让人力分配决策可解释，提升工业场景下的可信度与协作效率。
Trustworthy and Explainable Decision-Making for Workforce allocation
- 通过人机协同机制实现任务分配的可解释性
- 在不可行情况下提供清晰决策原因说明
- 适用于需要透明决策的工业人力调度场景
在工业场景中，高效的人力资源分配对运营效率至关重要。本文介绍一项正在进行的研究项目，旨在开发一种用于人力分配的决策支持工具，重点强调决策的可解释性以增强其可信度。目标是构建一个不仅能优化团队对预定任务的分配，还能在问题不可行时提供清晰、易懂的决策解释的系统。通过引入人机协同机制，该工具旨在提升用户信任度并支持交互式冲突解决。我们已在原型工具/数字演示器上实现了该方法，并计划在真实工业场景中评估其性能与用户接受度。
原文摘要 · Abstract (English)
In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed for workforce allocation, emphasising the explainability to enhance its trustworthiness. Our objective is to create a system that not only optimises the allocation of teams to scheduled tasks but also provides clear, understandable explanations for its decisions, particularly in cases where the problem is infeasible. By incorporating human-in-the-loop mechanisms, the tool aims to enhance user trust and facilitate interactive conflict resolution. We implemented our approach on a prototype tool/digital demonstrator intended to be evaluated on a real industrial scenario both in terms of performance and user acceptability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。