Jiannan Zhu, Chao Deng, Jiaofeng Pan, Fu Gu, Jianfeng Guo
In this study, we propose a big data-based method for characterizing the feature distributions of multiple technologies within a specific domain. Traditional approaches, such as Gartner’s hype cycle or S-curve model, portray the developmental trajectory of individual technologies. However, these approaches are insufficient to encapsulate the aggregate characteristic distribution of multiple technologies within a specific domain. Thus, this study proposes an innovative method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The research methodology involves that the features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or cause potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach by comparing historical trends with the literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.
在本研究中，我们提出了一种基于大数据的方法，用于描述特定领域内多种技术的特征分布。传统方法，如 Gartner 的炒作周期或 S 曲线模型，描绘了单项技术的发展轨迹。然而，这些方法不足以概括特定领域内多种技术的总体特征分布。因此，本研究提出了一种创新方法，即通过四个拟议特征（即通用性、重要性、商业性和颠覆性）来描述特定领域内的技术特征。研究方法包括在技术和应用的二维分析空间中，利用谷歌和谷歌学术搜索结果中的代表性关键词和返回量，对技术特征进行量化描述。我们使用智能机器人领域的 452 项技术演示了这一方法的适用性。我们的评估结果表明，通用性值呈正态分布，而重要性、商业性和破坏性值则呈幂律分布，其中很少有技术拥有较高的特征值。我们还表明，重要技术更有可能商业化或造成潜在破坏，因为这类技术在这些特征上的得分更高。此外，我们还将历史趋势与文献进行了比较，并在缩小的分析空间中对技术进行了特征描述，从而有效证明了我们方法的稳健性。我们的方法可广泛应用于分析不同领域技术的特征分布，并有可能在投资、贸易和科学政策等决策中加以利用。
{"title":"Feature Distributions of Technologies","authors":"Jiannan Zhu, Chao Deng, Jiaofeng Pan, Fu Gu, Jianfeng Guo","doi":"10.3390/systems12080268","DOIUrl":"https://doi.org/10.3390/systems12080268","url":null,"abstract":"In this study, we propose a big data-based method for characterizing the feature distributions of multiple technologies within a specific domain. Traditional approaches, such as Gartner’s hype cycle or S-curve model, portray the developmental trajectory of individual technologies. However, these approaches are insufficient to encapsulate the aggregate characteristic distribution of multiple technologies within a specific domain. Thus, this study proposes an innovative method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The research methodology involves that the features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or cause potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach by comparing historical trends with the literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"56 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mining is a high-risk industry where occupational accidents are common due to its complex nature. Therefore, providing a more holistic and dynamic risk assessment framework is essential to identify and minimize the potential risks and enhance safety measures. Unfortunately, traditional risk assessment methods have limitations and shortcomings, such as uncertainty, differences in experience backgrounds, and insufficiency to articulate the opinions of experts. In this paper, a novel risk assessment method precisely for such cases in the mining sector is proposed, applied, and compared with traditional methods. The objective of this study is to determine the risk scores of Turkish Coal Enterprises, based on non-fatal occupational accidents, which operates eight large-scale open-cast coal mine enterprises in Türkiye. The causes of the accidents were categorized into 25 sub-criteria under 6 main criteria. The risk scores for these criteria were computed using the Pythagorean fuzzy Analytical Hierarchy Process (PFAHP) method. The first shift (8–16 h) (0.6341) for the shift category is ranked highest out of the 25 sub-risk factors, followed by maintenance personnel (0.5633) for the occupation category; the open-cast mining area (0.5524) for the area category, the 45–57 age range (0.5279) for employee age category, and the mining machine (0.4247) for the reason category, respectively. The methodologies proposed in this study not only identify the most important risk factors in enterprises, but also provide a mechanism for risk-based rankings of enterprises by their calculated risk scores. The enterprises were risk-based ranked with the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method and Paksoy approach based on interval type-2 fuzzy sets (IT2FSs). The findings indicate that the first three risk score rankings of enterprises are the same for both approaches. To examine the consistency of the applied methods, sensitivity analyses were performed. The results of the study also indicate that the proposed approaches are recommended for effective use in the mining sector due to their ease of application compared to other methods and their dynamic nature in the risk assessment process.
{"title":"A Novel Risk Assessment Approach for Open-Cast Coal Mines Using Hybrid MCDM Models with Interval Type-2 Fuzzy Sets: A Case Study in Türkiye","authors":"Mert Mutlu, Nazli Ceren Cetin, Seyhan Onder","doi":"10.3390/systems12080267","DOIUrl":"https://doi.org/10.3390/systems12080267","url":null,"abstract":"Mining is a high-risk industry where occupational accidents are common due to its complex nature. Therefore, providing a more holistic and dynamic risk assessment framework is essential to identify and minimize the potential risks and enhance safety measures. Unfortunately, traditional risk assessment methods have limitations and shortcomings, such as uncertainty, differences in experience backgrounds, and insufficiency to articulate the opinions of experts. In this paper, a novel risk assessment method precisely for such cases in the mining sector is proposed, applied, and compared with traditional methods. The objective of this study is to determine the risk scores of Turkish Coal Enterprises, based on non-fatal occupational accidents, which operates eight large-scale open-cast coal mine enterprises in Türkiye. The causes of the accidents were categorized into 25 sub-criteria under 6 main criteria. The risk scores for these criteria were computed using the Pythagorean fuzzy Analytical Hierarchy Process (PFAHP) method. The first shift (8–16 h) (0.6341) for the shift category is ranked highest out of the 25 sub-risk factors, followed by maintenance personnel (0.5633) for the occupation category; the open-cast mining area (0.5524) for the area category, the 45–57 age range (0.5279) for employee age category, and the mining machine (0.4247) for the reason category, respectively. The methodologies proposed in this study not only identify the most important risk factors in enterprises, but also provide a mechanism for risk-based rankings of enterprises by their calculated risk scores. The enterprises were risk-based ranked with the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method and Paksoy approach based on interval type-2 fuzzy sets (IT2FSs). The findings indicate that the first three risk score rankings of enterprises are the same for both approaches. To examine the consistency of the applied methods, sensitivity analyses were performed. The results of the study also indicate that the proposed approaches are recommended for effective use in the mining sector due to their ease of application compared to other methods and their dynamic nature in the risk assessment process.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shared leadership is a dynamic phenomenon that has gained attention in behavioral science and management research over the last two decades. Network modeling is frequently employed to study this phenomenon, with the recent literature favoring a node-based approach over the traditional dyad-based approach. In this study, we investigate the differential impact of these approaches on shared leadership dynamics in student teams, specifically examining their effects on team task cohesion, team social cohesion, and team performance. We utilized multilevel structural equation modeling to compare node-based and dyad-based approaches in modeling shared leadership networks. Our findings indicate that increased leadership interactions positively influenced team performance and cohesion across both approaches. The dyad-based approach demonstrated a greater effect of leadership interactions on team performance, while leadership centrality significantly impacted performance exclusively in the node-based approach. This research contributes to the field by elucidating the differential impacts of node-based and dyad-based approaches, highlighting their strengths in capturing shared leadership dynamics and centrality effects. Our results underscore the critical importance of aligning theoretical foundations and research objectives with methodological choices in shared leadership studies. These insights enhance our understanding of shared leadership measurement and its implications for team outcomes, offering valuable guidance for future empirical investigations in this domain.
{"title":"On Shared Leadership Modeling: Contrasting Network and Dyadic Approaches","authors":"Giuliani Coluccio, Sebastián Muñoz-Herrera","doi":"10.3390/systems12070265","DOIUrl":"https://doi.org/10.3390/systems12070265","url":null,"abstract":"Shared leadership is a dynamic phenomenon that has gained attention in behavioral science and management research over the last two decades. Network modeling is frequently employed to study this phenomenon, with the recent literature favoring a node-based approach over the traditional dyad-based approach. In this study, we investigate the differential impact of these approaches on shared leadership dynamics in student teams, specifically examining their effects on team task cohesion, team social cohesion, and team performance. We utilized multilevel structural equation modeling to compare node-based and dyad-based approaches in modeling shared leadership networks. Our findings indicate that increased leadership interactions positively influenced team performance and cohesion across both approaches. The dyad-based approach demonstrated a greater effect of leadership interactions on team performance, while leadership centrality significantly impacted performance exclusively in the node-based approach. This research contributes to the field by elucidating the differential impacts of node-based and dyad-based approaches, highlighting their strengths in capturing shared leadership dynamics and centrality effects. Our results underscore the critical importance of aligning theoretical foundations and research objectives with methodological choices in shared leadership studies. These insights enhance our understanding of shared leadership measurement and its implications for team outcomes, offering valuable guidance for future empirical investigations in this domain.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"48 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous development of the global logistics industry, cold chain transportation and joint distribution, as critical strategies in supply chain management, are gradually becoming key means to ensure the safe transportation of perishable goods, pharmaceuticals, and other temperature-sensitive commodities. The present study is dedicated to an in-depth exploration of cold chain logistics and joint distribution, with a particular focus on a review of fresh food logistics modes, aiming to comprehensively understand their operational modes, advantages, challenges, and future development trends. The present study elucidates the basic concepts of fresh food logistics and underscores its significance in supply chain management. Through comparative analysis of different operational modes, it reveals their advantages in enhancing efficiency, reducing costs, and mitigating environmental impacts. The present study focuses on the operational mode of joint distribution, discussing its application in cold chain logistics and its differences from traditional logistics modes. Through case studies and empirical analysis, it evaluates the impact of joint distribution on logistics efficiency and costs, as well as its potential to enhance transportation efficiency and reduce carbon emissions. Lastly, the present study provides an outlook on the future development trends of cold chain logistics and joint distribution, discussing the influences of technological innovation, policy support, and industry collaboration and offering recommendations and prospects to drive the sustained development of the industry. Through a comprehensive summary of fresh food logistics, cold chain logistics operational modes, and joint distribution operational modes, this paper aims to provide in-depth theoretical support and practical guidance for related research and practices.
{"title":"Cold Chain Logistics and Joint Distribution: A Review of Fresh Logistics Modes","authors":"Huaixia Shi, Qinglei Zhang, Jiyun Qin","doi":"10.3390/systems12070264","DOIUrl":"https://doi.org/10.3390/systems12070264","url":null,"abstract":"With the continuous development of the global logistics industry, cold chain transportation and joint distribution, as critical strategies in supply chain management, are gradually becoming key means to ensure the safe transportation of perishable goods, pharmaceuticals, and other temperature-sensitive commodities. The present study is dedicated to an in-depth exploration of cold chain logistics and joint distribution, with a particular focus on a review of fresh food logistics modes, aiming to comprehensively understand their operational modes, advantages, challenges, and future development trends. The present study elucidates the basic concepts of fresh food logistics and underscores its significance in supply chain management. Through comparative analysis of different operational modes, it reveals their advantages in enhancing efficiency, reducing costs, and mitigating environmental impacts. The present study focuses on the operational mode of joint distribution, discussing its application in cold chain logistics and its differences from traditional logistics modes. Through case studies and empirical analysis, it evaluates the impact of joint distribution on logistics efficiency and costs, as well as its potential to enhance transportation efficiency and reduce carbon emissions. Lastly, the present study provides an outlook on the future development trends of cold chain logistics and joint distribution, discussing the influences of technological innovation, policy support, and industry collaboration and offering recommendations and prospects to drive the sustained development of the industry. Through a comprehensive summary of fresh food logistics, cold chain logistics operational modes, and joint distribution operational modes, this paper aims to provide in-depth theoretical support and practical guidance for related research and practices.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"61 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since 2014, China has been actively promoting the transformation of manufacturing servitization, clarifying the importance of manufacturing servitization. This paper investigates the correlation between manufacturing servitization and cost stickiness, supplementing the research on the economic consequences of manufacturing servitization and the influencing factors of cost stickiness. This paper launches an empirical study with a sample of A-share manufacturing companies from 2014 to 2022. The research results show that, first, manufacturing servitization can inhibit enterprise cost stickiness; second, manufacturing servitization affects enterprise cost stickiness through the path of reducing enterprise adjustment costs, reducing managers’ optimistic expectations and reducing enterprise agency costs; third, the negative relationship between manufacturing servitization and cost stickiness is stronger among firms with a low level of internal control, a strong degree of financing constraints, a good quality internal information environment, a strong degree of competition in the market, and firms that are in capital-intensive manufacturing industries; fourth, the role of embedded servitization on enterprise cost stickiness is not significant, while hybrid servitization can have a significant negative effect on enterprise cost stickiness; and fifth, the impact of manufacturing servitization on enterprise cost stickiness mainly lies in the cost of material resources rather than the cost of human resources.
{"title":"A Study of the Impact of Manufacturing Servitization on Firms’ Cost Stickiness","authors":"Ming Bai, Hao Guan, Ye Hong, Haoyi Sun","doi":"10.3390/systems12070266","DOIUrl":"https://doi.org/10.3390/systems12070266","url":null,"abstract":"Since 2014, China has been actively promoting the transformation of manufacturing servitization, clarifying the importance of manufacturing servitization. This paper investigates the correlation between manufacturing servitization and cost stickiness, supplementing the research on the economic consequences of manufacturing servitization and the influencing factors of cost stickiness. This paper launches an empirical study with a sample of A-share manufacturing companies from 2014 to 2022. The research results show that, first, manufacturing servitization can inhibit enterprise cost stickiness; second, manufacturing servitization affects enterprise cost stickiness through the path of reducing enterprise adjustment costs, reducing managers’ optimistic expectations and reducing enterprise agency costs; third, the negative relationship between manufacturing servitization and cost stickiness is stronger among firms with a low level of internal control, a strong degree of financing constraints, a good quality internal information environment, a strong degree of competition in the market, and firms that are in capital-intensive manufacturing industries; fourth, the role of embedded servitization on enterprise cost stickiness is not significant, while hybrid servitization can have a significant negative effect on enterprise cost stickiness; and fifth, the impact of manufacturing servitization on enterprise cost stickiness mainly lies in the cost of material resources rather than the cost of human resources.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"26 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective.
{"title":"Integrated Optimization of Route and Frequency for Rail Transit Feeder Buses under the Influence of Shared Motorcycles","authors":"Jing Cai, Zhuoqi Li, Sihui Long","doi":"10.3390/systems12070263","DOIUrl":"https://doi.org/10.3390/systems12070263","url":null,"abstract":"In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"69 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.
本研究为城市地铁系统故障后维护资源供应点的战略位置和多种资源的调度开发了一个优化模型，目的是提高系统的恢复能力。该模型采用了多目标优化框架，主要侧重于最大限度地减少资源调度时间和降低成本。它包含了空间位置、网络拓扑结构、车站规模和客流量等关键因素。该模型采用了非支配排序遗传算法 III 和理想解相似度排序技术相结合的混合方法进行求解，并通过南京地铁系统的案例研究证实了该方法的有效性。模拟结果得出了 21 个维修资源供应站的最佳数量，并提供了它们的位置。在大规模故障情况下，最优资源调度策略确保关键站点的需求满足率超过 90%，总体满足率保持在 87.09%，从而显著提高了资源调度效率和系统的应急响应能力，增强了城市地铁系统的物理弹性和恢复能力。此外，该模型还考虑了经济因素，通过有效的维护策略和资源利用，努力实现应急响应能力与生产连续性和成本效益之间的平衡。这种方法为城市地铁系统管理突发故障提供了一个系统框架，可确保在资源有限的情况下迅速恢复正常运营，并最大限度地减少运营中断。
{"title":"Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience","authors":"Lingyi Tang, Shiqi Chen, Qiming Li","doi":"10.3390/systems12070262","DOIUrl":"https://doi.org/10.3390/systems12070262","url":null,"abstract":"This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"10 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To achieve sustainability, industrial systems need to be modernized to improve resource efficiency while optimizing environmental and social performance. The implementation of environmental and technological projects is a complex management process and requires sufficient innovative potential and serious investments, which not every company can provide. Network integration of companies, providing synergy of resources and potentials, is an effective tool for the development and implementation of innovative technologies that allow achieving optimal resource efficiency indicators. An integrated complex approach to the formation of a cross- industrial system on the principles of network integration and partnerships ensures technological interaction between companies, optimizes the methods and forms of their economic activities, allows integration participants to achieve strategic goals and ensure environmental and social effects for the territory of presence. The sustainability of such a system is expressed in its ability to withstand internal threats and external challenges. Approaches to balancing environmental and technological effects while simultaneously analysing social efficiency have not received sufficient development in scientific research. This article discusses an approach to the selection of environmental-technological projects based on criteria for assessing the sustainability and resilience of industrial systems. The authors’ approach has been tested using two industrial symbioses of advanced socio-economic development territories in the city of Novotroitsk (Orenburg region, Russian Federation). The authors presented calculated indicators of resource efficiency before and after the formation of a cross-sectoral industrial system in order to identify social and environmental effects in Novotroitsk. This approach to the assessment of environmental and technological projects allows to concentrate government support measures on the general priorities of the implementation of regional economic and industrial policies.
{"title":"The Complex Approach to Environmental and Technological Project Management to Enhance the Sustainability of Industrial Systems","authors":"Leyla Gamidullaeva, Nadezhda Shmeleva, Tatyana Tolstykh, Tatiana Guseva, Svetlana Panova","doi":"10.3390/systems12070261","DOIUrl":"https://doi.org/10.3390/systems12070261","url":null,"abstract":"To achieve sustainability, industrial systems need to be modernized to improve resource efficiency while optimizing environmental and social performance. The implementation of environmental and technological projects is a complex management process and requires sufficient innovative potential and serious investments, which not every company can provide. Network integration of companies, providing synergy of resources and potentials, is an effective tool for the development and implementation of innovative technologies that allow achieving optimal resource efficiency indicators. An integrated complex approach to the formation of a cross- industrial system on the principles of network integration and partnerships ensures technological interaction between companies, optimizes the methods and forms of their economic activities, allows integration participants to achieve strategic goals and ensure environmental and social effects for the territory of presence. The sustainability of such a system is expressed in its ability to withstand internal threats and external challenges. Approaches to balancing environmental and technological effects while simultaneously analysing social efficiency have not received sufficient development in scientific research. This article discusses an approach to the selection of environmental-technological projects based on criteria for assessing the sustainability and resilience of industrial systems. The authors’ approach has been tested using two industrial symbioses of advanced socio-economic development territories in the city of Novotroitsk (Orenburg region, Russian Federation). The authors presented calculated indicators of resource efficiency before and after the formation of a cross-sectoral industrial system in order to identify social and environmental effects in Novotroitsk. This approach to the assessment of environmental and technological projects allows to concentrate government support measures on the general priorities of the implementation of regional economic and industrial policies.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"44 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.
{"title":"Enhancing Inventory Management through Safety-Stock Strategies—A Case Study","authors":"Sema Demiray Kırmızı, Zeynep Ceylan, Serol Bulkan","doi":"10.3390/systems12070260","DOIUrl":"https://doi.org/10.3390/systems12070260","url":null,"abstract":"Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"82 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model’s capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples.
{"title":"XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring","authors":"Yuxuan Xia, Shanshan Jiang, Lingyi Meng, Xin Ju","doi":"10.3390/systems12070254","DOIUrl":"https://doi.org/10.3390/systems12070254","url":null,"abstract":"Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model’s capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"103 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：