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SA04 3 - Mathematical Models Implemented for the Propagation and Mitigation of the Avian Influenza Virus (H5N1) in Colombia Diana Ramirez, Scientific Director, Fundacion Centro de Investigacion en Modelacion Empresarial del Caribe, Carrera 53 No. 79 - 315 L202, Barranquilla, Colombia, dramirez@fcimec.org Given the recent influenza pandemic outbreak, the World Health Organization has been even more worried about the strategies that the countries have been implementing in order to control the spread of the disease. Also, they have been alerting about the coming of a new pandemic outbreak of the avian influenza (H5N1) among humans, which is said to be even more dangerous. Mathematical models have been studied, for decades now, on the propagation of every influenza virus, yet each country presents its own reality. This research is based on the implementation of these models in Colombia, not only to model the propagation of the disease within the country but also for an optimal implementation of the control and mitigation strategies needed for a future outbreak of this disease. 4 - Discrete Event Simulation to Determine when Performing a Breast Cancer Biopsy is Cost-effective John Jairo Rios Rodriguez, Universidad de los Andes, Cra 1 Este # 19A-40 Ed. Mario Laserna, Bogotá, Colombia, joh-rios@uniandes.edu.co, Mario Castillo This work develops a Discrete Event Simulation model programmed in Arena® to evaluate the appropriate moment to perform a biopsy to women who are at risk of developing breast cancer. The model follows women’s disease progression and performs a cost-effectiveness analysis. Disease progression parameters were obtained from published data; only direct medical costs were considered and were based on local information. 5 - A Tool for Management of the Patient-visit Network within a Chilean Public Health Service Mario Tarride, University of Santiago, Chile, Industrial Engineering Department, 3769, Ecuador Avenue, Santiago, Chile, mario.tarride@usach.cl, Oscar C. Vasquez, Julia Gonzalez, Tamara Riquelme, Gisel Lagos This paper presents a tool for management of the patient-visit network within a Chilean public health service based on the simulation of system. This is developed in Powersim Studio 2005, management requirements according to the Chilean’s Law are certificated and its validation performance is realized. Results show determination coefficients upper to 0.8 and average simulation time of 20 minutes. In addition, management scenarios to reduce the patient-visit queue are simulated and analyzed. ■ SA04 Aula 372- Third Floor OR Applications I Contributed Session Chair: Elias D. Niño, Professor, Universidad del Norte, KM5 Via Puerto Colombia, Barranquilla, Colombia, enino@uninorte.edu.co 1 - Multiobjective Optimization Algorithm with Self-adaptation for Scheduling Problems Jaime Mora-Vargas, Head Graduate Program in Industrial Engineering, Tec de Monterrey, Carr. Lago de Guadalupe km 3.5, Col. Margarita Maza de Juárez, Atizapán de Zaragoza, 52926, Mexico, jmora@itesm.mx, Nestor Velasco-Bermeo, Miguel Gonzalez-Mendoza It is well known that the computational complexity of scheduling problems has been determined to be NP-hard and the a different scenario solution is a basic consideration in order to solve a problem with its most realistic modeling. Although this paper is limited to analyze a bi-objective objective function the results obtained show the effect a self-adaptation module has on the evolution and the improved results over a MOGA with a Local Search strategy implemented and a plain MOGA. 2 - Optimization of Infomation Exchange in Distributed Decision Networks Hela Masri, Larodec Laboratory, Institut Superieur de Gestion, University of Tunis, 41 Rue de la Liberte, Le Bardo, 2000, Tunisia, masri_hela@yahoo.fr, Adel Guitouni, Saoussen Krichen A decision network is a set of interconnected nodes through heterogeneous edges. Nodes are information provider, information consumer or information relay. We propose a nonlinear integer mathematical program to optimize the multiple quickest paths between multiple nodes. Each edge is has a capacity, a transmission delay and a cost. The decision problem is to optimize information exchange. Solution include the quickest transmission paths and scheduling of the information transmission. <strong>ALIO</strong> / INFORMS International – 2010 22 3 - A Mathematical Programming Model Applied to the Optimization of Cleaning Procedures in a Sunlight Collector Field Pedro L. González-R, PhD, Escuela Superior de Ingenieros. Universidad de Sevilla, C/Camino de los Descubrimientos, s/n, Sevilla, Spain, pedroluis@esi.us.es, David Canca Nowadays, renewable energy, and more specifically those based on solar energy, are a clear alternative to the fossil fuel energy. Many solar energy plants are opening throughout the world. Procedures for increasing these solar plants efficiency pass through an appropriate policy in cleaning and maintaining processes. However, these aspects are not usually taken into account in the actual design of plants. In this work a mathematical model is introduced in order to address this issue. 4 - A New Metaheuristic Based on Deterministic Finite Automaton for Multi-objective Optimization of Combinatorial Problems Elias D. Niño, Professor, Universidad del Norte, KM5 Via Puerto Colombia, Barranquilla, Colombia, enino@uninorte.edu.co, Carlos J. Ardila, Carlos Paternina-Arboleda In this paper we state a metaheuristic based on Deterministic Finite Automaton (DFA) for multi-objective optimization of combinatorial problems. First, we propose a new DFA based on Swapping (DFAS). DFAS allows the representation of feasible solutions space for combinatorial problems. Last, we define an algorithm that works with DFAS, it is named Exchange Deterministic Algorithm (EDA). The results of EDA were compared with the results of a Pareto Local Search (PLS). ■ SA05 Velez Sarfield- Second Floor Retail Pricing and Inventory Decisions: Theory and Applications Cluster: Revenue Management and Pricing Invited Session Chair: Juan-Carlos Ferrer, Associate Professor, Pontificia Universidad Católica de Chile, Casilla 06 Correo 22 Santiago, Escuela de Ingeniería, Santiago, RM, Chile, jferrer@ing.puc.cl 1 - A Multi-period Competitive Model of Retail Revenue Management Pedro León, Research Engineer, Pricing-UC, Casilla 306 Correo 22, Santiago, RM, Chile, pleon@pricing.uc.cl, Juan-Carlos Ferrer, Hugo Mora This paper analyzes a multi-period stochastic retail revenue management problem considering competition. An optimal pricing policy is obtained using a game theoretic approach combined with dynamic programming, for periodic revisions in a non-cooperative duopoly market where two firms competing with substitutable perishable products have perfect information on the competitor’s inventory level at all times. Managerial insights are provided to understand the impact of competition. 2 - On Pricing of Retail Products with Demand Learning: A Real Application Eduardo Flores, Research Engineer, Pricing-UC, Casilla 306 Correo 22, Santiago, RM, Chile, eflores@pricing.uc.cl, Juan-Carlos Ferrer We address the pricing problem of a retailer offering a fixed inventory of products in a finite sales horizon. The demand is characterized by a Poisson process with an uncertain arrival rate. The retailer may choose to learn about that rate throughout the season. Computational results indicate that incorporating learning into the pricing decision produces a significant increase in expected revenues. An empirical study in a Chilean retail chain supports the results of this research. 3 - Risk Averse Retail Pricing with Robust Demand Forecasting Diego Oyarzún, Research Engineer, Pricing-UC, Casilla 306 Correo 22, Santiago, RM, Chile, doyarzun@pricing.uc.cl, Juan-Carlos Ferrer, Jorge Vera Good demand estimates are the key to an effective pricing decision making. However, they are subject to high uncertainty levels due to various factors that are unpredictable or difficult to model, and pricing decisions are therefore risky. Uncertainty is explicitly considered for two coefficients of both a linear and an exponential demand function, price expressions are derived and a criterion is proposed for defining the degree of risk aversion. 4 - Effective Inventory Management in a Pharmaceutical Retail Chain Juan-Carlos Ferrer, Associate Professor, Pontificia Universidad Católica de Chile, Casilla 06 Correo 22 Santiago, Escuela de Ingeniería, Santiago, RM, Chile, jferrer@ing.puc.cl, Pedro León, Diego Oyarzún We present an application of demand forecasting models to significantly reduce stock-outs in the pharmaceutical industry, where revenues are low and extremely variable at a SKU/store level. The trade-off between lost sales and inventory is modeled. Promotions, lead-times, fill rates and replenishment policies are taken into account. The model keeps a balanced and coordinated inventory flow between the distribution center and the 310 stores. We conclude presenting the implementation results.
■ SA06 Aula 352- Third Floor Electrical Markets Contributed Session Chair: Tiago Filomena, PhD Student, George Washington University, 1776 G Street NW, Washington, DC, 20005, United States of America, tiagopf@gwu.edu 1 - The Colombian Wholesale Electricity Market View from a Minimum Cost Optimization Software Jose Eduardo Quintero Restrepo, Ingeniero Electrico, Superintendencia de Servicios Publicos, cra 18 No. 84-35, cll 168A no. 62-86, Bogotá, Colombia, jequintero@superservicios.gov.co In this paper, the reader can find an analysis about the behavior of the Colombian wholesale market. It was based on the results obtained with the implementation of a Dispatch Optimization Model Interconnected Electric System (MODSEI). It is determined how close is the behavior of Colombian wholesale electricity market to a market of perfect competition, and it is determined how accurate can be the utilization of a minimum cost software to analyze a competitive market. 2 - Competition in Electricity Markets under Emission Caps Enrique Campos-Nanez, Assistant Professor, George Washington University, 1776 G Street NW, Washington, DC, 20005, United States of America, ecamposn@gwu.edu, Tiago Filomena We study deregulated electricity markets where firms are subject to emission constraints. We characterize the equilibrium generation for firms with asymmetric costs. Our result shows that under emission constraints will concentrate the use of emission allowances in high-demand low-risk periods. The result is used to understand socially optimal allocation of permits. 3 - Market Power in a Power Market with Transmission Constraints Victoria Gribkovskaia, Research Scholar (PhD), Norwegian School of Economics and Business Administration, Helleveien 30, Bergen, 5045, Norway, victoria.gribkovskaia@nhh.no, Mette Björndal, Kurt Jörnsten We present a model for analysing strategic behaviour of a generator and its shortrun implications on an electricity network with transmission constraints. The problem is formulated as a Stackelberg leader-follower game. Generator maximises its profit subject to the solution of optimal power flow problem by the system operator. Numerical examples show effects of strategic bidding and inclusion of irrelevant constraints on prices, production and social welfare. 4 - A Statistical Tool to Identify Potential Clients to Make use of a Thermo Accumulation Scheme Reinaldo Souza, PhD, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), DEE. Rua Marquís de São Vicente, 225, Rio de Janeiro, RJ, 22451-041, Brazil, reinaldo@ele.puc-rio.br, Jose Aguinaldo Pinho, Fernanda Particelli, Mauricio Frota, Patricia Queiroz, Oraldo D’Avila This paper shows the use of a new approach to identify utility clients to use in their premises a thermo accumulation scheme, whereby energy, in its thermal form is produced during the low energy cost periods and storage as ice or cold water, to be used later, during peak hours, where the costs are higher, to produce cool . This way, the client not only produces a continuous load shape curve, but also rationalizes its use of energy resulting in a reduction of his electricity bill. 5 - Technology Portfolio and Capacity Investments under Emissions Cost Uncertainty Tiago Filomena, PhD Student, George Washington University, 1776 G Street NW, Washington, DC, 20005, United States of America, tiagopf@gwu.edu, Michael Duffey, Enrique Campos-Nanez We develop a model which helps to understand the impact of emissions policies in electricity generation investment behavior. The model is based on a Nash-Cournot open-loop dynamic optimization and the emissions cost is considered as a stochastic parameter. The model helps to forecast the portfolio of technologies, capacity availability and electricity clearing price for an oligopoly under emissions cost uncertainty. We also present a reformulation with stochastic demand and cost simultaneously. <strong>ALIO</strong> / INFORMS International – 2010 SA07 23 ■ SA07 Aula 353- Third Floor Multiobjective Scheduling Contributed Session Chair: Mariano Frutos, Universidad Nacional del Sur, Av. Alem 1253, Bahía Blanca, B8000CPB, Argentina, mfrutos@uns.edu.ar 1 - Multi-objective Assignment and Scheduling of Heterogeneous Surveillance Assets Adel Guitouni, Professor, C2 Decision Support System Section, DRDC, 2459 Pie-XI North, Quebec, QC, G3J 1X5, Canada, adel.guitouni@drdc-rddc.gc.ca, Olfa Dridi, Saoussen Krichen We propose a multi-objective optimization to schedule and assignment heterogeneous surveillance assets to surveillance tasks in a continuous and connected space. Surveillance tasks are characterized by their dependencies, priorities and time and space constraints. Assets require transition time between tasks and release time. The objectives include minimizing the mission makespan, total cost and maximizing the probability of success. A meta-heuristic is proposed to solve the problem. 2 - Multiobjective Operating Room Scheduling using Simulation Optimization Felipe Baesler, Industrial Engineering Professor, Universidad del Desarrollo, Ainavillo 456, Concepcion, Chile, fbaesler@udd.cl, Juan Pedro Sepúlveda This paper presents an approach for operating room scheduling considering two main conflicting objectives. The approach uses discrete simulation to capture the resources randomness involved in the patient flow process. The model interacts with a metaheuristic that searches for better schedules. Pareto frontiers for different experiments were constructed using the e-constraint technique and showed that the scheduling approach improves the hospital performance in comparison to the current method. 3 - A Multi-objective Hybrid Genetic Algorithm for Solving an Invoice Printing Scheduling Problem Yan Zhang, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, 710049, China, yanerxh@gmail.com, Zhili Zhou This paper considers a multi-objective order scheduling problem in an invoice printing system. The objectives considered are the minimization of the makespan, the average flowtime, the maximum tardiness and the average tardiness. A multiobjective hybrid genetic algorithm is proposed and numerical experiment indicates that the algorithm is efficient and effective. 4 - Bi-objective Heuristic Algorithm to Solve the Resource-constrained Project Scheduling Problem Juan Carlos Rivera Agudelo, Eafit University, Cr 49 No. 7 sur - 50, Bl. 38, of. 424, Medellín, Colombia, jrivera6@eafit.edu.co, Maria Clara Giraldo Rico Resource-Constrained Project Scheduling Problem, RCPSP, is a NP-Hard combinatorial optimization problem, which consists of assigning start times to a series of activities with fixed duration. Activities have precedence and resource constraints for their execution. This research proposes a GA-based algorithm to solve the bi-objective version, in which makespan minimization and robustness maximization criteria are considered. Comparisons are made using benchmark instances from the PSPLIB library. 5 - A Study of The Flexible Job Shop Scheduling Problem with Memetic Algorithms Mariano Frutos, Universidad Nacional del Sur, Av. Alem 1253, Bahía Blanca, B8000CPB, Argentina, mfrutos@uns.edu.ar, Fernando Tohmé, Gabriel Winter Althaus While the theoretical solutions of Multi-objective Optimization Problems are Pareto optimal, the actual solutions of these problems, due to the many sources of complexity that hamper them, tend to exhibit a clear lack of efficiency. This particular problem has been addressed in the last decades by means of the successful application of techniques based on GAs (MOGA, NPGAII, NSGAII, SPEAII, PAES). In this paper we assess these techniques in the framework of Job-Shop Scheduling Problems.
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methods can reduce the inference ex
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Gay, David M. TB04 Gazzola, Rosaura
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