With the reform of the power system further deepening,the reliance on electricity and importance attached to the reliable power supply are increasing year by year,and the establishment of a high resilient power system...With the reform of the power system further deepening,the reliance on electricity and importance attached to the reliable power supply are increasing year by year,and the establishment of a high resilient power system has considerable economic,environmental and social benefits.Reconfiguring the network is one of the well-known tactics to enhance reliability.Accordingly,this paper proposes a reconfiguration method of distribution network considering the enhancement of reliability,which reconfigures the network structure both under normal operation conditions and outage scenarios,and considers factors such as power loss,load distribution and voltage quality considered in conventional reconfiguration methods.In this paper,the reliability assessment is integrated into the process of distribution network reconfiguration by using binary variables to represent the operating state of switchable devices.Based on the concept of fictitious fault flows,the reliability indices of distribution network are linearized expressed,and the network loss is reduced by minimizing the voltage deviation.A mixed integer linear programming(MILP)model is established for distribution network reconfiguration problem,which can guarantee the global optimal solution with high solution efficiency.Finally,the applicability and effectiveness of the proposed method are verified by numerical tests on a 54-node test system.展开更多
The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain o...The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming solvers.In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.展开更多
Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as ...Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.展开更多
To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming （REILP） approach was developed in previous studies to address decision risks and system returns...To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming （REILP） approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeofffactor （α） into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty- based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of a was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demon- strate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.展开更多
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20221165).
文摘With the reform of the power system further deepening,the reliance on electricity and importance attached to the reliable power supply are increasing year by year,and the establishment of a high resilient power system has considerable economic,environmental and social benefits.Reconfiguring the network is one of the well-known tactics to enhance reliability.Accordingly,this paper proposes a reconfiguration method of distribution network considering the enhancement of reliability,which reconfigures the network structure both under normal operation conditions and outage scenarios,and considers factors such as power loss,load distribution and voltage quality considered in conventional reconfiguration methods.In this paper,the reliability assessment is integrated into the process of distribution network reconfiguration by using binary variables to represent the operating state of switchable devices.Based on the concept of fictitious fault flows,the reliability indices of distribution network are linearized expressed,and the network loss is reduced by minimizing the voltage deviation.A mixed integer linear programming(MILP)model is established for distribution network reconfiguration problem,which can guarantee the global optimal solution with high solution efficiency.Finally,the applicability and effectiveness of the proposed method are verified by numerical tests on a 54-node test system.
文摘The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems.The complexity of industrial-scale supply chain optimization,however,often poses limits to the application of general mixed-integer programming solvers.In this paper we describe algorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set,modeling real-world scenarios supplied by our industry partner SAP.
基金supported by the National Natural Science Foundation of China(72201229,72025103,72394360,72394362,72361137001,72071173,and 71831008).
文摘Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.
基金This paper was supported by the National Natural Science Foundation of China （Grant No. 41222002）, Research Fund for the Doctoral Program of Higher Education of China （20100001120020） and ＂China National Water Pollution Control Program＂ （2013ZX07102-006）. Special thanks to Dr. Daniel Obenour in University of Michigan.
文摘To enhance the effectiveness of watershed load reduction decision making, the Risk Explicit Interval Linear Programming （REILP） approach was developed in previous studies to address decision risks and system returns. However, REILP lacks the capability to analyze the tradeoff between risks in the objective function and constraints. Therefore, a refined REILP model is proposed in this study to further enhance the decision support capability of the REILP approach for optimal watershed load reduction. By introducing a tradeofffactor （α） into the total risk function, the refined REILP can lead to different compromises between risks associated with the objective functions and the constraints. The proposed model was illustrated using a case study that deals with uncertainty- based optimal load reduction decision making for Lake Qionghai Watershed, China. A risk tradeoff curve with different values of a was presented to decision makers as a more flexible platform to support decision formulation. The results of the standard and refined REILP model were compared under 11 aspiration levels. The results demon- strate that, by applying the refined REILP, it is possible to obtain solutions that preserve the same constraint risk as that in the standard REILP but with lower objective risk, which can provide more effective guidance for decision makers.
基金Supported by the China Nature Science Foundation(41071270)the Natural Science Fund of Hubei Province(2010CDB03305)+2 种基金the Open Fund of Hubei Province Key Laboratory of Systems Science in Metallurgical Process(C201007)the Wuhan Chenguang Program(201150431096)the Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics(SOED1102)
