An iterative Constraint Programming approach to integrate maximum workload constraints in preemptive jobshop scheduling

TL;DR

This paper presents a Constraint Programming method for efficiently incorporating maximum workload constraints into preemptive jobshop scheduling, improving solution quality and computational performance.

Contribution

It introduces an iterative approach that integrates workload constraints without decomposing activities, along with heuristics to enhance search efficiency.

Findings

Effective on large instances, outperforming IBM's CP Optimizer.

Avoids activity decomposition, reducing computational complexity.

Demonstrates practical applicability in real-world scheduling scenarios.

Abstract

Optimizing schedules in real-world settings often requires considering workload constraints, specially for human resources, to ensure regulatory compliance, impose rest periods, or level the workload over the working horizon. This paper focuses on tackling this family of constraints in the context of preemptive jobshop scheduling, as preemption is particularly relevant when human resources are involved (allowing personnel to flexibly switch between tasks). Preemption also offers theoretical insights as a relaxation of non-preemptive problems. The main contribution of this paper is a Constraint Programming approach designed to handle effectively maximum workload constraints in a preemptive setting, without decomposing activities into unit-duration tasks (which may be computationally prohibitive). Since workload constraints introduce significant additional complexity, we further propose a method that iteratively introduces the workload constraints into the problem, along with tailored heuristics specifically designed to guide the search efficiently. The experimental results demonstrate the effectiveness of our approach on a large set of instances, highlighting its performance compared to a well-known industrial solver, IBM's CP Optimizer.