Achieving computational intelligence by resource optimization

Abstract

This paper presents a general resource management and optimization (RMO) paradigm, known as the constrained resource planning (CRP) model, which has been shown broadly suitable for solving most planning and scheduling applications under stringent solution requirements, tightly interacting constraints, as well as restricted resource availability and utilization. By effectively deploying two domain-independent guiding principles - the most-constrained strategy for task identification and the least-impact strategy for solution selection - the algorithmic procedure of CRP strikes a balance between resource utilization and task completion to allow a wide variety of RMO problems to be mapped into this model and solved. The broad applicability of CRP has been demonstrated for over 40 resource allocation and activity scheduling problems by mapping the problem specifics to the key concepts of the CRP model in order for the solution process to execute. The CRP system is offered as a general, problem-solving paradigm for complex RMO problems, with the possibility of even achieving solutions that are, sometimes, beyond human intelligence.