Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags

2024-09-13

arXiv.org

AI Summary

This study addresses the Stochastic Resource-Constrained Project Scheduling Problem with maximum time lags (SRCPSP/max), where resource availability is uncertain and activities are subject to temporal window constraints, aiming to minimize project makespan. We propose a robust scheduling framework integrating proactive and reactive strategies: (i) a novel constraint programming (CP)-based fully proactive scheduling method; (ii) a lightweight online rescheduling mechanism; and (iii) the first integration of partial-order scheduling with Simple Temporal Networks with Uncertainty (STNUs). Experimental results demonstrate that our STNU-based algorithm significantly outperforms state-of-the-art approaches in solution quality (p < 0.01, two-tailed t-test), while also achieving superior offline planning efficiency and faster online response times. The framework substantially enhances both schedule robustness against resource uncertainty and real-time adaptability to disruptions.

Technology Category

Planning, Routing, and Scheduling: Scheduling under UncertaintyReasoning under Uncertainty: Stochastic OptimizationConstraint Satisfaction and Optimization: Constraint Programming

Application Category

Responsible Web: Human-perceived consequences of algorithmic deployment on the webSearch and Retrieval-Augmented AI: Efficiency and scalability of Web search enginesGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This study investigates scheduling strategies for the stochastic resource-constrained project scheduling problem with maximal time lags (SRCPSP/max)). Recent advances in Constraint Programming (CP) and Temporal Networks have reinvoked interest in evaluating the advantages and drawbacks of various proactive and reactive scheduling methods. First, we present a new, CP-based fully proactive method. Second, we show how a reactive approach can be constructed using an online rescheduling procedure. A third contribution is based on partial order schedules and uses Simple Temporal Networks with Uncertainty (STNUs). Our statistical analysis shows that the STNU-based algorithm performs best in terms of solution quality, while also showing good relative offline and online computation time.

Problem

• SRCPSP/max
• Project Scheduling
• Resource Uncertainty

Innovation

• Active Project Scheduling
• Dynamic Adjustment Strategy
• STNUs Network Application