Resource allocation using task similarity distance in business process management systems

被引:0
|
作者
Yaghoubi, Mehdi [1 ,2 ]
Zahedi, Morteza [1 ,2 ]
机构
[1] Shahrood Univ Technol, Comp & IT Engn Dept, Shahrood, Iran
[2] Shahrood Univ Technol, Comp & IT Engn Dept, Shahrood, Iran
来源
2016 2ND INTERNATIONAL CONFERENCE OF SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS) | 2016年
关键词
Business Process; Workflow management; Resource allocation; Reinforcement learning; Entropy-based optimization;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Resource allocation 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 for resource allocation to optimize cycle time by minimizing entropy of work list while keeping workloads balanced. The idea of the entropy of work list 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 in a sporadic manner. To this end, an entropy measure is defined, which represents task similarities on some work list. Furthermore, workload balancing is regarded as an objective because not only is cycle time reduction important, but workload fairness should also be met. Experimental results on a real-life event log of BPI challenge 2012 show that the proposed method leads the reduction in cycle time, compared to some other well-known algorithms.
引用
收藏
页码:18 / 22
页数:5
相关论文
共 50 条
  • [1] A Task Operation Model for Resource Allocation Optimization in Business Process Management
    Huang, Zhengxing
    Lu, Xudong
    Duan, Huilong
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2012, 42 (05): : 1256 - 1270
  • [2] Reinforcement learning based resource allocation in business process management
    Huang, Zhengxing
    van der Aalst, W. M. P.
    Lu, Xudong
    Duan, Huilong
    DATA & KNOWLEDGE ENGINEERING, 2011, 70 (01) : 127 - 145
  • [3] Preference-Based Resource and Task Allocation in Business Process Automation
    Bidar, Reihaneh
    ter Hofstede, Arthur
    Sindhgatta, Renuka
    Ouyang, Chun
    ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2019 CONFERENCES, 2019, 11877 : 404 - 421
  • [4] A Method to Enable Ability-Based Human Resource Allocation in Business Process Management Systems
    Erasmus, Jonnro
    Vanderfeesten, Irene
    Traganos, Konstantinos
    Jie-A-Looi, Xavier
    Kleingeld, Ad
    Grefen, Paul
    PRACTICE OF ENTERPRISE MODELING (POEM 2018), 2018, 335 : 37 - 52
  • [5] Mining association rules to support resource allocation in business process management
    Huang, Zhengxing
    Lu, Xudong
    Duan, Huilong
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (08) : 9483 - 9490
  • [6] Resource allocation in business process executions-A systematic literature study
    Pufahl, Luise
    Stiehle, Fabian
    Ihde, Sven
    Weske, Mathias
    Weber, Ingo
    INFORMATION SYSTEMS, 2025, 132
  • [7] Integration of resource allocation and task assignment for optimizing the cost and maximum throughput of business processes
    Yi Xie
    Shitao Chen
    Qianyun Ni
    Hanqing Wu
    Journal of Intelligent Manufacturing, 2019, 30 : 1351 - 1369
  • [8] Integration of resource allocation and task assignment for optimizing the cost and maximum throughput of business processes
    Xie, Yi
    Chen, Shitao
    Ni, Qianyun
    Wu, Hanqing
    JOURNAL OF INTELLIGENT MANUFACTURING, 2019, 30 (03) : 1351 - 1369
  • [9] Resource Allocation Optimization in Business Processes Supported by Reinforcement Learning and Process Mining
    Neubauer, Thais Rodrigues
    da Silva, Valdinei Freire
    Fantinato, Marcelo
    Peres, Sarajane Marques
    INTELLIGENT SYSTEMS, PT I, 2022, 13653 : 580 - 595
  • [10] An entropy-based clustering ensemble method to support resource allocation in business process management
    Weidong Zhao
    Haitao Liu
    Weihui Dai
    Jian Ma
    Knowledge and Information Systems, 2016, 48 : 305 - 330