Dynamic Programming Approaches for Resource Allocation in Project Scheduling: Maximizing Efficiency under Time and Budget Constraints

Abstract

This study investigates dynamic programming methodologies for resource allocation in project scheduling, aiming to optimize efficiency while adhering to time and budget limitations. Exploring the theoretical underpinnings of dynamic programming, including time and budget restrictions into resource allocation models, and conducting case studies to assess actual applications are the primary goals of this project. This study utilized a thorough review process, including a synthesis of the relevant literature, an analysis of the case study, and policy implications. Among the most important discoveries are the following: the versatility of dynamic programming techniques in addressing resource allocation challenges across industries, the significance of incorporating time and budget constraints into decision-making processes, and the necessity of addressing limitations related to computational complexity, data requirements, and risk management. In the context of policy consequences, investments in computing infrastructure, data management techniques, and risk mitigation strategies are highlighted. In general, the findings of this study highlight the potential of dynamic programming methodologies to improve the efficiency and effectiveness of resource allocation within the context of a project that is affected by time and financial restrictions.