The Project Scheduling Interdiction Problem with Delay Groups

2026-08-27Discrete Mathematics

Discrete Mathematics
AI summary

The authors study how delays in big projects can happen when many tasks rely on a shared resource that gets disrupted at once. They model these delays as "delay groups" affected by common causes and explore how an adversary could intentionally extend task durations to maximize project delays. The problem they analyze is very complex and hard to solve exactly, so the authors develop heuristic methods that find good solutions faster. Their experiments show that these heuristics perform nearly as well as exact methods but more efficiently, sometimes even better.

Project SchedulingDelay GroupsUncertainty SetsNP-hardnessInterdiction ProblemHeuristicsApproximation GuaranteePolyhedral SetsBudgeted UncertaintyMakespan
Authors
Fei Wu, Erik Demeulemeester, Jannik Matuschke
Abstract
Large-scale projects are frequently delayed by correlated disruptions: when a shared input such as a common supplier, a specialized team, or a supporting platform degrades, all dependent activities are slowed down simultaneously. This paper introduces delay groups to capture such disruptions: a delay group is a set of activities whose delays stem from a common cause, described jointly by an uncertainty set. Our model takes the perspective of an interdictor that, subject to a budget of $k$ groups, selects which groups to disrupt so as to maximize the project makespan. The interdictor can extend activity durations within each disrupted group by delays from a group-specific uncertainty set, while non-disrupted activities keep their nominal duration. We study the complexity of the resulting Project Scheduling Interdiction Problem with Delay Groups (PSIP-DG), which provides a worst-case stress test of the schedule. The problem is computationally intractable ($N\!P$-hard) for general polyhedral uncertainty sets, even for a single delay group with a continuous knapsack constraint. For budgeted uncertainty sets, we prove $N\!P$-hardness both when all activities of a disrupted group are delayed and when only one activity per group may be delayed, and derive an inapproximability bound of $1-1/e+ε$ for the former case. We further develop a greedy heuristic with approximation guarantee $k$ and two structure-based heuristics with initializations and neighborhoods from tractable special cases. Experiments on $5{,}000$-activity networks show that the heuristics match the solution quality of an exact solver at substantially lower running times, in some cases finding strictly better solutions.