cspy

cspy is a dedicated collection of algorithms designed to tackle the Resource Constrained Shortest Path (RCSP) problem. This fundamental challenge in operations research and network optimization extends the classic shortest path problem by introducing one or more resource consumption limits, such as time, cost, or capacity. The tool provides robust computational methods to find optimal paths within a network while adhering to these complex constraints.

The capabilities of cspy are broadly applicable across various scientific and engineering domains that involve discrete optimization and decision-making under resource limitations. In logistics and transportation, it can optimize drone delivery networks, determining the most efficient routes that minimize time while respecting critical constraints like battery life or cargo capacity. For complex scheduling and planning tasks, such as creating airline crew pairings or developing intricate space mission timelines, cspy enables the generation of viable schedules by managing resource allocations and enforcing strict packing constraints over timeline states.

Beyond traditional optimization, cspy serves as a vital component in advanced algorithmic design, for instance, in comparing the efficiency of different dominance pruning strategies within Branch and Bound (BnB) frameworks for RCSP. Furthermore, it supports the modeling of equity constraints in network flow, ensuring paths avoid neighborhoods exceeding a specified risk quota. These applications highlight cspy's utility in providing foundational algorithmic solutions for highly constrained, real-world problems.