Intelligent resource management through the constrained resource planning model

Objective: Introduce a resource management methodology, known as the Constrained Resource Planning (CRP), together with a flexible and powerful computing "engine", suitable for most planning and scheduling applications under stringent solution requirements rightly interacting constraints, as well as restricted resource availability and utilization. Methodology: By the effective utilization of two domain-independent guiding principles (i) the most-constrained strategy for task identification and the least-impact strategy for solution selection (ii) the algorithmic procedure of CRP allows widely varying problems to be mapped and solved without changing the underlying problem solving mechanisms - the CRP engine, Quality: Unlike most heuristic approaches that cart be trapped into locally- or non-optimal solutions, CRP has been shown to produce provably optimal solutions for a variation of the classic, NP-complete traveling salesman problem, known as the diamond lattice problem. Applicability: Over 40 resource allocation and activity scheduling problems have been solved using the CRP methodology and computing engine. Capitalizing on CRP's ability to delicately integrate and balance complementary and conflicting objectives, CRP-solved applications have demonstrated a consistency to achieve quality solutions and display surprising intelligence for well known difficult problems, such as multiprocessor scheduling 30 model discovery, and DNA folding, in addition to providing intelligent solutions for most challenging and NP-hard problems as maximal common subgraph. Significance: Due to its broad applicability, solution quality and computational efficacy, CRP is offered both as a general, problem-solving methodology for tackling difficult problems and as an executable computing engine capable of achieving solutions even beyond human intelligence. CRP also guides a problem solver to tackle resource confined decision problems with simultaneous objectives and conflicting constraints through a set of disciplined strategies and balanced principles.