Unveiling Hidden Values of Optimization Models with Metaheuristic Approach

Considering that the decision making process for constrained optimization problem is based on modeling, there is always room for alternative solutions because there is usually a gap between the model and the real problem it depicts. This study looks into the problem of finding such alternative solutions, the non-optimal solutions of interest for constrained optimization models, the SoI problem. SoI problems subsume finding feasible solutions of interest (FoIs) and infeasible solutions of interest (IoIs). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making come into play and for this purpose the SoIs can be very valuable. An evolutionary computation approach (in particular, a population-based metaheuristic) is proposed for solving the SoI problem and a systematic approach with a feasible-infeasible- two-population genetic algorithm is demonstrated. In this study, the effectiveness of the proposed approach on finding SoIs is demonstrated with generalized assignment problems and generalized quadratic assignment problems. Also, the applications of the proposed approach on the multi-objective optimization and robust-optimization issues are examined and illustrated with two-sided matching problems and flowshop scheduling problems respectively

Portfolio optimization and bidding valuation methodology for combinatorial auctions of new power plants

Orientador: Paulo de Barros CorreiaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Leilões são empregados na comercialização de energia elétrica brasileira por se tratarem de mecanismos rápidos, seguros e eficientes para a alocação de bens. O objetivo do trabalho é sugerir uma possível implementação de um leilão combinatório como um mecanismo de investimento em novos empreendimentos de geração na exploração das complementaridades entre as fontes. A partir da visão de um participante do mecanismo, uma otimização de portfólio com diferentes empreendimentos de geração de energia é realizada na busca da combinação que minimize os riscos de exposição do agente ao PLD. Escolhido o portfólio ótimo, uma avaliação de lances é feita através do método de Monte Carlo na busca da distribuição dos lances prováveis no jogo, de forma a embasar a tomada de decisão do participante na competição contra seus adversários. A combinação de fontes complementares contribui na diminuição dos riscos de exposição ao preço spot, atuando principalmente numa melhora do valor de lances para agentes avessos ao risco relacionado às oscilações de preços do mercadoAbstract: Auctions are used in the Brazilian electricity market for being a rapid, safe and efficient method for allocation of goods. The objective of this work is to suggest a possible implementation of a combinatorial auction as a mechanism for investment in new generation projects by the exploitation of the existing complementarities between sources. From the perspective of a participant of the mechanism, a portfolio optimization with power plants of different sources is performed searching the combination of assets that minimizes the risks of exposure of the agent to the spot prices. After choosing the optimal portfolio, an evaluation of bids is done using the Monte Carlo method in order to find the distribution of bids that is likely to happen during the game in order to base the decision making of the participant in the competition against their opponents. The combination of complementary sources helps to reduce risks of exposure to the spot prices, working mainly in improved value bids for risk averse agents concerned with the market prices fluctuationsMestradoPlanejamento de Sistemas EnergeticosMestra em Planejamento de Sistemas Energético

An Economic Framework For Resource Management And Pricing In Wireless Networks With Competitive Service Providers

A paradigm shift from static spectrum allocation to dynamic spectrum access (DSA) is becoming a reality due to the recent advances in cognitive radio, wide band spectrum sensing, and network aware real--time spectrum access. It is believed that DSA will allow wireless service providers (WSPs) the opportunity to dynamically access spectrum bands as and when they need it. Moreover, due to the presence of multiple WSPs in a region, it is anticipated that dynamic service pricing would be offered that will allow the end-users to move from long-term service contracts to more flexible short-term service models. In this research, we develop a unified economic framework to analyze the trading system comprising two components: i) spectrum owner--WSPs interactions with regard to dynamic spectrum allocation, and ii) WSP--end-users interactions with regard to dynamic service pricing. For spectrum owner--WSPs interaction, we investigate various auction mechanisms for finding bidding strategies of WSPs and revenue generated by the spectrum owner. We show that sequential bidding provides better result than the concurrent bidding when WSPs are constrained to at most single unit allocation. On the other hand, when the bidders request for multiple units, (i.e., they are not restricted by allocation constraints) synchronous auction mechanism proves to be beneficial than asynchronous auctions. In this regard, we propose a winner determination sealed-bid knapsack auction mechanism that dynamically allocates spectrum to the WSPs based on their bids. As far as dynamic service pricing is concerned, we use game theory to capture the conflict of interest between WSPs and end--users, both of whom try to maximize their respective net utilities. We deviate from the traditional per--service static pricing towards a more dynamic model where the WSPs might change the price of a service almost on a session by session basis. Users, on the other hand, have the freedom to choose their WSP based on the price offered. It is found that in such a greedy and non-cooperative behavioral game model, it is in the best interest of the WSPs to adhere to a price threshold which is a consequence of a price (Nash) equilibrium. We conducted extensive simulation experiments, the results of which show that the proposed auction model entices WSPs to participate in the auction, makes optimal use of the common spectrum pool, and avoids collusion among WSPs. We also demonstrate how pricing can be used as an effective tool for providing incentives to the WSPs to upgrade their network resources and offer better services

Combinatorial Auctions and Knapsack Problems

This note summarizes the connection between winner determination problems (WDPs) in multi-unit combinatorial exchanges and generalized knapsack problems (KPs); see [2] for an extensive treatment. Taxonomies of sealedbid auction WDPs and of KPs align precisely, i.e., the two classes of problems are identical. The relationship between KPs and WDPs has received remarkably little attention in E-commerce and multi-agent systems literature. However the WDP-KP connection is important because it allows us to leverage a vast body of Operations Research literature for developing and benchmarking WDP solvers. For low-dimensional multi-unit CA WDPs (i.e., few types of goods), extremely simple dynamic-programming algorithms are available whose time and memory requirements are linear (in the pseudo-polynomial sense) in three out of four natural measures of problem size