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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.07932 |
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| _version_ | 1866917800042299392 |
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| author | Stratman, Eric G. Boutilier, Justin J. Albert, Laura A. |
| author_facet | Stratman, Eric G. Boutilier, Justin J. Albert, Laura A. |
| contents | Many organizations depend on human decision-makers to make subjective decisions, especially in settings where information is scarce. Although workers are often viewed as interchangeable, the specific individual assigned to a task can significantly impact outcomes due to their unique decision-making processes and risk tolerance. In this paper, we introduce a novel framework that utilizes machine learning to predict worker behavior and employs integer optimization to strategically assign workers to tasks. Unlike traditional methods that treat machine learning predictions as static inputs for optimization, in our approach, the optimal predictive model used to represent a worker's behavior is determined by how that worker is allocated within the optimization process. We present a decision-aware optimization framework that integrates predictive model selection with worker allocation. Collaborating with an auto-insurance provider and using real-world data, we evaluate the effectiveness of our proposed method by applying three different techniques to predict worker behavior. Our findings show the proposed decision-aware framework outperforms traditional methods and offers context-sensitive and data-responsive strategies for workforce management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decision-Aware Predictive Model Selection for Workforce Allocation Stratman, Eric G. Boutilier, Justin J. Albert, Laura A. Optimization and Control Machine Learning Many organizations depend on human decision-makers to make subjective decisions, especially in settings where information is scarce. Although workers are often viewed as interchangeable, the specific individual assigned to a task can significantly impact outcomes due to their unique decision-making processes and risk tolerance. In this paper, we introduce a novel framework that utilizes machine learning to predict worker behavior and employs integer optimization to strategically assign workers to tasks. Unlike traditional methods that treat machine learning predictions as static inputs for optimization, in our approach, the optimal predictive model used to represent a worker's behavior is determined by how that worker is allocated within the optimization process. We present a decision-aware optimization framework that integrates predictive model selection with worker allocation. Collaborating with an auto-insurance provider and using real-world data, we evaluate the effectiveness of our proposed method by applying three different techniques to predict worker behavior. Our findings show the proposed decision-aware framework outperforms traditional methods and offers context-sensitive and data-responsive strategies for workforce management. |
| title | Decision-Aware Predictive Model Selection for Workforce Allocation |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2410.07932 |