Approximation algorithm for resource-constrained project scheduling problems with net present value objective

TL;DR

This paper introduces the first approximation algorithm for resource-constrained project scheduling problems with net present value objectives, enabling efficient solutions for large, real-world instances with complex cost functions.

Contribution

The paper presents a novel approximation algorithm for RCPSP with discounted costs, using geometrically increasing intervals to handle precedence, resources, and timing constraints.

Findings

Successfully solves large underground mining instances

Achieves small optimality gaps in reasonable time

First known approximation algorithm for RCPSP-DC

Abstract

Resource-constrained project scheduling problems (RCPSP) are at the heart of many production planning problems across a plethora of applications. Although the problem has been studied since the early 1960s, most developments and test instances are limited to problems with less than 300 jobs, far from the thousands present in real-life scenarios. Furthermore, the RCPSP with discounted cost (DC) is critical in many of these settings, which require decision makers to evaluate the net present value of the finished tasks, but the non-linear cost function makes the problem harder to solve or analyze. In this work, we propose a novel approximation algorithm for the RCPSP-DC. Our main contribution is that, through the use of geometrically increasing intervals, we can construct an approximation algorithm, keeping track of precedence constraints, usage of multiple resources, and time requirements. To our knowledge, this is the first approximation algorithm for this problem. Finally, through experimental analysis over real instances, we report the empirical performance of our approach, showing that our technique allows us to solve sizeable underground mining problems within reasonable time frames and gaps much smaller than the theoretically computed ones.