Business process modeling and analysis based on logical data Petri net

doi:10.13196/j.cims.2017.05.001

›› 2017, Vol. 23 ›› Issue (第5期): 921-930.DOI: 10.13196/j.cims.2017.05.001

Online:2017-05-31 Published:2017-05-31
Supported by:
Project supported by the National Natural Science Foundation,China(No.61472228,61502280,61170078),the Applied Basic Research of Qingdao City(Special Youth Project),China(No.14-2-4-55-jch),and the Natural Science Foundation of Shandong Province,China(No.ZR2014FM009,ZR2015FM013).

基于逻辑数据Petri网的业务过程建模与分析

王聘,刘伟⁺,杜玉越

山东科技大学计算机科学与工程学院

基金资助:
国家自然科学基金资助项目(61472228,61502280,61170078);青岛市应用基础研究计划资助项目(青年专项)(14-2-4-55-jch);山东省自然科学基金资助项目(ZR2014FM2009,ZR2015FM013)。

Abstract

Abstract: To solve the problem of token priority in logical Petri nets,the logical data Petri nets with firing rules and its reachability graph were put forward by combining with the advantage of time Petri nets and logic Petri nets.Attributes and arrival time were added for each token.Batch waiting time and duration were added in logic input and output transitions,and duration was added in ordinary transitions.Deletion function and writing function were defined for tokens,and their attributes and arrival time were operated.Moreover,the logical data Petri net model of an electronic commerce system was constructed and the reachability graph was produced based on new states.Time analysis of tokens with different priority and the fairness of user requests were discussed.

Key words: logical data Petri nets, electronic commerce system, priority, time

摘要： 为了解决逻辑Petri网中处理托肯优先级的问题,结合逻辑Petri网和时间Petri网的思想,提出逻辑数据Petri网、引发规则及其可达图。在该网中,为每个托肯添加了属性和到达时间,为逻辑输入、输出变迁添加了批处理等待时间和变迁引发持续时间,为普通变迁添加了引发持续时间。针对托肯定义了属性、时间删除函数和写函数,对库所中托肯的属性和到达时间进行了操作。建立了基于逻辑数据Petri网的电子商务系统模型,构建了可达图并重新定义了可达图中的状态信息,分析了优先级不同的托肯的时间优越性以及系统处理用户请求时的公平性。

关键词: 逻辑数据Petri网, 电子商务系统, 优先级, 时间

CLC Number:

TP391.9

王聘,刘伟,杜玉越. 基于逻辑数据Petri网的业务过程建模与分析[J]. 计算机集成制造系统, 2017, 23(第5期): 921-930.

[1]	. Improved whale algorithm for solving engineering design optimization problems [J]. , 2021, 27(7): 1884-1897.
[2]	. Mixed no-idle permutation flow shop scheduling problem based on gravitational search algorithm [J]. , 2021, 27(7): 1909-1917.
[3]	. Deep learning algorithm for resource investment problem [J]. , 2021, 27(6): 1558-1568.
[4]	. Visual real-time monitoring method for discrete manufacturing workshop based on digital twin [J]. , 2021, 27(6): 1605-1616.
[5]	. Multi-timing production of differentiated products in cloud manufacturing mode [J]. , 2021, 27(6): 1681-1692.
[6]	. Decision optimization support model for matching problem with decision time window [J]. , 2021, 27(6): 1728-1740.
[7]	. Multi-echelon inventory optimization of spare parts under imperfect maintenance conditions [J]. , 2021, 27(6): 1749-1759.
[8]	. Multi-objective scheduling algorithm for one worker job shop considering learning effect [J]. , 2021, 27(5): 1361-1370.
[9]	. Plug-in technology connecting Simulink model with run-time infrastructure [J]. , 2021, 27(5): 1422-1428.
[10]	. Ordering decision behavior under recency effect [J]. , 2021, 27(5): 1483-1493.
[11]	. Modified machined surface topography modeling in ball-end milling process [J]. , 2021, 27(4): 973-980.
[12]	. Pollution routing problem based on time window assignment [J]. , 2021, 27(4): 1178-1187.
[13]	. Inventory decision and coordination contract design for two-echelon supply chain considering delivery time uncertainty [J]. , 2021, 27(4): 1211-1225.
[14]	. Fresh food vehicle routing problem with split deliveries and customer classification [J]. , 2021, 27(4): 1238-1248.
[15]	. Performance analysis of flexible assembly job shop scheduling under variable disturbance intensity [J]. , 2021, 27(3): 800-814.