Un hypercube latin (LHD) maximin est un ensemble de points contenus dans un hypercube tel que les points ne partagent de coordonnées sur aucune dimension et tel que la distance minimale entre deux points est maximale. Les LHDs maximin sont particulièrement utilisés pour la construction de métamodèles en raison de leurs bonnes propriétés pour l’échantillonnage. Comme la plus grande partie des travaux concernant les LHD se sont concentrés sur leur construction par des algorithmes heuristiques, nous avons décidé de produire une étude détaillée du problème, et en particulier de sa complexité et de son approximabilité en plus des algorithmes heuristiques permettant de le résoudre en pratique.Nous avons généralisé le problème de construction d’un LHD maximin en définissant le problème de compléter un LHD entamé en respectant la contrainte maximin. Le sous-problème dans lequel le LHD partiel est vide correspond au problème de construction de LHD classique. Nous avons étudié la complexité du problème de complétion et avons prouvé qu’il est NP-complet dans de nombreux cas. N’ayant pas déterminé la complexité du sous-problème, nous avons cherché des garanties de performances pour les algorithmes résolvant les deux problèmes.D’un côté, nous avons prouvé que le problème de complétion n’est approximable pour aucune norme en dimensions k ≥ 3. Nous avons également prouvé un résultat d’inapproximabilité plus faible pour la norme L1 en dimension k = 2. D’un autre côté, nous avons proposé un algorithme d’approximation pour le problème de construction, et avons calculé le rapport d’approximation grâce à deux bornes supérieures que nous avons établies. En plus de l’aspect théorique de cette étude, nous avons travaillé sur les algorithmes heuristiques, et en particulier sur la méta-heuristique du recuit simulé. Nous avons proposé une nouvelle fonction d’évaluation pour le problème de construction et de nouvelles mutations pour les deux problèmes, permettant d’améliorer les résultats rapportés dans la littérature. / A maximin Latin Hypercube Design (LHD) is a set of point in a hypercube which do not share a coordinate on any dimension and such that the minimal distance between two points, is maximal. Maximin LHDs are widely used in metamodeling thanks to their good properties for sampling. As most work concerning LHDs focused on heuristic algorithms to produce them, we decided to make a detailed study of this problem, including its complexity, approximability, and the design of practical heuristic algorithms.We generalized the maximin LHD construction problem by defining the problem of completing a partial LHD while respecting the maximin constraint. The subproblem where the partial LHD is initially empty corresponds to the classical LHD construction problem. We studied the complexity of the completion problem and proved its NP-completeness for many cases. As we did not determine the complexity of the subproblem, we searched for performance guarantees of algorithms which may be designed for both problems. On the one hand, we found that the completion problem is inapproximable for all norms in dimensions k ≥ 3. We also gave a weaker inapproximation result for norm L1 in dimension k = 2. On the other hand, we designed an approximation algorithm for the construction problem which we proved using two new upper bounds we introduced.Besides the theoretical aspect of this study, we worked on heuristic algorithms adapted for these problems, focusing on the Simulated Annealing metaheuristic. We proposed a new evaluation function for the construction problem and new mutations for both the construction and completion problems, improving the results found in the literature.

Delay is known to be a critical performance metric for various real-world routing applications including multimedia communication and freight delivery. Provisioning delay-minimal (or at least delay-bounded) routing services for all traffic of an application is highly important. As a basic paradigm of networking, multi-path routing has been proven to be able to obtain lower delay performance than the single-path routing, since traffic congestions can be avoided. However, to our best knowledge, (i) many of existing delay-aware multi-path routing studies only consider the aggregate traffic delay. Considering that even the solution achieving the optimal aggregate traffic delay has a possibly unbounded delay performance for certain individual traffic unit, those studies may be insufficient in practice; besides, (ii) most existing studies which optimize or bound delays of all traffic are best-effort, where the achieved solutions have no theoretical performance guarantee. In this dissertation, we study four delay-aware multi-path routing problems, with the delay performances of all traffic taken into account. Three of them are in communication and one of them is in transportation. Note that our study differ from all related ones as we are the first to study the four fundamental problems to our best knowledge. Although we prove that our studied problems are all NP-hard, we design approximation algorithms with theoretical performance guarantee for solving each of them. To be specific, we claim the following contributions. Minimize maximum delay and average delay. First, we consider a single-unicast setting where in a multi-hop network a sender requires to use multiple paths to stream a flow at a fixed rate to a receiver. Two important delay metrics are the average sender-to-receiver delay and the maximum sender-to-receiver delay. Existing results say that the two delay metrics of a flow cannot be both within bounded-ratio gaps to the optimal. In comparison, we design three different flow solutions, each of which can minimize the two delay metrics simultaneously within a $(1/epsilon)$-ratio gap to the optimal, at a cost of only delivering $(1-epsilon)$-fraction of the flow, for any user-defined $epsilonin(0,1)$. The gap $(1/epsilon)$ is proven to be at least near-tight, and we further show that our solutions can be extended to the multiple-unicast setting. Minimize Age-of-Information (AoI). Second, we consider a single-unicast setting where in a multi-hop network a sender requires to use multiple paths to periodically send a batch of data to a receiver. We study a newly proposed delay-sensitive networking performance metric, AoI, defined as the elapsed time since the generation of the last received data. We consider the problem of minimizing AoI subject to throughput requirements, which we prove is NP-hard. We note that our AoI problem differs from existing ones in that we are the first to consider the batch generation of data and multi-path communication. We develop both an optimal algorithm with a pseudo-polynomial time complexity and an approximation framework with a polynomial time complexity. Our framework can build upon any polynomial-time $alpha$-approximation algorithm of the maximum delay minimization problem, to construct an $(alpha+c)$-approximate solution for minimizing AoI. Here $c$ is a constant dependent on throughput requirements. Maximize network utility. Third, we consider a multiple-unicast setting where in a multi-hop network there exist many network users. Each user requires a sender to use multiple paths to stream a flow to a receiver, incurring an utility that is a function of the experienced maximum delay or the achieved throughput. Our objective is to maximize the aggregate utility of all users under throughput requirements and maximum delay constraints. We observe that it is NP-complete either to construct an optimal solution under relaxed maximum delay constraints or relaxed throughput requirements, or to figure out a feasible solution with all constraints satisfied. Hence it is non-trivial even to obtain approximate solutions satisfying relaxed constraints in a polynomial time. We develop a polynomial-time approximation algorithm. Our algorithm obtains solutions with constant approximation ratios under realistic conditions, at the cost of violating constraints by up to constant-ratios. Minimize fuel consumption for a heavy truck to timely fulfill multiple transportation tasks. Finally, we consider a common truck operation scenario where a truck is driving in a national highway network to fulfill multiple transportation tasks in order. We study an NP-hard timely eco-routing problem of minimizing total fuel consumption under task pickup and delivery time window constraints. We note that optimizing task execution times is a new challenging design space for saving fuel in our multi-task setting, and it differentiates our study from existing ones under the single-task setting. We design a fast and efficient heuristic. We characterize conditions under which the solution of our heuristic must be optimal, and further prove its optimality gap in case the conditions are not met. We simulate a heavy-duty truck driving across the US national highway system, and empirically observe that the fuel consumption achieved by our heuristic can be $22%$ less than that achieved by the fastest-/shortest- path baselines. Furthermore, the fuel saving of our heuristic as compared to the baselines is robust to the number of tasks. / Doctor of Philosophy / We consider a network modeled as a directed graph, where it takes time for data to traverse each link in the network. It models many critical applications both in the communication area and in the transportation field. For example, both the European education network and the US national highway network can be modeled as directed graphs. We consider a scenario where a source node is required to send multiple (a set of) data packets to a destination node through the network as fast as possible, possibly using multiple source-to-destination paths. In this dissertation we study four problems all of which try to figure out routing solutions to send the set of data packets, with an objective of minimizing experienced travel time or subject to travel time constraints. Although all of our four problems are NP-hard, we design approximation algorithms to solve them and obtain solutions with theoretically bounded gaps as compared to the optimal. The first three problems are in the communication area, and the last problem is in the transportation field. We claim the following specific contributions. Minimize maximum delay and average delay. First, we consider the setting of simultaneously minimizing the average travel time and the worst (largest) travel time of sending the set of data packets from source to destination. Existing results say that the two metrics of travel time cannot be minimized to be both within bounded-ratio gaps to the optimal. As a comparison, we design three different routing solutions, each of which can minimize the two metrics of travel time simultaneously within a constant bounded ratio-gap to the optimal, but at a cost of only delivering a portion of the data. Minimize Age-of-Information (AoI). Second, we consider the problem of minimizing a newly proposed travel-time-sensitive performance metric, i.e., AoI, which is the elapsed time since the generation of the last received data. Our AoI study differs from existing ones in that we are the first to consider a set of data and multi-path routing. We develop both an optimal algorithm with a pseudo-polynomial time complexity and an approximation framework with a polynomial time complexity. Maximize network utility. Third, we consider a more general setting with multiple source destination pairs. Each source incurs a utility that is a function of the experienced travel time or the achieved throughput to send data to its destination. Our objective is to maximize the aggregate utility under throughput requirements and travel time constraints. We develop a polynomial-time approximation algorithm, at the cost of violating constraints by up to constant-ratios. It is non-trivial to design such algorithms, as we prove that it is NPcomplete either to construct an optimal solution under relaxed delay constraints or relaxed throughput requirements, or to figure out a feasible solution with all constraints satisfied. Minimize fuel consumption for a heavy truck to timely fulfill multiple transportation tasks. Finally, we consider a truck and multiple transportation tasks in order, where each task requires the truck to pick up cargoes at a source timely, and deliver them to a destination timely. The need of coordinating task execution times is a new challenging design space for saving fuel in our multi-task setting, and it differentiates our study from existing ones under the single-task setting. We design an efficient heuristic. We characterize conditions under which the solution of our heuristic must be optimal, and further prove its performance gap as compared to the optimal in case the conditions are not met.

Dans cette thèse, nous nous intéressons à plusieurs problèmes liés à la conception de réseaux géométriques et aux plongements isométriques dans le plan.Nous commençons par étudier la généralisation du problème du réseau de Manhattan classique aux plans normés. Étant donné un ensemble de terminaux, nous recherchons le réseau de longueur totale minimum qui connecte chaque paire de terminaux par un plus court chemin dans la métrique définie par la norme. Nous proposons un algorithme d'approximation facteur 2.5 pour ce problème en temps O(mn^3) avec n le nombre de terminaux et m le nombre de directions de la boule unitaire. Le deuxième problème étudié est une version orientée des réseaux de Manhattan dont le but est de construire un réseau orienté de taille minimum dans lequel pour chaque paire de terminaux u, v est relié par un plus court chemin rectilinéaire de u vers v et un autre de v vers u. Nous proposons un algorithme d'approximation facteur 2 pour ce problème en temps O(n^3) où n est le nombre de terminaux.Nous nous intéressons ensuite à la recherche d'un spanner (un sous-graphe approximant les distances) planaire pour les graphes de disques unitaires (UDG) qui modélise les réseaux ad hoc sans fils. Nous présentons un algorithme qui construit un spanner planaire avec un facteur d'étirement constant en terme de distance de graphe pour UDG. Cet algorithme utilise uniquement des propriétés locales et peut donc être implémenté de manière distribuée.Finalement nous étudions le problème de la reconnaissance des espaces plongeables isométriquement dans le plan l_1 pour lequel nous proposons un algorithme en temps optimal O(n^2) pour sa résolution, ainsi que la généralisation de ce problème aux plans normés dont la boule unitaire est un polygone convexe central symétrique. / In this thesis, we study several problems related to the design of geometric networks and isometric embeddings into the plane.We start by considering the generalization of the classical Minimum Manhattan Network problem to all normed planes. We search the minimum network that connects each pair of terminals by a shortest path in this norm. We propose a factor 2.5 approximation algorithm in time O(mn^3), where n is the number of terminals and m is the number of directions of the unit ball.The second problem presented is an oriented version of the minumum Manhattan Network problem, we want to obtain a minimum oriented network such that for each pair u, v of terminals, there is a shortest rectilinear path from u to v and another path from v to u.We describe a factor 2 approximation algorithm with complexity O(n^3) where n is the number of terminals for this problem.Then we study the problem of finding a planar spanner (a subgraph which approximates the distances) of the Unit Disk Graph (UDG) which is used to modelize wireless ad hoc networks. We present an algorithm for computing a constant hop stretch factor planar spanner for all UDG. This algorithm uses only local properties and it can be implemented in distributed manner.Finally, we study the problem of recognizing metric spaces that can be isometrically embbed into the rectilinear plane and we provide an optimal time O(n^2) algorithm to solve this problem. We also study the generalization of this problem to all normed planes whose unit ball is a centrally symmetric convex polygon.

Nessa tese consideramos problemas de alocação e precificação de itens, onde temos um conjunto de itens e um conjunto de compradores interessados em tais itens. Nosso objetivo é escolher uma alocação de itens a compradores juntamente com uma precificação para tais itens para maximizar o lucro obtido, considerando o valor máximo que um comprador está disposto a pagar por um determinado item. Em particular, focamos em três problemas: o Problema da Compra Máxima, o Problema da Precificação Livre de Inveja e o Leilão de Anúncios de Segundo Preço. O Problema da Compra Máxima e o Problema da Precificação Livre de Inveja modelam o problema que empresas que vendem produtos ou serviços enfrentam na realidade, onde é necessário escolher corretamente os preços dos produtos ou serviços disponíveis para os clientes para obter um lucro interessante. Já o Leilão de Anúncios de Segundo Preço modela o problema enfrentado por empresas donas de ferramentas de busca que desejam vender espaço para anunciantes nos resultados das buscas dos usuários. Ambas as questões, tanto a precificação de produtos e serviços quanto a alocação de anunciantes em resultados de buscas, são de grande relevância econômica e, portanto, são interessantes de serem atacadas dos pontos de vista teórico e prático. Nosso foco nesse trabalho é considerar algoritmos de aproximação e algoritmos de programação inteira mista para os problemas mencionados, apresentando novos resultados superiores àqueles conhecidos previamente na literatura, bem como determinar a complexidade computacional destes problemas ou de alguns de seus casos particulares de interesse. / In this thesis we consider allocation and pricing problems, where we have a set of items and a set of consumers interested in such items. Our objective is to choose an allocation of items to consumers, considering the maximum value a consumer is willing to pay in a specific item. In particular, we focus in three problems: the Max-Buying Problem, the Envy-Free Pricing Problem and the Second-Price Ad Auction. The Max-Buying Problem and the Envy-Free Pricing Problem model a problem faced in reality by companies that sell products or services, where it is necessary to correctly choose the price of the products or services available to clients in order to obtain an interesting profit. The Second-Price Ad Auction models the problem faced by companies that own search engines and desire to sell space for advertisers in the search results of the users. Both questions, the pricing of items and services and the allocation of advertisers in search results are of great economical relevance and, for this, are interesting to be attacked from a theoretical and a practical perspective. Our focus in this work is to consider approximation algorithms and mixed integer programming algorithms for the aforementioned problems, presenting new results superior than the previously known in the literature, as well as to determine the computational complexity of such problems or some of their interesting particular cases.

The Steiner Tree problem has appeared in the Karp's list of the first 21 NP-hard problems and is well known as one of the most fundamental problems in Network Design area. We study the Node-Weighted version of the Prize Collecting Steiner Tree problem. In this problem, we are given a simple graph with a cost and penalty value associated with each node. Our goal is to find a subtree T of the graph minimizing the cost of the nodes in T plus penalty of the nodes not in T. By a reduction from set cover problem it can be easily shown that the problem cannot be approximated in polynomial time within factor of (1-o(1))ln n unless NP has quasi-polynomial time algorithms, where n is the number of vertices of the graph. Moss and Rabani claimed an O(log n)-approximation algorithm for the problem using a Primal-Dual approach in their STOC'01 paper \cite{moss2001}. We show that their algorithm is incorrect by providing a counter example in which there is an O(n) gap between the dual solution constructed by their algorithm and the optimal solution. Further, evidence is given that their algorithm probably does not have a simple fix. We propose a new algorithm which is more involved and introduces novel ideas in primal dual approach for network design problems. Also, our algorithm is a Lagrangian Multiplier Preserving algorithm and we show how this property can be utilized to design an O(log n)-approximation algorithm for the Node-Weighted Quota Steiner Tree problem using the Lagrangian Relaxation method. We also show an application of the Node Weighted Quota Steiner Tree problem in designing algorithm with better approximation factor for Technology Diffusion problem, a problem proposed by Goldberg and Liu in \cite{goldberg2012} (SODA 2013). In Technology Diffusion, we are given a graph G and a threshold θ(v) associated with each vertex v and we are seeking a set of initial nodes called the seed set. Technology Diffusion is a dynamic process defined over time in which each vertex is either active or inactive. The vertices in the seed set are initially activated and each other vertex v gets activated whenever there are at least θ(v) active nodes connected to v through other active nodes. The Technology Diffusion problem asks to find the minimum seed set activating all nodes. Goldberg and Liu gave an O(rllog n)-approximation algorithm for the problem where r and l are the diameter of G and the number of distinct threshold values, respectively. We improve the approximation factor to O(min{r,l}log n) by establishing a close connection between the problem and the Node Weighted Quota Steiner Tree problem.

This thesis proposes equilibrium models to link the wholesale and retail electricity markets which allow for reconciliation of the differing time scales of responses of producers (e.g., hourly) and consumers (e.g., monthly) to changing prices. Electricity market equilibrium models with time of use (TOU) pricing scheme are formulated as large-scale variational inequality (VI) problems, a unified and concise approach for modeling the equilibrium. The demand response is dynamic in these models through a dependence on the lagged demand. Different market structures are examined within this context. With an illustrative example, the welfare gains/losses are analyzed after an implementation of TOU pricing scheme over the single pricing scheme. An approximation of the welfare change for this analysis is also presented. Moreover, break-up of a large supplier into smaller parts is investigated. For the illustrative examples presented in the dissertation, overall welfare gains for consumers and lower prices closer to the levels of perfect competition can be realized when the retail pricing scheme is changed from single pricing to TOU pricing. These models can be useful policy tools for regulatory bodies i) to forecast future retail prices (TOU or single prices), ii) to examine the market power exerted by suppliers and iii) to measure welfare gains/losses with different retail pricing schemes (e.g., single versus TOU pricing). With the inclusion of linearized DC network constraints into these models, the problem size grows considerably. Dantzig-Wolfe (DW) decomposition algorithm for VI problems is used to alleviate the computational burden and it also facilitates model management and maintenance. Modification of the DW decomposition algorithm and approximation of the DW master problem significantly improve the computational effort required to find the equilibrium. These algorithms are applied to a two-region energy model for Canada and a realistic Ontario electricity test system. In addition to empirical analysis, theoretical results for the convergence properties of the master problem approximation are presented for DW decomposition of VI problems.

In this thesis, we consider multiobjective combinatorial optimization problems. We address two main topics. We first address the polynomially solvable cases of the Traveling Salesperson Problem and the Bottleneck Traveling Salesperson Problem. We consider multiobjective versions of these problems with different combinations of objective functions, analyze their computational complexities and develop exact algorithms where possible. We next consider generating extreme supported nondominated points of multiobjective integer programming problems for any number of objective functions. We develop two algorithms for this purpose. The first one is an exact algorithm and finds all such points. The second algorithm finds only a subset of extreme supported nondominated points providing a worst case approximation for the remaining points.

We design and analyze algorithms for the following two robot navigation problems: 1. TARGET SEARCH. Given a robot located at a point s in the plane, how will a robot navigate to a goal t in the presence of unknown obstacles ? 2. LOCALIZATION. A robot is "lost" in an environment with a map of its surroundings. How will it find its true location by traveling the minimum distance ? Since efficient algorithms for these two problems will make a robot completely autonomous, they have held the interest of both robotics and computer science communities. Previous work has focussed mainly on designing competitive algorithms where the robot's performance is compared to that of an omniscient adversary. For example, a competitive algorithm for target search will compare the distance traveled by the robot with the shortest path from s to t. We analyze these problems from the worst-case perspective, which, in our view, is a more appropriate measure. Our results are : 1. For target search, we analyze an algorithm called Dynamic A*. The robot continuously moves to the goal on the shortest path which it recomputes on the discovery of obstacles. A variant of this algorithm has been employed in Mars Rover prototypes. We show that D* takes O(n log n) time on planar graphs and also show a comparable bound on arbitrary graphs. Thus, our results show that D* combines the optimistic possibility of reaching the goal very soon while competing with depth-first search within a logarithmic factor. 2. For the localization problem, worst-case analysis compares the performance of the robot with the optimal decision tree over the set of possible locations. No approximation algorithm has been known. We give a polylogarithmic approximation algorithm and also show a near-tight lower bound for the grid graphs commonly used in practice. The key idea is to plan travel on a "majority-rule map" which eliminates uncertainty and permits a link to the half-Group Steiner problem. We also extend the problem to polygonal maps by discretizing the domain using novel geometric techniques.

Στα πλαίσια της διατριβής αναπτύξαμε δύο από τους πρώτους αλγορίθμους υπολογισμού μιας ε-προσεγγιστικής ισορροπίας Nash για την περίπτωση όπου το ε είναι κάποια σταθερά. Οι προσεγγίσεις που επιτυγχάνουν οι αλγόριθμοί μας είναι ε=3/4 και ε=(2+λ)/4 αντίστοιχα, όπου λ είναι το ελάχιστο, μεταξύ όλων των ισορροπιών Nash, κέρδος για έναν παίκτη. Επιπλέον, μελετήσαμε μια ευρεία κλάση τυχαίων παιγνίων δύο παικτών, για την οποία υπολογίσαμε μια πολύ καλή ε-προσεγγιστική ισορροπία Nash, με το ε να τείνει στο 0 καθώς το πλήθος των διαθέσιμων στρατηγικών των παικτών τείνει στο άπειρο. Οι αρχές της θεωρίας παιγνίων είναι χρήσιμες στην ανάλυση της επίδρασης που έχει στην καθολική απόδοση ενός συστήματος διαμοιραζόμενων πόρων η εγωιστική και ανταγωνιστική συμπεριφορά των χρηστών του. Προς την κατεύθυνση αυτή, εστιάσαμε στο πρόβλημα της εξισορρόπησης φορτίου. Μελετήσαμε διάφορα μοντέλα πληροφόρησης (π.χ. όταν όλα τα φορτία είναι άγνωστα ή όταν κάθε παίκτης γνωρίζει το μέγεθος του δικού του φορτίου) και αναλύσαμε για το καθένα το σύνολο και τις ιδιότητες των ισορροπιών Nash. Yπολογίσαμε επίσης φράγματα στο λόγο απόκλισης, ο οποίος εκφράζει την επίδραση που έχει στην απόδοση του συστήματος η εγωιστική συμπεριφορά των χρηστών του. Εκτός από τα υπολογιστικά θέματα που σχετίζονται με τη θεωρία παιγνίων, έχει ενδιαφέρον να μελετηθεί κατά πόσο μπορεί η θεωρία παιγνίων να βοηθήσει στην ανάπτυξη και ανάλυση αλγορίθμων για υπολογιστικά δύσκολα προβλήματα συνδυαστικής βελτιστοποίησης. Προς αυτήν την κατεύθυνση, μελετήσαμε από παιγνιοθεωρητική σκοπιά το πρόβλημα χρωματισμού των κορυφών ενός γραφήματος. Ορίσαμε κατάλληλα το παίγνιο χρωματισμού γραφήματος και αποδείξαμε ότι κάθε παίγνιο χρωματισμού γραφήματος έχει πάντα μια αγνή ισορροπία Nash, και ότι κάθε αγνή ισορροπία Nash αντιστοιχεί σε ορθό χρωματισμό του γραφήματος. Δείξαμε επίσης ότι υπάρχει πάντα μια αγνή ισορροπία Nash που χρησιμοποιεί βέλτιστο αριθμό χρωμάτων, δηλαδή ίσο με το χρωματικό αριθμό του γραφήματος. Επιπλέον, περιγράψαμε και αναλύσαμε έναν πολυωνυμικό αλγόριθμο που υπολογίζει μια αγνή ισορροπία Nash για ένα οποιοδήποτε παίγνιο χρωματισμού γραφήματος και χρησιμοποιεί συνολικά ένα πλήθος χρωμάτων που ικανοποιεί ταυτόχρονα τα περισσότερα κλασικά γνωστά φράγματα στο χρωματικό αριθμό. / We developed two algorithms for computing an e-approximate Nash equilibrium for the case where e is an absolute constant. The approximations achieved by our algorithms are e=3/4 and e=(2+l)/4 respectively, where $\lambda$ is the minimum, among all Nash equilibria, payoff of either player. Furthermore, we studied a wide class of random two player games, for which we showed how to compute an e-approximate Nash equilibrium, where e tends to zero as the number of strategies of the players tends to infinity. Game theoretic concepts are useful in determining the impact that selfish behavior plays on the global performance of a system involving selfish entities. Towards this direction, we focused on the problem of load balancing. We studied the case where the agents are not necessarily fully informed about the exact values of their loads. We focused on several models of information (e.g. when all agents know nothing about the loads, or when each agents knows her own load) and, for each model, we characterized the set of Nash equilibria and analyzed their properties. Moreover, we bounded the coordination ratio, a measure which captures the impact that selfish behavior has to the global performance of the system, in contrast to the performance achieved by an optimum centralized algorithm. Besides the computational issues related to game theory, it is interesting to investigate whether game theory can help us in developing and analyzing algorithms for computationally difficult combinatorial optimization problems. Towards this direction, we studied from a game theoretic point of view the problem of vertex coloring. In particular, we properly defined the graph coloring game and we proved that every graph coloring game has a pure Nash equilibrium, and each pure Nash equilibrium corresponds to a proper coloring of the graph. We also showed that there exists a pure Nash equilibrium that uses an optimum number of colors, i.e. equal to the chromatic number. Furthermore, we developed and analyzed a polynomial time algorithm that computes a pure Nash equilibrium for any graph coloring game, using a number of colors satisfying most of the known classical bounds on the chromatic number.

This thesis proposes equilibrium models to link the wholesale and retail electricity markets which allow for reconciliation of the differing time scales of responses of producers (e.g., hourly) and consumers (e.g., monthly) to changing prices. Electricity market equilibrium models with time of use (TOU) pricing scheme are formulated as large-scale variational inequality (VI) problems, a unified and concise approach for modeling the equilibrium. The demand response is dynamic in these models through a dependence on the lagged demand. Different market structures are examined within this context. With an illustrative example, the welfare gains/losses are analyzed after an implementation of TOU pricing scheme over the single pricing scheme. An approximation of the welfare change for this analysis is also presented. Moreover, break-up of a large supplier into smaller parts is investigated. For the illustrative examples presented in the dissertation, overall welfare gains for consumers and lower prices closer to the levels of perfect competition can be realized when the retail pricing scheme is changed from single pricing to TOU pricing. These models can be useful policy tools for regulatory bodies i) to forecast future retail prices (TOU or single prices), ii) to examine the market power exerted by suppliers and iii) to measure welfare gains/losses with different retail pricing schemes (e.g., single versus TOU pricing). With the inclusion of linearized DC network constraints into these models, the problem size grows considerably. Dantzig-Wolfe (DW) decomposition algorithm for VI problems is used to alleviate the computational burden and it also facilitates model management and maintenance. Modification of the DW decomposition algorithm and approximation of the DW master problem significantly improve the computational effort required to find the equilibrium. These algorithms are applied to a two-region energy model for Canada and a realistic Ontario electricity test system. In addition to empirical analysis, theoretical results for the convergence properties of the master problem approximation are presented for DW decomposition of VI problems.