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A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Engineering".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 14325

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Special Issue Editors

Prof. Dr. Pingyu Jiang

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Guest Editor

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: networked digital manufacturing; service-oriented manufacturing systems engineering; RFID/ IOT; logistics engineering; product family design theory; collaborative engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guozhu Jia

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Guest Editor

School of Management, Beihang University, Beijing 100084, China
Interests: production and operations management; supply Chain management; process management

Prof. Dr. Yuchun Xu

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Guest Editor

School of Engineering and Technology, Aston University, Birmingham B4 7ET, UK
Interests: smart and sustainable manufacturing; life cycle engineering and optimisation; digital product development and manufacturing; cost modelling & engineering economic analysis; circular economy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bernd Kuhlenkötter

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Productionsystems, Ruhr-University Bochum, 44801 Bochum, Germany
Interests: mechanical design; sheet metal forming; tool path; forming limit diagram

Prof. Dr. Petri Helo

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School of Technology and Innovations, Industrial Management, University of Vaasa, 65200 Vaasa, Finland
Interests: business, management and accounting engineering computer science decision sciences social sciences energy mathematics environmental science economics, econometrics and finance chemical engineering mat

Dr. Wei Guo

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Guest Editor

State Key Lab for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 610049, China
Interests: social manufacturing

Special Issue Information

Dear Colleagues,

Under scenarios of Industry 4.0/5.0, manufacturing and service systems have emerged as key technologies for improving productivity, efficiency, and quality. The manufacturing and service systems with the core of digitalization, networking, and intelligence are becoming a powerful driving force enabling the manufacturing and services of high value-added products. New-generation information technologies (New ITs) are the core support for Industry 4.0/5.0. They greatly stimulate the development of manufacturing and service systems, such as artificial intelligence, Internet of Things (IoT), cloud computing, cyber-physical systems (CPS), blockchain, and big data analytics. Besides that, New ITs sharply increase the development of human-to-human, human-to-machine, and machine-to-machine systems with high quality and integration levels. As a result, New ITs are revolutionizing product design, manufacturing, services, and supply chains. However, the organic combination of manufacturing and service systems with New ITs still has a long way to go in order to realize intelligent manufacturing services, service-oriented intelligent manufacturing, smart service supply chains, etc.

This Special Issue welcomes a wide range of emerging topics related to advances and applications in intelligent manufacturing and service systems for Industry 4.0/5.0, with high-quality contributions addressing related theoretical and practical aspects. In this Special Issue, potential contributions related to original research articles, reviews, communications and technical notes are welcome. Research areas may include (but are not limited to) the following:

Debates regarding the conation and concept architecture of manufacturing and service systems;
Digital, networked and intelligent manufacturing systems within the context of Industry 4.0/5.0;
Manufacturing servitization within the context of Industry 4.0/5.0;
Smart, cloud and social manufacturing service systems based on new-generation information technologies;
Big data analytics to enable on-demand manufacturing service systems;
IoT-based manufacturing and service systems;
Smart product-service systems;
Product design for services, additive manufacturing, or others;
Generative and computational product design systems;
Product life-cycle engineering and systems;
Product remote monitoring, fault diagnosis and maintenance systems;
Manufacturing service supply chain and logistics management;
Intelligent and sustainable supply chain management;
Human factors within the context of Industry 4.0/5.0;
Blockchain technologies in manufacturing and service systems;
Green manufacturing and low-carbon manufacturing systems;
Artificial intelligence in manufacturing and service systems;
Manufacturing and service systems with large multi-modal models such as ChatGPT series;
Systems engineering in manufacturing and service systems;
System of systems in manufacturing and service systems;
Technology and innovation management in manufacturing and service systems;
New CAD/CAE/CAPP/CAM/MES/PDM/PLM/ERP systems and integration on the Internet or Web;
Next-generation industrial software models for Industry 4.0/5.0;
Intelligent and interconnected equipment, protocols, and sensor networks;
Industrial Internet service platforms or distributed networks;
Collective intelligence in the context of distributed manufacturing and services;
Social impacts in manufacturing and service systems;
Next-generation manufacturing paradigms for Industry 4.0/5.0;
Industrial applications of intelligent manufacturing and service systems;
Case studies of manufacturing and service systems for industrial scenarios, especially in large and complex manufacturing systems.

Please note that authors can submit their papers to the Special Issue at any time. Papers will be published online immediately after their acceptance and without delays caused by whether all paper collections are ready. This is different from the policy of a traditional Special Issue.

We look forward to hearing from you.

Prof. Dr. Pingyu Jiang
Prof. Dr. Guozhu Jia
Prof. Dr. Yuchun Xu
Prof. Dr. Bernd Kuhlenkötter
Prof. Dr. Petri Helo
Dr. Wei Guo
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. Systems is an international peer-reviewed open access monthly 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

manufacturing and service systems
product service systems
product design for services
supply chain
product life-cycle engineering and systems
industry 4.0/5.0
intelligent and interconnected equipment
fault diagnosis and maintenance
cyber physical social systems
artificial intelligence in manufacturing
systems engineering in manufacturing
system of systems in manufacturing
big data
industrial Internet
industrial Internet of Things
industrial software models
social manufacturing
cloud manufacturing
smart manufacturing

Published Papers (10 papers)

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Research

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20 pages, 2287 KiB

Open AccessArticle

Modeling Dynamic Bargaining and Stability in a Star-Shaped Trans-Shipment Network

by Shiyong Peng, Qingren He, Fei Xu and Wanhua Qiu

Systems 2024, 12(4), 108; https://doi.org/10.3390/systems12040108 - 23 Mar 2024

Viewed by 577

Abstract

The star-shaped trans-shipment network causes the retailer’s bargaining power to be different, which leads to the misalignment of trans-shipment profit. Aimed at this, we take retailers and the trans-shipment paths as the nodes and edges of the trans-shipment network. Based on this, we model the multilateral negotiations between the central retailer and the local retailer and adopt the Generalized Nash Bargaining game to derive the optimal solution of the value function for the incomplete trans-shipment network under the bargaining mechanism. Furthermore, we reveal the convexity of the optimal trans-shipment value function and give the condition that the allocation of the bargaining mechanism is in the core. Based on this, we introduce the concept of pairwise Nash equilibrium and show the star-shaped trans-shipment network is the optimal endogenous formation of the trans-shipment network. In practice, the central retailer should introduce as many local retailers as possible to join this trans-shipment alliance, which will achieve Pareto improvement. Meanwhile, the central retailer should order as many as possible. Finally, it is more appropriate to establish a star-shaped trans-shipment network when one retailer has stronger negotiation power compared to other retailers in a distribution system, which not only ensures the stability of the allocation of trans-shipment profits but also the stability of the trans-shipment network. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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32 pages, 2858 KiB

Open AccessArticle

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

by Enrico Alberti, Sergio Alvarez-Napagao, Victor Anaya, Marta Barroso, Cristian Barrué, Christian Beecks, Letizia Bergamasco, Sisay Adugna Chala, Victor Gimenez-Abalos, Alexander Graß, Daniel Hinjos, Maike Holtkemper, Natalia Jakubiak, Alexandros Nizamis, Edoardo Pristeri, Miquel Sànchez-Marrè, Georg Schlake, Jona Scholz, Gabriele Scivoletto and Stefan Walter

Systems 2024, 12(2), 48; https://doi.org/10.3390/systems12020048 - 02 Feb 2024

Cited by 1 | Viewed by 1943

Abstract

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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16 pages, 5077 KiB

Open AccessArticle

Modeling and Solving the Joint Replenishment Problem with Cross-Selling Effects Considering One Shared Minor Item

by Meng Yi and Renqian Zhang

Systems 2024, 12(1), 6; https://doi.org/10.3390/systems12010006 - 22 Dec 2023

Viewed by 1161

Abstract

In this paper, we provide a model to handle multiple replenishment cycles and the cross-selling of multiple major items with one minor item, while allowing partial late delivery. The optimization analytic expression of the model is finally obtained by utilizing the convexity of cost function for F and using the first-order conditions in optimization theory. Numerical examples and sensitivity analysis demonstrate the effectiveness of the model and algorithm, which offers a competent solution for practical applications. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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21 pages, 3484 KiB

Open AccessArticle

Modeling and IAHA Solution for Task Scheduling Problem of Processing Crowdsourcing in the Context of Social Manufacturing

by Gaohong Zhu and Dianting Liu

Systems 2023, 11(8), 383; https://doi.org/10.3390/systems11080383 - 27 Jul 2023

Viewed by 699

Abstract

The paper addresses the discrete characteristics of the processing crowdsourcing task scheduling problem in the context of social manufacturing, divides it into two subproblems of social manufacturing unit selecting and subtask sorting, establishes its mixed-integer programming with the objective of minimizing the maximum completion time, and proposes an improved artificial hummingbird algorithm (IAHA) for solving it. The IAHA uses initialization rules of global selection, local selection, and random selection to improve the quality of the initial population, the Levy flight to improve guided foraging and territorial foraging, the simplex search strategy to improve migration foraging to enhance the merit-seeking ability, and the greedy decoding method to improve the quality of the solution and reduce solution time. For the IAHA, orthogonal tests are designed to obtain the optimal combination of parameters, and comparative tests are made with variants of the AHA and other algorithms on the benchmark case and a simulated crowdsourcing case. The experimental results show that the IAHA can obtain superior solutions in many cases with economy and effectiveness. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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21 pages, 14463 KiB

Open AccessArticle

An Industrial Case Study on the Monitoring and Maintenance Service System for a Robot-Driven Polishing Service System under Industry 4.0 Contexts

by Yuqian Yang, Maolin Yang, Siwei Shangguan, Yifan Cao, Wei Yue, Kaiqiang Cheng and Pingyu Jiang

Systems 2023, 11(7), 376; https://doi.org/10.3390/systems11070376 - 22 Jul 2023

Viewed by 1694

Abstract

Remote monitoring and maintenance are important for improving the performance of production systems. However, existing studies on this topic usually focus on the monitoring and maintenance of the working conditions of the equipment and pay relatively less attention to the processing craft and processing quality. In addition, as far as we know, there are relatively few industrial case studies on the real applications of remote monitoring and maintenance systems that include both conventional and advanced maintenance techniques under the context of Industry 4.0. Addressing these issues, an industrial case study on the monitoring and maintenance service system for a robot-driven carbon block polishing service system is presented, including its application background and engineering problems, software/hardware architecture and running logic, the monitoring and maintenance-related enabling techniques, and the configuration and operation workflows of the system in the form of screenshots of the functional WebAPPs of the software system. The case study can provide real examples and references for the industrial application of remote monitoring and maintenance service systems on industrial product service systems under the context of Industry 4.0. Advanced techniques such as the Industrial Internet of Things, digital twins, deep learning, and edge/cloud/fog computing have been applied to the system. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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30 pages, 7015 KiB

Open AccessArticle

Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions

by Honglin Yi, Ting Qu, Kai Zhang, Mingxing Li, George Q. Huang and Zefeng Chen

Systems 2023, 11(7), 371; https://doi.org/10.3390/systems11070371 - 19 Jul 2023

Cited by 2 | Viewed by 1854

Abstract

At present, the development of the global manufacturing industry is still in the transition stage from Industry 3.0 to Industry 4.0 (i.e., Industry 3.X), and the production logistics system is becoming more and more complex due to the individualization of customer demands and the high frequency of order changes. In order to systematically analyze the research status and dynamic evolution trend of production logistics in the Industry 3.X stage, this paper designed a Log-Likelihood ratio-based latent Dirichlet allocation (LLR-LDA) algorithm based on bibliometrics and knowledge graph technology, taking the literature of China National Knowledge Infrastructure and Web of Science database as the data source. In-depth bibliometric analysis of literature was carried out from research progress, hotspot evolution, and frontier trends. At the same time, taking the case of scientific research projects overcome by our research group as an example, it briefly introduced the synchronized decision-making framework of digital twin-enabled production logistics system. It is expected to broaden the research boundary of production logistics in the Industry 3.X stage, promote the development and progress of the industry, and provide valuable reference for steadily moving towards the Industry 4.0 stage. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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15 pages, 1378 KiB

Open AccessArticle

Decentralized Inventory Transshipments with Quantal Response Equilibrium

by Qingren He, Taiwei Shi, Fei Xu and Wanhua Qiu

Systems 2023, 11(7), 357; https://doi.org/10.3390/systems11070357 - 12 Jul 2023

Cited by 1 | Viewed by 916

Abstract

Despite the benefits of inventory transshipment, numerous behavioral experiments have revealed that retailers often deviate from the Nash-equilibrium ordering quantities, which in turn impacts the potential advantages. Motivated by this issue, we developed a behavioral model to analyze the deviation of ordering quantities among two independent retailers who engage in inventory transshipment from the perspective of analytical modeling. In our model, we incorporated bounded rationality with the quantal response equilibrium. Firstly, we established the existence of such a quantal response equilibrium and provided the conditions for its uniqueness. Secondly, we compared the quantal response equilibrium with the Nash equilibrium within a certain range of transshipment prices and observed that the limiting quantal response equilibrium is equivalent to the Nash equilibrium. Lastly, we design an iterative algorithm that incorporates the learning effects of the retailers to determine the quantal response equilibrium for the ordering quantity. The results indicate that the optimal ordering quantity and the nearby ordering quantities should be chosen with higher probabilities. Additionally, the retailer should gradually enhance their cognitive or computational abilities through repeated transshipment games to improve their decision-making process. Furthermore, to ensure a balanced inventory-sharing system, the evaluation of inventory strategies should consistently prioritize avoiding surplus instead of shortage. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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15 pages, 2267 KiB

Open AccessArticle

Reinforcement Learning for Optimizing Can-Order Policy with the Rolling Horizon Method

by Jiseong Noh

Systems 2023, 11(7), 350; https://doi.org/10.3390/systems11070350 - 07 Jul 2023

Viewed by 1112

Abstract

This study presents a novel approach to a mixed-integer linear programming (MILP) model for periodic inventory management that combines reinforcement learning algorithms. The rolling horizon method (RHM) is a multi-period optimization approach that is applied to handle new information in updated markets. The RHM faces a limitation in easily determining a prediction horizon; to overcome this, a dynamic RHM is developed in which RL algorithms optimize the prediction horizon of the RHM. The state vector consisted of the order-up-to-level, real demand, total cost, holding cost, and backorder cost, whereas the action included the prediction horizon and forecasting demand for the next time step. The performance of the proposed model was validated through two experiments conducted in cases with stable and uncertain demand patterns. The results showed the effectiveness of the proposed approach in inventory management, particularly when the proximal policy optimization (PPO) algorithm was used for training compared with other reinforcement learning algorithms. This study signifies important advancements in both the theoretical and practical aspects of multi-item inventory management. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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13 pages, 618 KiB

Open AccessArticle

Modified DEMATEL Method Based on Objective Data Grey Relational Analysis for Time Series

by Qun Wang, Kai Huang, Mark Goh, Zeyu Jiao and Guozhu Jia

Systems 2023, 11(6), 267; https://doi.org/10.3390/systems11060267 - 24 May 2023

Cited by 1 | Viewed by 1106

Abstract

Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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Review

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20 pages, 1263 KiB

Open AccessReview

Bibliometric Analysis of a Product–Service System’s Rebound Effect: Identification of a Potential Mitigation Strategy

by Salman Alfarisi, Yuya Mitake, Yusuke Tsutsui, Hanfei Wang and Yoshiki Shimomura

Systems 2023, 11(9), 452; https://doi.org/10.3390/systems11090452 - 01 Sep 2023

Viewed by 1110

Abstract

A product–service system (PSS) is a concept concerning sustainability, as it offers the opportunity to decouple economic success from material consumption, thereby reducing the environmental impact of economic activities. However, researchers have identified significant barriers frequently impeding sustainability potential, which are called rebound effects. Unfortunately, the existing studies are insufficient, and there are few published studies on the actual avoidance of the rebound effect, which is a significant limitation for practical applications for decision-makers and policymakers. Therefore, this study aims to conduct a comprehensive bibliometric review of the relationship between the rebound effect and PSSs, including its drivers and mitigation strategies. This study incorporates multiple perspectives to map and analyze the landscape of rebound effect research in the context of PSSs and used 152 articles from a systematic literature review covering all publication years. Using the Scopus and Web of Science database, journals, citations, authors, and keywords were identified. This study identified the annual trend of research, listed the most influential articles, and uncovered six research topic clusters related to the rebound effect and PSSs. As an innovative feature of this study, it categorised the identified drivers based on their contextual dependencies to elucidate their interrelationships. This study also presents a categorisation of mitigation strategies based on the type of approach. This study is expected to support decision-makers and practitioners in developing sustainable PSS implementation strategies. Full article

(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)

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