Volume 15, Issue 2 (9-2018)                   JSDP 2018, 15(2): 103-118 | Back to browse issues page


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Yaghoubi M, Zahedi M, Ahmadyfard A. A Dynamic Programing Algorithm for Tuning Concurrency of Business Processes. JSDP. 2018; 15 (2) :103-118
URL: http://jsdp.rcisp.ac.ir/article-1-623-en.html
Shahrood university of technology
Abstract:   (192 Views)

Business process management systems (BPMS) are vital complex information systems to compete in the global market and to increase economic productivity. Workload balancing of resources in BPMS is one of the challenges have been long studied by researchers. Workload balancing of resources increases the system stability, improves the efficiency of the resources and enhances the quality of their products. Workload balancing of resources in BPMS is considered as an important factor of the performance and the stability in systems. Setting the workload of each source at a certain level increases the efficiency of the resources.
The main objectives of this research are the concept of resource workload balance and uniformity of the workload for each source at a specified level. To optimize the balance workload and uniformity of each source, the ​​setting multi-process concurrency was offered and studied. Also, the regulation of multi-process concurrency was mentioned as an optimization problem. In this paper, tuning concurrency of the business process is introduced as a problem in BPMS, which is an application issue to improve at workload balance of resources and uniformity in the workload of each resource.
To solve this problem, a delay vector is defined, each element of delay vector makes the synthetic delay at the first of each business process, then a dynamic optimization algorithm is presented to compute delay vector and the speed of the proposed algorithms is compared with and state-space search algorithm and evolutionary algorithm of PSO. The comparison shows that the speed of the proposed algorithm is 37 hours to 5.8 years compared to the state-space search algorithm, while the POS algorithm solves the same problem in just 3 minutes. The experimental results on a real dataset show 21.64 percent improvement in the performance of the proposed algorithm.
 

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Type of Study: Research | Subject: Paper
Received: 2016/12/19 | Accepted: 2017/08/20 | Published: 2018/09/16 | ePublished: 2018/09/16

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