Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Dong Yang, Qidong Liu, Jia Li and Yongji Jia
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Dong Yang: School of Business and Management, Donghua University, Shanghai 200051, China
Qidong Liu: School of Business and Management, Donghua University, Shanghai 200051, China
Jia Li: School of Business and Management, Donghua University, Shanghai 200051, China
Yongji Jia: School of Business and Management, Donghua University, Shanghai 200051, China

Sustainability, 2020, vol. 12, issue 18, 1-19

Abstract: 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.

Keywords: cloud manufacturing; multi-objective optimization; carbon emissions; sustainable manufacturing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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