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

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.

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

“…Jian et al [7] developed relevant mathematical optimisation models based on the dynamic implementation of the tasks in the cloud manufacturing to save the shop floor production time and cost. Liu et al [8] developed a multi-objective mathematical model to minimise the total cost, carbon emission, and water resources from economic and environmental perspectives while meeting time, cost, and quality requirements. Mehdi Zeynivand et al [9] used the concept of alternative process routing for task decomposition, which enhances the flexibility of the system to complete resource allocation, and task delivery at the lowest cost.…”

Resource Scheduling Method for Equipment Maintenance Based on Dynamic Pricing Model in Cloud Manufacturing

“…Many experts and scholars have carried out research on resource scheduling in cloud manufacturing. Yang et al studied the multi-objective optimization of cloud manufacturing service selection and scheduling considering environmental sustainability [11]. Akbaripour et al studied distributed resource scheduling in cloud manufacturing services considering transportation factors [12].…”

Tripartite Evolutionary Game Analysis of Trust Relationship between Enterprises in a Cloud Manufacturing Environment: A Service Composition Perspective

“…e cloud manufacturing environment is dynamic and open, and it is subject to a variety of uncertainties, including unpredictable service increases and decreases, service failures, and changes in service quality. Some scholars have considered the influence of the history of interactions [39], quality [40], service satisfaction [41], time decay [42], and other factors in cloud manufacturing trust research, effectively controlling the risk of trust caused by dishonesty or malicious falsehood. However, in the cloud manufacturing system, the amount of transactions is often huge and mainly nonfinished product transactions, the quality of the service cannot be evaluated in time, the enterprise information is relatively scattered, and the package of services is not accurate [43,44].…”