agent competition for supply chain management (TAC SCM), several manufacturer agents compete in a reverse auction in order to sell assembled computers to customers. The manufacturer agents tasks include acquiring supplies, selling products and managing its local manufacturing process. The agent decide whether to accept an arriving bid in order to maximise its long-term expected prot. In this paper, we use dynamic programming to provide a pricing strategy for the TAC SCM. We consider a competition between an individual manufacturer agent and other automated agents in TAC SCM. The experiment results show that this strategy improves the agents revenue signicantly comparing to several other heuristics in the current practice. This approach can also be applied to similar bidding problems in other e-commerce applications.

Investment Decisions in the Rent-to-Own Industry in the Absence of Inventory

This study addresses the product investment decision faced by firms in the rent-to-own industry. In this setting, a customer arrives according to a random process and requests one unit of a product to rent (and eventually own should he/she choose to make all the required payments). At the time of request, if the product is available in inventory, the firm enters into a contractual agreement (by accepting the customers offer) and rents the merchandise. More interesting and the case considered here, if the requested item is not in inventory, the firm must decide whether to purchase the item in order to rent it out or to simply reject the request. The customers offer specifies the desired maximum contract length and the payment frequency—from which the firm determines the fixed periodic payment charged. The firm makes its investment decision based on the characteristics of the offer as well as those of the product (eg, initial and resale values, useful life and carrying costs) in essence performing a complicated cost benefit analysis. An extension is also considered whereby instead of simply rejecting the request the firm can adjust the required payment amount. Dynamic programming techniques are used to address the problem and to solve for the firms optimal decision.

On establishing initial inventory levels in the rent-to-own industry: an empirical analysis

A theoretic model is developed to calculate the optimal inventory investment for a firm in the rent-to-own industry. This investment question is shown to be equivalent to a queueing problem and queueing technology is employed to fashion a solution. The initial inventory level is a critical decision which should be made to maximise expected profit. The decision is complicated by the fact that the arrival process of new customers, the total number of times that an item will be rented, and the length of time an item is kept by a customer are all random variables. Despite this, the model is able to provide closed-form solutions for various parameters, e.g., the probability that a potential customer will be lost due to product unavailability. A compelling feature of the current study is our access to a large and detailed transactional database. This is used to gain insight into actual contract usage and to numerically estimate the input parameters to our model. The resultant optimal inventory level is then compared against the actual levels providing a clear goodness-of-fit test for our overall approach.

An Optimization Method for Agent’s Bidding Strategy in TAC-SCM Game

Intelligent agents have been developed for a number of e-commerce applications including supply chain management. In Trading Agent Competition for Supply Chain Management (TAC SCM), several manufacturer agents compete in a reverse auction in order to sell assembled computers to customers. In this paper, we consider an individual manufacturer agent in TAC SCM and we focus on the sales task. Using a dynamic programming method, the manufacturer agent is able to find an optimized bidding strategy to decide whether to bid for each arriving request for quote (RFQ). The experiment results show that this strategy improves the agent’s revenue significantly comparing to several other heuristics in the current practice. This approach can also be applied to similar bidding problems in other e-commerce applications.

Revenue management of auto train at Amtrak

Abstract This study involves working with Amtrak, the national railroad passenger corporation, to develop a revenue management model. The revenue management department of Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to bring their vehicles on the train. We analyze the demand structure from sales data and build a mathematical model to describe the sales process for Auto Train. We further develop an algorithm to calculate the optimal pricing strategy that yields the maximum revenue. Because of the distinctive service provided by Auto Train, our findings make important contribution to the revenue management literature.