Dear Colleagues,

Simulation optimisation has attracted a great deal of attention from researchers and practitioners in a wide range of real-world applications such as supply chain management (Llaguno et al. 2022 ; Miranzadeh et al., 2015, Sun et al. 2022), health care and crisis management (Mousavi et al, 2022; Sepehri, et. al., 2015, Mahabadi et.al 2015, Sajadi et al. 2016a, Hatami-Marbini et al. 2022), production planning (Sajadi et al., 2011; Amelian et al. 2015; Hatami-Marbini et al., 2020; Emami et. al., 2014, Sajadi et al., 2016b, Malekpour et al,2016), manufacturing management (Amelian et al., 2019; Rad et. al., 2014; Soroush et al., 2014), sales and operations (Aiassi et al. 2020), maintenance management (Ak et al, 2022; Davari et al, 2022; Eslami et al., 2014), scheduling (Amelian et al., 2022, Salehi et al., 2022), transportation (Dui et al.2022), project management, risk management, environmental pollution (Behnamfar et al. 2022; Afshar-Bakeshloo et al., 2018), finance and revenue management, marketing, entrepreneurship (Jamshidi et al. 2021), etc. The surge in the use of this research method is due to the extensive complexity and uncertainty in the nature of real-world problems and the ability of the method to simplify the problem, segregate the problem parameters, and evaluate how each parameter may lead to a solution.

Simulation has emerged as a promising technique for modelling and analysing complex problems. It allows for a controlled examination of complex and uncertain interactions under different potential solutions for the problem and shows each solution's implications. The challenge in using simulation, however, is the large number of potential solutions associated with each problem and the time required to identify the optimal one. Researchers can deal with the challenge by combining an optimisation strategy with simulation models. This combination, which is known as simulation optimisation, enables the determination of the best input variable values from among all possible values without explicitly evaluating each one (Carson et al., 1997).

Business analytics is the science of manipulating data by applying various models and statistical formulae to it to discover insights. Business analytics are also employed to recognise and foresee trends and outcomes. Due to its potential to address business problems, investment in business analytics has been among the top business priorities in recent years (Kappelman et al., 2021). The scope of business analytics has been expanding due to the increased use of IT tools to incorporate statistics in decision-making. A subset of several methodologies, including data mining, statistical analysis, and predictive analytics, are referred to as "business analytics" and are used to analyse and turn data into useful information. Making data-driven business decisions is made simpler with the aid of these results.

Despite the potential and the cross-connection between business analytics and simulation-based optimization, the combination has not received enough attention in the extant literature. Consequently, the purpose of this Special Issue is to investigate the role and application of simulation in business analytics. This Special Issue aims to disseminate research that applies simulation and business analytics to describe, diagnose, predict, and prescribe in business environments. Research in this area can focus on questions, topics, and theories, including:

• How can combined simulation-based optimisation and business analytics be employed for describing, forecasting, and prescribing complex business problems?
• How can discrete event simulation be employed in business analytics related to production, transportation, and scheduling problem?
• How can agent-based simulation be employed in business analytics regarding marketing, healthcare, and supply chain problems?
• How can system dynamics help business analytics in regarding economic, social, and environmental issues?
• How can simulation-based optimisation be employed for describing, forecasting, and prescribing the impact of I4.0 technologies such as IoT, Blockchain, augmented reality, and big data analytics in businesses?
• How can we use simulation-based approaches to analyse the use of business analytics in organization and facilitate the decision-making process?

References

Afshar-Bakeshloo, M., Bozorgi-Amiri, A., Sajadi, S. M., & Jolai, F. (2018). A multi-objective Environmental Hedging Point Policy with customer satisfaction criteria. Journal of Cleaner Production, 179, 478-494.

Aiassi, R., Sajadi, S. M., Hadji-Molana, S. M., & Zamani-Babgohari, A. (2020). Designing a stochastic multi-objective simulation-based optimization model for sales and operations planning in built-to-order environment with uncertain distant outsourcing. Simulation Modelling Practice and Theory, 104, 102103.

Akl, A. M., El Sawah, S., Chakrabortty, R. K., & Turan, H. H. (2022). A Joint Optimization of Strategic Workforce Planning and Preventive Maintenance Scheduling: A Simulation–Optimization Approach. Reliability Engineering & System Safety, 219, 108175.

Amelian, S., Sajadi, S. M., & Alinaghian, M. (2015). Optimal production and preventive maintenance rate in a failure-prone manufacturing system using discrete event simulation. International Journal of Industrial and Systems Engineering, 20(4), 483-496.

Amelian, S. S., Sajadi, S. M., Navabakhsh, M., & Esmaelian, M. (2019). Multi-objective optimization of stochastic failure-prone manufacturing system with consideration of energy consumption and job sequences. International journal of environmental science and technology, 16(7), 3389-3402.

Amelian, S. S., Sajadi, S. M., Navabakhsh, M., & Esmaelian, M. (2022). Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method. Expert Systems, 39(2), e12455.

Behnamfar, R., Sajadi, S. M., & Tootoonchy, M. (2022). Developing environmental hedging point policy with variable demand: A machine learning approach. International Journal of Production Economics, 254, 108640.

Carson, Y., & Maria, A. (1997, December). Simulation optimization: methods and applications. In Proceedings of the 29th conference on Winter simulation (pp. 118-126).

Davari, A., Ganji, M., & Sajadi, S. M. (2022). An integrated simulation-fuzzy model for preventive maintenance optimisation in multi-product production firms. Journal of Simulation, 16(4), 374-391.

Dui, H., Zheng, X., Chen, L., & Wang, Z. (2022). Model and simulation analysis for the reliability of the transportation network. Journal of Simulation, 16(2), 194-203.

Emami, S. B., Arabzad, S. M., & Sajjadi, S. M. (2014). A simulation study on warehouse loading system: the case of poultry feed production factory. International Journal of Logistics Systems and Management, 19(3), 347-355.

Eslami, S., Sajadi, S. M., & Kashan, A. H. (2014). Selecting a preventive maintenance scheduling method by using simulation and multi criteria decision making. International Journal of Logistics Systems and Management, 18(2), 250-269.

Hatami-Marbini, A., Sajadi, S. M., & Malekpour, H. (2020). Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods. Computers & Industrial Engineering, 146, 106614.

Hatami-Marbini, A., Varzgani, N., Sajadi, S. M., & Kamali, A. (2022). An emergency medical services system design using mathematical modeling and simulation-based optimization approaches. Decision Analytics Journal, 3, 100059.

Jamshidi, Z., Sajadi, S. M., Talebi, K., & Hosseini, S. H. (2021). Applying System Dynamics Approach to Modelling Growth Engines in the International Entrepreneurship Era. In Empirical International Entrepreneurship (pp. 491-513). Springer, Cham.

Kappelman, L., Torres, R., McLean, E., Srivastava, S., Johnson, V., Maurer, C., & Guerra, K. (2021). A Preview of the 2021 SIM IT Trends Study. MIS Quarterly Executive, 20(4), 3.

Llaguno, A., Mula, J., & Campuzano-Bolarin, F. (2022). State of the art, conceptual framework and simulation analysis of the ripple effect on supply chains. International Journal of Production Research, 60(6), 2044-2066.

Mahabadi, A., Ketabi, S., & Sajadi, S. M. (2015). Investigate the parameters which affect the patients waiting time in emergency department of orthopedic services in Ayatollah Kashani hospital with the lean management approach. Health Information Management, 11(7), 1016-1025.

Malekpour, H., Sajadi, S. M., & Vahdani, H. (2016). Using discrete-event simulation and the Taguchi method for optimising the production rate of network failure-prone manufacturing systems with perishable goods. International Journal of Services and Operations Management, 23(4), 387-406.

Miranzadeh, A., Sajadi, S. M., & Tavakoli, M. M. (2015). Simulation of a single product supply chain model with ARENA. International Journal of Industrial and Systems Engineering, 19(1), 18-33.

Mousavi, S., Sajadi, S. M., AlemTabriz, A., & Najafi, S. E. (2022). A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief. Scientia Iranica.

Rad, M. H., Sajadi, S. M., & Tavakoli, M. M. (2014). The efficiency analysis of a manufacturing system by TOPSIS technique and simulation. International Journal of Industrial and Systems Engineering, 18(2), 222-236.

Sajadi, S. M., Seyed Esfahani, M. M., & Sörensen, K. (2011). Production control in a failure-prone manufacturing network using discrete event simulation and automated response surface methodology. The International Journal of Advanced Manufacturing Technology, 53(1), 35-46.

Sajadi, S. M., Ghasemi, S., & Vahdani, H. (2016a). Simulation optimisation for nurse scheduling in a hospital emergency department (case study: Shahid Beheshti Hospital). International Journal of Industrial and Systems Engineering, 23(4), 405-419.

Sajadi, S. M., & Rad, M. F. (2016b). Optimal production rate in production planning problem with simulation optimisation approach by simulated annealing. International Journal of Industrial and Systems Engineering, 22(3), 262-280.

Salehi, F., Sajadi, S. M., & Yousefi, H. (2022). A novel model for production optimization with stochastic rework and failure-prone job shop schedule problem via hybrid simulation–heuristic optimization. Scientia Iranica.

Sepehri, Z., Arabzad, S. M., & Sajadi, S. M. (2015). Analysing the performance of emergency department by simulation: the case of Sirjan Hospital. International Journal of Services and Operations Management, 20(3), 289-301.

Soroush, H., Sajjadi, S. M., & Arabzad, S. M. (2014). Efficiency analysis and optimisation of a multi-product assembly line using simulation. International Journal of Productivity and Quality Management, 13(1), 89-104.

Sun, J. Y., Tang, J. M., & Chen, Z. R. (2022). Multi-agent learning mechanism design and simulation of multi-echelon supply chain. Computers & Industrial Engineering, 168, 108034.

Dr. Seyed Mojtaba Sajadi
Dr. Mohammad Daneshvar Kakhki
Guest Editors

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• simulation optimization
• operation research
• business analytics
• data science
• project management
• information systems strategy
• supply chain management
• operations management