Methods of Solving Transport-Production Problems

Abstract

Transport-production problems represent a critical challenge in various domains, including logistics, supply chain management, and urban planning. This abstract provides an overview of methods employed to address these complex problems. Traditional techniques, such as linear programming, have long been utilized for optimization tasks in transport and production systems. However, advancements in computational methods have led to the emergence of diverse approaches, including metaheuristic algorithms, simulation-based optimization, and hybrid methods combining mathematical models with machine learning techniques. This abstract discusses the characteristics, advantages, and limitations of each method, highlighting their suitability for different problem scenarios. Moreover, it explores recent trends, such as the integration of sustainability objectives and real-time data analytics, shaping the landscape of transport-production problem-solving methodologies. By synthesizing existing knowledge, this abstract aims to provide researchers and practitioners with insights into selecting appropriate methods and fostering innovation in addressing contemporary transport-production challenges.