A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems

By Angel A. Juan, Javier Faulin, Scott E. Grasman, M. Rabe, Gonçalo Figueira. 2015

422 citations recorded by the source corpus.

Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology.

Subfields: Industrial and Manufacturing Engineering, Management Science and Operations Research, Software

View Paper: https://www.semanticscholar.org/paper/ab48762a0df0318c818887805a50f316fb11edaa

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In Industrial and Manufacturing Engineering

In Management Science and Operations Research

Citations in the corpus

A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “From an optimization perspective, the use of simulators for problem solving (simulation-based optimization or simheuristics [142]) constitutes a straightforward approach to circumvent an issue that appears concurrently in real-world problems: the impossibility of formulating objective functions and…”