16 results about "Crew scheduling" patented technology

The invention relates to the technical field of intelligent manufacturing and dynamic personnel scheduling optimization, and discloses a dynamic personnel scheduling optimization method based on DDQN. According to the method, a DDQN intelligent decision framework oriented to the industrial scheduling problem is constructed, and the problem of Q value over-estimation in a traditional DQN algorithm is effectively solved by designing a multi-dimensional state space containing a process state, a team and a parallel mark and directly mapping an action space of an actual scheduling decision; and process states and worker behaviors are modeled through a deep reinforcement learning framework, and a task allocation and resource scheduling scheme is dynamically adjusted in combination with a real-time worker behavior prediction module, so that the total completion time is minimized and the resource utilization rate is maximized in a multi-process and multi-constraint scene, and the working efficiency is improved. The method is suitable for complex industrial environments such as chemical workshops and flexible manufacturing systems, scheduling challenges caused by process dependence, worker heterogeneity and dynamic task requirements can be effectively handled, and the production efficiency and the response capability of the intelligent manufacturing system are improved.

The invention relates to a production management system oriented to automatic control. The production management system comprises a production planning module, a production resource scheduling module, a production control module, a quality detection module and a logistics management module. The production planning module is used for generating a production plan according to production requirements; the production resource scheduling module is used for raw material scheduling, equipment scheduling and personnel scheduling; the production control module is used for controlling production process parameters according to collected real-time production data; the quality detection module is used for detecting the quality of the produced product; and the logistics module is used for managing raw material and product distribution.

The present application relates to the technical field of energy security, and more particularly to a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method and system, comprising: obtaining active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output and load curve and numerical value; the post-disaster recovery model of active power distribution network is constructed in the guidance of elasticity, the adjustment strategy of distributed power generation resources is determined based on the topology reconstruction and island segmentation strategy method; the optimization scheduling model of repair personnel and network reconstruction coordination is constructed in the guidance of elasticity, and the second order cone relaxation method is used for relaxation processing to determine the fault repair strategy; through the active power distribution network topology parameters, the adjustable resource parameters and the renewable energy predicted output, the optimization problem is reconstructed based on the active power distribution network fault scene under the disaster; the optimization problem is solved with the minimum fault economic loss as the target, and the active power distribution network topology structure and the fault repair strategy are obtained.

This invention discloses a crew scheduling method, system, and equipment based on multi-dimensional fatigue prediction and TEM optimization, relating to the field of aviation operation control technology. It generates crew member capability parameters by acquiring multi-dimensional flight mission data and performing time alignment and correlation integration; obtains a demand summary result based on the TEM threat knowledge base mapping, calculates the coupling increment by combining the situation-coupled threat map, and forms a situation-enhanced demand summary result; synthesizes basic operational requirements and situation-enhanced requirements into comprehensive capability requirements, and dynamically adjusts them according to a phased dynamic amplification coefficient under unexpected events; generates vulnerability correction parameters based on crew operation adaptation data, and calculates situation-effective maneuverability and cognitive ability; obtains energy state parameters through a fatigue prediction model, defines resilience margin indicators based on situation-effective capability and comprehensive capability requirements, and constructs capability matching constraints to generate a scheduling plan; updates parameters and performs rescheduling and decision chain output when data changes are detected.

The application discloses a crew scheduling method, system and equipment based on multi-dimensional fatigue prediction and TEM optimization, and relates to the technical field of aviation operation control. Multi-dimensional data of a flight task is acquired, time alignment and associated integration are performed, and crew member capability parameters are generated. A demand summary result is obtained based on a TEM threat knowledge base mapping, a coupling increment is calculated in combination with a situation coupling threat graph, a situation enhanced demand summary result is formed, basic operation demand and situation enhanced demand are summarized to form comprehensive capability demand, and dynamic adjustment is performed according to a phased dynamic amplification coefficient under an unexpected event. A vulnerability correction parameter is generated based on crew operation adaptation data, situation effective operating capability and cognitive capability are calculated, an energy state parameter is obtained through a fatigue prediction model, a resilience margin index is defined based on situation effective capability and comprehensive capability demand, capability matching constraints are constructed, a scheduling scheme is generated, and parameters are updated when data changes are monitored, and rescheduling and decision chain output are performed.

The invention relates to the technical field of intelligent building and parking lot management, and discloses a parking lot operation and maintenance event automatic disposal and personnel scheduling method, which comprises the following steps: firstly, obtaining operation and maintenance event information in real time through various sensing devices; secondly, a dynamic priority calculation model is constructed, and the model integrates the type severity of the events, the potential influence range based on the equipment dependency graph and the time urgency, and gives quantitative priorities to the events; secondly, vectorizing task requirements of events and multi-dimensional state information such as capabilities, workloads and positions of operation and maintenance personnel, determining an optimal matching personnel by calculating a matching score, and performing a final scheduling decision in combination with a task preemption mechanism; and finally, the system performs closed-loop parameter self-optimization on the priority calculation model and the personnel ability model according to feedback data of task completion. According to the invention, quick response and intelligent accurate scheduling of the operation and maintenance event can be realized, and the resource allocation efficiency is remarkably improved.

This invention provides an intelligent scheduling and configuration method and system for shared vehicle maintenance personnel, relating to the field of shared vehicle technology. Based on a preset time period, it retrieves historical scheduling data of shared vehicles, constructs a scheduling task matching module and a scheduling path generation module, generates a vehicle maintenance scheduling model, obtains user vehicle demand applications and integrates them as batch target application information, generates a single set of scheduling tasks and multiple scheduling paths, wherein each single set of scheduling tasks corresponds one-to-one with the multiple scheduling paths, and achieves intelligent vehicle scheduling and maintenance based on the single set of scheduling tasks and multiple scheduling paths. This invention solves the technical problem that existing shared vehicle maintenance personnel scheduling and configuration methods cannot establish reasonable indicators based on the demand for shared vehicle resources in different times and spaces, leading to unreasonable allocation of maintenance personnel. It achieves multi-model coordination and optimization, performs optimal allocation of maintenance tasks, and achieves the technical effect of improving vehicle utilization and reducing the workload of maintenance personnel.

The invention provides a unit scheduling method, device and equipment and a storage medium, relates to the technical field of data processing, and is used for improving the flight scheduling efficiency. The method comprises the following steps: acquiring a function interface array of a flight sub-rule; the function interface array comprises a plurality of function interfaces, and one function interface corresponds to one flight sub-rule. Under the condition that a first function interface in the function interface array is called, obtaining a calling value corresponding to the first function interface; the first function interface is any function interface in the plurality of function interfaces. Under the condition that the calling value is the first numerical value, obtaining a first adaptation result corresponding to the first function interface; the first numerical value is a numerical value obtained after the first function interface is called, and the adaptation result is a first adaptation result obtained by performing adaptation on the employee information of the flight crew by the first function interface based on a flight sub-rule corresponding to the first function interface. And arranging flights for the flight crew based on the first adaptation result.

This application relates to the field of intelligent adjustment and compilation technology for train crew routes, and provides a method and system for optimizing high-speed train crew scheduling plans based on heuristic algorithms. The method includes: generating virtual routes with consistent origin and destination stations by pairing routes in the route set where the origin and destination stations are inconsistent; solving the problem using a heuristic algorithm to obtain a single-cycle route sequence covering all routes once; and generating a crew scheduling plan based on the single-cycle route sequence. This method, through the design of a specialized route preprocessing and matching mechanism, intelligently pairs outbound and return routes to form virtual routes, and combines feasible connection rules and connection time cost matrices to transform the scheduling problem into a single-cycle route sequence optimization problem for heuristic solution. This not only ensures the feasibility of the scheduling plan, but also improves overall operational efficiency by optimizing pairing and compressing non-duty time such as outbound stops.

The invention relates to the technical field of airport analog simulation, in particular to an adaptive optimization method for airport dynamic simulation parameters based on SAC reinforcement learning, and the method comprises the steps: combining an SAC algorithm with an improved Transform model for multi-modal spatial-temporal feature extraction; static configuration and multi-time-frame real-time states of various business objects such as airport flights, airport positions, runways, guarantee vehicles and personnel are used as input, and parameters such as release intervals, sliding path priorities and resource allocation modes required by simulation are output through a strategy network; a strategy is continuously optimized in a simulation-real error closed loop by using a double-Q network and an entropy regularization mechanism, and key control parameters such as runway distribution, sliding path priority, release interval and guarantee vehicle and personnel scheduling are learned and adjusted online in real time from the simulation-real error closed loop; therefore, the simulation precision and the system robustness are ensured in a complex dynamic environment.

The application relates to a substation inspection scheme planning method for an extreme load scene, which comprises the following steps: constructing a substation inspection model fusing a value-keeping scene; proposing a dynamic updating mechanism of an inspection path adapting to the change of the number of stations; establishing a time-cost collaborative planning model considering resource constraints to generate an optimal inspection scheme considering the inspection time length and fuel cost; defining work length, station inspection proportion, overtime intensity, work frequency and fatigue degree as work quantity evaluation indexes, constructing an inspection task allocation model, and optimizing and solving the substation inspection scheme with the most balanced work quantity allocation. The application constructs a multi-dimensional work quantity evaluation index system and an inspection task allocation model, realizes the balanced allocation of work quantities among various inspection teams, and provides a scientific decision basis for personnel scheduling in operation and maintenance management.

The invention discloses a temporary employment scheduling and community autonomy method and device based on a block chain, and a storage medium. The method comprises the steps of performing data mining on historical service data when service personnel serve a community based on a data mining and machine learning algorithm to obtain a service knowledge rule, and constructing a knowledge base according to the service knowledge rule; setting an optimization problem and constructing an intelligent optimization scheduling model based on knowledge driving based on the optimization problem; obtaining a to-be-scheduled service order task, and obtaining a service personnel order receiving allocation scheme of the to-be-scheduled service order task according to the intelligent optimization scheduling model based on knowledge driving and all service personnel registered in the system, so as to realize order receiving of the to-be-scheduled service order personnel; and meanwhile, the order receiving record of the service order task to be scheduled is uploaded to the block chain. According to the invention, the problems of difficulty in personnel identity verification, poor personnel scheduling efficiency and the like in the existing resident autonomous cross-community process can be solved.

The application discloses a high-speed rail business travel service personnel scheduling method based on data analysis, and relates to the technical field of personnel scheduling management. The method comprises the following steps: data collection and integration: collecting high-speed rail station operation data, passenger information, service personnel information and environment data; performing stream data processing and space-time alignment on the collected data, and constructing a dynamic main data matrix; service demand prediction: extracting passenger multi-dimensional features from the dynamic main data matrix, and generating passenger multi-dimensional feature labels; based on the passenger multi-dimensional feature labels, combining environment data, performing bimodal demand prediction, and obtaining initial passenger service demand. Through the establishment of the bimodal service demand prediction method, the extraction of passenger multi-dimensional features, the combination of environment data and the prediction of passenger demand, the application realizes the transition from passive response to active prediction, accelerates the response speed, reduces the waiting time of passengers and improves the service quality and efficiency.

The application provides a rail transit crew scheduling optimization method based on a space-time connection network. The method comprises the following steps: dividing a rail transit crew segment, generating a crew work shift according to the crew segment, constructing a scheduling plan space-time connection network based on the crew work shift, establishing a target function of a rail transit crew scheduling plan optimization model based on the scheduling plan space-time connection network, and solving the target function of the rail transit crew scheduling plan optimization model through a Lagrange relaxation algorithm to obtain a rail transit crew scheduling plan. The method divides a rail transit diagram according to a vehicle depot and a value station, combines the generated crew segments with each other to obtain corresponding crew work shifts, constructs a scheduling plan space-time connection network, establishes a rail transit crew scheduling plan optimization model, and finally designs a Lagrange relaxation algorithm to solve the model, so that the rail transit crew scheduling plan can be effectively optimized.

This invention discloses a low-disturbance-impact method for railway passenger service scheduling, comprising: generating several initial scheduling plans sequentially using a greedy algorithm constrained by scheduling rules; using these initial scheduling plans as the initial population for a particle swarm optimization algorithm; globally optimizing the initial scheduling plans with scheduling rule satisfaction and disturbance impact indicators as optimization objectives to obtain a baseline passenger service scheduling scheme; acquiring sudden impact events; performing semantic extraction and credibility calculation on the sudden impact events to obtain structured event objects, and converting them into constraint objects; extracting the set of affected resources and the set of affected task links from the constraint objects; determining the repair area based on the set of affected resources and the set of affected task links; using the baseline passenger service scheduling scheme as the initial value, performing a local update search based on task relationships within the repair area to generate candidate solutions in the domain; selecting the optimal feasible local solution with minimal disturbance to update the baseline passenger service scheduling scheme.

The embodiments of the present disclosure provide methods and systems for scheduling operation and maintenance personnel based on an Internet of Things (IoT) system for smart gas installation management. The method may include: obtaining user installation information of at least one operation and maintenance area; determining target installation information of the at least one operation and maintenance area; determining a door-to-door service plan for the at least one operation and maintenance area; obtaining collection data of a gas device configured in the operation and maintenance area; determining a count of on-call personnel of the at least one operation and maintenance area; determining a scheduling capability value of the at least one operation and maintenance area; determining a real-time scheduling instruction; and in response to compensation time off information, updating the scheduling capability value, and adjusting the real-time scheduling instruction.