Hybrid Approaches Combining Metaheuristics and Methods of Mathematical Analysis for Environmental Unit Commitment Pro

1. Programme Gaspard Monge pour l'Optimisation et la Recherche Opérationnelle – 3 et 4 octobre 2013 Hybrid Approaches They combine relaxation, heuristics, metaheuristics and exact methods to provide efficient methods for hard problems. Hard Optimization Problem f* = Max{f(x) : xX E} Hard OptimizationProblems NP-hard problems are oftentackled in fieldssuch as mathematics, statistics, computer science, physics, engineering, economics, and social sciences to solve real-world business problemsappearing in Airline, Telecommunications, Manufacturing, Healthcare, Scheduling, Planning, Data mining , Transportation and Energy. Hybrid Approaches Combining Metaheuristics and Methods of Mathematical Analysis for Environmental Unit CommitmentProblem • Enviromental aspects • Majorpolluters of the environment are power plants • Globally, more than 70% of power plants use fossil fuels such as coal, naturalgas and oil • After combustion more CO2 is produced than fuel is used • Almost all countries enforce certain environmental penalties • In some countries CO2 taxes are more expensive than the fuel itself Unit Commitment Problem • Unit commitment Problem (UCP) is a well-known combinatorial optimizationproblem. It consists of determining an optimal production plan for a given setof power plants over a given time horizon so that the total production cost isminimized, while various constraints are satisfied. • The constraints that must be respected are: • Unit power generation limits - upper and lower bounds of production for each unit • Load balance - the total production of all active plants must satisfy the required demand in each time period • Spinning reserve constraints • Minimum up/down time constraints – minimal number of consecutive time periods during which units must be turned on/off Environmental Unit Commitment Problem • Proposed Animations • Optimization Seminars • - Seminar Organization • Debates between participants and speakers driven by the chairman • Real time synthesis to precise developments, complements and needs • - Structure of Half Day Seminars • Presentation of the basics focused on the area and recent developments • Discussion to define links between other research fields and synthesis to precise needs • - Main Topics • Hybrid Approaches in Combinatorial Optimization • Some Goals • Effective methods for bi-objective problem that makes balance between supply and demand. • Hybrid approaches combining exact methods, heuristics and relaxations for solving resource allocation and scheduling problems. • Variable neighborhood search for the problem of mobilization of production units, taking into account both economic and ecological aspects. • Continuous Reflection on Education and Profession • Methodological issues of optimization • Key points on mastering complexity for both methods andformulation • Optimization in concrete situations and link between the numerical and the realworld • Improvement of the link by common vocabulary and concepts on optimization • References • F. Glover, S. Hanafi (2010). Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization. International Journal of Applied Metaheuristic Computing. • S. Hanafi, C. Wilbaut. (2011). Improved Convergent Heuristic for the 0-1 Multidimensional Knapsack Problem. Annals of Operations Research. • J. Lazić, S. Hanafi, N. Mladenović, D. Urošević (2010). Variable Neighbourhood Decomposition Search for 0-1 Mixed Integer Programs. Computers and Operations Research. • M. Vasquez, Y. Vimont (2005). Improvedresults on the 0-1 Multi DimensionalKnapsackproblem. European Journal of OperationalResearch. • R. Todosijevic, M. Mladenovic, S. Hanafi, I. Crévits (2012). VNS based heuristic for solving the Unit Commitment problem. Electronic Notes in Discrete Mathematics. • Y.W.Jeong, J.B. Park, S.H. Jang, K. Lee(2010).A new quantum inspired binarypso: Application to unit commitment problems for power systems.IEEE TransPower Systems. • P. Hansen, N. Mladenovic, J.A. Moreno-Perez (2010). Variable neighbourhood search: methods and applications.Annals of Operation Research.