Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Economics. / Vita. / Bibliography: leaves 213-214. / Ph.D.

Decreasing block pricing refers to the practice of selling a product at successively lower marginal prices as the amount purchased in any one time period increases. In more familiar terms, this practice can be thought of as any quantity discount scheme as long as marginal price does not vary continuously with quantity. Decreasing block pricing results in a faceted, non-convex budget set, and under standard assumptions concerning consumer preferences, yields several nonstandard theoretical implications. The central goal of this paper is to formulate an estimation technique which is consistent with these implications. When the budget set is not convex, the uniqueness of consumer equilibrium is no longer guaranteed. It also follows that discontinuities in demand occur whenever consumer equilibrium shifts from one facet of the budget constraint to another. Prior empirical studies have not made use of demand functions consistent with these results. In Chapter 2, a utility-maximizing algorithm was developed to determine consumer equilibrium given the declining block pricing schedule and income for a Cobb-Douglas utility function. In developing this algorithm, it was made clear that the proper approach for estimating individual demand was through the use of a block-dependent independent variable. The coefficient of this block-department independent variable provided an estimate of a utility function parameter which completely specified the Cobb-Douglas form. Incorporating this utility function estimate into the utility-maximation algorithm made it possible to obtain estimates of consumption given changes in any or all of the rate schedule components. While the use of a block-dependent independent variable is the theoretically correct method for estimating demand, it poses an inescapable problem of errors-in-variables. A Monte Carlo study was performed in Chapter 2 to investigate, among other things, the seriousness of the errors-in-variables bias. The results were quite encouraging. When using data incorporating extremely large error variances, amazingly precise estimates were obtained. Another encouraging Monte Carlo result was when comparing samples not containing a discontinuity with those with one, it was found that the latter produced estimates with statistically significant superiority. Chapter 3 generalized the estimation technique of the previous chapter to allow the estimation of demand using cross-sectional data. The data base recorded monthly electricity consumption for households from a number of cities whose utilities had decreasing block rates. Seven of these cities were selected for analysis. The data also included various demographic characteristics and electric appliance stock information. The generalization was accomplished by assuming that all households had a Stone-Geary utility function. Also, the utility function parameter representing the minimum required quantity of electricity was assumed to depend linearly on the household's appliance stock and demographic characteristics. This allowed demand to vary across households on the basis of this parameter and income. The results of applying this regression technique to the cross-sectional data were then compared with results from a conventional, non-theoretically based demand specification. The data were used in pooled and individual month form with the former yielding much better statistical results. The Stone-Geary form provided a greater number of significant coefficients for price and income variables than the conventional version. The predominant failure of the conventional version was that the coefficient of marginal price was rarely significant and when significant, frequently of the wrong sign. For the same samples, the Stone-Geary results were quite acceptable except for the regressions involving one of the cities. Thus, it was demonstrated that a method consistent with the theoretical implications of decreasing block pricing is easily applied to cross-sectional data and produces better results than conventional techniques.

Assuming that all unmet demand is fully lost, we begin our study by confining the sales lever to be price only, that is, z = p , and ignoring the cost for executing the sales lever. Given a stationary (s, S, p) policy, we find that the profit function for the lost-sales case exhibits the same structure as the one for the backlogging case. We further show that the relaxed assumption on the news-vendor type profit function can also be satisfied by a broad class of demand function. We can therefore extend the optimizing algorithm and the optimality analysis developed earlier to the lost-sales case. We further demonstrate that the results can be extended to the general sales lever decisions. / Assuming that unmet demand is fully backlogged, a newsvendor-type profit function which is defined as the resulting expected one-period profit with sales lever being optimized for every inventory level, fails to be unimodal. By assuming the newsvendor-type profit function to have a finite number of local maxima, we develop an efficient algorithm for finding the optimal ( s, S, z) policy with the long-run average profit derived by the renewal theory. We further identify the conditions under which the (s, S, z) policy is globally optimal. / Issues on the interfaces between operations management and marketing research have attracted much attention recently. The developments integrating marketing decisions into inventory management are not only of academic interest, but also of practical importance. With uncertain demand, this research studies the joint determination of inventory and sales lever decisions such as price, incentives to salesforce, and short-term promotions, or a combination of them. / We consider a single-item, periodic-review system with the objective of maximizing the long-run average profit over an infinite planning horizon. Demand in a period is a non-negative, discrete random variable with its distribution dependent on the sales lever chosen for the period. A replenishment order can be placed at the beginning of a period incurring both fixed and variable ordering costs. The sales lever is determined jointly, and its execution may incur possible cost, for example, promotion cost. For such a model, we take particular interest in a so-called (s, S, z) policy, which operates as follows: whenever the inventory level falls to or below s, an order is placed to bring it up to S; when the inventory level is above s, no order is issued; the choice of sales lever z depends on the inventory level. / We finally conduct an extensive numerical study for both the backlogging and lost-sales cases. We compare the benefits of the dynamic sales lever strategy with those of the semi-dynamic as well as the static sales lever strategy, and find that the profit gains are significant. By sensitivity analysis, we bring out the impact of cost parameters on the optimal solutions. / Wei Ying. / "December 2006." / Adviser: Youhua Frank Chen. / Source: Dissertation Abstracts International, Volume: 68-09, Section: A, page: 3961. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 125-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Location-allocation problems can be described generically as follows: Given the location or distribution (perhaps, probabilistic) of a set of customers and their associated demands for a given product or service, determine the optimum location of a number of service facilities and the allocation of products or services from facilities to customers, so as to minimize total (expected) location and transportation costs. This study is concerned with a particular subclass of location-allocation problems involving capacitated facilities and a continuum of demand. Specifically, two minisum, network-based location-allocation problems are analyzed in which facilities having known finite capacities are to be located so as to optimally supply/serve a known continuum of demand. The first problem considered herein, is an absolute p-median problem in which p > l capacitated facilities are to be located on a chain graph having both nodal and link demands, the latter of which are defined by nonnegative, integrable demand functions. In addition, the problem is balanced, in that it is assumed the total demand equals the total supply. An exact solution procedure is developed, wherein the optimality of a certain location-allocation scheme (for any given ordering of the facilities) is used to effect a branch and bound approach by which one can identify an optimal solution to the problem. Results from the chain graph analysis are then used to develop an algorithm with which one can solve a dynamic, sequential location-allocation problem in which a single facility per period is required to be located on the chain. Finally, an exact solution procedure is developed for locating a capacitated, absolute 2-median on a tree graph having both nodal and link demands and for which the total demand is again equal to the total supply. This procedure utilizes an algorithm to construct two subtrees, each of whose ends constitute a set of candidate optimal locations for one of the two elements of an absolute 2-median. Additional localization results are used to further reduce the number of candidate pairs (of ends) that need to be considered, and then a post-localization analysis provides efficient methods of comparing the relative costs of the remaining pairs. / Ph. D.

Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a new conceptual framework of self-other agreement and used three samples to illustrate how national culture affected self-other agreement. These three samples included 428 participants from China, 818 participants from the US, and 871 participants from globally dispersed teams (GDTs). An EQS procedure and a polynomial regression procedure were used to examine whether the covariance matrices were equal across samples and whether the relationships between self-other agreement and performance would be different across cultures, respectively. The results indicated MSF could be applied to China and GDTs, but the pattern of relationships between self-other agreement and performance was different across samples, suggesting that the results found in the U.S. sample were the exception rather than rule. Demographics also affected self-other agreement disparately across perspectives and cultures, indicating self-concept was susceptible to cultural influences. The proposed framework only received partial support but showed great promise to guide future studies. This study contributed to the literature by: (a) developing a new framework of self-other agreement that could be used to study various contextual factors; (b) examining the relationship between self-other agreement and performance in three vastly different samples; (c) providing some important insights about consensus between raters and self-other agreement; (d) offering some practical guidelines regarding how to apply MSF to other cultures more effectively.

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