The purpose of this study has been to specify and estimate a complete demand system for fresh fruits in Canada. The Almost Ideal Demand System (AIDS) is used as the functional form to accomplish the purpose of this study. Fresh fruit was assumed to be weakly separable from all other goods at the first stage of the budgeting process and a conditional demand analysis for fresh fruit was carried out at the second stage of the budgeting process, treating total expenditure as an exogenous variable. At the second stage, expenditure on fresh fruit is allocated to five groups: apples, bananas, grapefruit, oranges, and other fresh fruit. The other fresh fruit group includes: apricots, blueberries, cherries, grapes, lemons, pineapples, and strawberries. The second stage allocation was estimated utilising Zellner's iterative SURE procedure with homogeneity and symmetry conditions imposed on the restricted model. Data used in this study were obtained from Statistics Canada and consist of thirty-four observations from 1960--1993. / Results of the likelihood ratio test failed to reject the restricted model at 5% significant level. The R-square and Durbin-Watson test statistics indicated that the fit of the model is satisfactory. This study showed that a system of fresh fruit demand is inelastic to total expenditure, own-price, and cross-price effects. All the expenditure elasticity estimates were positive and significant over the study period, and indicate that apples, grapefruit, and oranges were relatively normal goods, while bananas and other fresh fruit category were relative luxury goods. Apples and oranges, grapefruit and other fruit category, oranges and other fruit category, bananas and other fresh fruit category are substitutes; and apples and other fresh fruit category, oranges and the other fruits category are complements.

Thesis (M.Econ)--University of Hong Kong, 2003. / Includes bibliographical references (leaves 26). Also available in print.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1984. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 170-173).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 69-74).

This dissertation deals with theoretical and empirical aspects of factor demand and output supply decisions of firms. In the theoretical part of the thesis, some major existing theories of investment are discussed and their formulation is extended to the case of firms which extract an exhaustible resource. Those theories are then incorporated into a model which exploits complementarities between some of them and can reflect some well-known hypotheses, such as the putty-clay hypothesis, as special cases. This model relies on a general notion of irreversibility: a decision is defined as irreversible if it introduces a new constraint to a firm. This constraint may be a non negativity constraint, but may also mean the appearance of costs of adjustments. Such an approach implies a distinction between ex ante phases and ex post phases in the life of firms, those phases being separated by the irreversible decisions. Two empirical applications are presented. The first one corresponds to the ex ante phase of the theoretical model, and deals with the capacity selection decision of some North-American open-pit metal mines. According to the evidence, this decision takes account of economic parameters, such as expected prices, as well as geological and technological parameters. The second empirical application correspond to the ex post phase of the theoretical model, and deals with the short-run production decisions of some mines. Both empirical studies provide support for the putty-clay hypothesis. / Arts, Faculty of / Vancouver School of Economics / Graduate

This thesis seeks to advance our understanding of how quantitative models can be developed and applied to marketing in complex dynamic environments characterized by demand perishability. Specifically, it involves three essays on the dynamic shelf-space management of movies. The problem is particularly complex for exhibitors - the retailers in the motion picture supply chain - given the short life cycles of movies, their perishable and uncertain demand, and complicated contracts. Our objective is to understand, formalize, and develop optimal normative policies for such decision making situations. Essay 1 considers this problem from a theoretical standpoint by addressing the stochastic aspects of movie replacement, which is analogous to equipment replacement in maintenance theory. We formulate this problem as a Markov decision process model. A scenario analysis reveals that the exhibitor is better off when shelf-space becomes scarcer for the distributors. A smart exhibitor associates a cost with contract parameters and bears it if it makes economic sense. The results underscore the importance of precise information for making smart replacement decisions. The optimal policy under special conditions resembles a control limit policy, which is easy to implement and compute. Essay 2 applies the theoretical concepts developed in Essay 1 to a special case of the movie replacement problem. The output of this essay is SilverScreener, which is a decision support model for movie exhibitors. The model helps the exhibitors both select (which) and schedule (when, how long) the movies. The model is readily implementable and appears to lead to considerable improvement in profitability in different comparative cases. The general nature of optimal policy emerges as: choose fewer "right" movies and run them longer. The robustness of the results is shown through sensitivity analyses. Essay 3 proposes a two-tier application of the SilverScreener model to show its effectiveness as a managerial aid. The master plan helps the manager in bidding and planning for movies before a season. The rolling horizon approach is useful for weekly replacement decisions during the season. Our results show that SilverScreener can improve the manager's profitability and promises to be an effective scheduling and planning tool. / Business, Sauder School of / Graduate

No description available.

This dissertation contains a number of varied, yet closely related, results that are relevant to the construction of mathematical and statistical models of inventory systems. Its primary focus is on the sensitivity of some specific inventory models to errors in certain modeling assumptions. Motivation for this research is provided through the development of analytical expressions that show that the deterministic economic order quantity can be quite sensitive to errors in the forecast of the demand rate whenever the lead time is non-zero. Similar results are provided for the stochastic case by means of a carefully designed experiment that shows that the specific form or "shape" of the distribution chosen to represent the stochastic behavior of the lead time demand can have a significant impact on a minimum cost (Q,R) policy. Together, these results refute the "conventional wisdom" that inventory models are generally insensitive to errors in model specification or parameter estimation. Considerable attention is also given to the postulation of a "robust" model for the lead time demand distribution (LTDD). This discussion culminates with the introduction of a new probability distribution, based on a hyperbolic cosine transformation of normal random variables, that appears to be well-suited for modeling the LTDD. Furthermore, it is concluded that the two- and three-parameter versions of the lognormal and inverse Gaussian distributions can also be considered as viable candidates to model the LTDD in a wide variety of inventory systems. A number of new algorithms for computing optimal (Q,R) policies are also introduced. These significantly reduce both the amount and complexity of computation required by the standard iterative method. Two additional sets of analytical results are chronicled in this work. The first allows the LTDD to be characterized (by its first four moments) on the basis of information about the distributions of the lead time and demand rate. The second expresses the linear loss functions (LLF's) for a number of probability distributions whose LLF's are not readily available in the inventory control literature. Complete and intuitive proofs of these results are included.

This dissertation consists of three papers in revenue management: on-line assortment optimization with reusable resources, spatial distribution of surge price under incentive compatible assignment for drivers and optimal price rebates for demand response under power flow constraints. In Chapter 2, we study an on-line assortment optimization problem of substitutable products with fixed reusable capacities. At any time, a potential user with her preference model (possibly adversarially chosen) arrives to the selling platform and the platform offers a subset of products from the available set of products to the user. The user selects a product with probability given by her preference model, uses it for a random duration, which is distributed according to a distribution that only depends on the product selected, and generates revenue to the seller. The revenue contribution depends on the product selected and the actual usage time of this user. The goal of the seller is to find a policy for determining the assortment offered to each arrival to maximize the expected cumulative revenue over a time horizon. We find that a simple myopic policy offering the available assortment that maximizes the expected revenue from a single user at her arrival time provides a good approximation for the problem. In particular, we show that the myopic policy is $1/2$-competitive, i.e., the expected cumulative revenue of the myopic policy is at least $1/2$ times the expected cumulative revenue of an optimal clairvoyant policy that has full information about the adversarially chosen user sequence, including their preference models and arrival epochs. The proof is based on partitioning the expected revenue of optimal clairvoyant policy into two parts and a coupling argument that allows us to bound the two parts in terms of the expected revenue of the myopic policy. In Chapter 3, we study the surge pricing problem on a ride sharing platform when there is a demand shock to the traffic network. The goal of the platform is to maximize the revenue by setting the prices over the network and the assignments between drivers and riders. In particular, we model the city as a continuous two dimensional network with exogenous arrivals of baseline riders, available drivers and demand shocks. We consider the demand shock only exists in a short time scale, so the rider chooses to request the ride or not depending on their willingness to pay and the price quoted to them, and the driver accepts any price to provide service. Since drivers can see the price distribution on driver app, they only accept the assignment from the locations that are incentive compatible for them. Thus, the price change at one location may affect the operations over the network and the platform must consider the incentive of drivers when assigning them. We develop a model for this surge pricing problem and show the structural properties of an optimal solution. Once the prices at the location with demand shock is determined, we can determine the optimal prices on other part of the network. Then, the optimal assignments between riders and drivers can be determined analytically. The surge pricing problem reduces to one that only depends on the price at the location with demand shock. We then extend our model by including strategic behavior of riders, using throughput as objective, dealing with multiple demand shocks, un-constraining the price and considering movement time. We also conduct numerical experiments to study the properties of the model which can not be explored analytically. In Chapter 4, we study the demand response problem of computing price rebates to offer to the customers to reduce the consumption in the presence of power flow constraints and transmission losses on the distribution grid. In particular, we employ alternating current power flow model for the power flow constraints with transmission loss. However, the demand response problem with alternating current power flow constraints is known as a non-convex problem, which is in-tractable to solve. To overcome this, we apply a semi-definite relaxation of alternating current power flow model to obtain a convex approximation for the problem. At the same time, to handle the uncertainty in the power reduction of customers, we use sample average to approach the expected cost and linear injection approximation to estimate the impact of uncertainty in the power reduction. Based on these relaxations and approximations, we propose an efficient iterative heuristic to solve the near-optimal offer price under alternating current power flow constraints and transmission losses. We conduct a substantial amount of numerical tests on our heuristic and compare its performance with other popular models. The result shows that our iterative heuristic leads to a significant reduction in the rebates that one needs to offer to shed a certain demand than the solution which does not consider full transmission loss in its model.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 237-250). Also available on the Internet.