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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >A Petri Nets based Generic Genetic Algorithm framework for resource optimization in business processes
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A Petri Nets based Generic Genetic Algorithm framework for resource optimization in business processes

机译：基于Petri网的业务流程资源优化的泛型遗传算法框架

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摘要

Business process simulation (BPS) enables detailed analysis of resource allocation schemes prior to actually deploying and executing the processes. Although BPS has been widely researched in recent years, less attention has been devoted to intelligent optimization of resource allocation in business processes by exploiting simulation outputs. This paper endeavors to combine the power of a genetic algorithm (GA) in finding optimum resource allocation scheme and the benefits of the process simulation. Although GA has been successfully used for finding optimal resource allocation schemes in manufacturing processes, in this previous work the design of these algorithms is ad hoc, meaning that the chromosomes, crossover and selection operators, and fitness functions need to be manually tailored for each problem. In this research, we pioneer to design and implement a Petri Nets based Generic Genetic Algorithm (GGA) framework that can be used to optimize any given business processes which are modeled in Color Petri Nets (CPN). Specifically, the proposed GGA framework is capable of producing an optimized resource allocation scheme for any CPN process model, its task execution times, and the constraints on available resources. The effectiveness of the proposed framework was evaluated on archive management workflow at Macau Historical Archives and an insurance claim workflow from an Australian insurance company. In both case studies, the framework identified significantly improved resource allocation scheme relative to the one that existed when the data for the case studies were collected.
机译：业务流程仿真（BPS）可以在实际部署和执行进程之前对资源分配方案进行详细分析。虽然近年来，BPS已被广泛研究，但通过利用仿真输出，尚未致力于智能优化业务流程中的资源分配。本文努力将遗传算法（GA）的力量结合在找到最佳资源分配方案中的功率和过程模拟的益处。尽管GA已成功用于在制造过程中找到最佳资源分配方案，但在此之前的工作中，这些算法的设计是临时，这意味着染色体，交叉和选择操作员以及适用于每个问题的手动量身定制的健身功能。在本研究中，我们先导要设计并实施基于Petri网的泛型遗传算法（GGA）框架，可用于优化在彩色Petri网（CPN）中建模的任何给定的业务流程。具体地，所提出的GGA框架能够为任何CPN进程模型，任务执行时间和可用资源的约束产生优化的资源分配方案。拟议框架的有效性在澳门历史档案馆归档管理工作流程和澳大利亚保险公司的保险索赔工作流程进行了评估。在两种情况下，该框架鉴定了收集了案例研究数据存在的相对于存在的资源分配方案显着改善。

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