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Data-Driven Simulation-Optimization (DSO): An Efficient Approach to Optimize Simulation Models with Databases

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Optimization and Learning (OLA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1684))

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Abstract

Simulation-optimization is instrumental to solve stochastic problems with complexity. Over the past half-century, simulation-optimization methods have progressed theoretically and methodologically across different disciplines. The majority of commercial simulation packages - to some degree - offer an optimizer that allows decision-makers to conveniently determine an optimal or near-optimal system design. With the latest advancements in simulation techniques, such as data-driven modeling and Digital Twins, optimizer platforms need a redesign to include new capabilities. This paper proposes a Data-driven Simulation-Optimization (DSO) platform to narrow this gap. By considering data-tables as a decision variable (control), DSO can systematically generate new tables, run experiments, and determine the best table entries to optimize the model. To implement DSO, three software packages (MATLAB, Simio, and MS Excel) are integrated via a customized coded interface, called Simio-API. The applicability of this Simulation-optimization tool is tested in two experimental settings to evaluate its effectiveness and provide some insights for future extensions. The DSO initial results are promising and should stimulate further research in academia and industry.

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Author information

Authors and Affiliations

  1. Mechanical and Industrial Engineering Department, Northeastern University, Boston, MA, USA

    Mohammad Dehghanimohammadabadi

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  1. Mohammad Dehghanimohammadabadi
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Correspondence to Mohammad Dehghanimohammadabadi .

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Paris-Est, Paris, France

    Amir Nakib

  4. Université de Lille, Lille, France

    El-Ghazali Talbi

Appendix A

Appendix A

Particle Swarm Optimization or PSO is a population-based Metaheuristic algorithm developed by Kennedy and Eberhart in 1995 [27] PSO is a swarm-based algorithm and by moving particles in a specific exploration field [28]. Due to its effective balancing of exploration and exploitation [29], PSO has been widely used in the development of Simheuristic models and in solving SO problems. Recent examples include using PSO to deal with stochastic models in supply chain management [30], healthcare systems [31], and manufacturing [32]. The general pseudocode of PSO is shown in Algorithm 2.

figure b

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Dehghanimohammadabadi, M. (2022). Data-Driven Simulation-Optimization (DSO): An Efficient Approach to Optimize Simulation Models with Databases. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-22039-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22038-8

  • Online ISBN: 978-3-031-22039-5

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