Simulation-Based Optimisation in Business Analytics
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: 16 October 2024 | Viewed by 7311
Special Issue Editors
Interests: simulation optimization; operation research; business analytics; data science
Special Issue Information
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
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Dr. Seyed Mojtaba Sajadi
Dr. Mohammad Daneshvar Kakhki
Guest Editors
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Keywords
- simulation optimization
- operation research
- business analytics
- data science
- project management
- information systems strategy
- supply chain management
- operations management
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