A Data-Driven Neuro-Operations Research Model for Urban Drone Delivery Optimization

Abstract

This study examines the use of Neuro-Operations Research (Neuro-OR) in strategic decision-making for businesses operating in a dynamic, complex, and uncertain environment. In order to provide cutting-edge decision assistance in strategic management and improve accuracy and efficiency in strategic choices, Neuro-OR focuses on integrating the concepts of operations research into artificial neural networks. Dealing with nonlinear interactions between the variables, dynamic changes, and uncertainty factors influencing a strategic decision has been proven to have certain general flaws. In order to solve this problem, this study develops a hybrid strategy in which mathematical models of optimization identify the optimal course of action, while neural networks analyze and forecast choice variables at the data analysis level. As a result, it uses the descriptive-analytic approach to develop the theoretical underpinnings of Neuro-Operations Research and applied technique in the development of a mathematical model in conjunction with a neural network. The model that is being provided will be tested using either simulated or real data, and its performance will be evaluated by comparing it to traditional models in three areas: accuracy, efficiency, and robustness of the results against uncertainty. The hybrid technique of Neuro-OR significantly enhances the results of the tests.