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

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 "firstresort" 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.

Bi-Level Model on Freight Distribution Network Integration in Archipelagic Region with Milk Run Time Windows and Uncertainty

“…The currently developing network integration of freight distribution in sea transport is stochastic and real time data, where system will adjust with real condition on the field. A research by Fu et al [19] regarding simulasion based optimization (SBO) is a decent stochastic programming to solve stochastic problem that is used further by Oliveira and Montevechi [20] and Chica et al [21]. A paper by Layeb et al [22] developed stochastic programming simulation by simplifying uncertainty variables to achieve on time full delivery (OTFD) of 90%.…”

Benefits of robust multiobjective optimization for flexible automotive assembly line balancing

“…Future works may focus on adding the current robust EMO algorithms and models with more realistic industrial features such as ergonomic factors (Bautista et al 2016). Furthermore, and although we have considered uncertain demand in our case study, the use of more advanced simulationoptimization approaches such as simheuristics (Juan et al 2016, Chica et al 2017 could promote the integration of simulation techniques within the optimization procedure. Additionally, visualization processes to enhance the decision making process are, in our opinion, another important and promising line in the area.…”

The Role of Simulation and Serious Games in Teaching Concepts on Circular Economy and Sustainable Energy

“…Another work where simheuristics are used to design efficient distribution plans under uncertainty scenarios, while considering monetary, environmental, and social criteria, can be found in Reyes-Rubiano et al [79]. A recent review on simheuristics is provided by Chica et al [80].…”