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Energies
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Smart Design, Smart Manufacturing and Industry 4.0
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Smart Design, Smart Manufacturing and Industry 4.0

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 December 2016) | Viewed by 40708

Special Issue Editors

Prof. Dr. Yun Li

E-Mail Website
Guest Editor
School of Engineering, University of Glasgow, Glasgow G12 8LT, UK
Interests: Industry 4.0, smart design for manufacture, energy efficient engines, parallel computing and control, machine learning and optimisation, computational intelligence
Dr. Yi (Leo) Chen

E-Mail Website
Guest Editor
School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
Interests: cyber-physical systems, dynamics and control, robotics, multidisciplinary design and optimisation, reliability and risk analysis, computational intelligence
Dr. Erfu Yang

E-Mail Website
Guest Editor
Department of Design, Manufacturing and Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK
Interests: robotics and autonomous systems; mechatronics and automation; data analytics; intelligent control; computational intelligence; digital manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongnian Yu

E-Mail Website
Guest Editor
Department of Computing, Bournemouth University, Poole BH12 5BB, UK
Interests: computer science, informatics, energy-efficient computing, robotics
Prof. Dr. Jun Zhang

E-Mail
Guest Editor
School of Computer Science, South China University of Technology, Guangzhou, China
Interests: evolutionary computation, machine learning, computer applications, power electronics
Prof. Dr. Huaqiang Yuan

E-Mail
Guest Editor
College of Computer Science and Technology, Dongguan University of Technology, Dongguan, China
Interests: computer science and technology, network and communication technology, cyber security

Special Issue Information

Dear Colleagues,

Smart manufacturing lies in the centre of “Industrie 4.0” or “Industry 4.0”, the first a-priori engineered and the fourth “industrial (r)evolution”, referring to “the industrial value chain and technological evolution upgrading the factory floor to a mass be-spoke innovation centre” (Siemens 2015). Its revolutionary value lies in the mass customisation made available at mass production through cyber-physical integration, information and communication technology, and computer-automated design, in which computer science and technology play a key role.

Energy-efficient design for smart manufacturing and Industry 4.0 is applicable to smart designs for customised production, networked embedded systems, “smart factories”, “industrial internet”, “Internet of Things”, “Internet of Everything”, and “Internet+”. This Special Issue welcomes original, high-quality research papers or reviews on these topics, including developments of energy-efficient methodologies and business environments for Industry 4.0. It also welcomes related topics on energy-efficient autonomous manufacturing, computer technology for manufacturing automation, autonomous systems, smart robotic systems, computational intelligence aided engineering, machine learning, system monitoring, and advanced control.

Prof. Dr. Yun Li
Dr. Yi (Leo) Chen
Dr. Erfu Yang
Prof. Dr. Hongnian Yu
Prof. Dr. Jun Zhang
Prof. Dr. Huaqiang Yuan
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy efficiency
  • energy efficient design
  • energy efficient systems
  • smart manufacturing
  • autonomous manufacturing
  • smart factories
  • customised production
  • industrial automation
  • industry 4.0
  • industrial internet
  • cyber-physical systems
  • internet of everything
  • smart design
  • autonomous design
  • computer-automated design
  • machine learning
  • evolutionary computation
  • computational intelligence
  • business informatics

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Published Papers (4 papers)

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Research

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916 KiB  
Article
An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing
by Xiao-Fang Liu, Zhi-Hui Zhan and Jun Zhang
Energies 2017, 10(5), 609; https://doi.org/10.3390/en10050609 - 1 May 2017
Cited by 32 | Viewed by 4665
Abstract
Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM [...] Read more.
Energy efficiency is a significant topic in cloud computing. Dynamic consolidation of virtual machines (VMs) with live migration is an important method to reduce energy consumption. However, frequent VM live migration may cause a downtime of service. Therefore, the energy save and VM migration are two conflict objectives. In order to efficiently solve the dynamic VM consolidation, the dynamic VM placement (DVMP) problem is formed as a multiobjective problem in this paper. The goal of DVMP is to find a placement solution that uses the fewest servers to host the VMs, including two typical dynamic conditions of the assignment of new coming VMs and the re-allocation of existing VMs. Therefore, we propose a unified algorithm based on an ant colony system (ACS), termed the unified ACS (UACS), that works on both conditions. The UACS firstly uses sufficient servers to host the VMs and then gradually reduces the number of servers. With each especial number of servers, the UACS tries to find feasible solutions with the fewest VM migrations. Herein, a dynamic pheromone deposition method and a special heuristic information strategy are also designed to reduce the number of VM migrations. Therefore, the feasible solutions under different numbers of servers cover the Pareto front of the multiobjective space. Experiments with large-scale random workloads and real workload traces are conducted to evaluate the performance of the UACS. Compared with traditional heuristic, probabilistic, and other ACS based algorithms, the proposed UACS presents competitive performance in terms of energy consumption, the number of VM migrations, and maintaining quality of services (QoS) requirements. Full article
(This article belongs to the Special Issue Smart Design, Smart Manufacturing and Industry 4.0)
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1809 KiB  
Article
Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems
by Shichang Xiao, Shudong Sun and Jionghua (Judy) Jin
Energies 2017, 10(4), 543; https://doi.org/10.3390/en10040543 - 16 Apr 2017
Cited by 15 | Viewed by 4490
Abstract
This study focuses on surrogate measures (SMs) of robustness for the stochastic job shop scheduling problems (SJSSP) with uncertain processing times. The objective is to provide the robust predictive schedule to the decision makers. The mathematical model of SJSSP is formulated by considering [...] Read more.
This study focuses on surrogate measures (SMs) of robustness for the stochastic job shop scheduling problems (SJSSP) with uncertain processing times. The objective is to provide the robust predictive schedule to the decision makers. The mathematical model of SJSSP is formulated by considering the railway execution strategy, which defined that the starting time of each operation cannot be earlier than its predictive starting time. Robustness is defined as the expected relative deviation between the realized makespan and the predictive makespan. In view of the time-consuming characteristic of simulation-based robustness measure (RMsim), this paper puts forward new SMs and investigates their performance through simulations. By utilizing the structure of schedule and the available information of stochastic processing times, two SMs on the basis of minimizing the robustness degradation on the critical path and the non-critical path are suggested. For this purpose, a hybrid estimation of distribution algorithm (HEDA) is adopted to conduct the simulations. To analyze the performance of the presented SMs, two computational experiments are carried out. Specifically, the correlation analysis is firstly conducted by comparing the coefficient of determination between the presented SMs and the corresponding simulation-based robustness values with those of the existing SMs. Secondly, the effectiveness and the performance of the presented SMs are further validated by comparing with the simulation-based robustness measure under different uncertainty levels. The experimental results demonstrate that the presented SMs are not only effective for assessing the robustness of SJSSP no matter the uncertainty levels, but also require a tremendously lower computational burden than the simulation-based robustness measure. Full article
(This article belongs to the Special Issue Smart Design, Smart Manufacturing and Industry 4.0)
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1762 KiB  
Article
Examining the Feasibilities of Industry 4.0 for the Hospitality Sector with the Lens of Management Practice
by Saqib Shamim, Shuang Cang, Hongnian Yu and Yun Li
Energies 2017, 10(4), 499; https://doi.org/10.3390/en10040499 - 7 Apr 2017
Cited by 101 | Viewed by 14729
Abstract
Industry 4.0 and its impact in the manufacturing sector are well documented. However, the service sector is understudied, and it is also facing the challenges of mass customization, digital enhancement, smart work environment, and efficient supply chain. The aim of this study is [...] Read more.
Industry 4.0 and its impact in the manufacturing sector are well documented. However, the service sector is understudied, and it is also facing the challenges of mass customization, digital enhancement, smart work environment, and efficient supply chain. The aim of this study is to fill this research gap by exploring the issues of Industry 4.0 in the service sector, with cases in the hospitality industry. All the challenges of Industry 4.0 require continuous innovation and learning, which is dependent on people and the enterprise’s capabilities. Appropriate management approaches can play a vital role in the development of dynamic capabilities, and an effective learning and innovation environment. This paper proposes a framework of management practices which can promote the environment of innovation and learning in an organization, and hence facilitate business to match the pace of Industry 4.0 by facilitating technology acceptance e.g., digital enhancements and implementation of cyber physical systems (CPS). This study integrates the literature with logical beliefs to suggest the appropriate management practices for Industry 4.0. It represents one of the initial attempts to draw research attention towards the important role of management practices in Industry 4.0, as most of the recent studies have been restricted to the technological aspects. Semi-structured interviews of hospitality employees are conducted to explore the management practices suitable for meeting the challenges of Industry 4.0, specifically for informing the service sector. Full article
(This article belongs to the Special Issue Smart Design, Smart Manufacturing and Industry 4.0)
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Review

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1516 KiB  
Review
Energy-Efficient Through-Life Smart Design, Manufacturing and Operation of Ships in an Industry 4.0 Environment
by Joo Hock Ang, Cindy Goh, Alfredo Alan Flores Saldivar and Yun Li
Energies 2017, 10(5), 610; https://doi.org/10.3390/en10050610 - 29 Apr 2017
Cited by 102 | Viewed by 15313
Abstract
Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in [...] Read more.
Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future. Full article
(This article belongs to the Special Issue Smart Design, Smart Manufacturing and Industry 4.0)
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Submitted Papers

Dear Colleagues,

Smart manufacturing lies in the centre of “Industrie 4.0” or “Industry 4.0”, the first a-priori engineered and the fourth “industrial (r)evolution”, referring to “the industrial value chain and technological evolution upgrading the factory floor to a mass be-spoke innovation centre” (Siemens 2015). Its revolutionary value lies in the mass customisation made available at mass production through cyber-physical integration, information and communication technology, and compute-automated design, in which computer science and technology play a key role.

Energy-efficient design for smart manufacturing and Industry 4.0 is applicable to smart designs for customised production, networked embedded systems, “smart factories”, “industrial internet”, “Internet of Things”, “Internet of Everything”, and “Internet+”. This Special Issue welcomes original, high-quality research papers or reviews on these topics, including developments of energy-efficient methodologies and business environments for Industry 4.0. It also welcomes related topics on energy-efficient autonomous manufacturing, computer technology for manufacturing automation, autonomous systems, smart robotic systems, computational intelligence aided engineering, machine learning, system monitoring, and advanced control.

Prof. Dr. Yun Li

Dr. Yi (Leo) Chen

Dr. Erfu Yang

Prof. Dr. Hongnian Yu

Prof. Jun Zhang

Prof. Huaqiang Yuan

Guest Editors
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