  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. The first method leverages Deep Reinforcement Learning (DRL) to
learn near-optimal policies by taking action in the business process. 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. To evaluate the proposed approaches, we first designed six
distinct business processes with archetypal process flows and characteristics.
These business processes were then connected to form three realistically sized
business processes. 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 individual business processes. The DRL
approach outperforms all benchmarks in all three composite business processes
and finds a policy that is, on average, 13.1% better than the best-performing
benchmark.
