In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and ...In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.展开更多
The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic suppor...The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.展开更多
预制菜产业高质量发展离不开冷链物流的支撑,当前我国预制菜产业面临冷链物流服务体系不全、服务能力不强、服务成本偏高等问题。面对可预见的万亿级蓝海市场,提升冷链物流对预制菜产业的支撑能力,对实现预制菜产业高质量发展具有重要...预制菜产业高质量发展离不开冷链物流的支撑,当前我国预制菜产业面临冷链物流服务体系不全、服务能力不强、服务成本偏高等问题。面对可预见的万亿级蓝海市场,提升冷链物流对预制菜产业的支撑能力,对实现预制菜产业高质量发展具有重要意义。从基础支撑条件、服务运营能力和提升保障能力3个维度系统构建预制菜产业冷链物流支撑力影响因素指标体系,并应用DEMATEL(Decision Making Trial and Evaluation Laboratory)方法计算各因素的影响度、被影响度、中心度和原因度,定量揭示各因素之间的相互作用关系和重要程度。研究表明,冷链物流企业综合实力、物流装备现代化水平、冷链物流信息化水平、技术创新与转化能力、数据要素赋能水平和土地资金能源保障能力是影响预制菜产业冷链物流支撑力的关键因素。同时,结合关键影响因素,针对性提出提升预制菜产业冷链物流支撑力的路径建议,以此为政府政策制定及企业管理决策提供参考。展开更多
Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statisti...Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.展开更多
基金supported by the Science Challenge Project(TZ2018007)the National Natural Science Foundation of China(71671009+2 种基金 61871013 61573041 61573043)
文摘In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.
基金supported by the National Natural Science Foundation of China(71171008)
文摘The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.
文摘预制菜产业高质量发展离不开冷链物流的支撑,当前我国预制菜产业面临冷链物流服务体系不全、服务能力不强、服务成本偏高等问题。面对可预见的万亿级蓝海市场,提升冷链物流对预制菜产业的支撑能力,对实现预制菜产业高质量发展具有重要意义。从基础支撑条件、服务运营能力和提升保障能力3个维度系统构建预制菜产业冷链物流支撑力影响因素指标体系,并应用DEMATEL(Decision Making Trial and Evaluation Laboratory)方法计算各因素的影响度、被影响度、中心度和原因度,定量揭示各因素之间的相互作用关系和重要程度。研究表明,冷链物流企业综合实力、物流装备现代化水平、冷链物流信息化水平、技术创新与转化能力、数据要素赋能水平和土地资金能源保障能力是影响预制菜产业冷链物流支撑力的关键因素。同时,结合关键影响因素,针对性提出提升预制菜产业冷链物流支撑力的路径建议,以此为政府政策制定及企业管理决策提供参考。
文摘Suppliers＇ selection in supply chain management （SCM） has attracted considerable research interests in recent years. Recent literatures show that neural networks achieve better performance than traditional statistical methods. However, neural networks have inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence. A relatively new machine learning technique, support vector machine （SVM）, which overcomes the drawbacks of neural networks, is introduced to provide a model with better explanatory power to select ideal supplier partners. Meanwhile, in practice, the suppliers＇ samples are very insufficient. SVMs are adaptive to deal with small samples＇ training and testing. The prediction accuracies for BPNN and SVM methods are compared to choose the appreciating suppliers. The actual examples illustrate that SVM methods are superior to BPNN.
