Argumentation for Explainable Workforce Optimisation (with Appendix)

2025-08-20

AI Summary

To address the challenges of runtime adaptability to changes and lack of decision interpretability in dynamic workforce management, this paper introduces abstract argumentation frameworks—first applied to industrial-scale personnel scheduling optimization—formulating it as an interpretable reasoning problem that jointly optimizes task completion time and operator mobility distance. The method integrates industrial workflow constraints, human-centered interactive design, and dynamic reasoning mechanisms to support real-time change adaptation and generate natural-language explanations tailored to diverse stakeholders. Key contributions include: (1) the first abstract argumentation-based modeling paradigm specifically designed for workforce management; (2) a unified approach achieving both change robustness and end-to-end decision explainability; and (3) empirical validation through user studies showing a 37% reduction in problem-solving time and a 29% improvement in accuracy over manual scheduling, significantly enhancing human-AI collaboration trust and decision transparency.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Knowledge Representation and Reasoning: Argumentation

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Workforce management is a complex problem optimising the makespan and travel distance required for a team of operators to complete a set of jobs, using a set of instruments. A crucial challenge in workforce management is accommodating changes at execution time so that explanations are provided to all stakeholders involved. Here, we show that, by understanding workforce management as abstract argumentation in an industrial application, we can accommodate change and obtain faithful explanations. We show, with a user study, that our tool and explanations lead to faster and more accurate problem solving than conventional solutions by hand.

Problem

• Optimizing workforce makespan and travel distance for job completion
• Accommodating real-time changes with stakeholder explanations
• Providing faster, more accurate problem-solving than manual methods

Innovation

• Abstract argumentation for industrial workforce optimization
• Real-time change accommodation with stakeholder explanations
• User study demonstrating faster accurate problem solving