The Optimization of Resource Allocation Based on Process Mining

Zhang, Weidong; Liu, Yang; Liu, Haitao; Wu, Ran

doi:10.1007/978-3-319-22053-6_38

Cited by 17 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While some studies create advanced queue models (3 studies) [21,27,65] as basis of their resource allocation approach, Hirsch et al [24] simply use the constraints between the activities defined in the process model. As direct input (12 studies), traditional process models are used to create heuristics (5 studies) [9,16,32,45,70] for the resource allocation, or for genetic or machine learning (ML) algorithms (3 studies) [28,51,60]. We discuss the concrete solution techniques in Section 5.4 in detail.…”

Section: To Cluster Tasks (1)mentioning

confidence: 99%

“…Eleven primary studies use them for preparation and five of them as input to the resource allocation technique directly. Eight out of the eleven studies in the former class employ the process data to identify some insights on resources of the process (i.e., characteristics of resources), e.g., their previous performance [2,26,36,70], their expertise [2,5,38], their workload [2,5,70], their team compatibility [34],…”

Section: To Cluster Tasks (1)mentioning

confidence: 99%

See 1 more Smart Citation

Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

Pufahl,

Ihde,

Stiehle

et al. 2021

Preprint

View full text Add to dashboard Cite

For delivering products or services to their clients, organizations execute manifold business processes. During such execution, upcoming process tasks need to be allocated to internal resources. Resource allocation is a complex decision-making problem with high impact on the effectiveness and efficiency of processes. A wide range of approaches was developed to support research allocation automatically. This systematic literature survey provides an overview of approaches and categorizes them regarding their resource allocation goals and capabilities, their use of models and data, their algorithmic solutions, and their maturity. Rule-based approaches were identified as dominant, but heuristics and learning approaches also play a relevant role.

show abstract

Section: To Cluster Tasks (1)mentioning

confidence: 99%

Section: To Cluster Tasks (1)mentioning

confidence: 99%

Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

Pufahl,

Ihde,

Stiehle

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The spectrum of approaches is wide. In [10], [11], [12], [13] and [14] we can find solutions based on static, rule based algorithms.…”

Section: Resource Allocationmentioning

confidence: 99%

Deep Reinforcement Learning for Resource Allocation in Business Processes

Żbikowski¹,

Ostapowicz²,

Gawrysiak³

2021

Preprint

View full text Add to dashboard Cite

Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues. In this work, we first propose a novel representation that allows modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes. The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.

show abstract

“…In [23], the authors have provided the resource allocation method under constraints of preference, availability and the total cost constraints. Then they analyzed the influence of collaboration between resources on process performances.…”

Section: Related Workmentioning

confidence: 99%

A Resource Recommendation Approach based on Co-Working History

Abdulhameed¹,

Helal²,

Awad³

et al. 2018

ijacsa

View full text Add to dashboard Cite

Abstract-Recommending the right resource to execute the next activity of a running process instance is of utmost importance for the overall performance of the business process, as well as the resource and for the whole organization. Several approaches have recommended a resource based on the task requirements and the resource capabilities. Moreover, the process execution history and the logs have been used to better recommend a resource based on different human-resource recommender criteria like frequency and speed of execution, etc. These approaches considered the recommendation based on the individual's execution history of the task that will be allocated to the resource. In this paper, a novel approach based on the co-working history of resources has been proposed. This approach considers the resources that had executed the previous tasks in the current running process instances. Then, it recommends a resource that has the best harmony with the rest of the resources.

show abstract

The Optimization of Resource Allocation Based on Process Mining

Cited by 17 publications

References 17 publications

Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

Automatic Resource Allocation in Business Processes: A Systematic Literature Survey

Deep Reinforcement Learning for Resource Allocation in Business Processes

A Resource Recommendation Approach based on Co-Working History

Contact Info

Product

Resources

About