T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Please use this identifier to cite or link to this item:
|Title: ||Optimal Pricing and Capacity Planning in Operations Management|
|Authors: ||Tong, Dehui|
|Advisor: ||Berman, Oded|
|Issue Date: ||16-Nov-2011|
|Abstract: ||Pricing and capacity allocation are two important decisions that a service provider needs to make to maximize service quality and profit. This thesis attempts to address the pricing and capacity planning problems in operations management from the following three aspects.
We first study a capacity planning and short-term demand management problem faced by firms with industrial customers that are insensitive to price incentives when placing orders. Industrial customers usually have downstream commitments that make it too costly to instantaneously adjust their schedule in response to price changes. Rather, they can only react to prices set at some earlier time. We propose a hierarchical planning model where price decisions and capacity allocation decisions must be made at different points of times. Customers first sign a service contract specifying how capacity at different times will be priced. Then, when placing an order, they choose the service time that best meets their needs. We study how to price the capacity so that the customers behave in a way that is consistent with a targeted demand profile at the order period. We further study how to optimally allocate capacity. Our numerical computations show that the model improves the operational revenue substantially.
Second, we explore how a profit maximizing firm is to locate a single facility on a general network, to set its capacity and to decide the price to charge for service. Stochastic demand is generated from nodes of the network. Customers demand is sensitive to both the price and
the time they expect to spend on traveling and waiting. Considering the combined effect of location and price on the firm's profit while taking into account the demand elasticity, our model provides managerial insights about how the interactions of these decision variables impact the firm's profit.
Third, we extend this single facility problem to a multiple facility problem. Customers have multiple choices for service. The firm maximizes its profit subject to customers' choice criteria. We propose a system optimization model where customers cooperate with the firm to choose the facility for service and a user equilibrium model where customers choose the facilities that provide the best utility to them. We investigate the properties of the optimal solutions. Heuristic algorithms are developed for the user equilibrium model.
Our results show that capacity planning and location decisions are closely related to each other. When customers are highly sensitive to waiting time, separating capacity planning and location decisions could result in a highly suboptimal solution.|
|Appears in Collections:||Doctoral|
Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.