In this dissertation, we study the interplay between a firm’s operations strategy, with regard to its capacity management, and its marketing decision of product differentiation. For this, we study a market comprising heterogeneous customers who differ in their preferences for time and price. Time sensitive customers are willing to pay a price premium for a shorter delivery time, while price sensitive customers are willing to accept a longer delivery time in return for a lower price. Firms exploit this heterogeneity in customers’ preferences, and offer a menu of products/services that differ only in their guaranteed delivery times and prices. From demand perspective, when customers are allowed to self-select according to their preferences, different products act as substitutes, affecting each other’s demand. Customized product for each segment, on the other hand, results in independent demand for each product. On the supply side, a firm may either share the same processing capacity to serve the two market segments, or may dicate capacity for each segment. Our objective is to understand the interaction between product substitution and the firm’s operations strategy (dedicated versus shared capacity), and how they shape the optimal product differentiation strategy. To address the above issue, we first study this problem for a single monopolist firm, which offers two versions of the same basic product: (i) regular product at a lower price but with a longer delivery time, and (ii) express product at a higher price but with a shorter delivery time. Demand for each product arrives according to a Poisson process with a rate that depends both on its price and delivery time. In addition, if the products are substitutable, each product’s demand is also influenced by the price and delivery time of the other product. Demands within each category are served on a first-come-first-serve basis. However, customers for express product are always given priority over the other category when they are served using shared resources. There is a standard delivery time for the regular product, and the firm’s objective is to appropriately price the two products and select the express delivery time so as to maximize its profit rate. The firm simultaneously needs to decide its installed processing capacity so as to meet its promised delivery times with a high degree of reliability. While the problem in a dedicated capacity setting is solved analytically, the same becomes very challenging in a shared capacity setting, especially in the absence of an analytical characterization of the delivery time distribution of regular customers in a priority queue. We develop a solution algorithm, using matrix geometric method in a cutting plane framework, to solve the problem numerically in a shared capacity setting. Our study shows that in a highly capacitated system, if the firm decides to move from a dedicated to a shared capacity setting, it will need to offer more differentiated products, whether the products are substitutable or not. In contrast, when customers are allowed to self-select, such that independent products become substitutable, a more homogeneous pricing scheme results. However, the effect of substitution on optimal delivery time differentiation depends on the firm’s capacity strategy and cost, as well as market characteristics. The optimal response to any change in capacity cost also depends on the firm’s operations strategy. In a dedicated capacity scenario, the optimal response to an increase in capacity cost is always to offer more homogeneous prices and delivery times. In a shared capacity setting, it is again optimal to quote more homogeneous delivery times, but increase or decrease the price differentiation depending on whether the status-quo capacity cost is high or low, respectively. We demonstrate that the above results are corroborated by real-life practices, and provide a number of managerial implications in terms of dealing with issues like volatile fuel prices. We further extend our study to a competitive setting with two firms, each of which may either share its processing capacities for the two products, or may dedicate capacity for each product. The demand faced by each firm for a given product now also depends on the price and delivery time quoted for the same product by the other firm. We observe that the qualitative results of a monopolistic setting also extend to a competitive setting. Specifically, in a highly capacitated system, the equilibrium prices and delivery times are such that they result in more differentiated products when both the firms use shared capacities as compared to the scenario when both the firms use dedicated capacities. When the competing firms are asymmetric, they exploit their distinctive characteristics to differentiate their products. Further, the effects of these asymmetries also depend on the capacity strategy used by the competing firms. Our numerical results suggest that the firm with expensive capacity always offers more homogeneous delivery times. However, its decision on how to differentiate its prices depends on the capacity setting of the two firms as well as the actual level of their capacity costs. On the other hand, the firm with a larger market base always offers more differentiated prices as well as delivery times, irrespective of the capacity setting of the competing firms.

In this dissertation, we study the interplay between a firm’s operations strategy, with regard to its capacity management, and its marketing decision of product differentiation. For this, we study a market comprising heterogeneous customers who differ in their preferences for time and price. Time sensitive customers are willing to pay a price premium for a shorter delivery time, while price sensitive customers are willing to accept a longer delivery time in return for a lower price. Firms exploit this heterogeneity in customers’ preferences, and offer a menu of products/services that differ only in their guaranteed delivery times and prices. From demand perspective, when customers are allowed to self-select according to their preferences, different products act as substitutes, affecting each other’s demand. Customized product for each segment, on the other hand, results in independent demand for each product. On the supply side, a firm may either share the same processing capacity to serve the two market segments, or may dicate capacity for each segment. Our objective is to understand the interaction between product substitution and the firm’s operations strategy (dedicated versus shared capacity), and how they shape the optimal product differentiation strategy. To address the above issue, we first study this problem for a single monopolist firm, which offers two versions of the same basic product: (i) regular product at a lower price but with a longer delivery time, and (ii) express product at a higher price but with a shorter delivery time. Demand for each product arrives according to a Poisson process with a rate that depends both on its price and delivery time. In addition, if the products are substitutable, each product’s demand is also influenced by the price and delivery time of the other product. Demands within each category are served on a first-come-first-serve basis. However, customers for express product are always given priority over the other category when they are served using shared resources. There is a standard delivery time for the regular product, and the firm’s objective is to appropriately price the two products and select the express delivery time so as to maximize its profit rate. The firm simultaneously needs to decide its installed processing capacity so as to meet its promised delivery times with a high degree of reliability. While the problem in a dedicated capacity setting is solved analytically, the same becomes very challenging in a shared capacity setting, especially in the absence of an analytical characterization of the delivery time distribution of regular customers in a priority queue. We develop a solution algorithm, using matrix geometric method in a cutting plane framework, to solve the problem numerically in a shared capacity setting. Our study shows that in a highly capacitated system, if the firm decides to move from a dedicated to a shared capacity setting, it will need to offer more differentiated products, whether the products are substitutable or not. In contrast, when customers are allowed to self-select, such that independent products become substitutable, a more homogeneous pricing scheme results. However, the effect of substitution on optimal delivery time differentiation depends on the firm’s capacity strategy and cost, as well as market characteristics. The optimal response to any change in capacity cost also depends on the firm’s operations strategy. In a dedicated capacity scenario, the optimal response to an increase in capacity cost is always to offer more homogeneous prices and delivery times. In a shared capacity setting, it is again optimal to quote more homogeneous delivery times, but increase or decrease the price differentiation depending on whether the status-quo capacity cost is high or low, respectively. We demonstrate that the above results are corroborated by real-life practices, and provide a number of managerial implications in terms of dealing with issues like volatile fuel prices. We further extend our study to a competitive setting with two firms, each of which may either share its processing capacities for the two products, or may dedicate capacity for each product. The demand faced by each firm for a given product now also depends on the price and delivery time quoted for the same product by the other firm. We observe that the qualitative results of a monopolistic setting also extend to a competitive setting. Specifically, in a highly capacitated system, the equilibrium prices and delivery times are such that they result in more differentiated products when both the firms use shared capacities as compared to the scenario when both the firms use dedicated capacities. When the competing firms are asymmetric, they exploit their distinctive characteristics to differentiate their products. Further, the effects of these asymmetries also depend on the capacity strategy used by the competing firms. Our numerical results suggest that the firm with expensive capacity always offers more homogeneous delivery times. However, its decision on how to differentiate its prices depends on the capacity setting of the two firms as well as the actual level of their capacity costs. On the other hand, the firm with a larger market base always offers more differentiated prices as well as delivery times, irrespective of the capacity setting of the competing firms.

The interaction of theoretical computer science with game theory and economics has resulted in the emergence of two very interesting research directions. First, it has provided a new model for algorithm design, which is to optimize in the presence of strategic behavior. Second, it has prompted us to consider the computational aspects of various solution concepts from game theory, economics and auction design which have traditionally been considered mainly in a non-constructive manner. In this thesis we present progress along both these directions. We first consider optimization problems that arise in the design of combinatorial auctions. We provide an online algorithm in the important case of budget-bounded utilities. This model is motivated by the recent development of the business of online auctions of search engine advertisements. Our algorithm achieves a factor of $1-1/e$, via a new linear programming based technique to determine optimal tradeoffs between bids and budgets. We also provide lower bounds in terms of hardness of approximation in more general submodular settings, via a PCP-based reduction. Second, we consider truth-revelation in auctions, and provide an equivalence theorem between two notions of strategy-proofness in randomized auctions of digital goods. Last, we consider the problem of computing an approximate Nash equilibrium in multi-player general-sum games, for which we provide the first subexponential time algorithm.

The occurrence of catastrophic incidents in the process industry leave a marked legacy of resulting in staggering economic and societal losses incurred by the company, the government and the society. The work described herein is a novel approach proposed to help predict and mitigate potential catastrophes from occurring and for understanding the stakes at risk for better risk informed decision making. The methodology includes societal impact as risk measures along with tangible asset damage monetization. Predicting incidents as leading metrics is pivotal to improving plant processes and, for individual and societal safety in the vicinity of the plant (portfolio). From this study it can be concluded that the comprehensive judgments of all the risks and losses should entail the analysis of the overall results of all possible incident scenarios. Value-at-Risk (VaR) is most suitable as an overall measure for many scenarios and for large number of portfolio assets. FN-curves and F$-curves can be correlated and this is very beneficial for understanding the trends of historical incidents in the U.S. chemical process industry. Analyzing historical databases can provide valuable information on the incident occurrences and their consequences as lagging metrics (or lagging indicators) for the mitigation of the portfolio risks. From this study it can be concluded that there is a strong statistical relationship between the different consequence tiers of the safety pyramid and Heinrich‘s safety pyramid is comparable to data mined from the HSEES database. Furthermore, any chemical plant operation is robust only when a strategic balance is struck between optimal plant operations and, maintaining health, safety and sustaining environment. The balance emerges from choosing the best option amidst several conflicting parameters. Strategies for normative decision making should be utilized for making choices under uncertainty. Hence, decision theory is utilized here for laying the framework for choice making of optimum portfolio option among several competing portfolios. For understanding the strategic interactions of the different contributing representative sets that play a key role in determining the most preferred action for optimum production and safety, the concepts of game theory are utilized and framework has been provided as novel application to chemical process industry.

Ce travail porte sur l'étude des systèmes différentiels et algébriques du type Riccati issus de la théorie des jeux différentiels linéaires quadratiques. Ces systèmes dérivent de l'équilibre de Nash et de la commande optimale sous une contrainte différentielle stochastique. Ils sont le principal obstacle à franchir afin d'obtenir les stratégies optimales des joueurs. Dans le cas des systèmes différentiels, nous avons construit une méthode analytique pour le recherche d'une paire de solutions. Cette méthode s'appuie sur des changements de base de la matrice décrivant l'équilibre de Nash. Dans le cas des systèmes algébriques, nous avons proposé des itérations du type Lyapunov et des itérations du type Riccati. Des propriétés des solutions itératives ainsi que des conditions suffisantes de convergence de ces itérations sont également établies. Les résultats numériques obtenus avec ces deux types d'itérations sont présentées et comparés. Ces résultats démontrent une plus grande performance des itérations du type Riccati relativement aux itérations du type Lyapunov.

Zugl.: Oldenburg, Univ., Diss., 2007

Thèse doctorat : Department of English : Uppsala : 1993.

Strategic behaviors in electricity markets are analyzed. Three related topics are investigated. The first topic is a research about the NE search algorithm for complex non-cooperative games in electricity markets with transmission constraints. Hybrid co-evolutionary programming is suggested and simulated for complex examples. The second topic is an analysis about the competing pricing mechanisms of uniform and pay-as-bid pricing in an electricity market. We prove that for a two-player static game the Nash Equilibrium under pay-as-bid pricing will yield less total revenue in expectation than under uniform pricing when demand is inelastic. The third topic is to address a market power mitigation issue of the current Texas electricity market by limiting Transmission Congestion Right (TCR) ownership. The strategic coordination of inter zonal scheduling and balancing market manipulation is analyzed. A market power measurement algorithm useful to determine the proper level of TCR ownership limitation is suggested. / text

本文利用互補要素模型分析垂直相關市場中之整併策略，並討論在不同談判力下之各種均衡市場結構。當下游廠商所需生產要素之一為獨賣要素，另一由寡占廠商生產時，下游廠商與上游非獨賣要素廠商將視整併後談判力保存程度大小與獨占要素廠商進行補貼與否而決定是否進行垂直整併，上游獨賣要素廠商則會經由比較整併一家下游廠商和任由其他廠商垂直整併所能獲得之利潤，決定在下游廠商談判力較大的情況下併購下游廠商。而當上游非獨賣要素廠商談判力極小或極大時，上游獨賣要素廠商與下游廠商整併亦較為有利。 / This research investigates merger decisions in a vertically-related market with two complementary intermediate inputs by using the Nash bargaining model. The production of final good involves two complementary inputs, exclusive inputs and commonly available inputs. The downstream firms and the oligopolistic upstream firms would merge if the preservation of bargaining power after merger is large enough or the monopolistic input supplier subsidizes them to do so. The monopolistic input supplier compares the gains of merging a downstream firm and the profit of letting other firms integrate. The monopolistic supplier would merge a downstream firm when the bargaining power of the downstream firms is large. Also, merging with a downstream firm becomes more profitable for the monopolistic supplier if the bargaining power of oligopolistic upstream firms is extremely small or extremely large.

Planifier les actions d'un agent dans un environnement dynamique et incertain, a été largement étudié et le cadre des processus décisionnels de Markov offre les outils permettant de modéliser et de résoudre de tels problèmes. Le domaine de la théorie des jeux, a permis l'étude des interactions stratégiques entre plusieurs agents pour un jeu donné. Le cadre des jeux stochastiques, est considéré comme une généralisation du domaine des processus décisionnels de Markov et du champ de la théorie des jeux et permet de modéliser des systèmes ayant plusieurs agents et plusieurs états. Cependant, planifier dans unsystème multi-agents est considéré comme difficile, car la politique d'actions de l'agent dépend non seulement de ses choix mais aussi des politiques des autres agents. Le travail que nous présentons dans cette thèse porte sur la prise de décision distribuée dans les systèmes multi-agents. Les travaux existants dans le domaine, permettent la résolution théorique des jeux stochastiques mais imposent de fortes restrictions et font abstraction de certains problèmes cruciaux du modèle. Nous proposons un algorithme de planification décentralisée pour le modèle des jeux stochastiques, d'une part basé sur l'algorithme Value-Iteration et d'autre part basé sur la notion d'équilibre issue de la résolution des jeux matriciels. Afin d'améliorer le processus de résolution et de traiter des problèmes de taille importante, nous recherchons à faciliter la prise de décision et à limiter les possibilités d'actions à chaque étape d'interaction. L'algorithme que nous avonsproposé, a été validé sur un exemple d'interaction incluant plusieurs agents et différentes expérimentations ont été menées afin d'évaluer la qualité de la solution obtenue.