applsci-logo

Journal Browser

Journal Browser

Smart Manufacturing Networks for Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 20874

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Natural Sciences and Forestry, Department of Computer Science, University of Eastern Finland, 70211 Kuopio, Finland
Interests: nature-inspired computing methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart Manufacturing Networks for Industry 4.0 namely the integration of both the physical and virtual network world, is the trend of the global development era of production manufacturing. Effectively strengthening and improving the efficiency and performance of Smart Manufacturing Networks for Industry 4.0 by reinforcing operational reliability and deepening the interaction between humans and machines is crucial and is a top priority. Soft Computing, a critical part in the domain of artificial intelligence, has increasingly become an important modern computational intelligence in artificial intelligence and also has been proven to be efficient in solving numerous problems and optimizing processes/operations in various sciences and manufacturing network systems. Therefore, this Special issue will provide an excellent opportunity to present the latest scientific results and methods on the collaboration of Soft Computing in Smart Manufacturing Networks for Industry 4.0. This Special Collection aims to provide an international open access forum for the development, research, demonstration, and analysis of innovative knowledge along with information related to all topics in the collaboration of Soft Computing in Smart Manufacturing Networks for Industry 4.0. We welcome high-quality theoretical, conceptual, and empirical original research from all over the world.

Prof. Dr. Wei-Chang Yeh
Prof. Dr. Xiao-Zhi Gao
Dr. Omprakash Kaiwartya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • Smart Manufacturing Networks
  • Industry 4.0
  • Soft Computing
  • Soft Computing in Smart Manufacturing Networks for Industry 4.0
  • Smart Manufacturing Networks in reliability analytics
  • reliability for Industry 4.0
  • Smart Manufacturing Networks for Industry 4.0 in safety, security, and risk management
  • innovative research in Smart Manufacturing Networks for Industry 4.0
  • Smart Manufacturing Networks for Industry 4.0 in sustainable manufacturing
  • Smart Manufacturing Networks for Industry 4.0 in decision analysis

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 1888 KiB  
Article
Cloud Computing Considering Both Energy and Time Solved by Two-Objective Simplified Swarm Optimization
by Wei-Chang Yeh, Wenbo Zhu, Ying Yin and Chia-Ling Huang
Appl. Sci. 2023, 13(4), 2077; https://doi.org/10.3390/app13042077 - 6 Feb 2023
Cited by 5 | Viewed by 1528
Abstract
Cloud computing is an operation carried out via networks to provide resources and information to end users according to their demands. The job scheduling in cloud computing, which is distributed across numerous resources for large-scale calculation and resolves the value, accessibility, reliability, and [...] Read more.
Cloud computing is an operation carried out via networks to provide resources and information to end users according to their demands. The job scheduling in cloud computing, which is distributed across numerous resources for large-scale calculation and resolves the value, accessibility, reliability, and capability of cloud computing, is important because of the high development of technology and the many layers of application. An extended and revised study was developed in our last work, titled “Multi Objective Scheduling in Cloud Computing Using Multi-Objective Simplified Swarm Optimization MOSSO” in IEEE CEC 2018. More new algorithms, testing, and comparisons have been implemented to solve the bi-objective time-constrained task scheduling problem in a more efficient manner. The job scheduling in cloud computing, with objectives including energy consumption and computing time, is solved by the newer algorithm developed in this study. The developed algorithm, named two-objective simplified swarm optimization (tSSO), revises and improves the errors in the previous MOSSO algorithm, which ignores the fact that the number of temporary nondominated solutions is not always only one in the multi-objective problem, and some temporary nondominated solutions may not be temporary nondominated solutions in the next generation based on simplified swarm optimization (SSO). The experimental results implemented show that the developed tSSO performs better than the best-known algorithms, including nondominated sorting genetic algorithm II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and MOSSO in the convergence, diversity, number of obtained temporary nondominated solutions, and the number of obtained real nondominated solutions. The developed tSSO accomplishes the objective of this study, as proven by the experiments. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

35 pages, 2332 KiB  
Article
A Hybrid Algorithm Based on Simplified Swarm Optimization for Multi-Objective Optimizing on Combined Cooling, Heating and Power System
by Wei-Chang Yeh, Wenbo Zhu, Yi-Fan Peng and Chia-Ling Huang
Appl. Sci. 2022, 12(20), 10595; https://doi.org/10.3390/app122010595 - 20 Oct 2022
Cited by 2 | Viewed by 1182
Abstract
Energy demand is rising sharply due to the technological development and progress of modern times. Neverthless, traditional thermal power generation has several diadvantages including its low energy usage and emitting a lot of polluting gases, resulting in the energy depletion crisis and the [...] Read more.
Energy demand is rising sharply due to the technological development and progress of modern times. Neverthless, traditional thermal power generation has several diadvantages including its low energy usage and emitting a lot of polluting gases, resulting in the energy depletion crisis and the increasingly serious greenhouse effect. In response to environmental issues and energy depletion, the Combined Cooling, Heating and Power system (CCHP) combined with the power-generation system of renewable energy, which this work studied, has the advantages of high energy usage and low environmental pollution compared with traditional thermal power generation, and has been gradually promoted in recent years. This system needs to cooperate with the instability of renewable energy and the dispatch of the energy-saving system; the optimization of the system has been researched recently for this purpose. This study took Xikou village, Lieyu township, Kinmen county, Taiwan as the experimental region to solve the optimization problem of CCHP combined with renewable energy and aimed to optimize the multi-objective system including minimizing the operation cost, minimizing the carbon emissions, and maximizing the energy utilization rate. This study converted the original multi-objective optimization problem into a single-objective optimization problem by using the Technique for Order Preference by Similarity to and Ideal Solution (TOPSIS) approach. In addition, a hybrid of the simplified swarm optimization (SSO) and differential evolution (DE) algorithm, called SSO-DE, was proposed in this research to solve the studied problem. SSO-DE is based on SSO as the core of the algorithm and is combined with DE as the local search strategy. The contributions and innovations of the manuscript are clarified as follows: 1. a larger scale of CCHP was studied; 2. the parallel connection of the mains, allowing the exchange of power with the main grid, was considered; 3. the TOPSIS was adopted in this study to convert the original multi-objective optimization problem into a single-objective optimization problem; and 4. the hybrid of the DE algorithm with the improved SSO algorithm was adopted to improve the efficiency of the solution. The proposed SSO-DE in this study has an excellent ability to solve the optimization problem of CCHP combined with renewable energy according to the Friedman test of experimental results obtained by the proposed SSO-DE compared with POS-DE, iSSO-DE, and ABC-DE. In addition, SSO-DE had the lowest running time compared with POS-DE, iSSO-DE, and ABC-DE in all experiments. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

17 pages, 1355 KiB  
Article
Reliability of Social Networks on Activity-on-Node Binary-State with Uncertainty Environments
by Wei-Chang Yeh, Wenbo Zhu and Chia-Ling Huang
Appl. Sci. 2022, 12(19), 9514; https://doi.org/10.3390/app12199514 - 22 Sep 2022
Cited by 2 | Viewed by 1157
Abstract
Social networks (SNs) and many other industrial types of networks, structured by many nodes and relationships between nodes, have become an integral part of our daily lives. A binary-state network (BN) is often used to model structures and applications of SNs and other [...] Read more.
Social networks (SNs) and many other industrial types of networks, structured by many nodes and relationships between nodes, have become an integral part of our daily lives. A binary-state network (BN) is often used to model structures and applications of SNs and other networks. The BN reliability is the probability that a BN functions continuously, i.e., that there is always a path between a specific pair of nodes. This metric is a popular index for designing, managing, controlling, and evaluating networks. The traditional BN reliability assumes that the network is activity-on-arc, and the reliability of each arc is known in advance. However, this is not always the case. Functioning components operate in different environments; moreover, a network might have newly installed components. Hence, the reliability of these components is not always known. To resolve the aforementioned problems, in which the reliability of some components of a network is uncertain, we introduce the fuzzy concept for the analysis of these components and propose a new algorithm to solve this uncertainty-component activity-on-node BN reliability problem. The time complexity of the proposed algorithm is analyzed, and the superior performance of the algorithm is demonstrated through examples. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

28 pages, 2560 KiB  
Article
A New BAT and PageRank Algorithm for Propagation Probability in Social Networks
by Wei-Chang Yeh, Wenbo Zhu, Chia-Ling Huang, Tzu-Yun Hsu, Zhenyao Liu and Shi-Yi Tan
Appl. Sci. 2022, 12(14), 6858; https://doi.org/10.3390/app12146858 - 6 Jul 2022
Cited by 5 | Viewed by 1870
Abstract
Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations, including government organizations, academic research organizations and corporate organizations. Therefore, strategizing the optimal propagation strategy in social networks has [...] Read more.
Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations, including government organizations, academic research organizations and corporate organizations. Therefore, strategizing the optimal propagation strategy in social networks has also become more important. Increasing the precision of evaluating the propagation probability of social networks can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabási–Albert model, binary-addition tree (BAT) algorithm, PageRank algorithm, Personalized PageRank algorithm and a new BAT algorithm to calculate the propagation probability of social networks. The results obtained after implementing the simulation experiment of social network models show that the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

17 pages, 2289 KiB  
Article
Modeling and Profiling of Aggregated Industrial Network Traffic
by Mehrzad Lavassani, Johan Åkerberg and Mats Björkman
Appl. Sci. 2022, 12(2), 667; https://doi.org/10.3390/app12020667 - 11 Jan 2022
Cited by 5 | Viewed by 1309
Abstract
The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new technologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and [...] Read more.
The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new technologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

14 pages, 944 KiB  
Article
A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization
by Hota Chia-Sheng Lin, Chia-Ling Huang and Wei-Chang Yeh
Appl. Sci. 2021, 11(22), 10765; https://doi.org/10.3390/app112210765 - 15 Nov 2021
Cited by 4 | Viewed by 1568
Abstract
A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the [...] Read more.
A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the uncertainty condition of components used in the systems, which few studies have noticed this state over the years, is a concrete fact due to several reasons such as production conditions, different batches of raw materials, time reasons, and climatic factors. Therefore, this study adopts the fuzzy theory and credibility theory to solve the components uncertainty in the constraints of RRAP including cost, weight, and volume. Moreover, the simplified swarm optimization (SSO) algorithm has been adopted to solve the fuzzy constraints of RRAP. The effectiveness and performance of SSO algorithm have been experimented by four famous benchmarks of RRAP. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

27 pages, 1740 KiB  
Article
A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks
by Wenbo Zhu, Chia-Ling Huang, Wei-Chang Yeh, Yunzhi Jiang and Shi-Yi Tan
Appl. Sci. 2021, 11(21), 10197; https://doi.org/10.3390/app112110197 - 30 Oct 2021
Cited by 5 | Viewed by 1473
Abstract
The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors [...] Read more.
The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

Review

Jump to: Research

28 pages, 1323 KiB  
Review
The Vehicle Routing Problem: State-of-the-Art Classification and Review
by Shi-Yi Tan and Wei-Chang Yeh
Appl. Sci. 2021, 11(21), 10295; https://doi.org/10.3390/app112110295 - 2 Nov 2021
Cited by 37 | Viewed by 9781
Abstract
Transportation planning has been established as a key topic in the literature and social production practices. An increasing number of researchers are studying vehicle routing problems (VRPs) and their variants considering real-life applications and scenarios. Furthermore, with the rapid growth in the processing [...] Read more.
Transportation planning has been established as a key topic in the literature and social production practices. An increasing number of researchers are studying vehicle routing problems (VRPs) and their variants considering real-life applications and scenarios. Furthermore, with the rapid growth in the processing speed and memory capacity of computers, various algorithms can be used to solve increasingly complex instances of VRPs. In this study, we analyzed recent literature published between 2019 and August of 2021 using a taxonomic framework. We reviewed recent research according to models and solutions, and divided models into three categories of customer-related, vehicle-related, and depot-related models. We classified solution algorithms into exact, heuristic, and meta-heuristic algorithms. The main contribution of our study is a classification table that is available online as Appendix A. This classification table should enable future researchers to find relevant literature easily and provide readers with recent trends and solution methodologies in the field of VRPs and some well-known variants. Full article
(This article belongs to the Special Issue Smart Manufacturing Networks for Industry 4.0)
Show Figures

Figure 1

Back to TopTop