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Volume 12
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Systems, Volume 12, Issue 12 (December 2024) – 80 articles

Cover Story (view full-size image): This study explores the relationship between LMP and carbon emissions in electricity markets by combining system engineering and machine learning. Analyzing hourly data across New York State regions, we use neural networks to model market–emission interactions, examining LBMP, the marginal cost of losses, and congestion costs. Results reveal distinct regional variations in emission responses. Urban areas resist price signals, requiring targeted interventions beyond market mechanisms; rural regions demonstrate greater market sensitivity. Our findings indicate that effective emission reduction strategies must be tailored to regional characteristics rather than applying uniform approaches. This research provides valuable insights for policymakers aiming to optimize electricity markets for sustainability purposes. View this paper
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19 pages, 440 KiB  
Article
Understanding Individuals’ Continuance Intention to Use Advanced Driver Assistance Systems: An Integrated Application of Partial Least Squares Structural Equation Modeling and Necessary Condition Analysis
by Huijun Xiao, Weisheng Chiu and Shenglun Shen
Systems 2024, 12(12), 589; https://doi.org/10.3390/systems12120589 - 23 Dec 2024
Viewed by 455
Abstract
This study aimed to understand the factors that influence individuals’ intention to continue using advanced driver assistance systems (ADASs) through an integrated approach that extends the technology acceptance model (TAM). First, perceived safety, perceived quality, and satisfaction were incorporated into the traditional TAM [...] Read more.
This study aimed to understand the factors that influence individuals’ intention to continue using advanced driver assistance systems (ADASs) through an integrated approach that extends the technology acceptance model (TAM). First, perceived safety, perceived quality, and satisfaction were incorporated into the traditional TAM framework as additional constructs to address the complexities of ADAS usage. Second, an approach that combines partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) was employed to identify both the sufficient and necessary conditions for the continuous intention to use ADASs. This combined approach was directed toward data collected from 843 drivers hailing from the Greater Bay Area of China and experienced with ADAS usage. The findings revealed that perceived usefulness, perceived quality, perceived safety, and satisfaction significantly influenced continuance intention, while perceived ease of use indirectly affected it through perceived usefulness and satisfaction. This study underscores the paramount importance of safety and quality perceptions in ADAS adoption and offers practical insights that can help product design and marketing professionals enhance the acceptance and sustained use of ADAS technologies. Full article
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)
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28 pages, 2131 KiB  
Article
A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning
by Shanshan Zhu, Haotian Wu, Eric W. T. Ngai, Jifan Ren, Daojing He, Tengyun Ma and Yubin Li
Systems 2024, 12(12), 588; https://doi.org/10.3390/systems12120588 - 23 Dec 2024
Viewed by 602
Abstract
With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine [...] Read more.
With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors. Full article
(This article belongs to the Section Systems Practice in Social Science)
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29 pages, 5031 KiB  
Article
A Case of One Step Forward and Two Steps Back? An Examination of Herbicide-Resistant Weed Management Using a Simple Agroecosystem Dynamics Model
by Srinadh Kodali, Chris Flores-Lopez, Isabelle Lobdell, Branson Kim, James C. Russell, Lane Michna and Benjamin L. Turner
Systems 2024, 12(12), 587; https://doi.org/10.3390/systems12120587 - 22 Dec 2024
Viewed by 532
Abstract
Global herbicide-resistant weed populations continue rising due to selection pressures exerted by herbicides. Despite this, herbicides continue to be farmers’ preferred weed-control method due to cost and efficiency relative to physical or biological methods. However, weeds developing resistance to herbicides not only challenges [...] Read more.
Global herbicide-resistant weed populations continue rising due to selection pressures exerted by herbicides. Despite this, herbicides continue to be farmers’ preferred weed-control method due to cost and efficiency relative to physical or biological methods. However, weeds developing resistance to herbicides not only challenges crop production but also threatens ecosystem services by disrupting biodiversity, reducing soil health, and impacting water quality. Our objective was to develop a simulation model that captures the feedback between weed population dynamics, agricultural management, profitability, and farmer decision-making processes that interact in unique ways to reinforce herbicide resistance in weeds. After calibration to observed data and evaluation by subject matter experts, we tested alternative agronomic, mechanical, or intensive management strategies to evaluate their impact on weed population dynamics. Results indicated that standalone practices enhanced farm profitability in the short term but lead to substantial adverse ecological outcomes in the long term, indicated by elevated herbicide resistance (e.g., harm to non-target species, disrupting natural ecosystem functions). The most management-intensive test yielded the greatest weed control and farm profit, albeit with elevated residual resistant seed bank levels. We discuss these findings in both developed and developing-nation contexts. Future work requires greater connectivity of farm management and genetic-resistance models that currently remain disconnected mechanistically. Full article
(This article belongs to the Section Systems Practice in Social Science)
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25 pages, 1284 KiB  
Article
Synergies of Heterogeneous Environmental Regulation on the Quality of Foreign Direct Investment
by Zhaoyang Zhao, Yuhong Chen, Chong Ye and Lorenzo Lotti
Systems 2024, 12(12), 586; https://doi.org/10.3390/systems12120586 - 22 Dec 2024
Viewed by 533
Abstract
Expanding a high level of openness and attracting high-quality foreign direct investment (FDI) while preventing foreign-invested enterprises from relocating to host countries to reduce costs and circumvent environmental regulation (ER) in their home countries, which can transform host countries into “pollution heaven”, present [...] Read more.
Expanding a high level of openness and attracting high-quality foreign direct investment (FDI) while preventing foreign-invested enterprises from relocating to host countries to reduce costs and circumvent environmental regulation (ER) in their home countries, which can transform host countries into “pollution heaven”, present a significant challenge for emerging markets such as China. Based on a theoretical analysis that integrates various frameworks, this study constructs a panel regression model to empirically investigate the relationship between ER and the quality of FDI. This analysis is conducted from the perspectives of administrative means and market mechanisms, utilizing panel data from 267 prefectural-level cities in China spanning the years 2005 to 2021. This study reveals the following conclusions: (1) The implementation of ER significantly enhances the quality of FDI within cities, a conclusion that remains robust across various tests. (2) ER improves the quality of FDI through two key pathways: enhancing green competitiveness and fostering green technological innovation. (3) In comparison to the isolated effects of administrative and market mechanism policies, the synergistic effect of these two approaches proves to be more pronounced in elevating the quality of FDI. (4) ER exerts a significant impact on the quality of FDI, particularly within sub-samples of cities characterized by higher levels of environmental protection and a focus on non-resource-oriented activities. (5) ER has a negative spatial spillover effect on FDI quality. This study serves as a valuable guide for emerging markets to enhance environmental policy effectiveness and assess the potential for a new open economic system. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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17 pages, 3264 KiB  
Article
Understanding the Intersection of Central Environmental Protection Inspections and Green Investment Through Game Theory
by Tingru Zhao, Paijie Wan, Feng He, Hongjie Zhang and Xiaoqing Hou
Systems 2024, 12(12), 585; https://doi.org/10.3390/systems12120585 - 22 Dec 2024
Viewed by 415
Abstract
The Central Environmental Protection Inspector (CEPI) is an innovation in China’s environmental regulation. This paper uses game theory to analyze the influence of the CEPI on enterprises’ green investment. Firstly, by constructing the game model of “central government-local government-polluting enterprises”, the factors affecting [...] Read more.
The Central Environmental Protection Inspector (CEPI) is an innovation in China’s environmental regulation. This paper uses game theory to analyze the influence of the CEPI on enterprises’ green investment. Firstly, by constructing the game model of “central government-local government-polluting enterprises”, the factors affecting green investment strategy are analyzed. Then, with the help of a system simulation model, the influence of parameters on system stability and convergence trends is analyzed, so as to obtain the influence of the CEPI on enterprise green investment. The results show the following: (1) The CEPI can effectively promote preventive green investment, and the promotion effect of preventive green investment is proportional to its risk; (2) The effect of the CEPI on local governments is not obvious; (3) The cost of the CEPI is too high, and polluting enterprises are quick to choose remedial green investment. Full article
(This article belongs to the Section Systems Practice in Social Science)
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23 pages, 4707 KiB  
Article
Measuring the Systemic Risk of Clean Energy Markets Based on the Dynamic Factor Copula Model
by Wensheng Wang and Rui Wang
Systems 2024, 12(12), 584; https://doi.org/10.3390/systems12120584 - 21 Dec 2024
Viewed by 389
Abstract
This study is based on the stock returns of 11 subindustry markets in the international clean energy market from 2010 to 2024 and constructs a skewed t distribution dynamic factor copula model. The time-varying load factor is used to characterize the correlation between [...] Read more.
This study is based on the stock returns of 11 subindustry markets in the international clean energy market from 2010 to 2024 and constructs a skewed t distribution dynamic factor copula model. The time-varying load factor is used to characterize the correlation between a single subindustry market and the entire system, and the joint probability of distress is calculated as a measure of the overall level of systemic risk. Two indicators, Systemic Vulnerability Degree and Systemic Importance Degree, are introduced to evaluate the vulnerability of a single subindustry market in systemic risk and its contribution to systemic risk. A conditional risk-spillover index is constructed to measure the risk-spillover level between subindustry markets. This method fully considers the individual differences and inherent correlations of the international clean energy market subsectors, as well as the fat tail and asymmetry of returns, thus capturing more information and more timely information. This study found that the correlation between subindustry markets changes over time, and during the crisis, the market correlation shows a significant upward trend. In the measurement of the overall level of systemic risk, the joint probability of distress can identify the changes in systemic risk in the international clean energy market. The systemic risk of the international clean energy market presents the characteristics of rapid and multiple outbreaks, and the joint default risk probability of the whole system can exceed 0.6. The outbreak of systemic risk is closely related to a series of major international events, showing a strong correlation. In addition, the systemic vulnerability analysis found that the biofuel market has the lowest systemic vulnerability, and the advanced materials market has the highest vulnerability. The energy efficiency market is considered to be the most important market in the system. The advanced materials market and renewable energy market play a dominant role in the risk contribution to other markets, while the geothermal market, solar market, and wind energy market are net risk overflow parties in the tail risk impact, and the developer market and fuel cell market are net risk receivers. This study provides a theoretical basis for systemic risk management and ensuring the stability of the international clean energy market. Full article
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24 pages, 3662 KiB  
Article
An Exploration of Groundwater Resource Ecosystem Service Sustainability: A System Dynamics Case Study in Texas, USA
by Julianna Leal, Morgan Bishop, Caleb Reed and Benjamin L. Turner
Systems 2024, 12(12), 583; https://doi.org/10.3390/systems12120583 - 20 Dec 2024
Viewed by 533
Abstract
Groundwater, a crucial natural resource on a global scale, plays a significant role in Texas, impacting various essential ecosystem services either directly or indirectly. Despite efforts of state- and community-level regulations and conservation efforts, there is an ongoing trend of declining groundwater levels [...] Read more.
Groundwater, a crucial natural resource on a global scale, plays a significant role in Texas, impacting various essential ecosystem services either directly or indirectly. Despite efforts of state- and community-level regulations and conservation efforts, there is an ongoing trend of declining groundwater levels in the state of Texas. In this study, we utilized the systems thinking and system dynamics modeling approach to better understand this problem and investigate possible leverage points to achieve more sustainable groundwater resource levels. After conceptualizing a causal loop diagram (CLD) of the underlying feedback structure of the issue (informed by the existing literature), a small system dynamics (SD) model was developed to connect the feedback factors identified in the CLD to the stocks (groundwater level) and flows (recharge rate and groundwater pumping) that steer the behaviors of groundwater systems across time. After completing model assessment, experimental simulations were conducted to evaluate the current state relative to simulated treatments for improved irrigation efficiency, restricted pumping rates, cooperative conservation protocols among users, and combination strategy (of all treatments above) in the long-term. Results showed that groundwater stress (and the associated repercussions on related ecosystem service) could be alleviated with a combination strategy, albeit without complete groundwater level recovery. Full article
(This article belongs to the Section Systems Practice in Social Science)
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7 pages, 710 KiB  
Editorial
Introduction to the Special Issue on Systems Thinking and Strategic Management
by Martin Kunc and Federico Barnabè
Systems 2024, 12(12), 582; https://doi.org/10.3390/systems12120582 - 19 Dec 2024
Viewed by 633
Abstract
This paper summarizes the aim of the Special Issue and the insights from the papers submitted and accepted for it [...] Full article
(This article belongs to the Special Issue The Systems Thinking Approach to Strategic Management)
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22 pages, 900 KiB  
Article
Winning by Intelligence: Leveraging the Innovative Advantages of Intelligent Transformation in Market Competition
by Jingwen Lv, Lu Feng, Wei Xiao and Wei He
Systems 2024, 12(12), 581; https://doi.org/10.3390/systems12120581 - 19 Dec 2024
Viewed by 643
Abstract
Intelligent transformation plays an important role in enhancing innovation capabilities. This study utilizes data from 5000 A-share firms in China to investigate the effects of intelligent transformation on innovation capability and to elucidate the underlying mechanisms. We find that (1) there is a [...] Read more.
Intelligent transformation plays an important role in enhancing innovation capabilities. This study utilizes data from 5000 A-share firms in China to investigate the effects of intelligent transformation on innovation capability and to elucidate the underlying mechanisms. We find that (1) there is a positive relationship between intelligent transformation and innovation capability. Furthermore, pursuing profit maximization and sustainable competitive advantage drives firms to capitalize on the technological dividends of intelligent transformation to enhance substantive innovation capability. (2) Intelligent transformation affects the innovation capability and structure through the mechanism of total factor productivity and market competition. (3) Within the intelligent transformation, non-state-owned enterprises exert a stronger influence on enhancing innovation capability, but state-owned enterprises remain the primary drivers of substantive innovation. The synergistic effect of intelligent transformation, combined with the scale advantages of large enterprises, create substantial opportunities for firms to enhance their innovation capability. Additionally, intelligent transformation notably enhances both the innovation capability and the innovation quality of labor-intensive enterprises. While capital-intensive enterprises experience significant improvements in their overall innovation capability, their substantive innovation capability has not shown comparable advancements. These findings contribute to a more accurate evaluation of the effects of intelligent transformation on innovation capability. The findings also help cultivate differentiated competitive advantages and drive high-quality development through intelligent empowerment. Full article
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26 pages, 409 KiB  
Article
The Impact of Digital Trade on the Export Competitiveness of Enterprises—An Empirical Analysis Based on Listed Companies in the Yangtze River Economic Belt
by Lifan Yang, Weixin Yang, Longjiang Nan and Yuxun Gu
Systems 2024, 12(12), 580; https://doi.org/10.3390/systems12120580 - 19 Dec 2024
Viewed by 590
Abstract
With the in-depth development of globalization and informatization, digital trade, as an emerging form of trade, is gradually reshaping the global economic landscape and becoming a new engine for driving economic growth. Among them, the impact of digital trade on the export competitiveness [...] Read more.
With the in-depth development of globalization and informatization, digital trade, as an emerging form of trade, is gradually reshaping the global economic landscape and becoming a new engine for driving economic growth. Among them, the impact of digital trade on the export competitiveness of enterprises in developing countries has become a common concern in academia. To reveal the causal relationship between the development of digital trade and the enhancement of export competitiveness in developing countries, this paper first constructs a theoretical model based on product quality heterogeneity and analyzes the impact of digital trade on the export competitiveness of enterprises on the basis of achieving supply and demand equilibrium; then, this paper constructs a comprehensive index system for measuring digital trade and enterprise export competitiveness, and establishes an empirical analysis model; on this basis, this paper uses the data of listed companies in the A-share market in the Yangtze River Economic Belt area from 2011 to 2021 for empirical analysis. The results of the empirical analysis show that for every one-unit increase in the level of digital trade development in the region, there will be a positive impact of 0.9041 units on the export competitiveness of enterprises. After a series of robustness tests and endogeneity analyses, the above empirical results are confirmed to be robust and reliable. Furthermore, this paper conducts a heterogeneity analysis and finally puts forward corresponding policy recommendations based on the above theoretical and empirical research results. Full article
22 pages, 2023 KiB  
Article
Research on the Structure of Disciplinary Knowledge Systems from the Perspective of a Knowledge Behavior Strategy
by Huiying Zhang, Le Chang, Zuguo Yang and Juan Lu
Systems 2024, 12(12), 579; https://doi.org/10.3390/systems12120579 - 19 Dec 2024
Viewed by 511
Abstract
Examining the structure and acquisition mechanisms of a disciplinary knowledge system through the framework of knowledge behavior can greatly enhance science education and stimulate innovation in higher education in the long term. Within this framework, a disciplinary knowledge system can theoretically be segmented [...] Read more.
Examining the structure and acquisition mechanisms of a disciplinary knowledge system through the framework of knowledge behavior can greatly enhance science education and stimulate innovation in higher education in the long term. Within this framework, a disciplinary knowledge system can theoretically be segmented into a basic knowledge system and a knowledge network system. Drawing from knowledge structure theory and the philosophy of science, a basic knowledge system is characterized by a pyramid structure. When integrated with ecosystem research perspectives, the knowledge network system assumes a “center-periphery” circle structure which reveals the underlying meanings within the structure of disciplinary knowledge systems. On this basis, using energy chemical engineering as a case study, this paper examines a disciplinary knowledge system by analyzing citations and author collaborations in leading academic papers and explores interconnections within disciplinary knowledge systems. This process provides a methodological reference for other disciplines to identify the structure of their own knowledge systems. This study significantly contributes to educational reform and the development and innovation of academic disciplines by offering a robust framework for understanding and advancing the knowledge structures within various fields. Full article
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21 pages, 923 KiB  
Article
The Impact of the Industrial Innovation Ecosystem on Innovation Performance—Using the Equipment Manufacturing Industry as an Example
by Nan Qiao and Lixia Niu
Systems 2024, 12(12), 578; https://doi.org/10.3390/systems12120578 - 19 Dec 2024
Viewed by 603
Abstract
The innovation ecosystem can greatly enhance enterprises’ innovation performance. However, little is known about how the industrial innovation ecosystem (IIE) improves innovation ability within the equipment manufacturing industry (EMI). The fsQCA method is utilized in this study to explore the intricate causality behind [...] Read more.
The innovation ecosystem can greatly enhance enterprises’ innovation performance. However, little is known about how the industrial innovation ecosystem (IIE) improves innovation ability within the equipment manufacturing industry (EMI). The fsQCA method is utilized in this study to explore the intricate causality behind innovation performance. The conclusions are as follows: (1) There are six factors for high innovation performance, including the technological innovation subject, the knowledge innovation subject, research and development (R&D) investment, R&D personnel, the industrial internet platform, and government subsidies. None of these is a standalone prerequisite for high innovation performance. (2) Four configuration paths achieve remarkable performance. Three configuration paths achieve inefficient performance, and these have an asymmetric relationship with the above four paths. (3) Under the premise that a technological innovation network is perfect, R&D investment and industrial internet platforms both play a crucial role in innovation performance. Meanwhile, neglect in the application of industrial internet platforms and a lack of innovative subjects are important factors for low innovation performance. This study enriches the theoretical applications for innovation management within the EMI from an IIE perspective. It provides practical and management reference to promote innovative ability and enhance the manufacturing performance for China and other developing countries. Full article
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20 pages, 1352 KiB  
Article
Microelement Integration Drives Smart Manufacturing: A Mixed Method Study
by Chenguang Li, Jingtong Gong, Tao Fu and Zhiguo Liang
Systems 2024, 12(12), 577; https://doi.org/10.3390/systems12120577 - 19 Dec 2024
Viewed by 569
Abstract
Smart manufacturing is an important initiative to promote the transformation and upgrading of industries and the high-quality development of the economy. However, the current situation of digitalized smart transformation in manufacturing enterprises is not optimistic, which is primarily attributed to the ambiguity surrounding [...] Read more.
Smart manufacturing is an important initiative to promote the transformation and upgrading of industries and the high-quality development of the economy. However, the current situation of digitalized smart transformation in manufacturing enterprises is not optimistic, which is primarily attributed to the ambiguity surrounding the pathways. This study is based on the technology-organization-environment-individual (TOE-I) analytical framework; it selects 20 case studies of advanced manufacturing enterprises; and employs case studies and necessary condition fuzzy set qualitative comparative research methods (NCA and fsQCA) to investigate the pathways through which technology, organization, the environment, and individual microelements synergistically drive smart manufacturing from a configurational perspective. The study reveals that digital technology breakthroughs, digital infrastructure, digital talent, digital sharing, organizational resilience, organizational culture, and the entrepreneurial spirit are the core influencing factors in advancing smart manufacturing for manufacturing enterprises, and four implementation paths driven by smart manufacturing are analyzed. Among them, digital technology breakthroughs and digital infrastructure have a potential substitutive relationship in the “technology + talent” empowerment organizational model. Organizational resilience, organizational culture, and the entrepreneurial spirit are important safeguards for successful advancements in smart manufacturing. In contrast, digital infrastructure plays a more indirect, supporting role. Accordingly, this paper provides theoretical reference and practical guidance. Full article
(This article belongs to the Special Issue Management and Simulation of Digitalized Smart Manufacturing Systems)
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29 pages, 17282 KiB  
Article
Constant Companionship Without Disturbances: Enhancing Transparency to Improve Automated Tasks in Urban Rail Transit Driving
by Tiecheng Ding, Jinyi Zhi, Dongyu Yu, Ruizhen Li, Sijun He, Wenyi Wu and Chunhui Jing
Systems 2024, 12(12), 576; https://doi.org/10.3390/systems12120576 - 18 Dec 2024
Viewed by 492
Abstract
Enhancing transparency through interface design is an effective method for improving driving safety while reducing driver workloads, potentially fostering human–machine collaboration. However, to ensure system usability and safety, operator psychological factors and operational performance must be well balanced. This study investigates how the [...] Read more.
Enhancing transparency through interface design is an effective method for improving driving safety while reducing driver workloads, potentially fostering human–machine collaboration. However, to ensure system usability and safety, operator psychological factors and operational performance must be well balanced. This study investigates how the introduction of transparency design into urban rail transit driving tasks influences drivers’ situational awareness (SA), trust in automation (TiA), sense of agency (SoA), workload, operational performance, and visual behavior. Three transparency driver–machine interface (DMI) information conditions were evaluated: DMI1, which provided continuous feedback on vehicle operating status and actions; DMI1+2, which added inferential explanations; and DMI1+2+3, which further incorporated proactive predictions. Results from simulated driving experiments with 32 participants indicated that an appropriate level of transparency significantly enhanced TiA and SoA, thereby yielding the greatest acceptance. High transparency significantly aided in predictable takeover tasks but affected gains in TiA and SoA, increased workload, and disrupted perception-level SA. Compared with previous research findings, this study indicates the presence of a disparity in transparency needs for low-workload tasks. Therefore, caution should be exercised when introducing high-transparency designs in urban rail transit driving tasks. Nonetheless, an appropriate transparency interface design can enhance the driving experience. Full article
(This article belongs to the Topic Theories and Applications of Human-Computer Interaction)
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20 pages, 3167 KiB  
Article
A System Dynamics Stability Model for Discrete Production Ramp-Up
by Julian Haller, Bharath Kumar, Amon Göppert and Robert H. Schmitt
Systems 2024, 12(12), 575; https://doi.org/10.3390/systems12120575 - 18 Dec 2024
Viewed by 433
Abstract
Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail [...] Read more.
Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research. Full article
(This article belongs to the Special Issue Production Scheduling and Planning in Manufacturing Systems)
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42 pages, 719 KiB  
Article
The Impact of Optimism Bias on Strategic Decision-Making and Efficiency in Online Retail Supply Chains
by Jialu Li
Systems 2024, 12(12), 574; https://doi.org/10.3390/systems12120574 - 18 Dec 2024
Viewed by 436
Abstract
This paper examines the effects of optimism bias within online retail supply chains. Here, optimism refers to a cognitive bias wherein the third-party seller and the e-commerce platform underestimate the likelihood of facing low market potential. The analysis begins by exploring the impacts [...] Read more.
This paper examines the effects of optimism bias within online retail supply chains. Here, optimism refers to a cognitive bias wherein the third-party seller and the e-commerce platform underestimate the likelihood of facing low market potential. The analysis begins by exploring the impacts of each party’s respective biases. The results indicate that while seller optimism generally leads to self-detrimental outcomes, it can also benefit both the platform and the overall system. Conversely, platform optimism does not permanently harm the platform itself but consistently disadvantages the seller and negatively impacts the supply chain. This work then investigates the combined effects of seller and platform optimism on overall system performance, revealing that the entire channel can, in fact, gain from optimism bias. This research offers insights into strategic approaches that may enhance the efficiency of online retail supply chains. Full article
(This article belongs to the Section Supply Chain Management)
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26 pages, 3297 KiB  
Article
Minimization of Construction and Operation Costs of the Fuel Cell Bus Transportation System
by Po-Han Chiang, Bwo-Ren Ke, Shi-Jim Yen and Wei-Che Chien
Systems 2024, 12(12), 573; https://doi.org/10.3390/systems12120573 - 18 Dec 2024
Viewed by 498
Abstract
This paper took the actual bus transportation system as the object, simulated the operating state of the system, replaced all the current diesel engine buses with fuel cell buses using electrolysis-produced hydrogen, and completed the existing timetable and routes. In the study, the [...] Read more.
This paper took the actual bus transportation system as the object, simulated the operating state of the system, replaced all the current diesel engine buses with fuel cell buses using electrolysis-produced hydrogen, and completed the existing timetable and routes. In the study, the numbers of hydrogen production stations and hydrogen storage stations, the maximum hydrogen storage capacity of the buses, the supplementary hydrogen capacity of the buses, and the hydrogen production capacity of the hydrogen storage stations were used as the optimal adjustment parameters for minimizing the ten-year construction and operating costs of the fuel cell bus transportation system by the artificial bee colony algorithm. Two hydrogen supply methods, decentralized and centralized hydrogen production, were analyzed. This paper used the actual bus timetable to simulate the operation of the buses, including 14 transfer stations and 112 routes. The results showed that the use of centralized hydrogen production and partitioned hydrogen production transfer stations could indeed reduce the construction and operating costs of the fuel cell bus transportation system. Compared with the decentralized hydrogen production case, the construction and operating costs could be reduced by 6.9%, 12.3%, and 14.5% with one, two, and three zones for centralized hydrogen production, respectively. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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24 pages, 999 KiB  
Article
Research on Influence Mechanism of Consumer Satisfaction Evaluation Behavior Based on Grounded Theory in Social E-Commerce
by Ru Wang, Shuhui Xu, Shugang Li and Qiwei Pang
Systems 2024, 12(12), 572; https://doi.org/10.3390/systems12120572 - 17 Dec 2024
Viewed by 712
Abstract
For enterprises, exploring the influence mechanism of consumer satisfaction evaluation behavior (CSEB) holds significant research value for the advancement and further development of social e-commerce platforms. The existing literature primarily focuses on quantitative methods in investigating the influence mechanism of CSEB within social [...] Read more.
For enterprises, exploring the influence mechanism of consumer satisfaction evaluation behavior (CSEB) holds significant research value for the advancement and further development of social e-commerce platforms. The existing literature primarily focuses on quantitative methods in investigating the influence mechanism of CSEB within social e-commerce platforms. This study endeavors to expand the theoretical boundaries of CSEB through qualitative research. This study adopts a mixed-methods approach, combining primary data collected through in-depth interviews with 32 participants and secondary data gathered from 1000 users via web crawlers. Utilizing grounded theory as an analytical framework, the study meticulously summarizes, concludes, and refines the influencing factors of CSEB. Based on these findings, a robust CSEB model is constructed to provide a deeper understanding of the phenomenon. The study reveals that in the decision-making process of consumer evaluation, behavior is primarily driven by evaluation motivations. These motivations are intricately intertwined with product perception, social influence, and perceived behavior control. The interplay among these factors significantly shapes the manner in which consumers engage in satisfaction evaluation on social e-commerce platforms. This study complements existing quantitative research by providing nuanced insights into the complex interplay of factors, which drive consumer evaluation behavior. Furthermore, the study proposes actionable countermeasures and suggestions for businesses and platform managers to effectively promote and enhance consumer satisfaction evaluation activities, thereby contributing to the sustained growth and development of social e-commerce platforms. Full article
(This article belongs to the Special Issue Complex Systems for E-commerce and Business Management)
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21 pages, 4447 KiB  
Article
A Bi-Objective Model for the Location and Optimization Configuration of Kitchen Waste Transfer Stations
by Ming Wan, Ting Qu, George Q. Huang, Ruoheng Chen, Manna Huang, Yanghua Pan, Duxian Nie and Junrong Chen
Systems 2024, 12(12), 571; https://doi.org/10.3390/systems12120571 - 17 Dec 2024
Viewed by 480
Abstract
Since the implementation of China’s mandatory waste sorting policy, the recycling of kitchen waste has become one of the core tasks of waste classification. The problem of designing the locations and the optimization configuration strategy for kitchen waste transfer stations faces great challenges [...] Read more.
Since the implementation of China’s mandatory waste sorting policy, the recycling of kitchen waste has become one of the core tasks of waste classification. The problem of designing the locations and the optimization configuration strategy for kitchen waste transfer stations faces great challenges in reconstructing the municipal solid waste collection and transportation system. This paper establishes an integer programming model for the bi-objectives of the location and optimal configuration for a kitchen waste transfer station, with the goal of minimizing the total cost and overall negative environmental impact. An improved non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) is used to solve the problem, resulting in a Pareto-optimal solution set that includes several non-dominated solutions, thereby providing diversified choices for decision-makers. Finally, a pilot case involving cooperative enterprises is used as an example in this study, and the results demonstrate the effectiveness of the model and algorithm, as well as their feasibility in practice. Full article
(This article belongs to the Topic Smart Product Design and Manufacturing on Industrial Internet)
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21 pages, 821 KiB  
Article
Influence of Artificial Intelligence on Engineering Management Decision-Making with Mediating Role of Transformational Leadership
by Abdullah Abositta, Muri Wole Adedokun and Ayşen Berberoğlu
Systems 2024, 12(12), 570; https://doi.org/10.3390/systems12120570 - 17 Dec 2024
Viewed by 714
Abstract
The relationship between AI and management decision-making has received increasing attention in the literature, but the impact of AI on managerial decision-making through transformational leadership has not yet been thoroughly examined. Thus, this study investigates the impact of artificial intelligence on engineering management [...] Read more.
The relationship between AI and management decision-making has received increasing attention in the literature, but the impact of AI on managerial decision-making through transformational leadership has not yet been thoroughly examined. Thus, this study investigates the impact of artificial intelligence on engineering management decision-making through transformational leadership. The participants include 385 employees drawn from manufacturing, construction, and information technology firms in Turkey. The data were processed using WarpPLS (7.0), and the estimation was conducted with the use of “partial least squares structural equation modeling (PLS-SEM)”. A positive and significant direct influence of “artificial intelligence” and “transformational leadership” on engineering management decision-making practices was demonstrated in this study, while transformational leadership was also found to have a significant mediating role in the relationship between artificial intelligence and engineering management decision-making practices. This study concluded with theoretical and practical implications for policymakers in the engineering industry by providing an integrated framework that allows for a nuanced examination of how AI impacts engineering management decision-making. It accounts for individual perceptions, leadership influences, and organizational adaptations, providing a comprehensive lens through which to analyze the complex interplay between AI technology, leadership, and decision-making processes in engineering management contexts. In addition, the findings of our study have significant implications for engineers and for governments creating standards to help preserve engineering businesses. Leaders and practitioners should research the instillation of values inherent to AI for an organization like engineering businesses to ensure that AI is being used to enable effective decision-making towards ensuring the accomplishment of their sustainable competitive advantage. Full article
(This article belongs to the Special Issue Navigating Digital Transformation: Leadership and Decision Making in Today’s Systems)
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25 pages, 3383 KiB  
Article
Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks
by Katarína Marcineková and Andrea Janáková Sujová
Systems 2024, 12(12), 569; https://doi.org/10.3390/systems12120569 - 17 Dec 2024
Viewed by 563
Abstract
This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected [...] Read more.
This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency. Full article
(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)
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12 pages, 2843 KiB  
Article
Research on Compliance Thresholds Based on Analysis of Driver Behavior Characteristics
by Mingyue Ma, Weiqing Wang, Zelin Miao, Tao Wang and Guangming Zhao
Systems 2024, 12(12), 568; https://doi.org/10.3390/systems12120568 - 16 Dec 2024
Viewed by 526
Abstract
Traffic regulations provide a solid foundation for the safety of all road users; however, the ambiguous provisions and unclear safety thresholds within these regulations pose significant challenges to compliance, particularly concerning the safe operation of autonomous vehicles. To address this issue, this paper [...] Read more.
Traffic regulations provide a solid foundation for the safety of all road users; however, the ambiguous provisions and unclear safety thresholds within these regulations pose significant challenges to compliance, particularly concerning the safe operation of autonomous vehicles. To address this issue, this paper conducts an in-depth analysis of vehicle emergency braking behavior based on the Aerial Dataset for China Congested Highway and Expressway (AD4CHE). The extraction method for the emergency braking risk scenario of natural driving data is proposed, and the correlation between safe distance, safe speed, and driving safety under the scenario of a slightly congested expressway is elaborated in detail. The safety threshold of ambiguous traffic rules obtained can be used for the digitalization of traffic rules that can support the functional development and traffic safety testing of automated driving systems. Full article
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27 pages, 4376 KiB  
Article
A Unified Mission Ontology Based on Systematic Integration of Interdisciplinary Concepts
by Zelalem Mihret Belay and Jakob Axelsson
Systems 2024, 12(12), 567; https://doi.org/10.3390/systems12120567 - 16 Dec 2024
Viewed by 515
Abstract
The concept of a mission is important to system design and development, especially in system of systems (SoS) engineering. However, the diverse usage of the term ’mission’ across disciplines often results in ambiguity regarding its role in practical applications in mission-centric engineering tasks. [...] Read more.
The concept of a mission is important to system design and development, especially in system of systems (SoS) engineering. However, the diverse usage of the term ’mission’ across disciplines often results in ambiguity regarding its role in practical applications in mission-centric engineering tasks. Clearly defined and precisely represented missions improve communication among stakeholders and help bridge interdisciplinary gaps. This study aims to investigate and analyze the state of the art for mission conceptualizations and representations and proposes a unified mission ontology (UMO) that improves semantic interoperability across various domains. To achieve this goal, we conducted a systematic literature review (SLR) to examine how missions are conceptualized and represented, analyzed the findings to obtain insight about cross-domain concepts related to missions, and developed a UMO that can be adapted to domain specific applications. The UMO facilitates semantic interoperability across domains through a high-level abstraction of shared concepts. To validate the comprehensiveness and adaptability of the UMO, we conducted coverage analysis using semantic similarity estimates to assess the equivalence of ontological concepts. This evaluation quantified the extent to which concepts from various domain-specific ontologies, including the mission engineering guideline, align with those in the UMO. Full article
(This article belongs to the Special Issue System of Systems Engineering)
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22 pages, 5611 KiB  
Article
Numerical Design Structure Matrix–Genetic Algorithm-Based Optimization Method for Design Process of Complex Civil Aircraft Systems
by Qiucen Fan, Yanlong Han, An Zhang and Wenhao Bi
Systems 2024, 12(12), 566; https://doi.org/10.3390/systems12120566 - 16 Dec 2024
Viewed by 477
Abstract
In the requirement-driven forward design process of civil aircraft, the large number of design tasks of complex systems with varying difficulty and the complex relationships between design tasks lead to unnecessary repetitive design iterations. In order to solve the above problems, the concept [...] Read more.
In the requirement-driven forward design process of civil aircraft, the large number of design tasks of complex systems with varying difficulty and the complex relationships between design tasks lead to unnecessary repetitive design iterations. In order to solve the above problems, the concept of overlap coefficient is proposed to further sort out the forward and backward logical relationships between design tasks and the civil aircraft system design process optimization model based on a numerical design structure matrix. The algorithm NSGA-II is improved and verified with the flight control system design as a case study. The results show that the proposed method can effectively improve the efficiency of complex system design and provide technical support for the optimization of the design process of complex civil aircraft systems. Full article
(This article belongs to the Special Issue Advanced Model-Based Systems Engineering)
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14 pages, 920 KiB  
Article
Modeling Passengers’ Reserved Time Before High-Speed Rail Departure
by Zhenyu Zhang and Jian Wang
Systems 2024, 12(12), 565; https://doi.org/10.3390/systems12120565 - 16 Dec 2024
Viewed by 389
Abstract
The pre-departure reserved time (PDRV) for high-speed railway (HSR) passengers, which encompasses all the time between passengers leaving their origin and the departure of the HSR train they are going to take, is a crucial factor in planning intercity travel. Understanding how passengers [...] Read more.
The pre-departure reserved time (PDRV) for high-speed railway (HSR) passengers, which encompasses all the time between passengers leaving their origin and the departure of the HSR train they are going to take, is a crucial factor in planning intercity travel. Understanding how passengers select their PDRV is not only important for developing effective strategies to improve HSR efficiency but also for optimizing the integration between HSR hubs and urban transportation networks. However, analyzing passenger choice behavior regarding PDRV is complex due to numerous influencing factors. Despite this, few studies have explored how HSR passengers make their PDRV choices. This paper, using Nanjingnan Railway Station as a case study, presents a novel investigation into the PDRV choice behavior of HSR passengers. An integrated latent class model (LCM) and ordered probit model (OPM) are applied to identify the factors affecting passengers’ PDRV choices. The sample data are segmented based on individual characteristics using the LCM, and OPM models are then constructed for each segment to analyze PDRV choice behavior. The results reveal that several factors—such as travel purpose, the number of times passengers used HSR at Nanjingnan Station in the previous year, the duration of HSR travel, the number of companions, feeder trip duration, and departure time—significantly impact PDRV choices. The integrated LCM and OPM approach also uncovers choice heterogeneity among different passenger groups. These insights can serve as a valuable reference for forecasting HSR passenger demand and for designing integrated HSR hubs and urban transport systems. Full article
(This article belongs to the Section Systems Engineering)
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29 pages, 635 KiB  
Article
Harnessing Digital Technologies for Rural Industrial Integration: A Pathway to Sustainable Growth
by Jingkun Zhang and Wang Zhang
Systems 2024, 12(12), 564; https://doi.org/10.3390/systems12120564 - 16 Dec 2024
Viewed by 605
Abstract
Data have become a virtual factor of production, and when integrated with the traditional factors of labor, capital, and land form digital labor, digital capital, and digital land, thereby generating a multiplier effect that contributes to the comprehensive revitalization of rural areas. This [...] Read more.
Data have become a virtual factor of production, and when integrated with the traditional factors of labor, capital, and land form digital labor, digital capital, and digital land, thereby generating a multiplier effect that contributes to the comprehensive revitalization of rural areas. This paper utilizes panel data from 30 provinces (autonomous regions and municipalities) in China from 2013 to 2023 and employs a double machine learning model to empirically test the impact mechanism of rural digitalization on the integration of rural industries. The results indicate that digital villages significantly promote the integrated development of rural industries through three direct pathways—digital industry development, digital information infrastructure, and digital service levels—with this conclusion remaining valid after a series of robustness tests. A mechanism analysis shows that digital villages facilitate the integration of rural industries through three indirect pathways—alleviating urban–rural factor mismatches, adjusting the agricultural–industrial structure, and promoting agricultural technological advancement—with this conclusion still valid after various robustness tests. The heterogeneity results show that there is significant variability in how digital villages promote the development of integrated rural industries, with the effects being more pronounced in major grain-producing and eastern regions compared to non-major grain-producing and central-western regions. Based on this, this paper proposes policy recommendations focused on accelerating digital village construction, formulating differentiated strategies, and alleviating factor mismatches, aiming to provide references for achieving rural revitalization. We mainly propose countermeasures and suggestions from three aspects: digital dividend, differentiation strategy, and element mismatch. Our main purpose in writing this article is to make up for the shortcomings of existing theories, enrich the theoretical system of digital rural construction, contribute Chinese solutions for digital rural construction around the world, and improve the word’s level of digital rural construction. Full article
(This article belongs to the Special Issue Digital Solutions for Participatory Governance in Smart Cities)
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16 pages, 924 KiB  
Article
Energy-Efficient Pathways in the Digital Revolution: Which Factors Influence Agricultural Product Consumers’ Adoption of Low-Carbon Supply Chains on E-Commerce Platforms?
by Xuemei Fan, Yingdan Zhang and Jiahui Xue
Systems 2024, 12(12), 563; https://doi.org/10.3390/systems12120563 - 15 Dec 2024
Viewed by 570
Abstract
E-commerce platform-based supply chains have emerged as efficient and widely used channels for the distribution of agricultural products, with low-carbon supply chains capable of reducing the carbon footprint of these products from the initial to the terminal stage, which has led to their [...] Read more.
E-commerce platform-based supply chains have emerged as efficient and widely used channels for the distribution of agricultural products, with low-carbon supply chains capable of reducing the carbon footprint of these products from the initial to the terminal stage, which has led to their increasing adoption by consumers. Based on major e-commerce platforms in China, combined with an extended technology acceptance model, this study examines the factors affecting low-carbon supply chain adoption by agricultural product consumers on e-commerce platforms. The results showed that consumers’ perceived risk, perceived usefulness, and adoption attitude all influence their adoption behavior, with the effect of adoption attitude being the greatest. Furthermore, the effect of perceived usefulness on adoption attitude is stronger than that of perceived risk. Moreover, perceived usefulness is affected mainly by the perceived price, technical utility, green behavior tendency, and subjective norms, whereas the perceived risk is affected mainly by technical utility, information display and dissemination, and green behavior tendency. This study provides valuable insights for e-commerce platforms to better understand the needs of agricultural product consumers, improve market competitiveness, and enhance consumers’ awareness and willingness of adopting low-carbon approaches, which can promote the low-carbon sustainable development of agricultural product supply chains. Full article
(This article belongs to the Section Supply Chain Management)
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21 pages, 3767 KiB  
Article
Water Infrastructure Impacts of Agricultural Industry in China Under Extreme Weather: A System Dynamics Model of a Multi-Level, Climate Resilience Perspective
by Jiawen Li, Changzheng Zhang, Qiaozhi Huang, Mengyao Ding, Yuxin He, Mulan Liu and Chuchu Yang
Systems 2024, 12(12), 562; https://doi.org/10.3390/systems12120562 - 15 Dec 2024
Viewed by 602
Abstract
China is the world’s largest agricultural country and is also deeply affected by extreme weather. Water infrastructure is a crucial solution to improve the climate adaptability of the agricultural industry. This study aimed to explore the above adaptive processes of the agricultural industry [...] Read more.
China is the world’s largest agricultural country and is also deeply affected by extreme weather. Water infrastructure is a crucial solution to improve the climate adaptability of the agricultural industry. This study aimed to explore the above adaptive processes of the agricultural industry from a resilience perspective. This study builds a multi-level system dynamics (SD) model to assess the development of the agricultural industry and water infrastructure, predict the future resilience development trend, identify the key influencing factors, and simulate the effectiveness of different water infrastructure measures. The results show that (1) water infrastructure involving various climate adaptation measures significantly promotes the development of the agricultural industry. (2) Agricultural output, water infrastructure investment, and other fixed asset investments strongly improve the resilience, and the impact of the crop planting area is limited. (3) The resilience level is higher under the eco-friendly water conservation scenario than in the water supply security scenario and flood disaster prevention scenario. Such information will promote the sustainable development of the agricultural industry and future climate adaptation policy-making. Full article
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20 pages, 1104 KiB  
Article
How Do Dynamic Capabilities Enable a Firm to Convert the External Pressures into Environmental Innovation? A Process-Based Study Using Structural Equation Modeling
by Xiaoyan Jin, Daegyu Yang and Mooweon Rhee
Systems 2024, 12(12), 561; https://doi.org/10.3390/systems12120561 - 14 Dec 2024
Viewed by 651
Abstract
Recently, dealing with environmental issues has emerged as a critical part of various corporate social responsibility activities. To effectively address the environmental problems along with their generic purposes of increasing competitive advantages, firms pay attention to environmental innovation. Despite the growing importance of [...] Read more.
Recently, dealing with environmental issues has emerged as a critical part of various corporate social responsibility activities. To effectively address the environmental problems along with their generic purposes of increasing competitive advantages, firms pay attention to environmental innovation. Despite the growing importance of environmental innovation for achieving competitive advantages, there remains a significant gap in understanding how firms actually accomplish this innovation. This study aims to fill this gap by leveraging Teece’s theoretical framework to identify three key components of dynamic capabilities—sensing, seizing, and reconfiguring—that facilitate the development of an effective managerial system. Specifically, this study proposes that sensing and seizing guide a firm to correctly respond to the external requests of dealing with the environmental problems so that the firm may incorporate the external pressure in the environmental innovation outcomes, while reconfiguring leads directly to the realization of environmental innovation. Using a Korean Innovation Survey that includes direct questions about environmental innovation, we construct a structural equation model, PLS-SEM, to test our hypotheses, and the findings support all the hypotheses. The contributions and managerial implications are discussed based on the findings, and some limitations in methodology are also addressed. Full article
(This article belongs to the Special Issue Business Innovation: From Management Systems to Corporate Social Responsibility)
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32 pages, 5806 KiB  
Article
Modeling and Solving the Multi-Objective Vehicle Routing Problem with Soft and Fuzzy Time Windows
by Ailing Chen and Tianao Li
Systems 2024, 12(12), 560; https://doi.org/10.3390/systems12120560 - 13 Dec 2024
Viewed by 727
Abstract
In the distribution field, distribution costs and customer service satisfaction are extremely important issues for enterprises. However, both the Vehicle Routing Problem with Soft Time Windows (VRPSTW) and the Vehicle Routing Problem with Fuzzy Time Windows (VRPFTW) have certain deficiencies in describing real-world [...] Read more.
In the distribution field, distribution costs and customer service satisfaction are extremely important issues for enterprises. However, both the Vehicle Routing Problem with Soft Time Windows (VRPSTW) and the Vehicle Routing Problem with Fuzzy Time Windows (VRPFTW) have certain deficiencies in describing real-world scenarios. Therefore, this paper considers both soft time windows and fuzzy time windows, improving upon the traditional VRPSTW and VRPFTW models to establish a more comprehensive and realistic model called the Vehicle Routing Problem with Soft Time Windows and Fuzzy Time Windows (VRPSFTW). Secondly, to solve the relevant problems, this paper proposes a Directed Mutation Genetic Algorithm integrated with Large Neighborhood Search (LDGA), which fully utilizes the advantages of the Genetic Algorithm (GA) in the early stages and appropriately adopts removal and re-insertion operators from the Large Neighborhood Search (LNS). This approach not only makes efficient use of computational resources but also compensates for the weaknesses of crossover and mutation operators in the later stages of the genetic algorithm. Thereby, it improves the overall efficiency and accuracy of the algorithm and achieves better solution results. In addition, in order to solve multi-objective problems, this paper employs a two-stage solution approach and designs two sets of algorithms based on the principles of “cost priority” and “service-level priority”. Simulation experiments demonstrated that the algorithms designed in this study achieved a more competitive solving performance. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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