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Elham Taghizadeh Wayne State University |
Simulation is a primary tool to model complex systems, especially when the typical analytical techniques are not available due to modeling assumption complexity. Simulation helps decision-makers to define various scenarios to test the influence of alternative pre-specified decisions on system performance. Because simulation cannot find and suggest optimal decisions for a complex system, it is integrated with optimization techniques such as exact, heuristic, and metaheuristic methods. This combination, a developing area of research in operations research, is called "simulation-based optimization" or "simulation-optimization". Decision-makers are facing unprecedented challenges with increasing complexity and faster innovation cycles in the industry. On the other hand, advanced simulation techniques and high computational power have expanded simulation-based optimization methodologies as a powerful tool. Simulation-based optimization techniques have been utilized to optimize environmental systems, communication networks, complex supply chain networks, healthcare systems, and energy systems. The first step in a simulation-based optimization algorithm includes system analysis, parameter setting, and data collection. The second step consists of selecting and applying a suitable optimization algorithm to find the optimal decisions.
Three areas in simulation-based optimization have demonstrated vast developments and still need improvement. The first development uses dynamic simulation and optimization techniques to run real-world case studies. In dynamic simulation-based optimization, a varying behavior of system parameters for different time or scenarios is defined. Then, in the optimization section, time-dependent decision variables will be added to the model with analytical dynamics. Advancement in modeling and high-speed computational power has enabled engineering, and business research to involve dynamic simulation optimization in various operation research and management science topics such as supply chain, logistics, healthcare system, and transportation [1,3]. Dynamic simulation-based optimization has been addressed on large scale urban transportation and supply networks with time-dependent continuous decision variables to provide more practical scenarios that require high accuracy of solution [3]. For instance, a decision support system has been developed by performing a dynamic simulation to model water distribution system contamination and dynamic optimization to track time-varying optimal response protocols [12]. The dynamic simulation has been utilized to develop model complexity and enriches the optimization solution's accuracy by providing time-dependent variables. In healthcare research, dynamic simulation-based optimization has been used to optimize staff reallocation at an emergency medical center with arbitrary structures located in Austria. The results demonstrate a 7% system performance improvement [13].
Another enhancement is applying metaheuristic optimization algorithms. Various metaheuristics such as local search, random search, and simulated annealing, have been deployed to solve large-scale dynamic problems and find the local or near-global optimum solution within a reasonable time. Metaheuristics are also required when decision-makers or managers are willing to run simulations to optimize a complex multi-objective system. These objectives usually involve trade offs (e.g., increase service level by reducing cost), and their solution is not unique. For instance, multi-objective simulation-based optimization with an efficient heuristic algorithm has been employed in a residential area in the Italian city of Naples. The results show up to 56% cost reduction and around 20% energy transition improvement [4]. In a real case study, the decision tree and tabu search have been proposed for dispatching rules for shopping by Shahzad and Mebarki (2016). They represent metamodels that can generate similar results to the real simulation within a very reasonable time [5]. Integrating metaheuristic algorithms with simulation-based optimization has also been proposed for scheduling and production problems because decision-makers are dealing with two opposite objectives, which reduce cost and increase outputs [16]. Therefore, this integration can turn into a practical tool for managers and decision-makers.
The final development is employing machine learning to enrich simulation-optimization related techniques. Operation researchers' attention has been driven to employing modern methodologies, such as data mining, artificial intelligence, and machine learning, that can offer more tactical techniques to tackle complexity and challenges. A combination of Artificial Intelligence/Machine Learning (AI/ML), simulation and optimization helps industries enhance understanding of problems and make smarter and faster decisions. Especially, using AI/ML is a critical opportunity in simulation-based optimization to create metamodels that can deliver valuable insights into large scale and complex systems, such as using artificial intelligence in a material handling system [6]. Growing trends in Industry 4.0 make machine learning an appropriate tool to solve various manufacturing prediction problems [7]. The world's most innovative companies and industrial consultants bring up AI/ML to find the best optimal solutions for complex systems [8,9]. The most widely-known simulation-based optimization commercial tools, such as AnyLogic, are spearheading these changes and creating a new generation of simulation models by adding machine learning technology [10]. Many optimal models are computationally expensive due to system complexity, while the current AI/ML can solve such a problem efficiently to achieve better results. For instance, IBM combines the AI/ML with simulation-based optimization techniques and a new platform to overcome the lack of transaction and inventory visibility, which creates a lot of challenges and costs for industries [14].
To conclude, future simulation-based optimization studies should endeavor to investigate these developments and optional improvements. Simulation-based optimization is now recognized as a powerful tool for academics and industry to obtain the best solutions to challenging problems. Therefore, applying machine learning methods and heuristic algorithms to optimize solutions and improving the simulation phase by introducing dynamic parameters is particularly critical.
References:
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