Simheuristics: An Introductory Tutorial

Abstract

Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed. INTRODUCTIONSince uncertainty is present in most real-life systems, simulation methods are frequently employed to analyze complex systems in industries such as manufacturing and production logistics, transportation, health care, finance, smart cities, and telecommunication. Usually, the uncertainty in these systems is modeled via probability distributions, either theoretical or empirical ones. Then, a logical model of the system is built, and computer simulation is utilized to get insight on the system performance under different scenarios, each of which represent a possible system configuration. As pointed out by some authors, the advances in computer power and simulation software have transformed simulation into a 'first-resource' method for analyzing complex systems under uncertainty (Lucas et al. 2015).Still, simulation by itself is not an optimization tool, since it does not contain a mechanism to efficiently explore the vast solution spaces that arise in combinatorial optimization problems. Therefore, if our goal is

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