Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability
Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability
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DOI：
10.3390/su12187733
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发表时间：
2020-09
期刊：
影响因子：
3.9
通讯作者：
Dong Yang;Qi-dong Liu;Jia Li;YongJi Jia
中科院分区：
文献类型：
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作者：
Dong Yang;Qi-dong Liu;Jia Li;YongJi Jia
云制造是一种新兴的面向服务的范式，它利用分布式制造资源和能力协同执行制造任务，并考虑成本、时间和质量等服务质量(Quality Of Service)要求。由于激烈的市场竞争和客户日益增强的环境意识，将环境问题和可持续发展纳入云制造以生产更绿色的产品已成为一个紧迫的问题。本文提出了一种从经济和环境角度(包括碳排放和水资源)选择和调度云制造服务的多目标优化方法。在碳排放上限规定的约束下，以最小化总成本、碳排放和水资源使用为目标，建立了云制造任务的多目标模型。该模型考虑了云制造服务和运输活动的运输方式选择和碳排放。利用ε约束方法求出最优解的精确帕累托前沿。以汽车云制造为例，说明了该方法的有效性。通过数值实验对该方法与简单的加性加权法进行了比较。结果表明，所提出的ε约束方法能得到更好、更多样的帕累托解集，并能在合理的时间内求解模型。
Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε-constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.