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Data Driven Decision-Making for Complex Production Systems

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A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems and Cybernetics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 40389

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Special Issue Editors

Dr. Zaoli Yang

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Guest Editor

School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Interests: intelligence decision support systems; expert systems and decision support; information management; artificial intelligence; fuzzy set theory; data mining; their application in various fields
Special Issues, Collections and Topics in MDPI journals

Dr. Yuchen Li

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Guest Editor

School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Interests: combinatorial optimization; stochastic programming; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals

Dr. Ibrahim Kucukkoc

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Guest Editor

Industrial Engineering department, Balikesir University, Balıkesir 10145 , Turkey
Interests: modelling and optimisation of modern manufacturing systems (assembly line balancing in particular); production planning of additive manufacturing machines and application of modern algorithms on sophisticated combinatorial optimisation problems

Special Issue Information

Dear Colleagues,

The complexity of production processes is increasing as technology and lifestyles change. The complexity may reside in the production system or result from characteristics or events outside the system. The former is technological complexity, which is related to the inherent complexity of the system and its technologies for both products and systems. The latter is environmental complexity, which describes the co-ordination between the system and related industries or customers, e.g., raw material supplier and retailer. The complexity poses a great challenge to the production systems. With the development of big data technology, data-driven decision-making (DDM) algorithms provide new tools and perspectives for human to further explore the complexity and uncertain problems in production systems. In particular, deep learning and artificial intelligence technologies can be used to analyze multi-source complexity factors in the production process, production planning and control systems and develop multi-source DDM algorithms accordingly, thus becoming an important way to solve the above problems. For example, the big data mining technology can be applied to extract multi-type features of each component in complex production systems, investigate the correlation between features, so as to diagnose the problems existing in complex production system. Based on multi-source features, the causal relationships between features can be constructed to identify the causes and mechanisms of problems, and the information fusion theory and comprehensive decision technology are also introduced to evaluate the performance of complex production systems, and so on.

This Special Issue aims to publish rigorous research based on the application of data driven decision-making algorithms to solve the various problems associated with complex production systems.

Potential topics include but are not limited to the following:

Developing advanced DDM algorithms;
DDM algorithms for real-world production planning problem;
DDM algorithms for extracting multi-type features of complex production systems;
Evaluating the production system performance by DDM algorithms;
Efficient computational methods for solving new mathematical models under uncertainty;
Emergent DDM techniques for modelling the uncertain factors;
Identification of interaction relationships between technological complexity and environmental complexity by DDM techniques;
Modelling dynamics of the production systems based on the DDM algorithms;
Production system performance prediction using the DDM algorithms;
Production system optimization modeling using the DDM algorithms.

Dr. Zaoli Yang
Dr. Yuchen Li
Dr. Ibrahim Kucukkoc
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.

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Published Papers (14 papers)

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Research

45 pages, 2796 KiB

Open AccessArticle

Synergy Management of a Complex Industrial Production System from the Perspective of Flow Structure

by Jiekun Song, Zeguo He, Lina Jiang, Zhicheng Liu and Xueli Leng

Systems 2023, 11(9), 453; https://doi.org/10.3390/systems11090453 - 1 Sep 2023

Viewed by 2397

Abstract

Modern industry has become very complex and requires an equally complex engineering technology system, which includes resource utilization, energy conversion, product research and development, technological innovation, environmental protection and industrial ecology, and other aspects of the system. Continued development of large-scale, streamlined, and continuous processes is critical; however, there are also problems such as data redundancy, overcapacity, redundant construction, and waste of resources. Based on the system synergy theory, this paper introduces the system analysis method from the perspective of flow structure, with the purpose of solving the management defects of complex industrial production systems. First, we analyze the complex industrial production system as a collaborative structure of three subsystems: material flow, energy flow, and information flow. The following concepts are clarified: “material flow is the main body, energy flow is attached to and drives material flow, material flow and energy flow generate information flow, and information flow reversely drives material flow and energy flow”. Secondly, the collaborative evolution process of the complex industrial production system is divided into three periods, which are the generation period, the stalemate period, and the maturity period, and a synergy degree evaluation model is established, which considers the Theil index and subsystem gray correlation method, and extends the dynamic differential equation model of three-stage collaborative evolution. Subsequently, we used MATLAB numerical simulation to demonstrate that the collaborative evolution of production systems is related to four aspects. They are the self-organizing ability of the system, the dominant role of order parameters, the competition and cooperation between order parameters, and whether mutations can become order parameters. At the same time, it was also found that it is basically independent of other factors, such as attenuation inertia. Then, the self-organizing map network (SOM) algorithm was used for the rapid identification of mutation data. Finally, we use the empirical research of SG enterprises to show that their production level and management system are advanced, but they were in a non-cooperative state from 2014 to 2021. In 2022, they had the basic conditions and trends to enter the synergistic generation period, and a synergistic management model is required. At the end of the article, we give a collaborative management method for complex industrial enterprises with a good management foundation. These include the management mechanism based on flow structure collaboration and the management path based on collaborative evolution. Of course, the management countermeasures given in this study are also applicable to other complex process-based industrial enterprises. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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23 pages, 1151 KiB

Open AccessArticle

An Integrated EDAS Model for Fermatean Fuzzy Multi-Attribute Group Decision Making and Its Application in Green-Supplier Selection

by Shouzhen Zeng, Wendi Chen, Jiaxing Gu and Erhua Zhang

Systems 2023, 11(3), 162; https://doi.org/10.3390/systems11030162 - 21 Mar 2023

Cited by 14 | Viewed by 2003

Abstract

The environment and economy benefit from the sustained growth of a high-quality green supplier. During a supplier evaluation and selection process, DMs tend to use fuzzy tools to express evaluation information due to complex practical problems. Therefore, this study explores the green-supplier evaluation method in a complex Fermatean fuzzy (FF) environment. First, a group of indicators was created to evaluate the green capabilities and the social impact of suppliers. Second, by combining the merits of the Heronian mean and power average approaches, a FF power Heronian mean and its weighted framework were developed, and their related properties and special families were then presented. Third, to acquire the relative importance of indicators, a marvelous unification of the best–worst method (BWM) and FF entropy is then introduced. The challenge of choosing a green supplier was finally solved using an integrated evaluation based on distance from the average solution (EDAS) evaluation framework in the FF environment. Finally, the presented tool’s viability and robustness were confirmed by actual case analysis. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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13 pages, 3624 KiB

Open AccessArticle

A Study of the Strategic Interaction in Environmental Regulation Based on Spatial Effects

by Hewen Gao, Fei Li, Jinhua Zhang and Yu Sun

Systems 2023, 11(2), 62; https://doi.org/10.3390/systems11020062 - 23 Jan 2023

Viewed by 1611

Abstract

The incomplete enforcement of environmental regulations in China is a serious issue in environmental protection affairs, and this paper attempts to provide a new explanation for its prevalence from the perspective of strategic interaction. Under Chinese decentralization, environmental regulations are seen by local governments as a tool to compete for scarce resources, which leads to strategic interactions between regions. Therefore, under the theoretical framework of regional policy spillovers, this paper examines the strategic interaction behavior of local governments in environmental regulation with a spatial econometric approach research methodology based on panel data of 29 Chinese provinces (autonomous regions and municipalities directly under the central government) from 2015 to 2019, taking spatial interdependence and the strategic interaction relationship of local governments as the entry point. The study finds that the intensity of environmental regulation in a region is not only related to the characteristics of the region, but also related to the intensity of environmental regulation in competing provinces, and there is a significant strategic interaction of environmental regulation behavior between regions, which is manifested as complementary spatial strategies. If the neighboring provinces invest more in environmental regulation, the region will also strengthen its level of environmental regulation accordingly, showing the contagiousness of non-complete enforcement of environmental regulation. At the same time, the complementary strategic interaction behavior of environmental regulation between regions has weakened since 2017, which highlights the role of green environmental performance assessment. Based on this, this paper proposes to provide a policy reference to avoid the environmental regulation enforcement dilemma. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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18 pages, 3433 KiB

Open AccessArticle

Simulation of Manufacturing Scenarios’ Ambidexterity Green Technological Innovation Driven by Inter-Firm Social Networks: Based on a Multi-Objective Model

by Xuan Wei, Hongyu Wu, Zaoli Yang, Chunjia Han and Bing Xu

Systems 2023, 11(1), 39; https://doi.org/10.3390/systems11010039 - 10 Jan 2023

Cited by 5 | Viewed by 2424

Abstract

The mechanism of the impact of inter-firm social networks on innovation capabilities has attracted much research from both theoretical and empirical perspectives. However, as a special emerged and developing complex production system, how the scenario factors affect the relationship between these variables has not yet been analyzed. This study identified several scenario factors which can affect the firm’s technological innovation capabilities. Take the manufacturing scenario in China as an example, combined with the need for firms’ ambidexterity innovation and green innovation capability, a multi-objective simulation model is constructed. Past empirical analysis results on the relationship between inter-firm social network factors and innovation capabilities are used in the model. In addition, a numerical analysis was conducted using data from the Chinese auto manufacturing industry. The results of the simulation model led to several optimization strategies for firms that are in a dilemma of development in the manufacturing scenario. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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18 pages, 2724 KiB

Open AccessArticle

Parallel Learning of Dynamics in Complex Systems

by Xueqin Huang, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang and Ailin Liang

Systems 2022, 10(6), 259; https://doi.org/10.3390/systems10060259 - 15 Dec 2022

Cited by 2 | Viewed by 1866

Abstract

Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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20 pages, 548 KiB

Open AccessArticle

A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data

by Yun Zhang and Chaoxia Qin

Systems 2022, 10(6), 258; https://doi.org/10.3390/systems10060258 - 15 Dec 2022

Cited by 2 | Viewed by 2245

Abstract

Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping heterogeneous fuzzy data to a unified distribution space by adjusting the mean and variance of data from each information source. As a result of combining the normalized descriptions from various sources for an object, we can obtain a fused representation of that object. We then derive an adaptive Gaussian-shaped membership function based on the addition law of the Gaussian distribution. GFIS uses it to dynamically granulate fusion inputs and to design inference rules. This proposed membership function has the advantage of being able to adapt to changing information sources. Finally, we integrate the normalization method and adaptive membership function to the Takagi–Sugeno (T–S) model and present a modified fuzzy inference framework. Applying our methodology to four datasets, we confirm that the data do lend support to the theory implying the improved performance and effectiveness. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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21 pages, 2462 KiB

Open AccessArticle

Novel Hybrid MPSI–MARA Decision-Making Model for Support System Selection in an Underground Mine

by Miloš Gligorić, Zoran Gligorić, Suzana Lutovac, Milanka Negovanović and Zlatko Langović

Systems 2022, 10(6), 248; https://doi.org/10.3390/systems10060248 - 9 Dec 2022

Cited by 16 | Viewed by 2647

Abstract

An underground mine is a very complex production system within the mining industry. Building up the underground mine development system is closely related to the installation of support needed to provide the stability of mine openings. The selection of the type of support system is recognized as a very hard problem and multi-criteria decision making can be a very useful tool to solve it. In this paper we developed a methodology that helps mining engineers to select the appropriate support system with respect to geological conditions and technological requirements. Accordingly, we present a novel hybrid model that integrates the two following decision-making components. First, this study suggests a new approach for calculating the weights of criteria in an objective way named the Modified Preference Selection Index (MPSI) method. Second, the Magnitude of the Area for the Ranking of Alternatives (MARA) method is proposed as a novel multi-criteria decision-making technique for establishing the final rank of alternatives. The model is tested on a hypothetical example. Comparative analysis confirms that the new proposed MPSI–MARA model is a very useful and effective tool for solving different MCDM problems. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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14 pages, 410 KiB

Open AccessArticle

Type-1 Robotic Assembly Line Balancing Problem That Considers Energy Consumption and Cross-Station Design

by Yuanying Chi, Zhaoxuan Qiao, Yuchen Li, Mingyu Li and Yang Zou

Systems 2022, 10(6), 218; https://doi.org/10.3390/systems10060218 - 15 Nov 2022

Cited by 3 | Viewed by 2452

Abstract

Robotic assembly lines are widely applied to mass production because of their adaptability and versatility. As we know, using robots will lead to energy-consumption and pollution problems, which has been a hot-button topic in recent years. In this paper, we consider an assembly line balancing problem with minimizing the number of workstations as the primary objective and minimizing energy consumption as the secondary objective. Further, we propose a novel mixed integer linear programming (MILP) model considering a realistic production process design—cross-station task, which is an important contribution of our paper. The “cross-station task” design has already been applied to practice but rarely studied academically in type-1 RALBP. A simulated annealing algorithm is developed, which incorporates a restart mechanism and an improvement strategy. Computational tests demonstrate that the proposed algorithm is superior to two other classic algorithms, which are the particle swarm algorithm and late acceptance hill-climbing algorithm. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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20 pages, 3012 KiB

Open AccessArticle

An Approach for Predictive Maintenance Decisions for Components of an Industrial Multistage Machine That Fail before Their MTTF: A Case Study

by Francisco Javier Álvarez García and David Rodríguez Salgado

Systems 2022, 10(5), 175; https://doi.org/10.3390/systems10050175 - 29 Sep 2022

Cited by 6 | Viewed by 3269

Abstract

Making the correct maintenance strategy decision for industrial multistage machines (MSTM) is a constant challenge for industrial manufacturers. Preventive maintenance strategies are the most popular and provide interesting results but cannot prevent unexpected failures and consequences, such as time lost production (TLP). In these cases, a predictive maintenance strategy should be used to maintain the appropriate level of operation time. This research aims to present a model to identify the component that failed before its mean time to failure (MTTF) and, depending on whether the cause of the failure is known, propose the use of a predictive maintenance strategy and further decision-making to ensure the highest possible value from operating time. Also, it is necessary to check the reliable value of MTTF before taking certain decisions. For this research, a real case study of a MSTM was characterized component by component, setting the individual maintenance times. The initial maintenance strategy used for all the components is the preventive programming maintenance (PPM). If a component presents an unexpected failure, a method is proposed to decide whether the maintenance strategy should be changed, adding a predictive maintenance strategy to monitor said component. The research also provides a trust level to evaluate the reliable value of MTTF of each component. The authors consider this approach very useful for machine manufacturers and end users. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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16 pages, 512 KiB

Open AccessArticle

The Regulatory Architecture of Digital Platforms: A Perspective of Life Cycle and Risk Management

by Cong Xu and Yu-Min Wang

Systems 2022, 10(5), 145; https://doi.org/10.3390/systems10050145 - 8 Sep 2022

Cited by 1 | Viewed by 2146

Abstract

The rise of internet platforms meets people’s needs for a better life. However, the platforms also pose the risk of ecological monopolies. Using the methodology of economic analysis of law, with the help of ANT theory, the laws governing the operation of the platform ecosystem are discovered, and the paper analyzes the life cycle of digital platform development and figures out that the regulatory strategy for platforms should be adjusted to follow its life cycle and adopt more intuitive evaluation criteria for assessing market power. Meanwhile, the regulatory strategy for plat-forms could fully guarantee the active participation of multiple subjects, such as operators and consumers, in the platform’s governance. With the continuous advancement of data and algorithm technology, new content service providers will continue to emerge, and a new industry is developing. Besides the dynamic track analysis of platforms’ life cycles, another static research outcome is also given in this research. To ensure that the algorithmic technologies developed by the platform truly contribute to economic and social development and the well-being of people, the right to interpret algorithms and the establishment of scenario-based regulation of algorithms should be established. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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15 pages, 1082 KiB

Open AccessArticle

An Integrated Entropy-COPRAS Framework for Ningbo-Zhoushan Port Logistics Development from the Perspective of Dual Circulation

by Shouzhen Zeng, Zitong Fang, Yuhang He and Lina Huang

Systems 2022, 10(5), 131; https://doi.org/10.3390/systems10050131 - 25 Aug 2022

Cited by 3 | Viewed by 2854

Abstract

To promote the construction of new development patterns of dual circulation and to accelerate the smooth flow of logistics channels, port logistics has become a new growth point for the logistics industry that accelerates the connection between domestic and foreign dual circulation. Ningbo-Zhoushan Port, as one of the main hub ports in China, is facing the key issue of how to clarify its current development status and future development direction. To scientifically measure and evaluate the status quo of the logistics development of Ningbo-Zhoushan Port, clarify the advantages and disadvantages of the development and construction of the Ningbo-Zhoushan Port logistics industry, based on the situation of new standards and new requirements for the logistics industry in the dual circulation pattern, this study firstly constructs a scientific and reasonable evaluation index system of port logistics from seven aspects, including port infrastructure, international logistics capacity, and smart logistics capacity. An integrated comprehensive evaluation method based on entropy and Complex Proportional Assessment (COPRAS) is then proposed, and a comprehensive evaluation and longitudinal comparative analysis of the logistics level of Ningbo-Zhoushan Port are carried out. The results show that the development of Ningbo-Zhoushan Port in recent years is in line with that of many other ports due to the benefit of green logistics capacity, but it is seriously limited by smart logistics capabilities, and in the future, it should choose to continue to exert efforts in international logistics capabilities, green logistics capabilities, and total logistics capabilities. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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17 pages, 508 KiB

Open AccessArticle

Modeling Emergency Logistics Location-Allocation Problem with Uncertain Parameters

by Hui Li, Bo Zhang and Xiangyu Ge

Systems 2022, 10(2), 51; https://doi.org/10.3390/systems10020051 - 17 Apr 2022

Cited by 6 | Viewed by 3210

Abstract

In order to model the emergency facility location-allocation problem with uncertain parameters, an uncertain multi-objective model is developed within the framework of uncertainty theory. The proposed model minimizes time penalty cost, distribution cost and carbon dioxide emissions. The equivalents of the model are discussed via operational laws of uncertainty distribution. By employing the goal attainment technique, a series of Pareto-optimal solutions are generated that can be used for decision-making. Finally, several numerical experiments are presented to verify the validity of the proposed model and to illustrate decision-making strategy. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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48 pages, 1439 KiB

Open AccessArticle

Optimal Asynchronous Dynamic Policies in Energy-Efficient Data Centers

by Jing-Yu Ma, Quan-Lin Li and Li Xia

Systems 2022, 10(2), 27; https://doi.org/10.3390/systems10020027 - 2 Mar 2022

Cited by 3 | Viewed by 2858

Abstract

In this paper, we apply a Markov decision process to find the optimal asynchronous dynamic policy of an energy-efficient data center with two server groups. Servers in Group 1 always work, while servers in Group 2 may either work or sleep, and a fast setup process occurs when the server’s states are changed from sleep to work. The servers in Group 1 are faster and cheaper than those of Group 2 so that Group 1 has a higher service priority. Putting each server in Group 2 to sleep can reduce system costs and energy consumption, but it must bear setup costs and transfer costs. For such a data center, an asynchronous dynamic policy is designed as two sub-policies: The setup policy and the sleep policy, both of which determine the switch rule between the work and sleep states for each server in Group 2. To find the optimal asynchronous dynamic policy, we apply the sensitivity-based optimization to establish a block-structured policy-based Markov process and use a block-structured policy-based Poisson equation to compute the unique solution of the performance potential by means of the RG-factorization. Based on this, we can characterize the monotonicity and optimality of the long-run average profit of the data center with respect to the asynchronous dynamic policy under different service prices. Furthermore, we prove that a bang–bang control is always optimal for this optimization problem. We hope that the methodology and results developed in this paper can shed light on the study of more general energy-efficient data centers. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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27 pages, 10294 KiB

Open AccessArticle

Simulation of Cooperation Scenarios of BRI-Related Countries Based on a GVC Network

by Dawei Wang, Jun Guan, Chunxiu Liu, Chuke Jiang and Lizhi Xing

Systems 2022, 10(1), 12; https://doi.org/10.3390/systems10010012 - 3 Feb 2022

Cited by 6 | Viewed by 3140

Abstract

The inter-country input–output table is appropriate for presenting sophisticated inter-industry dependencies from a global perspective. Using the above table one can perceive the amount of production resources that sectors obtain from their upstream ones, as well as the number of productive capacities that sectors provide for their downstream ones. In other words, competition/collaboration occurs when sectors share the same providers/consumers because all sectors’ products and services outputted to downstream ones are limited. Thus, inter-industry competition for inputs from upstream sectors, or collaboration on outputs to downstream sectors, may be quantified with input–output matrix transformation. In this paper, a novel analytical framework of inter-industry collaborative relations is established based on the bipartite graph theory and the resource allocation process. The Collaborative Opportunity Index and Collaborative Threat index are designed to quantitatively measure the industrial influence hidden in the topological structure of the global value chain (GVC) network. Scenario simulations are carried out to forecast the potential and trends of international capacity cooperation within Asian, European, and African nations related to the Belt and Road Initiative, respectively. Full article

(This article belongs to the Special Issue Data Driven Decision-Making for Complex Production Systems)

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