# Learning policies for resource allocation in business processes

arXiv: https://arxiv.org/abs/2304.09970 · HTML: https://mlsanity.com/paper/2304.09970/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Prescriptive Process Monitoring: Quo Vadis?](https://mlsanity.com/paper/2112.01769/) (2021)
- [Deep Reinforcement Learning for Resource Allocation in Business Processes](https://mlsanity.com/paper/2104.00541/) (2021)

## Built on by (most cited first, 4 of 4)

- [An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring](https://mlsanity.com/paper/2502.10573/) (2025)
- [Automated decision-making for dynamic task assignment at scale](https://mlsanity.com/paper/2504.19933/) (2025)
- [A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes](https://mlsanity.com/paper/2504.11250/) (2025)
- [Planning and Scheduling Business Processes under Control-Flow Uncertainty: Extended Version](https://mlsanity.com/paper/2609.05578/) (2026)

## Abstract (arXiv)

Efficient allocation of resources to activities is pivotal in executing business processes but remains challenging. While resource allocation methodologies are well-established in domains like manufacturing, their application within business process management remains limited. Existing methods often do not scale well to large processes with numerous activities or optimize across multiple cases. This paper aims to address this gap by proposing two learning-based methods for resource allocation in business processes to minimize the average cycle time of cases. The first method leverages Deep Reinforcement Learning (DRL) to learn policies by allocating resources to activities. The second method is a score-based value function approximation approach, which learns the weights of a set of curated features to prioritize resource assignments. We evaluated the proposed approaches on six distinct business processes with archetypal process flows, referred to as scenarios, and three realistically sized business processes, referred to as composite business processes, which are a combination of the scenarios. We benchmarked our methods against traditional heuristics and existing resource allocation methods. The results show that our methods learn adaptive resource allocation policies that outperform or are competitive with the benchmarks in five out of six scenarios. The DRL approach outperforms all benchmarks in all three composite business processes and finds a policy that is, on average, 12.7% better than the best-performing benchmark.