Why Simheuristics? Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation

25 Pages Posted: 17 Feb 2017

See all articles by Manuel Chica

Manuel Chica

Open University of Catalunya (UOC) (Open University of Catalonia) - Internet Interdisciplinary Institute (IN3); The University of Newcastle, Australia

Angel A. Juan Pérez

Open University of Catalunya (UOC) (Open University of Catalonia) - Internet Interdisciplinary Institute (IN3)

Oscar Cordon

University of Granada

David Kelton

University of Cincinnati

Date Written: January 1, 2017

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.

Keywords: Optimization, Simheuristics, Metaheuristics, Simulation, Uncertainty

Suggested Citation

Chica, Manuel and Juan Pérez, Angel A. and Cordon, Oscar and Kelton, David, Why Simheuristics? Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation (January 1, 2017). Available at SSRN: https://ssrn.com/abstract=2919208 or http://dx.doi.org/10.2139/ssrn.2919208

Manuel Chica (Contact Author)

Open University of Catalunya (UOC) (Open University of Catalonia) - Internet Interdisciplinary Institute (IN3) ( email )

Barcelona
Spain

The University of Newcastle, Australia ( email )

University Drive
Callaghan, NSW 2308
Australia

HOME PAGE: http://www.manuchise.com

Angel A. Juan Pérez

Open University of Catalunya (UOC) (Open University of Catalonia) - Internet Interdisciplinary Institute (IN3)

Barcelona
Spain

Oscar Cordon

University of Granada ( email )

C/Rector López Argueta S/N
Granada, Granada 18071
Spain

David Kelton

University of Cincinnati ( email )

Cincinnati, OH 45221-0389
United States

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