Simulation Process as a Branch of Operations Research for Automation of Electronic Four-wheeler Vehicles: Algorithm and C++ Implementation

Ashutosh Pawan; Om Prakash Dubey

doi:10.36948/ijfmr.2026.v08i01.67106

Simulation Process as a Branch of Operations Research for Automation of Electronic Four-wheeler Vehicles: Algorithm and C++ Implementation

Author(s)	Mr. Ashutosh Pawan, Dr. Om Prakash Dubey
Country	India
Abstract	The study aims to explore the intersection of Operations Research (OR), Artificial Intelligence (AI), and simulation by examining how classical OR methodologies strengthen AI models, particularly in machine learning, robotics, natural language processing, and autonomous systems. It further investigates the critical role of simulation in training, testing, and validating AI algorithms, emphasizing its relevance for optimization, intelligent decision-making, and real-world system modelling. The study adopts a comprehensive analytical and literature-based methodology, reviewing foundational OR techniques, simulation principles, and modern AI applications. It synthesizes interdisciplinary research across mathematics, computer science, and engineering, supported by case analyses in robotics, healthcare, transportation, and autonomous systems. Additionally, a demonstration algorithm is developed to simulate automatic gear-control behavior in vehicles, illustrating how simulation models practically support AI-oriented operational decision-making. Findings reveal that OR optimization techniques significantly enhance AI efficiency, particularly in parameter tuning, resource allocation, and adaptive decision-making. Simulation is shown to be indispensable for AI training, offering controlled, safe, scalable, and cost-effective environments. The study identifies persistent challenges—including the reality gap, computational demands, and model bias—yet confirms that simulation and OR jointly accelerate AI development and broaden its practical reliability. The integrated OR–AI–simulation framework is applicable to numerous fields, including autonomous vehicle navigation, robotic motion planning, intelligent healthcare systems, logistics optimization, and smart city management. Industries benefit from improved forecasting, reduced operational costs, enhanced safety, and high-fidelity algorithm testing. Simulated environments also support reinforcement learning, surgical training, autonomous decision-making, and large-scale scenario evaluation, contributing to more efficient and intelligent real-world systems. The study’s novelty lies in its unified perspective that connects classical OR optimization principles with AI advancements through simulation-based experimentation. It uniquely synthesizes concepts from mathematics, computer science, and AI to highlight simulation as a bridge enabling intelligent automation. The inclusion of a practical simulation algorithm for automatic vehicle gear control further demonstrates how OR-driven simulation can concretely operationalize AI-based decision systems. Operations Research (OR) and Artificial Intelligence (AI) have both independently evolved as transformative fields which have shown impact on decision-making and problem-solving across diverse domains. While Operations Research makes available a foundation of mathematical modeling and optimization techniques, Artificial Intelligence sets up intelligence through learning, reasoning, and data-driven methods. This seminar paper presented by us explores how Operations Research gets involved in the development and enhancement of Artificial Intelligence (AI) systems. We, in this paper, have tried to discuss Key applications and case studies to highlight the synergies between these fields, remarkably in optimization, logistics, resource allocation, and automated decision-making. Simulation, an approach of Operations Research, has become a cornerstone in the field of Artificial Intelligence (AI), suggesting an experimental platform for testing hypotheses, training algorithms, and evaluating systems in controlled, cost-effective, and scalable environments. Our paper explores the central role simulations play in advancing Artificial Intelligence (AI) research and applications, with a focus on their integration in machine learning, robotics, and decision-making systems.
Keywords	Simulation, Algorithm, Four-wheeler Vehicle, automated decision-making, data-driven methods.
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-22
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.67106

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Simulation Process as a Branch of Operations Research for Automation of Electronic Four-wheeler Vehicles: Algorithm and C++ Implementation

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