Cycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Firouzian i.; ZAHEDI M.; HASSANPOUR H.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Engineering Year:2019 | Volume:32 | Issue:8 (TRANSACTIONS B: Applications) Page(s): 1090-1100

Download Full-Text

View:

142

Download:

Cites:

Information Journal Paper

Title

Cycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation

Author(s)

Firouzian i. | ZAHEDI M. | HASSANPOUR H. | Issue Writer Certificate

Keywords

Business Process

Entropy-based Optimization

Reinforcement Learning

Resource Allocation

Workflow Management

Abstract

time optimization could be one of the great challenges in Business Process management. Although there is much research on this subject, task similarities have been paid little attention. In this paper, a new approach is proposed to optimize cycle time by minimizing entropy of work lists in Resource Allocation while keeping workloads balanced. The idea of the entropy of work lists comes from the fact that the time it takes for a resource to do similar tasks in a rather consecutive order is less than the time it takes to do the same tasks separately. To this end, an entropy measurement is defined, which represents task similarities on some given work lists. Furthermore, workload balancing is also regarded as an objective because not only is cycle time optimization important, but also workload fairness should also be met. Experimental results on a real-life event log of BPI challenge 2012 showed that the proposed method leads to 32% reduction in cycle time, compared with a Reinforcement Learning Resource Allocation without involving the entropy.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Firouzian, i., ZAHEDI, M., & HASSANPOUR, H.. (2019). Cycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation. INTERNATIONAL JOURNAL OF ENGINEERING, 32(8 (TRANSACTIONS B: Applications)), 1090-1100. SID. https://sid.ir/paper/744818/en

Vancouver: Copy

Firouzian i., ZAHEDI M., HASSANPOUR H.. Cycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation. INTERNATIONAL JOURNAL OF ENGINEERING[Internet]. 2019;32(8 (TRANSACTIONS B: Applications)):1090-1100. Available from: https://sid.ir/paper/744818/en

IEEE: Copy

i. Firouzian, M. ZAHEDI, and H. HASSANPOUR, “Cycle Time Optimization of Processes Using an Entropy-Based Learning for Task Allocation,” INTERNATIONAL JOURNAL OF ENGINEERING, vol. 32, no. 8 (TRANSACTIONS B: Applications), pp. 1090–1100, 2019, [Online]. Available: https://sid.ir/paper/744818/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops