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Optimization and Engineering
Optim Eng
ISSN (printed): 1389-4420. ISSN (electronic): 1573-2924.
Optimization and Engineering is a multidisciplinary journal. Its primary goal is to promote the application of optimization methods in the general area of engineering sciences. This includes facilitating the development of advanced optimization methods sciences for direct or indirect use in engineering sciences. The journal provides a forum in which engineering scientists obtain information about recent advances of optimization sciences and researchers in mathematical optimization learn about the needs of engineering sciences and successful applications of optimization methods. Its aim is to close the gap between optimization theory and the practice of engineering. All optimization methods of relevance to applications in engineering sciences will be considered: deterministic and stochastic continuous mixed integer and discrete when they are relevant to applications in engineering sciences. The journal also strives to publish successful applications of optimization in various engineering areas. Topics of Interest: Optimization: All mathematical methods and algorithms of mathematical optimization. Numerical and implementation issues optimization software benchmarking case studies. Specifically: linear and convex optimization general nonlinear and nonlinear mixed-integer optimization combinatorial optimization equilibrium multilevel and multiobjective optimization stochastic optimization. Engineering Sciences: Electrical engineering VLSI design robotics mechanical and structural engineering geophysical engineering civil engineering industrial engineering chemical and process engineering aerospace engineering water management environmental and bioengineering transportation and communication sciences. Education: The goal of the education section is to promote understanding and appreciation of optimization theory and techniques. The Education section will publish material aimed at educating students engineers managers and potential users of optimization methodology. All submitted articles should be written at a level appropriate readers without a strong background in optimization.
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