Integrated Strategies to an Improved Genetic Algorithm for Allocating and Scheduling Multi-Task in Cloud Manufacturing Environment

被引：3
|
作者：
Elgendy, Abdelrahman [1 ]
Yan, Jihong [1 ]
Zhang, Mingyang [1 ]
机构：
[1] Harbin Inst Technol, Sch Mech Engn, Dazhi 92, Harbin 150001, Peoples R China
来源：
25TH INTERNATIONAL CONFERENCE ON PRODUCTION RESEARCH MANUFACTURING INNOVATION: CYBER PHYSICAL MANUFACTURING | 2019年 / 39卷
基金：
国家重点研发计划;
关键词：
Cloud Manufacturing; Multi-tasks scheduling; Matching strategy;
D O I：
10.1016/j.promfg.2020.01.251
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Cloud manufacturing (CMfg) is an emerged manufacturing paradigm oriented towards services via a network. In which, distributed manufacturing capabilities and resources have been collaborating together through CMfg platform to fulfill the required tasks in a perfect way. In this research, firstly, an architecture of the CMfg environment was proposed for machining complex parts and assembly lines. Then, a genetic algorithm was developed to accommodate the complexities involved in CMfg problems, represented in its larger size, diversity of tasks and dynamism In addition, two strategies have been proposed for integration with the genetic algorithm as a means of improving its efficiency. The experimental results indicate that merging these strategies into the genetic algorithm is effective in scheduling multiple tasks on a large number of resources in terms of improving multiple objectives such as makespan and the cost of transportation for the entire manufacturing life cycle. (C) 2019 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：1872 / 1879
页数：8
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