Pub Date : 2024-09-03DOI: 10.1016/j.cie.2024.110537
The intuitionistic fuzzy set is a generalization of the fuzzy set that performs noticeably better in expressing and managing uncertainty. The amount that one intuitionistic fuzzy set differs from the others is given by the distance measure. Certain distance measures that have been suggested by the various researchers do not satisfy the axioms of distance measures and also be counter-intuitive circumstances. In this paper we present a novel distance measure for intuitionistic fuzzy sets that is based on the difference between the cross-evaluation factor’s minimum and maximum, the membership degree and non-membership degree, respectively. The proposed measure satisfies all the axiomatic properties and also resolves the counter-intuitive cases. Consequently, this study provides an efficient symmetric distance formula for determining the distance between the information contained by intuitionistic fuzzy sets. By using numerical examples, it is shown that the new measurement is reliable. Also, we provide pattern recognition algorithms and employ them to solve diagnostic-related problems in medicine.
{"title":"An extended group decision-making algorithm with intuitionistic fuzzy set information distance measures and their applications","authors":"","doi":"10.1016/j.cie.2024.110537","DOIUrl":"10.1016/j.cie.2024.110537","url":null,"abstract":"<div><p>The intuitionistic fuzzy set is a generalization of the fuzzy set that performs noticeably better in expressing and managing uncertainty. The amount that one intuitionistic fuzzy set differs from the others is given by the distance measure. Certain distance measures that have been suggested by the various researchers do not satisfy the axioms of distance measures and also be counter-intuitive circumstances. In this paper we present a novel distance measure for intuitionistic fuzzy sets that is based on the difference between the cross-evaluation factor’s minimum and maximum, the membership degree and non-membership degree, respectively. The proposed measure satisfies all the axiomatic properties and also resolves the counter-intuitive cases. Consequently, this study provides an efficient symmetric distance formula for determining the distance between the information contained by intuitionistic fuzzy sets. By using numerical examples, it is shown that the new measurement is reliable. Also, we provide pattern recognition algorithms and employ them to solve diagnostic-related problems in medicine.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1016/j.cie.2024.110548
Distrust is the main obstacle to the green transformation of the supply chain. Blockchain can rebuild trust among multiple participants by employing distributed information technologies. Based on complex network evolutionary game models, this paper tries to reveal the transformative potential of blockchain in promoting the green transformation of the supply chain. Simulation experiments with real electric vehicle data show that if adopting blockchain technology: i) the greenness of the supply chain can increase by 200% even without government policy intervention; by 75% when jointly optimizing the intensity of rewards and penalties; by 40% when exclusively adjusting the reward or penalty intensity; ii) the profits of both suppliers and manufacturers can be improved when they adopt green investment strategies, creating a “win-win” scenario. Besides, optimal government rewards and penalties are provided to accomplish various high levels of greenness in a blockchain supply chain network. This study not only quantifies the significant impact of blockchain technology on enhancing the greenness of supply chains, but also reveals the decision-making mechanisms of enterprises and government under varying degrees of greenness, providing theoretical guidance for enterprises and government to manage green blockchain supply chains.
{"title":"The transformative potential of blockchain technology in developing green supply chain: An evolutionary perspective on complex networks","authors":"","doi":"10.1016/j.cie.2024.110548","DOIUrl":"10.1016/j.cie.2024.110548","url":null,"abstract":"<div><p>Distrust is the main obstacle to the green transformation of the supply chain. Blockchain can rebuild trust among multiple participants by employing distributed information technologies. Based on complex network evolutionary game models, this paper tries to reveal the transformative potential of blockchain in promoting the green transformation of the supply chain. Simulation experiments with real electric vehicle data show that if adopting blockchain technology: i) the greenness of the supply chain can increase by 200% even without government policy intervention; by 75% when jointly optimizing the intensity of rewards and penalties; by 40% when exclusively adjusting the reward or penalty intensity; ii) the profits of both suppliers and manufacturers can be improved when they adopt green investment strategies, creating a “win-win” scenario. Besides, optimal government rewards and penalties are provided to accomplish various high levels of greenness in a blockchain supply chain network. This study not only quantifies the significant impact of blockchain technology on enhancing the greenness of supply chains, but also reveals the decision-making mechanisms of enterprises and government under varying degrees of greenness, providing theoretical guidance for enterprises and government to manage green blockchain supply chains.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1016/j.cie.2024.110532
Continuous process manufacturing systems (CPMSs) are typical phased mission systems that require high standards of operational stability, reliability, and safety. With the variation of production mission profiles, CPMSs are required to run in diverse modes or conditions in different phases; therefore, to meet these standards, maintenance decisions applied to CPMSs should be adapted to such variations. Considering that the concept of “resilience” provides a systematic solution to evaluate system adaptability via “disruption absorption” and “recoverability,” this paper proposes a CPMS resilience evaluation model and utilizes it as guidance for the optimization of CPMS predictive maintenance (PdM). The proposed method consists of the following steps: (1) applying a customized Seasonal Trend Decomposition model to predict the future trend of production mission profile variations, (2) assessing the production mission accomplishment capability of CPMS based on a Gamma process model of equipment performance degradation, (3) using disruption response ratio to evaluate CPMS resilience based on mission accomplishment capability, and (4) proposing a Simulated Annealing Q-Learning algorithm for adaptive PdM optimization, which keeps resilience above a threshold level while minimizing maintenance costs. The applicability and effectiveness of the proposed method are validated by an industrial case study of a nuclear fuel rod shielding component CPMS.
{"title":"Resilience-oriented adaptive predictive maintenance optimization for continuous process manufacturing systems considering mission profile variation","authors":"","doi":"10.1016/j.cie.2024.110532","DOIUrl":"10.1016/j.cie.2024.110532","url":null,"abstract":"<div><p>Continuous process manufacturing systems (CPMSs) are typical phased mission systems that require high standards of operational stability, reliability, and safety. With the variation of production mission profiles, CPMSs are required to run in diverse modes or conditions in different phases; therefore, to meet these standards, maintenance decisions applied to CPMSs should be adapted to such variations. Considering that the concept of “resilience” provides a systematic solution to evaluate system adaptability via “disruption absorption” and “recoverability,” this paper proposes a CPMS resilience evaluation model and utilizes it as guidance for the optimization of CPMS predictive maintenance (PdM). The proposed method consists of the following steps: (1) applying a customized Seasonal Trend Decomposition model to predict the future trend of production mission profile variations, (2) assessing the production mission accomplishment capability of CPMS based on a Gamma process model of equipment performance degradation, (3) using disruption response ratio to evaluate CPMS resilience based on mission accomplishment capability, and (4) proposing a Simulated Annealing <em>Q</em>-Learning algorithm for adaptive PdM optimization, which keeps resilience above a threshold level while minimizing maintenance costs. The applicability and effectiveness of the proposed method are validated by an industrial case study of a nuclear fuel rod shielding component CPMS.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-03DOI: 10.1016/j.cie.2024.110542
Most data-driven methods for predicting remaining useful life assume that the data under different operating conditions follow the same distribution. However, this assumption rarely holds in real-world situation. Additionally, traditional methods do not fully utilize the hidden label information from the target domain or account for the transfer quality of source domain data. To address these issues, Label Adversarial Domain Adaptation (LADA) network is introduced in this paper. Specifically, LADA aims to filter the source domain data and maximize the use of hidden label information from the target domain. Firstly, a similarity measurement indicator based on the pearson correlation coefficient (PCC) and dynamic time warping (DTW) is employed to filter source domain data similar to the target domain data distribution. Then, in order to fully utilize the hidden label information from the target domain, the cloud model and golden section are utilized to create pseudo class labels. Furthermore, a feature difference module is established that minimizes the disparity between domain features. This is realized by using the maximum mean difference (MMD) and Kolmogorov–Smirnov (K–S) statistical test. The experimental results indicate that LADA has advantages in cross-domain RUL prediction.
{"title":"Label adversarial domain adaptation network for predicting remaining useful life based on cross-domain condition","authors":"","doi":"10.1016/j.cie.2024.110542","DOIUrl":"10.1016/j.cie.2024.110542","url":null,"abstract":"<div><p>Most data-driven methods for predicting remaining useful life assume that the data under different operating conditions follow the same distribution. However, this assumption rarely holds in real-world situation. Additionally, traditional methods do not fully utilize the hidden label information from the target domain or account for the transfer quality of source domain data. To address these issues, Label Adversarial Domain Adaptation (LADA) network is introduced in this paper. Specifically, LADA aims to filter the source domain data and maximize the use of hidden label information from the target domain. Firstly, a similarity measurement indicator based on the pearson correlation coefficient (PCC) and dynamic time warping (DTW) is employed to filter source domain data similar to the target domain data distribution. Then, in order to fully utilize the hidden label information from the target domain, the cloud model and golden section are utilized to create pseudo class labels. Furthermore, a feature difference module is established that minimizes the disparity between domain features. This is realized by using the maximum mean difference (MMD) and Kolmogorov–Smirnov (K–S) statistical test. The experimental results indicate that LADA has advantages in cross-domain RUL prediction.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.1016/j.cie.2024.110533
This study develops a new compact mathematical formulation to improve transplant planning in a kidney exchange program with a long cycle size. Our proposed integer programming model uses a new index, called timeslot, to significantly improve the model’s solution time for problems with large cycle sizes. To enhance the performance of obtaining an optimal solution, we apply two procedures. First, we define an appropriate upper bound for the defined index that reduces the number of variables and constraints as the cycle size increases. Furthermore, we develop a variable reduction procedure to eliminate the redundant variables and strengthen its performance. Finally, we assess the model performance in a systematic computational comparison and demonstrate its advantages compared to the existing formulations in the literature.
{"title":"Kidney exchange program: An efficient compact formulation","authors":"","doi":"10.1016/j.cie.2024.110533","DOIUrl":"10.1016/j.cie.2024.110533","url":null,"abstract":"<div><p>This study develops a new compact mathematical formulation to improve transplant planning in a kidney exchange program with a long cycle size. Our proposed integer programming model uses a new index, called timeslot, to significantly improve the model’s solution time for problems with large cycle sizes. To enhance the performance of obtaining an optimal solution, we apply two procedures. First, we define an appropriate upper bound for the defined index that reduces the number of variables and constraints as the cycle size increases. Furthermore, we develop a variable reduction procedure to eliminate the redundant variables and strengthen its performance. Finally, we assess the model performance in a systematic computational comparison and demonstrate its advantages compared to the existing formulations in the literature.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.cie.2024.110526
Governments frequently implement carbon trading (CT) and carbon subsidy (CS) policies for encouraging enterprises to engage in low-carbon efforts (LCE) and reduce emissions, ultimately aiming for sustainable development in environmental and economic realms. In response, enterprises increase their investments in process innovation (PI) to balance revenue generation and cost reduction while simultaneously accumulating knowledge. Exploring the relation between LCE and PI is crucial to guide enterprises in effectively balancing these inputs, as well as to examine the role of government regulations on carbon emissions in motivating enterprises to enhance their efforts. This study proposes a dynamic optimal control model integrating PI and LCE within the dual-carbon policy framework, considering the effect of knowledge accumulation (KA). Moreover, it evaluates changes in inputs and benefits under profit-optimal and social welfare-optimal conditions. Finally, a comparative analysis is conducted using numerical simulation and emulation. The findings indicate complementary and substitution effects between the two inputs: the KA effect enhances input stability, and the impact of social incentives consistently outweighs that of monopoly incentives. Furthermore, CT and CS policies exhibit cross-impacts on enterprise returns and the two inputs.
各国政府经常实施碳交易(CT)和碳补贴(CS)政策,鼓励企业开展低碳行动(LCE),减少排放,最终实现环境和经济领域的可持续发展。为此,企业增加了对工艺创新(PI)的投资,以在创收和降低成本之间取得平衡,同时积累知识。探索 LCE 与 PI 之间的关系对于指导企业有效平衡这些投入以及研究政府碳排放法规在激励企业加强努力方面的作用至关重要。考虑到知识积累(KA)的影响,本研究在双碳政策框架内提出了一个整合 PI 和 LCE 的动态优化控制模型。此外,它还评估了利润最优和社会福利最优条件下的投入和收益变化。最后,利用数值模拟和仿真进行了比较分析。研究结果表明,两种投入之间存在互补和替代效应:KA效应增强了投入的稳定性,社会激励的影响始终大于垄断激励的影响。此外,CT 和 CS 政策对企业回报和两种投入产生了交叉影响。
{"title":"Dynamic control of low-carbon efforts and process innovation considering knowledge accumulation under dual-carbon policies","authors":"","doi":"10.1016/j.cie.2024.110526","DOIUrl":"10.1016/j.cie.2024.110526","url":null,"abstract":"<div><p>Governments frequently implement carbon trading (CT) and carbon subsidy (CS) policies for encouraging enterprises to engage in low-carbon efforts (LCE) and reduce emissions, ultimately aiming for sustainable development in environmental and economic realms. In response, enterprises increase their investments in process innovation (PI) to balance revenue generation and cost reduction while simultaneously accumulating knowledge. Exploring the relation between LCE and PI is crucial to guide enterprises in effectively balancing these inputs, as well as to examine the role of government regulations on carbon emissions in motivating enterprises to enhance their efforts. This study proposes a dynamic optimal control model integrating PI and LCE within the dual-carbon policy framework, considering the effect of knowledge accumulation (KA). Moreover, it evaluates changes in inputs and benefits under profit-optimal and social welfare-optimal conditions. Finally, a comparative analysis is conducted using numerical simulation and emulation. The findings indicate complementary and substitution effects between the two inputs: the KA effect enhances input stability, and the impact of social incentives consistently outweighs that of monopoly incentives. Furthermore, CT and CS policies exhibit cross-impacts on enterprise returns and the two inputs.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.cie.2024.110523
Scheduling of service-oriented industrial businesses to meet the real-time requirements and improve the computing resources utilization is important. To this end, a Fine-grained Online Service Task Scheduling (FOSTS) method is proposed. First, a service task model that separates the invoked service from the task is established. Then, a service invocation-oriented allocation mechanism that allocates the invoked tasks based on priority is proposed to achieve fine-grained resource utilization. In addition, a task suspension mechanism to release resources for important tasks by suspending non-important tasks is proposed. The proposed FOSTS is tested in a typical industrial scenario and its performance is compared with many common and state-of-the-art methods. The simulation results show the effectiveness and superior performance of the proposed FOSTS compared to the other methods.
{"title":"Real-time service task scheduling with fine-grained resource utilization to benefit important industrial business","authors":"","doi":"10.1016/j.cie.2024.110523","DOIUrl":"10.1016/j.cie.2024.110523","url":null,"abstract":"<div><p>Scheduling of service-oriented industrial businesses to meet the real-time requirements and improve the computing resources utilization is important. To this end, a Fine-grained Online Service Task Scheduling (FOSTS) method is proposed. First, a service task model that separates the invoked service from the task is established. Then, a service invocation-oriented allocation mechanism that allocates the invoked tasks based on priority is proposed to achieve fine-grained resource utilization. In addition, a task suspension mechanism to release resources for important tasks by suspending non-important tasks is proposed. The proposed FOSTS is tested in a typical industrial scenario and its performance is compared with many common and state-of-the-art methods. The simulation results show the effectiveness and superior performance of the proposed FOSTS compared to the other methods.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.cie.2024.110524
With the rapid advancement of information technology, a new service model known as X as a Service has emerged. X can represent diverse resources such as platforms, infrastructure, farming, mobility, security, and more, allowing users to meet their needs flexibly and cost-effectively. The successful application of X as a Service heavily depends on end-users’ acceptance. This study explored the motivation factors for X as a Service based on the evidence from adopting Farming as a Service in Agriculture 4.0 among farmers in Northeastern China. We provided a theoretical framework for Farming as a Service adoption behavior, covering factors including personalization, perceived enjoyment, functionality, perceived risk, financial consequences, and perceived network externality. The effectiveness of the research model was assessed and validated through a two-stage procedural approach, utilizing partial least squares structural equation modeling. Results revealed that our proposed acceptance model for Farming as a Service exhibited a good model fit, accounting for 84.4 % of the variance in adoption intentions. Research findings highlighted that perceived network externality and functionality were the most influential factors in determining users’ adoption intentions for Farming as a Service. Conversely, perceived risk emerged as a significant negative factor influencing adoption. Furthermore, financial consequences, perceived enjoyment, and personalization also played crucial roles as determinants of user adoption. These findings offered valuable insights for service providers to improve their products, services, and marketing strategies.
{"title":"End-users’ acceptance of ’X as a Service’: Evidence from agriculture 4.0","authors":"","doi":"10.1016/j.cie.2024.110524","DOIUrl":"10.1016/j.cie.2024.110524","url":null,"abstract":"<div><p>With the rapid advancement of information technology, a new service model known as X as a Service has emerged. X can represent diverse resources such as platforms, infrastructure, farming, mobility, security, and more, allowing users to meet their needs flexibly and cost-effectively. The successful application of X as a Service heavily depends on end-users’ acceptance. This study explored the motivation factors for X as a Service based on the evidence from adopting Farming as a Service in Agriculture 4.0 among farmers in Northeastern China. We provided a theoretical framework for Farming as a Service adoption behavior, covering factors including personalization, perceived enjoyment, functionality, perceived risk, financial consequences, and perceived network externality. The effectiveness of the research model was assessed and validated through a two-stage procedural approach, utilizing partial least squares structural equation modeling. Results revealed that our proposed acceptance model for Farming as a Service exhibited a good model fit, accounting for 84.4 % of the variance in adoption intentions. Research findings highlighted that perceived network externality and functionality were the most influential factors in determining users’ adoption intentions for Farming as a Service. Conversely, perceived risk emerged as a significant negative factor influencing adoption. Furthermore, financial consequences, perceived enjoyment, and personalization also played crucial roles as determinants of user adoption. These findings offered valuable insights for service providers to improve their products, services, and marketing strategies.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.cie.2024.110528
Cities are increasingly pursuing sustainable development. To this end, we constructed two game models of cooperation between the electricity enterprise and the urban park manager to invest in charging stations (CSs) of energy vehicles (EVs). Model A involves revenue sharing between these entities, while Model B assumes that the electricity enterprise pays site leasing fees to the urban park manager. The CS investment capacity constraint is also considered. Based on the equilibrium solution of the models, we draw the following conclusions. Firstly, when the CS investment capacity constraint is small, the CS investment amount is equal under both models, while the leasing model provides a higher charging price and lower electricity demand for CS. Secondly, when the CS investment capacity constraint is moderate, the CS investment amount under the leasing model and the charging price are higher. The electricity demand for CS is mainly affected by the revenue sharing ratio. Thirdly, when the CS investment capacity constraint is high, the CS investment amount is affected by the finding cost of CS and the charging price changes accordingly with the electricity demand for CS. Finally, a decrease in the finding cost of CS does not necessarily lead to greater investment in CSs.
城市越来越追求可持续发展。为此,我们构建了电力企业与城市公园管理者在投资能源汽车(EV)充电站(CS)方面合作的两个博弈模型。模型 A 涉及这两个实体之间的收入共享,而模型 B 则假定电力企业向城市公园管理者支付场地租赁费。同时还考虑了 CS 投资能力约束。根据模型的均衡解,我们得出以下结论。首先,当 CS 投资能力约束较小时,两种模式下的 CS 投资额相等,而租赁模式提供了更高的充电价格和更低的 CS 用电需求。其次,当希尔思投资能力限制适中时,租赁模式下的希尔思投资额和充电价格都较高。CS 用电需求主要受收入分成比例的影响。第三,当希尔思投资能力约束较高时,希尔思投资额会受到希尔思寻找成本的影响,充电价格也会随着希尔思用电需求的变化而相应变化。最后,CS 寻找成本的降低并不一定导致 CS 投资的增加。
{"title":"Strategies of electricity enterprises and urban parks cooperatively investing in electric vehicle charging stations","authors":"","doi":"10.1016/j.cie.2024.110528","DOIUrl":"10.1016/j.cie.2024.110528","url":null,"abstract":"<div><p>Cities are increasingly pursuing sustainable development. To this end, we constructed two game models of cooperation between the electricity enterprise and the urban park manager to invest in charging stations (CSs) of energy vehicles (EVs). Model A involves revenue sharing between these entities, while Model B assumes that the electricity enterprise pays site leasing fees to the urban park manager. The CS investment capacity constraint is also considered. Based on the equilibrium solution of the models, we draw the following conclusions. Firstly, when the CS investment capacity constraint is small, the CS investment amount is equal under both models, while the leasing model provides a higher charging price and lower electricity demand for CS. Secondly, when the CS investment capacity constraint is moderate, the CS investment amount under the leasing model and the charging price are higher. The electricity demand for CS is mainly affected by the revenue sharing ratio. Thirdly, when the CS investment capacity constraint is high, the CS investment amount is affected by the finding cost of CS and the charging price changes accordingly with the electricity demand for CS. Finally, a decrease in the finding cost of CS does not necessarily lead to greater investment in CSs.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.cie.2024.110493
In group decision-making scenarios, consensus reaching is a crucial factor for resolving conflicts of opinion among groups, and social network analysis plays a significant role in fostering group consensus. This paper constructs a social network-driven minimum cost consensus framework for interval type-2 fuzzy group decision-making problems involving different individual attributes. Firstly, this paper proposes a theoretical social network analysis by the implementation of propagation efficiency, propagation reliability and opinion similarity to generate comprehensive trust relationships. The aim is to obtain missing trust relationships and individual centrality. Secondly, a minimum cost consensus model is constructed to give recommendation advice for identified inconsistent decision-makers according to their adjustment willingness. The novelty of the model lies in its capability to consider decision-makers’ individual attributes and group attitude or behavior. Then, this paper proposes an interval type-2 fuzzy Alternative by Alternative Comparison (ABAC) method for ranking multiple alternatives which address the rank reversal problem. Lastly, a case study on the selection of alternative charging point operators illustrates the effectiveness of the proposed method, and comparison and sensitivity analysis show the advantages of the proposed method.
{"title":"Enhanced minimum cost consensus model for interval type-2 fuzzy social network group decision making focusing on individual attributes and group attitude","authors":"","doi":"10.1016/j.cie.2024.110493","DOIUrl":"10.1016/j.cie.2024.110493","url":null,"abstract":"<div><p>In group decision-making scenarios, consensus reaching is a crucial factor for resolving conflicts of opinion among groups, and social network analysis plays a significant role in fostering group consensus. This paper constructs a social network-driven minimum cost consensus framework for interval type-2 fuzzy group decision-making problems involving different individual attributes. Firstly, this paper proposes a theoretical social network analysis by the implementation of propagation efficiency, propagation reliability and opinion similarity to generate comprehensive trust relationships. The aim is to obtain missing trust relationships and individual centrality. Secondly, a minimum cost consensus model is constructed to give recommendation advice for identified inconsistent decision-makers according to their adjustment willingness. The novelty of the model lies in its capability to consider decision-makers’ individual attributes and group attitude or behavior. Then, this paper proposes an interval type-2 fuzzy Alternative by Alternative Comparison (ABAC) method for ranking multiple alternatives which address the rank reversal problem. Lastly, a case study on the selection of alternative charging point operators illustrates the effectiveness of the proposed method, and comparison and sensitivity analysis show the advantages of the proposed method.</p></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":null,"pages":null},"PeriodicalIF":6.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}