Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e....Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.展开更多
How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention.With the continuous improvement on satellite payload capacity and the increasing complexity of observation re...How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention.With the continuous improvement on satellite payload capacity and the increasing complexity of observation requirements,the importance of satellite autonomous task scheduling research has gradually increased.This article first gives the problem description and mathematical model for the satellite autonomous task scheduling and then follows the steps of"satellite autonomous task scheduling,centralized autonomous collaborative task scheduling architecture,distributed autonomous collaborative task scheduling architecture,solution algorithm".Finally,facing the complex and changeable environment situation,this article proposes the future direction of satellite autonomous task scheduling.展开更多
We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing d...We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing data from a large U.S.power supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity costs.We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins.The features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior day.We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies.We observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy prices.We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage.Our test system includes an interactive voice interface via an intelligent personal assistant.展开更多
基金the financial support of the National Key Research and Development Plan(2021YFB3302501)the financial support of the National Natural Science Foundation of China(12102077)。
文摘Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft carrier.The primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch entities.In this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource constraints.Specifically,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as machines.By analyzing the constraints in sortie scheduling,a mixed-integer planning model is constructed.In particular,the constraint on preparing spot occupancy is improved to further enhance the sortie efficiency.The basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance issue.To efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is developed.The solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing strategies.And animation of the simulation results is available at www.bilibili.com/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.
基金supported by the National Natural Science Foundation of China(72001212,61773120)Hunan Postgraduate Research Innovation Project(CX20210031)+1 种基金the Foundation for the Author of National Excellent Doctoral Dissertation of China(2014-92)the Innovation Team of Guangdong Provincial Department of Education(2018KCXTD031)。
文摘How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention.With the continuous improvement on satellite payload capacity and the increasing complexity of observation requirements,the importance of satellite autonomous task scheduling research has gradually increased.This article first gives the problem description and mathematical model for the satellite autonomous task scheduling and then follows the steps of"satellite autonomous task scheduling,centralized autonomous collaborative task scheduling architecture,distributed autonomous collaborative task scheduling architecture,solution algorithm".Finally,facing the complex and changeable environment situation,this article proposes the future direction of satellite autonomous task scheduling.
文摘We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing data from a large U.S.power supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity costs.We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins.The features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior day.We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies.We observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy prices.We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage.Our test system includes an interactive voice interface via an intelligent personal assistant.
