Guohui Song, Yongbin Wang, Xiaosen Chen, Hongbin Hu, Fan Liu
Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.
{"title":"Evaluating User Engagement in Online News: A Deep Learning Approach Based on Attractiveness and Multiple Features","authors":"Guohui Song, Yongbin Wang, Xiaosen Chen, Hongbin Hu, Fan Liu","doi":"10.3390/systems12080274","DOIUrl":"https://doi.org/10.3390/systems12080274","url":null,"abstract":"Online news platforms have become users’ primary information sources. However, they focus on attracting users to click on the news and ignore whether the news triggers a sense of engagement, which could potentially reduce users’ participation in public events. Therefore, this study constructs four indicators by assessing user engagement to build an intelligent system to help platforms optimize their publishing strategies. First, this study defines user engagement evaluation as a classification task that divides user engagement into four indicators and proposes an extended LDA model based on user click–comment behavior (UCCB), using which the attractiveness of words in news headlines and content can be effectively represented. Second, this study proposes a deep user engagement evaluation (DUEE) model that integrates news attractiveness and multiple features in an attention-based deep neural network for user engagement evaluation. The DUEE model considers various elements that collectively determine the ability of the news to attract clicks and engagement. Third, the proposed model is compared with the baseline and state-of-the-art techniques, showing that it outperforms all existing methods. This study provides new research contributions and ideas for improving user engagement in online news evaluation.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"191 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872930","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 study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot.
{"title":"A Pricing Model Study of Shared Parking Area Charge Based on Game Theory","authors":"Chao Sun, Haodong Jing, Haowei Yin","doi":"10.3390/systems12080269","DOIUrl":"https://doi.org/10.3390/systems12080269","url":null,"abstract":"In this study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"245 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777719","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}
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}
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}
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}
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}
The development of digital technologies such as blockchain has provided new possibilities for solving the financing difficulties of small and medium-sized enterprises (SMEs). In order to explore the mutual influence of the participants in the supply chain, this paper constructs two static tripartite game models for traditional and digital supply chain finance, including a small and medium-sized enterprise (SME), a core enterprise (CE), and a financial institution (FI). The conditions for SME, CE, and FI to participate in digital supply chain finance, and the equilibrium strategy (repayment, repayment, loan) after participating in digital supply chain finance, are figured out. It is found that compared with the traditional supply chain, the digital supply chain expands the probability range of repayment for SME and CE by the change of pledge rate and default loss and broadens the probability range of repayment for CE by the change of default loss. Further, compared with the traditional supply chain finance, the greater the pledge rate of digital supply chain finance and the smaller the default loss, the stronger the willingness of the SME and CE to participate in the digital supply chain and the lower the willingness of the FI. After the three parties participate in the digital supply chain, however, the conclusion is the opposite. The smaller the pledge rate and the greater the default loss, the stronger the repayment willingness for the SME and CE and the stronger the loan willingness of the FI. Therefore, it is suggested to find the critical values of pledge rate and default loss and raise these two variables to an appropriate range to encourage all parties to voluntarily and consistently participate in digital supply chain financing.
{"title":"How to Reshape the Selection Boundaries between Traditional and Digital Supply Chain Finance Based on the Pledge Rate and Default Loss: Two Tripartite Game Models","authors":"Xiang Sun, Yue Wang, Yinzi Huang, Yue Zhang","doi":"10.3390/systems12070253","DOIUrl":"https://doi.org/10.3390/systems12070253","url":null,"abstract":"The development of digital technologies such as blockchain has provided new possibilities for solving the financing difficulties of small and medium-sized enterprises (SMEs). In order to explore the mutual influence of the participants in the supply chain, this paper constructs two static tripartite game models for traditional and digital supply chain finance, including a small and medium-sized enterprise (SME), a core enterprise (CE), and a financial institution (FI). The conditions for SME, CE, and FI to participate in digital supply chain finance, and the equilibrium strategy (repayment, repayment, loan) after participating in digital supply chain finance, are figured out. It is found that compared with the traditional supply chain, the digital supply chain expands the probability range of repayment for SME and CE by the change of pledge rate and default loss and broadens the probability range of repayment for CE by the change of default loss. Further, compared with the traditional supply chain finance, the greater the pledge rate of digital supply chain finance and the smaller the default loss, the stronger the willingness of the SME and CE to participate in the digital supply chain and the lower the willingness of the FI. After the three parties participate in the digital supply chain, however, the conclusion is the opposite. The smaller the pledge rate and the greater the default loss, the stronger the repayment willingness for the SME and CE and the stronger the loan willingness of the FI. Therefore, it is suggested to find the critical values of pledge rate and default loss and raise these two variables to an appropriate range to encourage all parties to voluntarily and consistently participate in digital supply chain financing.","PeriodicalId":36394,"journal":{"name":"Systems","volume":"59 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613671","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}
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