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


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Guest Editor
Operations and Information Department, Aston Business School, Aston University, Birmingham B4 7ET, UK
Interests: simulation optimization; operation research; business analytics; data science

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Guest Editor
Business Information Systems, Western Michigan University, 1903 West Michigan Avenue, Kalamazoo, MI 49008-5412, USA
Interests: business analytics; business value of information technology; information systems strategy

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

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 Production179, 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 Theory104, 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 Safety219, 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 Engineering20(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 technology16(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 Systems39(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 Simulation16(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 Simulation16(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 Management19(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 Management18(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 Journal3, 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 Research60(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 Management11(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 Management23(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 Engineering19(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 Engineering18(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 Engineering23(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 Engineering22(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 Management20(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 Management13(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 Engineering168, 108034.

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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Published Papers (6 papers)

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Research

21 pages, 3901 KiB  
Article
Developing a Robust Multi-Skill, Multi-Mode Resource-Constrained Project Scheduling Model with Partial Preemption, Resource Leveling, and Time Windows
by Ladan Hatami-Moghaddam, Mohammad Khalilzadeh, Nasser Shahsavari-Pour and Seyed Mojtaba Sajadi
Mathematics 2024, 12(19), 3129; https://doi.org/10.3390/math12193129 - 6 Oct 2024
Viewed by 343
Abstract
Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for [...] Read more.
Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim’s robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity. Full article
(This article belongs to the Special Issue Simulation-Based Optimisation in Business Analytics)
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19 pages, 1334 KiB  
Article
Production Planning Optimization in a Two-Echelon Multi-Product Supply Chain with Discrete Delivery and Storage at Manufacturer’s Warehouse
by Maedeh Tajik, Seyed Mohammad Hajimolana and Mohammad Daneshvar Kakhki
Mathematics 2024, 12(13), 1986; https://doi.org/10.3390/math12131986 - 27 Jun 2024
Cited by 2 | Viewed by 713
Abstract
In today’s competitive world, customers expect their demands to be met at the shortest possible time, while manufacturers aspire to deliver the orders within a convenient time and at a minimum cost. Thus, manufacturers are compelled to seek ways of lowering the costs [...] Read more.
In today’s competitive world, customers expect their demands to be met at the shortest possible time, while manufacturers aspire to deliver the orders within a convenient time and at a minimum cost. Thus, manufacturers are compelled to seek ways of lowering the costs of their services in order to satisfy customers and survive the competition in their respective industries. This research paper investigates a multi-product problem in a two-echelon supply chain consisting of a single manufacturer and several retailers. The main objective of this research is to develop and present a multi-product optimization model in which retailers receive their orders through discrete delivery and surplus manufactured goods are stored in the manufacturer’s warehouse. The objective function of the mathematical model in the economic dimension includes the minimization of the total supply chain costs and the maximization of profit. The retailers in this model place new orders when their inventory level drops to zero, and the manufacturer responds to the retailers’ orders at the same time as it begins processing each product. After delivering the last set of orders, the manufacturer stores surplus items in its warehouse in case the retailers place new orders. This optimization problem is modeled using mixed integer nonlinear programming, while numerical scenarios are coded using the MATLAB software which helps estimate the total cost within a short time. Finally, a sensitivity analysis is performed to determine the effects of a number of factors on the total cost, including problem parameters, demand and production rates, the production quantity, and the number of times the manufacturing machines are operated at each production cycle. Full article
(This article belongs to the Special Issue Simulation-Based Optimisation in Business Analytics)
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30 pages, 4355 KiB  
Article
Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies
by Tahereh Mohammadi, Seyed Mojtaba Sajadi, Seyed Esmaeil Najafi and Mohammadreza Taghizadeh-Yazdi
Mathematics 2024, 12(5), 679; https://doi.org/10.3390/math12050679 - 26 Feb 2024
Cited by 3 | Viewed by 1282
Abstract
With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed [...] Read more.
With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs. Full article
(This article belongs to the Special Issue Simulation-Based Optimisation in Business Analytics)
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42 pages, 9281 KiB  
Article
A Dynamic CGE Model for Optimization in Business Analytics: Simulating the Impact of Investment Shocks
by Ana Medina-López, Montserrat Jiménez-Partearroyo and Ángeles Cámara
Mathematics 2024, 12(1), 41; https://doi.org/10.3390/math12010041 - 22 Dec 2023
Cited by 1 | Viewed by 1491
Abstract
This study formulates a mathematical dynamic Computable General Equilibrium (CGE) model within a rational expectations framework, adhering to neo-classical principles. It emphasizes the significant role of agents’ expectations in determining the broader economic trajectory over time. The model combines microeconomic and macroeconomic perspectives [...] Read more.
This study formulates a mathematical dynamic Computable General Equilibrium (CGE) model within a rational expectations framework, adhering to neo-classical principles. It emphasizes the significant role of agents’ expectations in determining the broader economic trajectory over time. The model combines microeconomic and macroeconomic perspectives by merging the concept of intertemporal choice with savings behavior. Its mathematical foundations are derived and calibrated using data from a social accounting matrix to enhance its simulation capabilities. The paper presents a practical simulation investigating the economic implications of a strategic investment impact within an specific European region, Madrid as the case of study. Such demand shock affects sectors such as electronics, food, pharmaceuticals, and education. The study models the long-term effects of heightened investment and persistent demand-side shocks. The research demonstrates the CGE model’s ability to forecast economic shifts toward a new equilibrium after an investment shock, proving its utility for assessing the impacts of extensive environmental policies within a European context. The work’s originality lies in its detailed mathematical formulation, contributing to theoretical discourse and practical application in business analytics. Full article
(This article belongs to the Special Issue Simulation-Based Optimisation in Business Analytics)
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26 pages, 6616 KiB  
Article
Simulation-Based Models of Multi-Tier Financial Supply Chain Management Problem: Application in the Pharmacy Sector
by Mojtaba Azizian, Mohammad Mehdi Sepehri and Seyed Mohammad Javad Mirzapour Al-e-Hashem
Mathematics 2023, 11(19), 4188; https://doi.org/10.3390/math11194188 - 7 Oct 2023
Viewed by 1512
Abstract
A crucial role in the continuation of economic activities is played by the financing of services and production in supply chains. A key element of optimizing the financial flow of these complex networks is to pay attention to the financial aspects of these [...] Read more.
A crucial role in the continuation of economic activities is played by the financing of services and production in supply chains. A key element of optimizing the financial flow of these complex networks is to pay attention to the financial aspects of these complex networks since they are becoming more and more complex and expanding. This study aims to investigate the supply chain of a pharmaceutical company’s holding company and its subsidiaries while using internal resource valuation to develop a new strategy for financing the company’s operations. There is a process of money circulation through the chain, which consists of passing through two treasuries (primary and secondary), which provide liquidity to compensate the deficits of some institutions with the excess liquidity of other institutions. In this article, we present three simulation-based models based on a case study conducted at Shafa Darou Investment Company in Tehran-Iran, a leading pharmaceutical investment company in the country, to examine the impact of implementing this idea in the real world. Considering the study’s results, it has been shown that the supply chain as a whole has improved in terms of its working capital. Using a set of local treasuries is generally associated with reducing risks and a greater level of stability when relying on the excess liquidity of chain members provided that financial independence from external institutions, such as banks, is maintained. In addition, if the members’ excess liquidity is deposited in a set of local treasuries rather than a bank, the profit and internal financial flow within the chain will be circulated throughout the chain, and more added value will be generated. Full article
(This article belongs to the Special Issue Simulation-Based Optimisation in Business Analytics)
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21 pages, 1548 KiB  
Article
Solving the Problem of Reducing the Audiences’ Favor toward an Educational Institution by Using a Combination of Hard and Soft Operations Research Approaches
by Wenjing Xu, Seyyed Ahmad Edalatpanah and Ali Sorourkhah
Mathematics 2023, 11(18), 3815; https://doi.org/10.3390/math11183815 - 5 Sep 2023
Cited by 2 | Viewed by 1239
Abstract
Because of hyper-complexity, a difficulty to define, multiple stakeholders with conflicting perspectives, and a lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations research (OR) has been a valuable tool for managers to make informed decisions for years. However, as [...] Read more.
Because of hyper-complexity, a difficulty to define, multiple stakeholders with conflicting perspectives, and a lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations research (OR) has been a valuable tool for managers to make informed decisions for years. However, as we face increasingly complex and messy problems, it has become apparent that relying solely on either hard or soft OR approaches is no longer sufficient. We need to explore more innovative methodologies to address these wicked problems effectively. This study has bridged the research gap by proposing a structured process encompassing a subdivision-based problem structuring method for defining the wicked problem, a multi-attribute decision-making (MADM) for prioritizing subproblems, and a hard OR technique, data envelopment analysis (DEA) for tackling one of the most critical subdivisions. The proposed methodology, the subdivision-based problem structuring method (SPSM), implemented in a case study, focuses on a higher education institution experiencing a decline in student admissions and involves five steps. First, a diverse group of stakeholders is formed to ensure the comprehensive consideration of perspectives. Second, the wicked problem is defined, considering long-term consequences, multiple stakeholders, and qualitative stakeholder opinions. Third, a hierarchical structure is created to break down the wicked problem into manageable subproblems. Fourth, a multi-criteria decision-making (MCDM) method prioritizes subproblems. Finally, the subproblems are addressed one by one using a combination of soft and hard OR tools. The findings highlight the benefits of integrating hard and soft OR approaches. The study concludes with reflections on the implications of using a combined OR approach to tackle wicked problems in higher education and beyond. Full article
(This article belongs to the Special Issue Simulation-Based Optimisation in Business Analytics)
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