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

Jia Li,Qidong Liu,Dong Yang,Yongji Jia

doi:10.3390/su12187733

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.

Highlights

With the development of modern information technology, fierce market competition, and diverse customer needs, cloud manufacturing has emerged as a convenient manufacturing mode for organizing global manufacturing resources and capabilities to perform a specific manufacturing task by means of cloud computing [1]
We propose a multi-objective optimization approach for optimally selecting and scheduling cloud manufacturing services according to task requirement from a user and with consideration of environmental impacts, i.e., greenhouse gas emissions and water resource, in cloud manufacturing services
For service selection and scheduling in cloud manufacturing, an exact approach for solving multi-objective optimization is proposed in this study with the consideration of the sustainability of cloud manufacturing

Summary

Introduction

With the development of modern information technology, fierce market competition, and diverse customer needs, cloud manufacturing has emerged as a convenient manufacturing mode for organizing global manufacturing resources and capabilities to perform a specific manufacturing task by means of cloud computing [1]. Once a user submits the request for a manufacturing task, the cloud platform performs service discovery, service matching, and service selection and scheduling such that the user requirements for QoS (Quality of Service) like cost and due date are satisfied. Owing to various QoS requirements for user tasks, such as cost, due date, and reliability, service selection and scheduling is a multi-objective optimization problem; generally, in most studies, a simple additive weighting (SAW) method is used to convert a multi-objective optimization problem to a single objective one [14,15]. We propose a multi-objective optimization approach for optimally selecting and scheduling cloud manufacturing services according to task requirement from a user and with consideration of environmental impacts, i.e., greenhouse gas emissions and water resource, in cloud manufacturing services. Unlike existing studies that use a simple additive weighting to solve multi-objective optimization, we further develop an exact method to obtain the Pareto-optimal front by using the ε-constraint method [19] without a pre-determined weight for each objective

Objectives

Results

Conclusion