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http://hdl.handle.net/10609/85185
Title: Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation
Author: Chica Serrano, Manuel
Juan Pérez, Ángel Alejandro
Cordón García, Óscar
Kelton, David W.
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universidad de Granada
University of Cincinnati
Keywords: optimization
simheuristics
metaheuristics
simulation
uncertainty
Issue Date: Feb-2017
Publisher: SSRN
Citation: Chica, M., Juan, A.A., Cordón, Ó. & Kelton, D.W. (2017). Why simheuristics? Benefits, limitations, and best practices when combining metaheuristics with simulation. SSRN. doi: 10.2139/ssrn.2919208
Also see: http://dx.doi.org/10.2139/ssrn.2919208
Abstract: From smart cities to factories and business, many decision-making processes in our society involve NP-hard optimization problems. In a real environment, these problems are frequently large-scale, which limits the potential of exact optimization methods and justifies the use of metaheuristic algorithms in their resolution. Real-world problems are also distinguished by high levels of dynamism and uncertainty, which affect the formulation of the optimization model, its input data, and constraints. However, metaheuristic algorithms usually assume deterministic inputs and constraints, and thus end up solving oversimplified models of the real system being considered, casting doubt on validity and even meaning of the results and recommendations. Accordingly, this paper argues that approaches combining simulation with metaheuristics, i.e., simheuristics, not only constitute a natural extension of metaheuristics, but also should be considered as a 'first resort' method when dealing with large-scale stochastic optimization problems, which constitute most realistic problems in industry and business. To this end, this paper highlights the main benefits and limitations of these simheuristic algorithms, reviews some examples of applications to different fields, and analyzes the most suitable simulation paradigms to be used within a simheuristic. Finally, we outline a series of best practices to consider during the design and implementation stages of a simheuristic algorithm.
Language: English
URI: http://hdl.handle.net/10609/85185
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