An Overview of Combinatorial Auctions

An auction is combinatorial when bidders can place bids on combinations of items, called “packages,” rather than just individual items. Computer scientists are interested in combinatorial auctions because they are concerned with the expressiveness of bidding languages, as well as the algorithmic aspects of the underlying combinatorial problem. The combinatorial problem has attracted attention from operations researchers, especially those working in combinatorial optimization and mathematical programming, who are fascinated by the idea of applying these tools to auctions. Auctions have been studied extensively by economists, of course. Thus, the newly emerging field of combinatorial auctions lies at the intersection of computer science, operations research, and economics. In this article, we present a brief introduction to combinatorial auctions, based on our book, Combinatorial Auctions (MIT Press, 2006), in which we look at combinatorial auctions from all three perspectives.Auctions

Distributed and dynamic traffic congestion controls without requiring demand forecasting: Tradable network permits and its implementation mechanisms

Tohoku University赤松隆課

Fundamental Tradeoffs for Modeling Customer Preferences in Revenue Management

Revenue management (RM) is the science of selling the right product, to the right person, at the right price. A key to the success of RM, which now spans a broad array of industries, is its grounding in mathematical modeling and analytics. This dissertation contributes to the development of new RM tools by: (1) exploring some fundamental tradeoffs underlying any RM problems, and (2) designing efficient algorithms for some RM applications. Another underlying theme of this dissertation is the modeling of customer preferences, a key component of any RM problem. The first chapters of this dissertation focus on the model selection problem: many demand models are available but picking the right model is a challenging task. In particular, we explore the tension between the richness of a model and its tractability. To quantify this tradeoff, we focus on the assortment optimization problem, a very general and core RM problem. To capture customer preferences in this context, we use choice models, a particular type of demand model. In Chapters 1, 2, 3 and 4 we design efficient algorithms for the assortment optimization problem under different choice models. By assessing the strengths and weaknesses of different choice models, we can quantify the cost in tractability one has to pay for better predictive power. This in turn leads to a better understanding of the tradeoffs underlying the model selection problem. In Chapter 5, we focus on a different question underlying any RM problem: choos- ing how to sell a given product. We illustrate this tradeoff by focusing on the problem of selling ad impressions via Internet display advertising platforms. In particular, we study how the presence of risk-averse buyers affects the desire for reservation con- tracts over real time buy via a second-price auction. In order to capture the risk aversion of buyers, we study different utility models

Essays in Robust and Data-Driven Risk Management

Risk defined as the chance that the outcome of an uncertain event is different than expected. In practice, the risk reveals itself in different ways in various applications such as unexpected stock movements in the area of portfolio management and unforeseen demand in the field of new product development. In this dissertation, we present four essays on data-driven risk management to address the uncertainty in portfolio management and capacity expansion problems via stochastic and robust optimization techniques.The third chapter of the dissertation (Portfolio Management with Quantile Constraints) introduces an iterative, data-driven approximation to a problem where the investor seeks to maximize the expected return of his/her portfolio subject to a quantile constraint, given historical realizations of the stock returns. Our approach involves solving a series of linear programming problems (thus) quickly solves the large scale problems. We compare its performance to that of methods commonly used in finance literature, such as fitting a Gaussian distribution to the returns. We also analyze the resulting efficient frontier and extend our approach to the case where portfolio risk is measured by the inter-quartile range of its return. Furthermore, we extend our modeling framework so that the solution calculates the corresponding conditional value at risk CVaR) value for the given quantile level.The fourth chapter (Portfolio Management with Moment Matching Approach) focuses on the problem where a manager, given a set of stocks to invest in, aims to minimize the probability of his/her portfolio return falling below a threshold while keeping the expected portfolio returnno worse than a target, when the stock returns are assumed to be Log-Normally distributed. This assumption, common in finance literature, creates computational difficulties. Because the portfolio return itself is difficult to estimate precisely. We thus approximate the portfolio re-turn distribution with a single Log-Normal random variable by the Fenton-Wilkinson method and investigate an iterative, data-driven approximation to the problem. We propose a two-stage solution approach, where the first stage requires solving a classic mean-variance optimization model, and the second step involves solving an unconstrained nonlinear problem with a smooth objective function. We test the performance of this approximation method and suggest an iterative calibration method to improve its accuracy. In addition, we compare the performance of the proposed method to that obtained by approximating the tail empirical distribution function to a Generalized Pareto Distribution, and extend our results to the design of basket options.The fifth chapter (New Product Launching Decisions with Robust Optimization) addresses the uncertainty that an innovative firm faces when a set of innovative products are planned to be launched a national market by help of a partner company for each innovative product. Theinnovative company investigates the optimal period to launch each product in the presence of the demand and partner offer response function uncertainties. The demand for the new product is modeled with the Bass Diffusion Model and the partner companies\u27 offer response functions are modeled with the logit choice model. The uncertainty on the parameters of the Bass Diffusion Model and the logic choice model are handled by robust optimization. We provide a tractable robust optimization framework to the problem which includes integer variables. In addition, weprovide an extension of the proposed approach where the innovative company has an option to reduce the size of the contract signed by the innovative firm and the partner firm for each product.In the sixth chapter (Log-Robust Portfolio Management with Factor Model), we investigate robust optimization models that address uncertainty for asset pricing and portfolio management. We use factor model to predict asset returns and treat randomness by a budget of uncertainty. We obtain a tractable robust model to maximize the wealth and gain theoretical insights into the optimal investment strategies

Improving urban deliveries via collaboration

Distribution of goods is essential for the economic development of cities but at the same time it entails several problems to the urban systems and different stakeholders. Carriers spend a significant portion of their cost in the last-mile distribution due to traffic congestion and lack of available loading/unloading facilities. In turn, citizens undergo environmental effects like pollution, noise or space competition. Collaborative transportation is currently one of the major trends in transportation research due to its potential benefits with little need for big infrastructure or costly investments. This thesis deals with three different situations that appear repeatedly in the urban context, which can be improved by means of collaboration among private companies and/or public authorities. The first part of the thesis studies a little-disruptive collaboration approach, which is based on sharing loading/unloading urban facilities via an in-advance booking system, managed by local public authorities. In this context, the Parking Slot Assignment Problem is the mathematical problem that finds assignments of carriers to parking places that satisfy their time windows requests. We propose a feasibility model first, and then four other models with various objective functions that penalize in different ways the deviation from the requested time windows. We propose and compare two different formulations: one with time as a continuous variable and a second one with time discretization. Finally, we evaluate and compare the different proposals with extensive computational experiments in a set of test instances. An intermediate level of collaboration among carriers is studied in the second part of this thesis. Urban areas have high customers density and usually there are shared customers (customers with demand from different carriers in the same time horizon). We propose an innovative problem: the Shared Customer Collaboration Vehicle Routing Problem, where several carriers are willing to collaborate transferring part of the demand of their shared customers, if the overall transportation cost is reduced. A vehicle-based and a load-based formulation are studied, and experimented over a specifically generated instance set. The highest level of collaboration in urban deliveries resorts to Urban Consolidation Centers, which are normally led by public authorities but need the collaboration of carriers for a successful implementation. Urban Consolidation Centers are urban terminals where the load from different carriers is consolidated and then, a unique neutral carrier performs last-mile deliveries. In the third part of the thesis we propose continuous models that analyze the improvement in efficiency of urban distribution with the use of Urban Consolidation Centers under different assumptions. Continuous approximation models are known to produce robust solutions, which are useful to provide guidelines for general cases through sensitive analysis. In the three parts of the thesis, innovative models and approaches are proposed and validated on experiments that use data from real scenarios.La distribució urbana de mercaderies és una activitat essencial pel desenvolupament de les ciutats. Al mateix temps, però, comporta diversos problemes als nuclis urbans i als diferents actors involucrats. Els costos de la distribució urbana resulten una part molt significativa dels costos dels transportistes, especialment a causa de la congestió i la manca de zones de càrrega i descàrrega. Per altre banda, els ciutadans pateixen els efectes de la pol¢lució, el soroll o la competició per l’espai públic. El transport col¢laboratiu és actualment una de les principals tendències de recerca en transport, doncs ofereix beneficis atractius amb poca inversió. Aquesta tesi tracta tres situacions que trobem repetidament en el context urbà, situacions on diverses formes de col¢laboració poden representar una millora, i que consideren tant col¢laboració entre empreses privades com la col·laboració conjunta d’empreses privades amb les administracions. La primera part de la tesi estudia un nivell de col·laboració baix, basat en compartir les zones de càrrega i descàrrega gràcies a un sistema de reserves gestionat per l’administració. En aquest context, sorgeix el Parking Slot Assignment Problem (Problema d’assignació de places de parking), com el problemamatemàtic que assigna transportistes a places de parking satisfent els seus requeriments a través de finestres temporals. En primer lloc proposem un model de factibilitat, i després proposem quatremodelsamb funcions objectius desiguals que penalitzen la desviació de les finestres temporals de formes diferents. Es proposen i comparen dues formulacions: una amb el temps com una variable contínua, i la segona amb discretització temporal. Finalment, s’avaluen i es comparen les diferents propostes a través d’uns extensos experiments computacionals en un conjunt de test basat en dades reals. Un nivell intermedi de col¢laboració entre transportistes s’analitza en la segona part d’aquesta tesi. Les àrees urbanes presenten una alta densitat de clients i és comú trobar clients compartits (és a dir, clients que reben mercaderies a través de diferents transportistes en el mateix interval temporal). Proposem un problema innovador: el Shared Customer Collaboration Vehicle Routing Problem (Problema de rutes de vehicles amb col·laboració de clients compartits), on diferents transportistes estan disposats a col¢laborar transferint part de la demanda dels seus clients compartits, si el cost total de transport es redueix. S’estudien dues formulacions: una basada en els vehicles i una altra basada en la càrrega, i s’experimenta en un conjunt d’instàncies generades. El màxim nivell de col¢laboració en distribució urbana de mercaderies és l’ús de centres de consolidació urbana. Aquests centres estan normalment liderats per l’administració pública però necessiten l’activa col·laboració dels transportistes per aconseguir una implantació amb èxit. Els centres de consolidació urbana són terminals urbanes on es consolida la càrrega dels diferents transportistes i després, un únic transportista neutral realitza la distribució d’última milla. En aquesta tercera part de la tesi proposem models continus que analitzen la millora de l’eficiència en la distribució urbana a través de l’ús de centres de consolidació urbana amb diferents hipòtesis. Els models continus produeixen solucions robustes, que són útils per proporcionar guies en casos genèrics a través de l’anàlisi de sensibilitat. En les tres parts de la tesi es proposen nous enfocs i models que es validen a través d’experiments utilitzant dades obtingudes d’escenaris realsLa distribución urbana de mercancías es una actividad esencial para el desarrollo de las ciudades, aunque al mismo tiempo conlleva diversos problemas en los núcleos urbanos y los distintos actores involucrados. Los costes de la distribución urbana resultan una parte muy significativa de los costes de los transportistas, especialmente a causa de la congestión y la falta de zonas de carga y descarga. Por otro lado, los ciudadanos sufren los efectos de la contaminación, el ruido y la competición por el espacio público. El transporte colaborativo es actualmente una de las principales tendencias en la investigación en transporte, pues ofrece beneficios atractivos con poca inversión. Esta tesis trata tres situaciones que se reproducen repetidamente en el contexto urbano, donde distintas formas de colaboración (tanto entre compañías privadas como con administraciones) pueden representar una mejora. La primera parte de la tesis estudia un nivel de colaboración bajo, basado en compartir las zonas de carga y descarga a través de un sistema de reservas gestionado por la administración. En este contexto surge el Parking Slot Assignment Problem (Problema de asignación de plazas de parking), como el problema matemático que asigna transportistas a plazas de parking satisfaciendo sus requerimientos a través de ventanas temporales. En primer lugar proponemos un modelo de factibilidad, y después cuatro modelos con funciones objetivo que penalizan la desviación de las ventanas temporales de formas distintas. Se proponen y comparan dos formulaciones: una con el tiempo como una variable continua, y la segunda con discretización temporal. Finalmente, se evalúa y compara las distintas propuestas a través de unos extensos experimentos computacionales en un conjunto de test basado en datos reales. Un nivel intermedio de colaboración entre transportistas se analiza en la segunda parte de esta tesis. Las áreas urbanas presentan una alta densidad de clientes, y es común encontrar clientes compartidos (es decir, clientes que reciben mercancías a través de distintos transportistas en el mismo intervalo temporal). Proponemos un problema innovador: el Shared Customer Collaboration Vehicle Routing Problem (Problema de rutas de vehículos con colaboración de clientes compartidos), donde los distintos transportistas están dispuestos a colaborar transfiriendo parte de la demanda de sus clientes compartidos, si el coste total del transporte se reduce. Estudiamos dos formulaciones: una basada en los vehículos y otra basada en la carga, y se experimenta en un conjunto de instancias generadas. El máximo nivel de colaboración en distribución urbana de mercancías es el uso de centros de consolidación urbana. Estos centros, normalmente liderados por la administración pública, necesitan la activa colaboración de los transportistas para conseguir una exitosa implantación. Se trata de terminales urbanas donde se consolida la carga de distintos transportistas y, después, un único transportista neutral realiza la distribución de última milla. En esta tercera parte de la tesis proponemos modelos continuos que analizan la mejora de la eficiencia en la distribución urbana a través del uso de centros de consolidación urbana con distintas hipótesis. Los modelos continuos producen soluciones robustas, que son útiles para proporcionar guías en casos genéricos a través del análisis de sensibilidad. En las tres partes de la tesis se proponen nuevos enfoques y modelos que se validan con experimentos utilizando datos obtenidos en escenarios reale