🤖 AI Summary
To address suboptimal scheduling in resource-constrained project scheduling caused by overly restrictive constraints, this paper proposes a framework that automatically identifies critical bottleneck constraints and precisely locates relaxable ones. Methodologically, it integrates job-shop heuristics, constraint sensitivity analysis, iterative re-optimization, and two relaxation strategies—targeted and non-targeted. A key contribution is the empirical finding that non-targeted relaxation achieves performance comparable to targeted relaxation in reducing project tardiness, challenging the conventional intuition that explicit optimization direction is necessary. Experiments across multiple case studies demonstrate significant reductions in task delays, validate the accuracy of bottleneck identification, and confirm the effectiveness of constraint relaxation. The framework provides interpretable and actionable constraint tuning support for Advanced Planning and Scheduling (APS) systems.
📝 Abstract
In realistic production scenarios, Advanced Planning and Scheduling (APS) tools often require manual intervention by production planners, as the system works with incomplete information, resulting in suboptimal schedules. Often, the preferable solution is not found just because of the too-restrictive constraints specifying the optimization problem, representing bottlenecks in the schedule. To provide computer-assisted support for decision-making, we aim to automatically identify bottlenecks in the given schedule while linking them to the particular constraints to be relaxed. In this work, we address the problem of reducing the tardiness of a particular project in an obtained schedule in the resource-constrained project scheduling problem by relaxing constraints related to identified bottlenecks. We develop two methods for this purpose. The first method adapts existing approaches from the job shop literature and utilizes them for so-called untargeted relaxations. The second method identifies potential improvements in relaxed versions of the problem and proposes targeted relaxations. Surprisingly, the untargeted relaxations result in improvements comparable to the targeted relaxations.