Maxim Bushuev
Associate Professor and Interim Chair, Information Science and Systems
Morgan State University
Areas of Expertise: Supply Chain Management, Delivery Coordination, & Blockchain
Maxim A. Bushuev is an associate professor and interim chair of information science and systems at Morgan State University. His research focuses on supply chain delivery performance, analyzed from both the supplier and buyer perspectives, and production planning models that integrate financial elements and multiple objective functions. Bushuev primarily employs stochastic modeling and optimization techniques to address these areas while remaining active in cross-disciplinary collaborations that bridge his core expertise with other fields of study.
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Jay R. Brown & Maxim A. Bushuev (2024). Last Mile Delivery with Drones: A Carbon Emissions Comparison. International Journal of Sustainable Transportation
The development and potential adoption of drones or unmanned aerial vehicles as delivery vehicles creates incredible opportunities and unique challenges for last mile delivery. This research first presents a last mile delivery fleet model with drones that can be further modified and expanded over time. The model shows the optimal number of drones needed based on deterministic or stochastic demand using both traditional charging and battery swapping. The research then compares the carbon emissions of four delivery modes: traditional internal combustion delivery vehicles, all-electric vehicles, plug-in hybrids, and drones within the context of last mile delivery. Findings reveal that the breakdown of carbon emissions by delivery modality depends on parameter assumptions, ambient temperature, delivery radius, electric grid pollution rate, and number of customers.
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Liangyan Tao, Ailin Liang, & Maxim A. Bushuev (2024). Supply Chain Delivery Performance Improvement: A White-box Perspective. International Journal of Production Research
This paper proposes a white-box perspective that portrays a supply chain delivery process as a network of related activities which remains to be improved. It addresses a critical disadvantage of supply chain delivery performance models, namely considering a delivery process as a whole and ignoring characteristics and relationships between activities in the delivery process. A delivery process is modeled using the Graphical Evaluation and Review Technique based on the characteristic function (CF-GERT). Based on the CF-GERT model, a framework for applying managerial effort to activities to improve overall delivery performance is proposed. Then, particle swarm optimization (PSO) based on the penalty function is used to solve the delivery performance improvement framework. Finally, a numerical case shows how applying efforts to activities can effectively improve delivery performance and demonstrates the influence of several parameters on the related costs.
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Sergey Anokhin, Maxim Bushuev, Elena Akerman, Vladislav Spitsin, & Dmitry Anokhin. (2024). Arbitrage Opportunity Estimation: The Case of the Cobb-Douglas Production Function. International Journal of Operational Research
The entrepreneurship literature has recently become aware of the phenomenal promise of efficiency evaluation techniques for gauging one of its key concepts - arbitrage opportunities. Unfortunately, the use of DEA, the dominant efficiency evaluation approach, for this purpose is limited by some of the properties of the method. In this paper we develop an alternative method that could be used to assess opportunities for imitation (arbitrage) available to entrepreneurial firms. We adapt the minimum performance inefficiency technique to the Cobb-Douglas production function, compare the new method to the dominant efficiency estimation techniques that could be used to measure arbitrage opportunity, and run a Monte-Carlo experiment to explore its applicability to alternative types of production functions typically tackled with data envelopment analysis. We show that the new method may provide more accurate results than the mainstream approaches, and demonstrate a real-life application of the technique in the publishing industry setting.
