CN111967642A - Resource constraint type dynamic ship scheduling method and scheduling platform based on time window strategy - Google Patents
Resource constraint type dynamic ship scheduling method and scheduling platform based on time window strategy Download PDFInfo
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Abstract
The invention discloses a resource constraint type dynamic ship scheduling method and a scheduling platform based on a time window strategy, and belongs to the field of dynamic ship task scheduling. The scheduling platform comprises a data file module, a file configuration module, a scheduling algorithm module, a dynamic scheduling management module and a scheduling query module. Firstly, acquiring information of a ship task set from a test example, and selecting and initializing an objective function according to actual needs. Then sequencing all ship tasks by adopting a real number coding mode to obtain an execution sequence and an execution scheme of each task; and when a new task arrives or the instrument fails, adjusting by using a time window dynamic scheduling strategy, and updating the available starting time of the instrument. And finally, outputting the execution sequence and the execution scheme of each task and the available starting time of the instrument in real time to obtain a scheduling result of the current moment and a corresponding scheduling Gantt chart. The scheduling method of the invention improves the robustness, and the scheduling platform has flexibility and intuition.
Description
Technical Field
The invention belongs to the field of dynamic ship task scheduling, and particularly relates to a resource constraint type dynamic ship scheduling method and a scheduling platform based on a time window strategy.
Background
In order to adapt to the development trend of ship construction technologies at home and abroad, information technologies such as computers, microelectronics, network communication, sensors and the like are widely applied to ships, so that the complexity of ship equipment and systems is gradually improved, and the information quantity of whole-ship resource scheduling is rapidly increased. When a task is executed, the traditional manual scheduling method is bound by human memory, judgment and the like in the face of a large amount of inrush resource scheduling information, part of scheduling information is inevitably ignored, precious resource scheduling information such as real-time equipment conditions and personnel states is wasted, certain processing hysteresis exists, and ship efficiency is influenced.
In order to efficiently complete various tasks of a ship, quick comprehensive decisions and responses need to be made according to scheduling resource information such as the state of the current task, the condition of ship resources, the state of key equipment, the vital characteristics of ship personnel and the like, so that various tasks can be responded to at ease, the task execution efficiency is improved, and the real-time resource efficiency of the whole ship is optimized.
Traditional ship scheduling research focuses on the field of static scheduling, and the problems of sequencing and distribution of a plurality of ship tasks on a plurality of devices are solved. The scheduling problem is composed of a series of tasks which are executed in sequence or in parallel, the tasks are mutually independent or have a local priority relationship, and the scheduling aim is to distribute all the tasks to some mutually independent resources in a reasonable sequence and mode so as to enable a certain item or a plurality of items of performance indexes to be optimal.
During the actual work process of ship scheduling, various unexpected emergencies are inevitable, such as task arrival, instrument failure or new resource occupation, and the like, and these real-time events may cause that the previous scheduling scheme is no longer applicable, i.e. the ideal static scheduling cannot meet the actual requirement. Therefore, the scheduling process is often an uninterrupted reaction process, and needs to be adjusted or rescheduled according to actual conditions, so as to implement the response to the dynamic event. The dynamic scheduling problem considers various dynamic environment changes in practical application and is more consistent with the working practice of scheduling tasks.
From the viewpoint of dynamic scheduling technology, metaheuristic algorithms are widely applied to the solution of dynamic scheduling problems. The meta-heuristic algorithm is widely researched and paid attention because it does not need complicated mathematical formula deduction and quick operation capability based on computer simulation. Particularly on the basis of the multi-objective optimization scheduling problem, the algorithm shows excellent performances such as strong robustness, high convergence speed, strong searching capability and the like. In the ship resource scheduling problem with various targets, the scheduling optimization function has the characteristics of non-convexity, multiple peaks, non-smoothness and the like, and in general, if the problem is solved by adopting a traditional optimization method, only a feasible solution can be obtained, which is mainly because the traditional scheduling optimization method basically adopts a local optimization method, so that a satisfactory scheduling scheme cannot be obtained in the real whole-ship resource scheduling problem. The meta heuristic algorithm has the characteristics of simplicity, intuition, strong universality, strong robustness and the like, and can better solve the problem of complex ship resource scheduling no matter whether the problem is a single target or a multi-target.
Meta heuristic is a general heuristic strategy that guides conventional heuristic algorithms related to problems to make efficient searches of decision spaces. The meta-heuristic algorithm is formed by simulating natural phenomena such as biological evolution and the like and learning biological intelligence such as ant colony foraging and the like. Typical meta heuristic algorithms include Genetic Algorithm (GA), particle swarm algorithm (PSO), difference algorithm (DE), ant colony Algorithm (ACO), artificial bee colony Algorithm (ABC), and the like.
In the face of the higher and higher requirements of the current ship system on dynamic scheduling, the more and more inapplicable the current algorithm is under the large-scale and high-dynamic environment. In addition, a complete design and prototype simulation verification platform is lacked for dynamic ship scheduling research at home and abroad at present.
Therefore, the efficient dynamic ship scheduling method is researched, a complete dynamic ship scheduling platform with expandability is built, and the dynamic ship scheduling method plays an important role in improving the efficiency of the ship in executing tasks and accelerating dynamic task scheduling research.
Disclosure of Invention
The invention provides a resource constraint type dynamic ship scheduling method and a resource constraint type dynamic ship scheduling platform based on a time window strategy, which are used for solving the problems that the performance of the conventional scheduling method is poor in large-scale examples and dynamic environments, and the problem that the conventional research lacks a complete design and simulation platform to solve the dynamic ship scheduling problem.
The resource constraint type dynamic ship scheduling method based on the time window strategy mainly comprises a real number coding strategy and a time window dynamic scheduling strategy, and specifically comprises the following steps:
the method comprises the following steps that firstly, aiming at a ship task scheduling scene, information of a ship task set is obtained from a test example;
the method comprises the following steps: ship mission table T ═ T1,t2,…,tj,...,tN}，tjThe number is jth ship task, and N is the total number of current ship tasks; instrument resource table R ═ { R ═ R1,r2,…,ri,...,rM}，riThe number is the ith instrument, and M is the total number of instruments; task scheme collection table
K-th indicating j-th taskjScheme, kjRepresenting the total number of the schemes of the task j;
dynamic task table D ═ D1,d2,...,dQ}，dQRepresents the Q-th new task; q is the total number of new tasks;
failure instrument table B ═ B1,b2，...,bs,...,bS}，bsIndicating an s-th faulty instrument; and S is the total number of the fault instruments.
Step two: selecting and initializing a target function according to actual needs;
the objective function is used for completing each ship task according to the respective execution scheme, and the maximum completion time of all ship tasks is counted
Wherein the content of the first and second substances,
indicating a ship mission tjSelection scheme
Completion time of hour;
step three: in order to meet the objective function, sequencing all ship tasks in a real number coding mode to obtain an execution sequence of each task and an execution scheme of each task;
the method specifically comprises the following steps:
firstly, selecting a group of random real numbers between 0 and 1 as codes, respectively corresponding each code to a ship task, wherein the code length is the same as the number of the tasks, sequencing all the tasks according to the numerical value of the real numbers, and the larger the numerical value is, the more the execution sequence of the corresponding tasks is; the ordered task sequence is denoted T ═ Ts1,ts2,…,tsj,...,tsN}；
Then, for each task, multiplying the real number corresponding to each task by 1000 to obtain a new real number; dividing the total number of the schemes corresponding to each task by the new real number, and adding one to the remainder to obtain the scheme number corresponding to each task, wherein the formula is as follows: schemei＝mod(1000xi,numi)+1，
Wherein schemeiNumber, x, corresponding to the selected solution from the total number of solutions for the ith taskiIs the real number, num, corresponding to the ith taskiAnd the total number of the schemes corresponding to the ith task.
Step four: when a new task arrives or an instrument fails, a time window dynamic scheduling strategy is used for adjustment, and the available starting time of the instrument is updated;
the method comprises the following specific steps:
regarding a new task arrival event, taking the arrival time of the new task as a new time window starting point;
tasks that have completed before the new task arrives are removed from the task window;
the task which is being executed at the arrival time of the new task keeps the original scheduling scheme unchanged;
and adding the task which is not started to be executed at the arrival time of the new task and the new task into a task window, updating the initial condition, and rescheduling the task in the window for a new round, wherein the available starting time of each instrument is updated to the finished task and the finish occupied time of the task being executed.
Regarding the instrument fault event, taking the instrument fault moment as a new time window starting point;
then, judging whether the resources occupied by the executing tasks suddenly fail, if so, adding the executing tasks and the unexecuted tasks into a task window together, removing the processing schemes containing the failed resources, and rescheduling the tasks in the window after updating the initial conditions and the available resources; otherwise, the scheduling scheme is kept unchanged, only the processing scheme containing the fault resource in the unexecuted task is removed, and the starting condition and the available resource are updated and then re-scheduling is carried out.
The available start time for each instrument is updated to the end elapsed time for the completed task and the task being performed.
Step five: and aiming at the current moment, outputting the execution sequence of each task, the execution scheme of each task and the updated available starting time of the instrument in real time to obtain a scheduling result and a corresponding scheduling Gantt chart at the current moment.
The resource constraint type dynamic ship scheduling platform based on the time window strategy comprises the following modules: the system comprises a data file module, a file configuration module, a scheduling algorithm module, a dynamic scheduling management module and a scheduling query module.
The data file module records resource information, basic instrument information, initial task information, dynamic task information and task scheme information in a text form as input of the platform.
The file configuration module has three functions of resource configuration management, task configuration management and equipment configuration management, and in the resource configuration management, a user adds a new resource, self-defines the name of the new resource and imports a risk evaluation model or a risk threshold of the resource. In task configuration management, a user defines different types of tasks and their priorities, setting weights for different targets. In addition, in the task configuration, the user can add the task to be executed, select a different type and set the latest completion time. In device configuration management, a user adds different instruments, defining the precision of each instrument and the respective resource consumption.
The dynamic scheduling management module has three functions of dynamic task management, dynamic algorithm and dynamic equipment management, and in the dynamic task management, a user defines the type of a new task, adds a plurality of temporary tasks and modifies the priorities and target weights of different types of tasks. In the dynamic algorithm management, a user selects a specific single-target or multi-target algorithm for scheduling according to requirements. In dynamic device management, a user maintains information of different devices, including information of device accuracy, resource consumption, and the like.
The scheduling method module integrates user-selectable scheduling algorithms, including a single-target algorithm and a multi-target algorithm. The single-target algorithm comprises a genetic algorithm and a particle swarm algorithm, and the multi-target algorithm comprises NSGA-II and MOPSO. The scheduling algorithm can respond to the dynamic events according to the types of the dynamic events, search the solution space at the same time, and continuously optimize the found optimal solution through random iteration until the set termination condition is met.
The scheduling query module has three functions of querying a Gantt chart, querying a task ordering chart and querying a resource condition chart, and the Gantt chart can dynamically display tasks currently executed and tasks not executed on different instruments along with the time in the main interface of the platform. In the task ordering diagram, the priority of the tasks is visually displayed in the first row, and the completed tasks, the uncompleted tasks and the newly added tasks are arranged in sequence for the user to view. And in the resource situation graph, monitoring the resource usage situation in real time, and if the current resource occupancy is too high, giving a risk prompt and evaluating the risk.
All modules cooperate with each other to complete the dynamic dispatching of the ship task system together.
The main advantages of the invention are:
(1) a resource constraint type dynamic ship scheduling method based on a time window strategy can adapt to the requirements of large-scale problems and dynamic environments and efficiently improve the understanding quality.
(2) A resource constraint type dynamic ship scheduling method based on a time window strategy can enhance the compatibility of a scheduling algorithm based on a real number coding strategy, avoid the condition that the problem cannot be solved as far as possible, and improve the robustness of the algorithm.
(3) A resource constraint type dynamic ship scheduling platform based on a time window strategy has flexibility, supports rapid configuration of all dimensional data including resources, equipment, tasks and the like under the scheduling problem, and supports calculation of multiple scheduling tasks by using different algorithms.
(4) A resource constraint type dynamic ship scheduling platform based on a time window strategy has a user-friendly interface, supports a user to observe a task execution process from an equipment occupation view and a task execution view, can observe different resource usage and occupation ratios, and has intuitiveness.
Drawings
FIG. 1 is a flow chart of a resource constraint type dynamic ship scheduling method based on a time window strategy according to the present invention;
FIG. 2 is a diagram of the task ordering and scheme results of the real number encoding of the present invention;
FIG. 3 is a block diagram of a resource constrained dynamic vessel scheduling platform based on a time window strategy according to the present invention;
FIG. 4 is a resource allocation interface of a scheduling platform employed in an embodiment of the present invention;
FIG. 5 is a device configuration interface of a dispatch platform employed by embodiments of the present invention;
FIG. 6 is a task configuration interface of a scheduling platform employed by embodiments of the present invention;
FIG. 7 is a full task preview interface at a scheduling platform task perspective employed by an embodiment of the present invention;
FIG. 8 is a single task preview interface at a scheduling platform task perspective employed by embodiments of the present invention;
fig. 9 is a full device preview interface at a scheduling platform device view angle employed in the embodiment of the present invention;
fig. 10 is a preview interface of the single device under the view angle of the scheduling platform device adopted in the embodiment of the present invention;
FIG. 11 is a scheduling platform task window preview interface employed by embodiments of the present invention;
FIG. 12 is a preview interface of multiple resource types for a scheduling platform according to an embodiment of the present invention;
FIG. 13 is a scheduling platform dynamic configuration interface employed by embodiments of the present invention;
fig. 14 is a result interface of dynamic scheduling of the scheduling platform according to the embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The resource constraint type dynamic ship scheduling method based on the time window strategy, as shown in fig. 1, mainly includes a real number encoding strategy and a time window dynamic scheduling strategy, and specifically includes the following steps:
the method comprises the following steps that firstly, aiming at a ship task scheduling scene, information of a ship task set is obtained from a test example;
the method comprises the following steps: ship mission table T ═ T1,t2,…,tj,...,tN}，tjThe number is jth ship task, and N is the total number of current ship tasks; instrument resource table R ═ { R ═ R1,r2,…,ri,...,rM}，riThe number is the ith instrument, and M is the total number of instruments; task scheme collection table
K-th indicating j-th taskjScheme, kjRepresenting the total number of the schemes of the task j;
dynamic task table D ═ D1,d2,...,dQ}，dQRepresents the Q-th new task; q is the total number of new tasks;
failure instrument table B ═ B1,b2，...,bs,...,bS}，bsIndicating an s-th faulty instrument; and S is the total number of the fault instruments.
Step two: selecting and initializing a target function, a variable and a parameter according to actual needs;
the objective function is used for completing each ship task according to the respective execution scheme, and the maximum completion time of all ship tasks is counted
Wherein the content of the first and second substances,
indicating a ship mission tjSelection scheme
Completion time of hour;
besides, the target function also comprises execution precision, instrument load and the like; and selecting a proper search algorithm, such as a particle swarm algorithm, a genetic algorithm and the like, and configuring relevant parameters of the algorithm.
Step three: in order to meet the objective function, sequencing all ship tasks in a real number coding mode to obtain an execution sequence of each task and an execution scheme of each task;
the coding is to express the solution of the problem to be researched in a computer processing mode, and a good coding mode can generate a feasible solution in the subsequent algorithm operation, so that the execution efficiency is improved; otherwise, an infeasible solution is generated through algorithm operation, a certain repairing measure is needed, and the execution efficiency is reduced. It has direct relation to solving speed, calculating precision, etc. and has important influence on the method.
At present, the common coding modes in the scheduling field include integrated coding, segmented coding, matrix coding and the like. These discrete encoding approaches make continuous algorithms such as Particle Swarm Optimization (PSO) not directly applicable, since the particle positions are usually real values during the algorithm's operation, whereas discrete encoding requires that these values all be integers.
There are two approaches to achieve the above compatibility issues: one is to re-modify the representation after each particle update is completed, typically by a rounding operation. The other is to change the updating mode of the particle position, so that the updating process of the particle is carried out in a discrete domain; however, the two methods require adjusting or designing special operators, and are relatively complicated.
In order to avoid the complicated process and combine the characteristics of the scheduling problem, the embodiment adopts a real number coding mode, and only the information of task grouping or sequencing is represented in the coding.
When the grouping mode is determined, firstly, a group of random real numbers between 0 and 1 is selected as codes, each code corresponds to a ship task, the code length is the same as the number of the tasks, all the tasks are sequenced according to the numerical value of the real numbers, and the larger the numerical value is, the closer the execution sequence of the corresponding tasks is; if the number of tasks is N, the ordered task sequence is represented as:
T＝{ts1,ts2,…,tsj,...,tsN}
then, for each task, multiplying the real number corresponding to each task by 1000 to obtain a new real number; dividing the total number of the schemes corresponding to each task by the new real number, and adding one to the remainder to obtain the scheme number corresponding to each task, wherein the formula is as follows: schemei＝mod(1000xi,numi)+1，
Wherein schemeiNumber, x, corresponding to the selected solution from the total number of solutions for the ith taskiIs the real number, num, corresponding to the ith taskiAnd the total number of the schemes corresponding to the ith task.
For example, as shown in fig. 2, in a full-ship scheduling system having 8 tasks, if the number of solutions is 10, the correspondence between coding and decoding, that is, the task sequence and the solution number corresponding to the solution (0.5435,0.3562,0.1272,0.9513,0.8162,0.7281,0.2468,0.4057) are: the execution sequence of the task 1 is 4, and the task scheme number is 4; the execution sequence of the tasks 2 is 6, and the task scheme number is 7; the execution sequence of the tasks 3 is 8, and the task scheme number is 8; the execution sequence of the task 4 is 1, and the task scheme number is 2; the execution sequence of the tasks 5 is 2, and the task scheme number is 7; the execution sequence of the tasks 6 is 3, and the task scheme number is 9; the execution sequence of the tasks 7 is 7, and the task scheme number is 7; the execution order of the tasks 8 is 5 and the task plan number is 6.
Therefore, the encoding strategy can be used for determining the task sequence and the task scheme selection by adjusting the encoding length. The coding mode is simple to operate, cannot generate an infeasible solution in the solving process, has generality, is suitable for the problem, is also suitable for various scheduling problems such as FJSP, PMSP, TTSP and the like, and does not need to perform special processing on a scheduling algorithm. It establishes a fast path between the combinatorial optimization problem and the continuous optimization algorithm, so that the continuous algorithm such as PSO can be used to solve the combinatorial optimization problem
Step four: when a new task arrives or an instrument fails, a time window dynamic scheduling strategy is used for adjustment, and the available starting time of the instrument is updated;
when dynamic emergencies such as new task arrival or instrument failure occur, the completed task is moved out of the task window; for a new task arrival event, keeping the scheduling scheme of the task being executed unchanged, adding the new task and the task not being executed into a task window, updating the starting condition, and rescheduling the task in the window, wherein the specific steps are as follows:
regarding a new task arrival event, taking the arrival time of the new task as a new time window starting point;
tasks that have completed before the new task arrives are removed from the task window and are not considered;
the task which is being executed at the arrival time of the new task keeps the original scheduling scheme unchanged;
and the task which is not started to be executed at the arrival time of the new task and the new task are taken as scheduling objects in the time window to perform a new round of rescheduling, and at the moment, the available starting time of each instrument is updated to the finish occupied time of the completed task and the executing task.
Regarding the dynamic event of the instrument fault, taking the moment of the instrument fault as the starting point of a new time window; tasks that have been completed before this are not considered;
then, judging whether the resources occupied by the task which is being executed at the moment suddenly fail, if so, adding the task which is being executed and the task which is not being executed into a task window together, removing the processing schemes of the task which is being executed and the task which is not being executed, and rescheduling the tasks in the window after updating the initial conditions and the available resources; otherwise, the scheduling scheme is kept unchanged, only the processing scheme containing the fault resource in the unexecuted task is removed, and the starting condition and the available resource are updated and then re-scheduling is carried out.
In addition, the task availability scheme is updated to remove alternatives for each task that include failed instruments, and the available start time for each instrument is updated to the elapsed time for the task to complete and the task being performed.
The flow of the time window dynamic scheduling strategy is as follows:
1) identifying dynamically changing variables:
newly added task set delta T ═ delta T1,Δt2,…,Δtj,...,ΔtU}, newly added fault instrument table delta B ═ delta B1,...,ΔbLResource change amount Δ R ═ Δ R }1,Δr2,…,Δri,...,ΔrM}；
2) Calculating the current states of different tasks:
splitting an original set of tasks into affected sets of tasks
And unaffected task collections
The affected tasks need to be rescheduled and the unaffected tasks will continue to execute.
3) And (3) sorting the existing scheduling states:
arranging new task set T needing to be optimized during reschedulingNew＝T1+ Δ T, new resource usage RNewConstraint R + Δ R, etc., and calculate the last available time on different instruments:
TIMENew＝{time1+Δtime1,time2+Δtime2,…,timeV+ΔtimeV}。
4) rescheduling: adding new constraint according to the current scheduling state, and performing online scheduling planning by using an algorithm.
Step five: and aiming at the current moment, outputting the execution sequence of each task, the execution scheme of each task and the updated available starting time of the instrument in real time to obtain a scheduling result and a corresponding scheduling Gantt chart at the current moment.
And matching the real number coding strategy with the time window dynamic scheduling strategy to obtain an optimal solution of the whole scheduling problem, and decoding to obtain a scheduling scheme for outputting the current time in real time.
The resource constraint type dynamic ship scheduling platform based on the time window strategy, as shown in fig. 3, includes the following modules: the system comprises a data file module 1A, a file configuration module 2B, a scheduling method module 3C, a dynamic scheduling management module 4D and a scheduling query module 5E.
The data file module 1A records resource information, basic instrument information, initial task information, dynamic task information, and task plan information in the form of text as input to the platform.
The method specifically comprises the following steps: various information for ship scheduling, including: the resource information, the basic instrument information, the initial task information, the dynamic task information, the task scheme information and the like are stored in a text form, so that the subsequent calling of a user is facilitated, and each text has a uniform form. The texts mainly comprise four types of resource information texts, equipment information texts, task information texts and algorithm information texts.
The resource information text includes information of resource name, resource quantity unit, resource quantity limit and resource quantity risk threshold, as shown in table 1.
TABLE 1
| Name (R) | Data type | Description of the invention |
| ResourceName | CString | Resource name |
| ResourceUnit | CString | Resource quantity unit |
| ResourceNumber | Float | Resource amount limit |
| ResourceLimit | Float | Resource quantity risk threshold |
In the device information text, information of device name, device capability, device number, and resource usage is included, as shown in table 2.
TABLE 2
| Name (R) | Data type | Description of the invention |
| EquipmentName | CString | Device name |
| EquipmentPerformance | INT | Device performance |
| EquipmentNumber | INT | Number of devices |
| ResourceConsume | Float | Resource usage |
The task information text includes task name, task priority, task object, recipe number, instrument number, and instrument elapsed time, as shown in table 3.
TABLE 3
| Name (R) | Data type | Description of the invention |
| TaskName | CString | Task name |
| TaskPriority | INT | Task priority |
| TaskObject | CString | Task object |
| SchemeID | INT | Plan numbering |
| EquipmentID | INT | Instrument numbering |
| EquipmentConsume | Float | Time consuming of the instrument |
In the algorithm information text, the contents of the target number and the algorithm name are included, as shown in table 4.
TABLE 4
| Name (R) | Data type | Description of the invention |
| ObjectNumber | INT | Target number |
| AlgorithmName | CString | Name of algorithm |
The file configuration module 2B is an entry of the dynamic test task scheduling platform, and has three functions of resource configuration management, task configuration management, and device configuration management, and in the resource configuration management, a user can add a new resource, customize a name thereof, and import a risk evaluation model or a risk threshold of the resource. In task configuration management, a user may define different types of tasks and their priorities, and may also set weights for different goals. In addition, in the task configuration, the user can add the task to be executed, select a different type and set the latest completion time. In device configuration management, a user adds different instruments, and defines the precision of each instrument and the respective resource consumption condition.
In the module, a user can read the relevant configuration file, and can also store the configured relevant information as a new configuration file for subsequent use. The reset function will clear the entire configuration information for the page. In the resource allocation management, a user can set a resource type, a resource name, the amount and the amount of the resource of the type, a risk threshold and an amount unit. In device configuration management, a user may configure device numbers, device types, performance levels, and quantity information used by different resources. In task configuration management, a user can configure information such as a recipe number, a task name, a task type, the number of different devices used, and an execution time required on the device.
When a dynamic event occurs, through the dynamic scheduling management module 4D, the user can add a new task and adjust the priorities and target weights of different task types, and finally reschedule according to the current actual situation.
The dynamic scheduling management module 4D has three functions of dynamic task management, dynamic algorithm, and dynamic device management, and in the dynamic task management, a user defines the type of a new task, adds a plurality of temporary tasks, and modifies task priorities and target weights of different types. In the dynamic algorithm management, a user selects a specific single-target or multi-target algorithm for scheduling according to requirements. In dynamic device management, a user maintains information of different devices, including information of device accuracy, resource consumption, and the like.
The scheduling method module 3C integrates a plurality of meta heuristic scheduling algorithms selectable by the user, including a single-target algorithm and a multi-target algorithm. The single-target algorithm comprises a genetic algorithm and a particle swarm algorithm, and the multi-target algorithm comprises NSGA-II and MOPSO. And the user selects a proper scheduling algorithm according to actual needs, schedules the scheduling instance according to the type of the dynamic event, responds to the dynamic event, searches a solution space, continuously optimizes the found optimal solution through random iteration until a set termination condition is met, and feeds back the obtained scheduling result to the user through the scheduling query module 5E in real time.
After the scheduling information is configured, the corresponding scheduling algorithm is selected, and the dynamic event is processed, the scheduling information may enter the scheduling query module 5E. The scheduling and querying module 5E enables a user to view scheduling results in different forms, and monitor the ship state in real time, and has three functions of querying a gantt chart, querying a task ordering chart and querying a resource condition chart. In the task view, the occupation of each task on different devices is dynamically represented, and in the device view, the occupation of each device is dynamically represented.
In the task ranking graph, a user can obtain information such as task priority, completed tasks, uncompleted tasks, newly added tasks and the like. The priority of the tasks can be visually displayed on the first line, and the completed tasks, the uncompleted tasks and the newly added tasks can be arranged in sequence for the user to view. In the resource situation graph, a user monitors and checks the resource usage situation in real time, the platform can judge the risk state at the current moment according to the resource occupancy rate at the current moment, and if the current resource occupancy rate is too high, a risk prompt is fed back to the user in time to evaluate the risk.
All modules cooperate with each other to complete the dynamic dispatching of the ship task system together.
Example (b):
taking a ship scheduling example as an example, the operation process of the platform is explained, and the scheduling of the example and the response to the new task reaching the dynamic event are realized.
First, configuration management is performed
Step 1.1: and managing resource allocation.
As shown in FIG. 4, after clicking "configure manage", the resource configuration interface is opened. At the upper end of the interface, the user can read the configuration file of the page, and can also store the configured related information as a new configuration file for subsequent use. The "reset" button clears the entire configuration information for the page. In the algorithm setting, after a user sets a single-target algorithm or a multi-target algorithm, a specific meta heuristic algorithm can be selected for solving, wherein the meta heuristic algorithm comprises the single-target algorithm and the multi-target algorithm. The left side of the interface can be set with 'resource type', and the user can input the name of the resource to be added and set the information of the quantity limit, risk threshold value, quantity unit and the like of the type of resource.
In the example, five types of resources used on the ship are preset, including: job resources, search resources, communication resources, power resources, and computing resources, which are in different quantities and units. The right side of the interface can be set with a task type, a user can input a type name, task priority and weight values of three targets, and the targets of the algorithm comprise completion time, resource usage and equipment performance. In the priority setting, "0" indicates that no priority is set, "1" indicates the highest priority, and a larger number indicates a lower priority. Tasks with higher priorities are scheduled preferentially and executed as soon as possible in the scheduling algorithm.
The example comprises a fishing task, a first-aid repair task, an auxiliary task, a transportation task and a conventional task, and the priority and the target weight of different tasks are different. The fishing task is most important among all the different types of tasks, and therefore its priority is the highest, the first-aid task and the auxiliary task are also very important, and therefore the priorities are 2 and 3, respectively, and the transportation task and the conventional task are less important than the first three types of tasks and therefore their priorities are not set. When the target weight is set, the fishing combat mission has extremely high requirement on the reaction time, so the time is taken as a main measurement index; when the response time is sufficient, the urgent repair precision is taken as a main target, so that the time and the equipment performance are considered simultaneously when the target weight is set; the auxiliary task requires auxiliary precision as a unique target; the transportation task and the conventional task are used as the conventional tasks of the ships, and two targets of resource consumption and time consumption are considered in balance when the tasks are executed.
Step 1.2: and managing the device configuration.
Click next after completing the resource configuration, as shown in fig. 5, the device configuration interface is opened. In this interface, in addition to reading and saving as a configuration file, the user can set the type name and number of devices in the list box on the left side. After clicking on "confirm", a corresponding number of devices of that type will be generated in the detailed information list of the right device. The user can directly click corresponding information in the right list to modify and adjust, and can click an 'add equipment' button below to add a certain amount of equipment. The information contained in each type of device includes the device number, the device type, the performance level and the quantity information used by the different resources.
The embodiment presets radar equipment, sonar equipment, fishing equipment, emergency repair equipment, auxiliary equipment, transportation equipment, power supply equipment and communication equipment. The radar equipment and the sonar equipment need to use different amounts of search resources, power resources and calculation resources, the performance of the radar equipment and the sonar equipment is search accuracy, and the level is 1-3; the fishing equipment, the emergency repair equipment, the auxiliary equipment and the transportation equipment need to use working resources, electric power resources and computing resources, and the performance grade of the fishing equipment, the emergency repair equipment, the auxiliary equipment and the transportation equipment is 1-3 grade; communication resources, power resources and computing resources are required to be used by the communication equipment, and the communication distance and the error rate of the communication equipment determine the performance of the communication equipment.
Step 1.3: and managing task configuration.
Clicking "next" after device configuration is completed, as shown in fig. 6, the task configuration interface is opened. In this configuration, a user may manage tasks that need to be performed and modify their associated parameters. The left list is the tasks to be executed, after the user fills in the task name, the task description, the task type and the scheme number, the corresponding number of schemes can be randomly generated in the right list, and the user can double click corresponding information to adjust. In the column of "task description", it defaults to "none". The recipe list generated on the right side of the interface includes information such as a recipe number, a task name, a task type, numbers of different devices to be used, and execution time required for the devices. In an example, different types of tasks are divided into different sub-tasks, each type of task using a different type of device. For each subtask, the user may set a different scheme, which corresponds to a specific device number.
Then, a schedule display is performed
Step 2.1: scheduling gantt chart from task perspective. In the task view, the occupation of each task on different devices is presented in a dynamic form, as shown in fig. 7. All the tasks scheduled and the equipment used are shown in the gantt chart, where the type of equipment used and the numbering and the time taken by the fishing missions 1 and 2 can be seen. Selecting the drop-down box to view the content allows one to view the occupancy of a single task on different devices, as shown in fig. 8. Shown in the figure is the details of fishing task 3, which uses radar equipment No. 4, fishing equipment No. 1, transport equipment No. 1, power supply equipment No. 1 and communication equipment No. 2 in this dispatch.
Step 2.2: scheduling gantt chart from device perspective. As shown in fig. 9, the device view is selected, and the occupancy of each device is displayed in a dynamic manner. The user can view the use condition of each sub-device of different types of devices and the number of tasks executed on a certain device at any time. Selecting a drop-down box to view content allows viewing of the usage of a single different type of device, as shown in FIG. 10. The figure shows the occupancy of the communication device. The communication equipment has 5 models, and a first-aid repair task 1 and a first-aid repair task 2 are executed on the equipment No. 1; on device number 2, fishing task 3 will be performed; on equipment No. 3, a fishing task 1 will be performed, and on equipment No. 5, a fishing task 2 and an emergency repair task 3 will be performed.
Step 2.3: and (4) a task state table.
As shown in FIGS. 7-10, in the lower left corner of the main interface, the task priority and task completion and their ordering are displayed. The preset task priority is displayed in the first row, the task types with the priority setting are arranged in front of the vertical line, the sequence is the priority sequence, and the tasks without the priority setting are arranged behind the vertical line. The tasks that are completed at the current moment are placed in the second row, the tasks that are not completed in the third row, and the sequence of the tasks is arranged according to the sequence of the expected completion time. As shown in fig. 11, a part of the task has been completed at the present time.
Step 2.4: and a resource monitoring module.
And a resource monitoring and risk assessment interface is displayed on the right side of the interface, a user can check the real-time resource occupation condition, and software can judge the risk state of the current moment according to the resource occupation rate of the current moment and feed back the risk state to the user in time. As shown in FIG. 12, one may choose to view all resource types. After a certain resource type is selected, the solid line in the graph represents the usage amount of the resource at the current moment, the dotted line represents the risk threshold of the type at the current moment, and when the usage amount exceeds the early warning amount, the software can prompt that the current resource usage amount is too high. In this part, the user can obtain the number of resources currently occupied by different resources, the use condition of the resources currently occupied by different resources, and obtain the risk prompt. In the figure, the usage states of the job resource, the search resource, the communication resource, the power resource, and the calculation resource can be viewed.
Finally, dynamic scheduling is performed
The software supports adding tasks at any time during task execution. After clicking "add task" in the scheduling main interface, the add task interface is opened as shown in fig. 13. The user may add a new task in the upper left corner of the interface; adjusting the priorities and target weights of different task types at the lower right corner; and a new algorithm is set at the lower left corner for scheduling again.
Two tasks are added in the embodiment and are divided into a fishing type task and an emergency repair type task. After the new task is added, click "confirm add" as shown in FIG. 14, and the scheduling home interface is opened again. At the lower left corner, newly added task names, i.e. new task 1 and new task 2, are displayed, and new tasks are added to different devices for execution.
Claims (5)
1. A resource constraint type dynamic ship scheduling method based on a time window strategy mainly comprises a real number coding strategy and a time window dynamic scheduling strategy, and is characterized by comprising the following specific steps:
the method comprises the following steps that firstly, aiming at a ship task scheduling scene, information of a ship task set is obtained from a test example;
the method comprises the following steps: ship mission table T ═ T1,t2,…,tj,...,tN}，tjThe number is jth ship task, and N is the total number of current ship tasks; instrument resource table R ═ { R ═ R1,r2,…,ri,...,rM}，riThe number is the ith instrument, and M is the total number of instruments; task scheme collection table
K-th indicating j-th taskjScheme, kjRepresenting the total number of the schemes of the task j;
dynamic task table D ═ D1,d2,...,dQ}，dQRepresents the Q-th new task; q is the total number of new tasks;
failure instrument table B ═ B1,b2，...,bs,...,bS}，bsIndicating an s-th faulty instrument; s is the total number of fault instruments;
step two: selecting and initializing a target function according to actual needs;
the objective function is used for completing the respective execution schemes for each ship task, and the maximum completion time of all ship tasks is counted
Wherein the content of the first and second substances,
indicating a ship mission tjSelection scheme
Completion time of hour;
step three: in order to meet the objective function, sequencing all ship tasks in a real number coding mode to obtain an execution sequence of each task and an execution scheme of each task;
the method specifically comprises the following steps:
firstly, selecting a group of random real numbers between 0 and 1 as codes, respectively corresponding each code to a ship task, wherein the code length is the same as the number of the tasks, sequencing all the tasks according to the numerical value of the real numbers, and the larger the numerical value is, the more the execution sequence of the corresponding tasks is; the ordered task sequence is denoted T ═ Ts1,ts2,…,tsj,...,tsN}；
Then, for each task, multiplying the real number corresponding to each task by 1000 to obtain a new real number; dividing the total number of the schemes corresponding to each task by the new real number, and adding one to the remainder to obtain the scheme number corresponding to each task, wherein the formula is as follows:
schemei＝mod(1000xi,numi)+1；
wherein schemeiNumber, x, corresponding to the selected solution from the total number of solutions for the ith taskiFor the real number, num, corresponding to the ith taskiThe total number of the schemes corresponding to the ith task;
step four: when a new task arrives or an instrument fails, a time window dynamic scheduling strategy is used for adjustment, and the available starting time of the instrument is updated;
step five: and aiming at the current moment, outputting the execution sequence of each task, the execution scheme of each task and the updated available starting time of the instrument in real time to obtain a scheduling result and a corresponding scheduling Gantt chart at the current moment.
2. The resource-constrained dynamic ship scheduling method based on the time window strategy as claimed in claim 1, wherein for the new task arrival event in step four, the new task arrival time is taken as a new time window starting point;
tasks that have completed before the new task arrives are removed from the task window;
the task which is being executed at the arrival time of the new task keeps the original scheduling scheme unchanged;
and adding the task which is not started to be executed at the arrival time of the new task and the new task into a task window, updating the initial condition, and rescheduling the task in the window for a new round, wherein the available starting time of each instrument is updated to the finished task and the finish occupied time of the task being executed.
3. The resource-constrained dynamic ship scheduling method based on the time window strategy as claimed in claim 1, wherein for the instrument failure event in step four, the instrument failure time is used as a new time window starting point;
then, judging whether the resources occupied by the executing tasks suddenly fail, if so, adding the executing tasks and the unexecuted tasks into a task window together, removing the processing schemes containing the failed resources, and rescheduling the tasks in the window after updating the initial conditions and the available resources; otherwise, keeping the scheduling scheme unchanged, only removing the processing scheme containing the fault resource in the unexecuted task, and performing re-scheduling after updating the initial condition and the available resource;
the available start time for each instrument is updated to the end elapsed time for the completed task and the task being performed.
4. The scheduling platform applying the resource constraint type dynamic ship scheduling method based on the time window strategy as claimed in claim 1, comprising the following modules: the system comprises a data file module, a file configuration module, a scheduling algorithm module, a dynamic scheduling management module and a scheduling query module;
the data file module records resource information, basic instrument information, initial task information, dynamic task information and task scheme information in a text form and is used as the input of the platform;
the file configuration module has three functions of resource configuration management, task configuration management and equipment configuration management, and in the resource configuration management, a user adds new resources, self-defines the names of the new resources and imports a risk evaluation model or a risk threshold of the resources; in task configuration management, a user defines different types of tasks and priorities thereof, and weights are set for different targets; in addition, in the task configuration, the user can add the task to be executed, select different types and set the latest completion time; in the equipment configuration management, a user adds different instruments and defines the precision of each instrument and the respective resource consumption condition;
the dynamic scheduling management module has three functions of dynamic task management, dynamic algorithm and dynamic equipment management, in the dynamic task management, a user defines the type of a new task, adds a plurality of temporary tasks and modifies the priorities and target weights of different types of tasks; in the dynamic algorithm management, a user selects a specific single-target or multi-target algorithm for scheduling according to requirements; in the dynamic equipment management, a user maintains information of different equipment, including equipment precision, resource consumption and other information;
the scheduling method module integrates user-selectable scheduling algorithms, including a single-target algorithm and a multi-target algorithm; the scheduling algorithm can respond to the dynamic event according to the type of the dynamic event, search the solution space at the same time, and continuously optimize the found optimal solution through random iteration until the set termination condition is met;
the scheduling query module has three functions of querying a Gantt chart, a task query sequencing chart and a resource query situation chart, and the Gantt chart can dynamically display tasks currently executed and tasks not executed on different instruments along with the time in a main interface of the platform; in the task ordering diagram, the priority of the tasks can be visually displayed in the first row, and the completed tasks, the uncompleted tasks and the newly added tasks can be arranged in sequence for the user to view; monitoring the resource consumption condition in real time in the resource condition graph, and if the current resource occupancy is too high, prompting a risk and evaluating the risk;
all modules cooperate with each other to complete the dynamic dispatching of the ship task system together.
5. The time-window-strategy-based resource-constrained dynamic vessel scheduling platform of claim 4, wherein the single-target algorithm comprises a genetic algorithm and a particle swarm algorithm, and the multi-target algorithm comprises NSGA-II and MOPSO.
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