CN115619141A - Random scheduling method for ship maintenance tasks under consideration of milestone constraints - Google Patents
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Abstract
The invention provides a random scheduling method for ship maintenance tasks under consideration of milestone constraints, and relates to the technical field of task scheduling. The method comprises the steps of coding a task to be scheduled by acquiring data of the task to be scheduled in a project, and generating a task list; processing the task list based on heuristic rules and a probability selection method to generate an initial population; taking the initial population as an initial bird nest, and optimizing the initial bird nest by an improved discrete cuckoo search algorithm to obtain an optimal solution by taking the minimized maximum completion time and the minimized delay cost as targets; the invention aims at minimizing the maximum completion time and the minimum delay cost of a task, researches a task scheduling method of the time-varying task construction period and the resource demand under a milestone task, and provides a plurality of heuristic rules based on the characteristics of the milestone task and random scheduling. The method fills the gap of the current research, and obtains a task scheduling scheme of high-end equipment manufacture which is more in line with the realization of the production condition.
Description
Technical Field
The invention relates to the technical field of task scheduling, in particular to a random scheduling method for ship maintenance tasks under consideration of milestone constraints.
Background
The ship equipment maintenance safety requirement is high, the timeliness is strong, the important influence is brought to national economic safety and national defense construction, how to reasonably coordinate and arrange tasks and resources in a maintenance task and reduce the construction period and the cost of the whole maintenance task is a subject worthy of deep research. In the actual implementation process, the ship equipment maintenance task may be affected by factors such as mechanical failure, external environment, insufficient resource allocation, personnel change, diseases and the like, so that a deviation exists between an expected dispatching plan generated by deterministic parameters and the actual dispatching plan. There is also uncertainty about the resource demand of the partial tasks, which may change over time. For example, the requirements of maintenance personnel, machine equipment and the like generally do not change due to the start time of a task, while the energy source and the like may be affected by time, for example, when the temperature rises, a large machine tool needs to additionally dissipate heat, which may result in increase of energy consumption resources.
Meanwhile, the scheduling process has strict constraints on the completion time of part of tasks, namely, not only the whole task needs to be completed on schedule, but also the completion time of part of tasks needs to be completed within a specified time limit, otherwise, the tasks are lost or directly fail. This task with completion time constraints is called a milestone task.
However, in the existing method, a scheduling strategy of a time-varying task duration under the constraint of milestones is not researched, that is, the research and actual production conditions of the existing method do not meet, so that the practicability of the obtained scheduling scheme is too low.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a random scheduling method for ship maintenance tasks under consideration of milestone constraints, and solves the technical problem of low practicability of a scheduling scheme obtained by the conventional method.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a method of stochastic scheduling of marine repair tasks under consideration of milestone constraints, comprising:
s1, acquiring data of a task to be scheduled in a project, coding the task to be scheduled, and generating a task list;
s2, processing the task list based on heuristic rules and a probability selection method to generate an initial population;
s3, taking the initial population as an initial bird nest, and carrying out optimization processing on the initial bird nest through an improved discrete cuckoo search algorithm by taking the maximum completion time and the minimum delay cost as targets to obtain an optimal solution;
wherein the heuristic rules comprise:
heuristic rule 1: prioritizing hard milestone tasks of the tasks to be scheduled, without regard to resource conflicts, the hard milestone tasks being tasks whose completion time may not exceed a specified expiration date;
heuristic rule 2: each task is scheduled according to the earliest starting time rule, namely the task is scheduled as early as possible in the period when available resources exist;
heuristic rule 3: when two soft milestone tasks conflict and any one task is independently arranged not to exceed the deadline, the milestone task with the earlier deadline is preferentially arranged, and the soft milestone task has the completion time which can exceed the specified deadline but can generate the delay cost if the completion time exceeds the specified completion time.
Preferably, the probability selection method includes:
randomly sampling by using the repentance preference value, calculating the selection probability of all selectable tasks by combining the latest completion time of the tasks, and randomly selecting the next task according to the probability, wherein the calculation formula is as follows:
wherein ρ j Is the regret preference value, LE, of the task i And LE j Represents the latest ending time of the task i and the task j, v is an optional task set, eta j Is the selection probability of task j.
Preferably, S2 specifically includes:
s201, sequentially arranging tasks according to a probability selection method and a heuristic rule 1 until a feasible scheduling scheme is generated, and repeating M times to generate an initial population;
s202, decoding individuals in the population by combining the heuristic rule 2, the heuristic rule 3 and the random serial scheduling generation scheme, selecting corresponding tasks according to the coded task sequence by the random serial scheduling generation scheme, arranging the tasks into a plan on the basis of considering the priority, resource constraint and milestone constraint limit of the tasks, and generating the starting time and the ending time of the tasks by changing the positions of the tasks compared with the codes.
Preferably, the S3 specifically includes:
s301, taking the initial population as an initial bird nest, and initializing the current iteration times T and the maximum iteration times T max Generating an initial external archive Q of capacity N out ；
S302, calculating the fitness value f of each individual in the current population m1 And f m2 Wherein:
f m1 ＝E(S n+1 (λ))
in the formula (I), the compound is shown in the specification,
f m1 a desired completion time for individual m; s. the n+1 The starting time of the last virtual task, namely the ending time of the last actual task; f. of m2 A deferred cost for an individual m, m 2 For soft milestone tasks, B m To complete a set of soft milestone tasks beyond a defined time,
in order to be a penalty factor,
for task m 2 The start time of (c) is,
for task m 2 During the duration of the time instant t,
for task m 2 Saving the non-inferior solution in the limited completion time into an external archive Q;
s303, updating the current population individuals by using improved discrete Levis flight;
s304, calculating the fitness value of the individual in the population after the updating is finished, and storing the non-inferior solution in the population into an external archive Q;
s305, judging the number N of non-inferior solutions in Q par And capacity N out If N is the size of par <N out Then all individuals in the current Q are saved and N is regenerated by using the method in S1-S2 out -N par Supplementing for individual; otherwise, calculating the congestion degree of all individuals, and storing the top N with larger congestion degree out Individuals are used as next generation population;
s306, let T = T +1, judge T ≦ T max And if so, returning to S302, otherwise, ending the algorithm and outputting the current external archive Q as the optimal solution.
Preferably, the S303 specifically includes:
s303a, firstly, calculating the Euclidean distance of each bird nest in the population according to the following formula, wherein the bird nest with the minimum Euclidean distance is the optimal bird nest, and the calculation formula is as follows:
wherein, f lm For the ith objective function value of the mth individual,
representing the Euclidean distance of each bird nest;
s303b, calculating the individual distance between the current bird nest and the optimal bird nest, and taking the individual distance as the crossing probability of the individual, wherein the calculation formula of the individual distance is as follows:
wherein HD(s) 1 ,s 2 ) Is an individual (bird nest in the present example) s 1 And s 2 Hamming distance of, N max The upper limit of the hamming distance, i.e. the length of the individual,
representing individual distances between the front bird nest and the optimal bird nest;
s303c, generating a random number matrix of 1 × (N + 1), wherein each number ranges from 0 to 1; comparing the number of each position in the matrix with the cross probability, if the number is greater than the cross probability, retaining the task serial number of the position, and otherwise deleting the task serial number of the position;
s303d, deleting the task serial number of the optimal bird nest which is the same as the task serial number reserved by the current bird nest, and sequentially placing the rest task serial numbers of the optimal bird nest into the position of the deleted task serial number of the current bird nest;
s303e, sorting and grading the population through rapid non-dominated sorting, dividing the interval into subsets which are equally spaced and have the same length as the number of the levels, wherein the probability that the individual at the best level is discovered is 0, and the probability that the individual at the worst level is discovered is P max And carrying out local search operation on the discovered individuals.
Preferably, the local search operation includes:
each non-milestone task in an individual is selected with a certain probability, the selected task will move in the neighborhood between the tasks immediately before and after the closest task based on the principle of the maximum hamming distance, and the moving mode comprises the following steps:
when no milestone task exists in the neighborhood, calculating the Hamming distance between the sequence after the selected task moves to each position and the sequence before the movement, and moving the task to the position with the maximum Hamming distance;
when a milestone task exists on the left side of the selected task in the neighborhood, the movable position is changed into an immediately-after task closest to the selected task from the left-side milestone, the Hamming distance before and after movement is calculated, and the task is moved to the position with the largest Hamming distance.
Preferably, the congestion degree is calculated as follows:
stratify each solution according to their dominated case: firstly, find out the non-dominant solution set in the population, and mark it as the first non-dominant layer FR 1 And removed from the entire population; then, continuously finding out the non-dominant solution set in the rest population, and recording as a second non-dominant ranking layer FR 2 (ii) a Proceeding as such until the entire population is stratified;
the congestion degree is calculated for solutions in the same layer: make the degree of crowding d m N, and sorting the individuals in ascending order according to the l objective function, and giving d to the left and right individuals 1 ＝d n = infinity, calculating intermediate
Wherein f is l (m + 1) and f l (m-1) the objective function values of the next and previous individuals to the individual m,
and
the maximum value and the minimum value of the ith target function in the current level are obtained; for each objective functionUpdating the crowdedness according to the mode until the final crowdedness is calculated; the dominance condition refers to that when each target function index of the solution A is different from the target function index of the solution B, the solution A is named as dominated by the solution B.
In a second aspect, the present invention provides a stochastic scheduling system for maintenance tasks on a vessel under consideration of milestone constraints, comprising:
the encoding module is used for acquiring data of tasks to be scheduled in the project, encoding the tasks to be scheduled and generating a task list;
the initial population generation module is used for processing the task list based on heuristic rules and a probability selection method to generate an initial population;
the optimization module is used for taking the initial population as an initial bird nest, optimizing the initial bird nest by an improved discrete cuckoo search algorithm by taking the maximum completion time and the minimum delay cost as targets to obtain an optimal solution;
wherein, the heuristic rule comprises:
heuristic rule 1: prioritizing hard milestone tasks of the to-be-scheduled tasks without regard to resource conflicts, the hard milestone tasks being tasks whose completion time may not exceed a specified expiration date;
heuristic rule 2: each task is scheduled according to the earliest starting time rule, namely the task is scheduled as early as possible in the period when available resources exist;
heuristic rule 3: when two soft milestone tasks conflict and any one task is independently arranged not to exceed the deadline, the milestone task with the earlier deadline is preferentially arranged, and the soft milestone task has the completion time which can exceed the specified deadline but can generate the delay cost if the completion time exceeds the specified completion time.
In a third aspect, the present invention provides a computer readable storage medium storing a computer program for stochastic scheduling of marine service tasks under consideration of milestone constraints, wherein the computer program causes a computer to perform the method of stochastic scheduling of marine service tasks under consideration of milestone constraints as described above.
In a fourth aspect, the present invention provides an electronic device comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a stochastic scheduling method for maintenance tasks on a vessel under consideration of milestone constraints as described above.
(III) advantageous effects
The invention provides a random scheduling method for ship maintenance tasks under consideration of milestone constraints. Compared with the prior art, the method has the following beneficial effects:
the invention aims at minimizing the maximum completion time and the minimum delay cost of the task, researches the task scheduling method of the time-varying task construction period and the resource demand under the milestone task, and provides a plurality of heuristic rules based on the characteristics of the milestone task and random scheduling. The method fills the gap of the current research, and obtains a task scheduling scheme of high-end equipment manufacture which is more in line with the realization of the production condition.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a method for stochastic scheduling of maintenance tasks to a vessel under consideration of milestone constraints in an embodiment of the invention;
FIG. 2 is a flow chart of an improved discrete cuckoo search algorithm in an embodiment of the present invention;
FIG. 3 is a process diagram of a local search operation performed on an individual being discovered when there is no milestone mission within the neighborhood;
FIG. 4 is a schematic diagram of a process for performing a local search operation on an individual to be found when a milestone task exists on the left side of a selected task within a neighborhood.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete description of the technical solutions in the embodiments of the present invention, it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The embodiment of the application solves the technical problem that the practicability of a scheduling scheme obtained by the existing method is too low by providing the random scheduling method for the ship maintenance task under the consideration of the milestone constraint, and obtains the task scheduling scheme which is more consistent with the production condition and used for manufacturing high-end equipment by considering the milestone in the solving process.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in the prior art, partial research is carried out on task scheduling considering time-varying task duration, but the problem of resource-limited task scheduling considering time-varying task duration and resource demand is less researched. In the face of uncertainty existing in the scheduling process, how to reasonably and accurately evaluate and measure the specific influence of uncertainty on scheduling is a difficult problem, and the condition is more complicated due to milestone limitation existing in part of tasks, so that corresponding scheduling strategies related to the time-varying task duration, resource requirements and milestone tasks are not researched and provided at present.
Aiming at the problem, the embodiment of the invention solves the problem of resource-limited task scheduling considering the time-varying task duration and the resource requirement under the milestone task. Firstly, a mathematical model is established, the goal is to minimize completion time and delay cost, a plurality of heuristic rules are provided based on the characteristics of milestone tasks and random scheduling, an improved multi-target discrete cuckoo algorithm is used, discrete Levis flight based on Hamming distance is designed to carry out efficient individual updating, the capability of population escaping local convergence is enhanced by constructing self-adaptive local search based on a maximum Hamming distance left-right rotation mechanism, and finally a scheduling strategy with better construction period and delay cost is obtained through non-dominated sorting.
The embodiment of the invention provides a random scheduling method for ship maintenance tasks under consideration of milestone constraints, which comprises the following steps as shown in figure 1:
s1, acquiring data of a task to be scheduled in a project, coding the task to be scheduled, and generating a task list;
s2, processing the task list based on heuristic rules and a probability selection method to generate an initial population;
s3, taking the initial population as an initial bird nest, and carrying out optimization processing on the initial bird nest through an improved discrete cuckoo search algorithm by taking the maximum completion time and the minimum delay cost as targets to obtain an optimal solution;
wherein the heuristic rules comprise:
heuristic rule 1: prioritizing hard milestone tasks of the to-be-scheduled tasks without regard to resource conflicts, the hard milestone tasks being tasks whose completion time may not exceed a specified expiration date;
heuristic rule 2: each task is scheduled according to the earliest starting time rule, namely the task is scheduled as early as possible in the period when available resources exist;
heuristic rule 3: when two soft milestone tasks conflict and any one task is independently arranged not to exceed the deadline, the milestone task with the earlier deadline is preferentially arranged, and the soft milestone task has the completion time which can exceed the specified deadline but can generate the delay cost if the completion time exceeds the specified completion time.
The embodiment of the invention aims at minimizing the maximum completion time and the minimum delay cost, researches the task scheduling method of the time-varying task construction period and the resource demand under the milestone task, and provides a plurality of heuristic rules based on the characteristics of the milestone task and random scheduling. The method fills the gap of the current research, and obtains a task scheduling scheme of high-end equipment manufacture which is more in line with the realization of the production condition.
The following describes each step in detail:
in step S1, data of N tasks to be scheduled in the project are obtained, the tasks to be scheduled are coded, and a task list A is generated L ＝[a 0 ,a 1 ...a N+1 ]Each element in the list represents the number of the task and the position of the element represents the priority order of the task scheduling, wherein a 0 And a N+1 Is a virtual task representing the beginning and end of a project. The specific implementation process is as follows:
n tasks are waiting to be scheduled in the ship maintenance project, and the duration of each task is a random variable d which changes along with the start time of the task i,t The resource requirement of part of the task is also a random variable r which changes along with the start time of the task ik,t Priority relation exists between tasks, i belongs to P j Indicating that task i belongs to the task immediately preceding task j. The task has K renewable resources, and the maximum available amount of the resource K is R k . The scheduling process is not allowed to be preempted or interrupted.
Using task list A L ＝[a 0 ,a 1 ...a N+1 ]The form of the question being studied being encoded, each element in the list representing the number of the task, the position of the element representing the priority order in which the task is arranged, a 0 And a N+1 Is a virtual task that represents the beginning and end of a project.
In step S2, the task list is processed based on heuristic rules and a probability selection method to generate an initial population. The specific implementation process is as follows:
wherein the heuristic rules comprise:
heuristic rule 1: the hard milestone task among the tasks to be scheduled is prioritized without considering resource conflicts. (hard milestone tasks are those tasks whose completion time must not exceed a specified cutoff date)
Heuristic rule 2: each task is scheduled according to an earliest start time rule, i.e. the task is scheduled as early as possible during the period when there are available resources.
Heuristic rule 3: when two soft milestone tasks conflict and any one task is scheduled alone without exceeding the expiration date, the milestone task with the earlier expiration date is preferentially scheduled. (Soft milestone tasks refer to those tasks whose completion time can exceed a specified expiration date but which, once exceeded, can incur a delayed cost).
S2 specifically comprises the following steps:
s201, sequentially arranging tasks according to a probability selection method and a heuristic rule 1 until a feasible scheduling scheme is generated, and repeating M times to generate an initial population, wherein the calculation method for selecting the probability comprises the following steps:
the calculation method of the selection probability comprises the following steps:
randomly sampling by using the repentance preference value, calculating the selection probability of all selectable tasks by combining the latest completion time of the tasks, and randomly selecting the next task according to the probability, wherein the probability calculation formula is as follows:
where ρ is j Is the regret preference value, LE, of the task i And LE j Represents the latest ending time of the task i and the task j, v is an optional task set, eta j Is the selection probability of the task.
S202, decoding the individuals by combining the heuristic rule 2, the heuristic rule 3 and the random serial scheduling generation scheme, selecting corresponding tasks according to the coded task sequence by the random serial scheduling generation scheme, and arranging the tasks into a plan on the basis of considering the constraint limits of priority, resources, milestones and the like of the tasks, wherein the positions of the final tasks are changed compared with the codes. The start and end times of the task are generated.
In step S3, the initial population is used as an initial bird nest, and the initial bird nest is optimized by an improved discrete cuckoo search algorithm with the goal of minimizing the maximum completion time and minimizing the postponing cost, so as to obtain an optimal solution. As shown in fig. 2, the specific implementation process is as follows:
s301, taking the initial population as an initial bird nest, and initializing the current iteration times T and the maximum iteration times T max Generating an initial external archive Q of capacity N out The initial external archive Q is an empty set of stored individuals.
S302, calculating the fitness value (f) of each individual of the current population m1 ,f m2 )，
E(S n+1 (λ))，
Wherein f is m1 Desired completion time, S, for individual m n+1 The start time of the last task (the start time of the last virtual task, i.e. the end time of the last actual task), f m2 A deferred cost for an individual m, m 2 For soft milestone tasks, B m To complete a set of soft milestone tasks beyond a defined time,
in order to be a penalty factor,
for task m 2 The start time of (c) is,
for task m 2 During the duration of the time instant t,
for task m 2 E is a desired meaningAnd (4) storing the non-inferior solution in the external file Q.
And S303, updating the current population individuals by using the improved discrete Rivie flight. The method specifically comprises the following steps:
s303a, firstly, calculating the Euclidean distance of each bird nest in the population according to the following formula, wherein the bird nest with the minimum Euclidean distance is the optimal bird nest, and the calculation formula is as follows:
wherein f is lm For the ith objective function value of the mth individual,
representing the euclidean distance of each bird nest.
S303b, calculating the individual distance between the current bird nest and the optimal bird nest, and taking the individual distance as the crossing probability of the individual, wherein the calculation formula of the individual distance is as follows:
wherein HD(s) 1 ,s 2 ) Is an individual (bird nest in the present example) s 1 And s 2 Hamming distance of, N max The upper limit of the hamming distance, i.e. the length of the individual,
representing the individual distance between the front bird nest and the optimal bird nest.
S303c, generating a random number matrix of 1 × (N + 1), wherein each number ranges from 0 to 1. And comparing the number of each position in the matrix with the cross probability, if the number is greater than the cross probability, keeping the task sequence number of the position, and otherwise deleting the task sequence number of the position.
And S303d, deleting the task serial numbers of the optimal bird nest which are the same as the task serial numbers reserved by the current bird nest, and sequentially placing the residual task serial numbers of the optimal bird nest into the position of the deleted task serial numbers of the current bird nest.
S303e, sorting and grading the population through rapid non-dominated sorting, dividing the interval into subsets which are equally spaced and have the same length as the number of the levels, wherein the probability that the individual at the best level is discovered is 0, and the probability that the individual at the worst level is discovered is P max And carrying out local search operation on the discovered individuals. (each non-milestone task in an individual has a certain probability of being selected, and the selected task will move in the neighborhood between the immediately preceding and immediately succeeding tasks nearest to the selected task based on the principle of the maximum hamming distance. This time, there are two cases, namely, when there is no milestone task in the neighborhood, the hamming distance between the sequence after the selected task moves to each position and the sequence before moving is calculated, and the task moves to the position with the maximum hamming distance, as shown in fig. 3. When there is a milestone task on the left side of the selected task in the neighborhood, the movable position is changed to the immediately succeeding task nearest to the selected task from the left-side milestone, and the hamming distances before and after moving are calculated, and the task moves to the position with the maximum hamming distance, as shown in fig. 4.
S304, calculating the fitness value of the individual in the population after the updating is finished, and storing the non-inferior solution in the population into an external archive Q. The specific implementation process is similar to S302 and will not be described herein again.
S305, judging the number N of non-inferior solutions in Q par And capacity N out The size of (2). If N is present par <N out Then all individuals in the current Q are saved and N is regenerated by using the method in S1-S2 out -N par Supplementing for individual; otherwise, calculating the congestion degree of all individuals, and storing the top N with larger congestion degree out Individual individuals served as next generation populations. The congestion degree is calculated as follows:
first, each solution is layered according to its dominated case (when each target function index of solution a is worse than the target function of solution B, it is said that solution a is dominated by solution B): firstly, find out the non-domination solution set in the group, which is marked as the first non-domination layer FR 1 And removed from the entire population; then, continuously finding out a non-dominant solution set in the rest population, and marking as a second non-dominant solution setOrdering layer FR 2 (ii) a This was done until the entire population was stratified. The congestion degree is calculated for solutions in the same layer: make the degree of crowding d m N) and sorting the individuals in ascending order according to the l target function, and giving d to the left and right individuals 1 ＝d n = infinity, calculating the intermediate
Wherein f is l (m + 1) and f l (m-1) the objective function values of the next and previous individuals to the individual m,
and
the maximum and minimum values of the ith objective function in the current hierarchy. The congestion degree is updated for each objective function in the above manner until the final congestion degree is calculated.
S306, let T = T +1, judge T ≦ T max And if so, returning to the S302, otherwise, ending the algorithm and outputting the current external file Q as the optimal pareto solution.
The embodiment of the invention also provides a random scheduling system for ship maintenance tasks under consideration of milestone constraints, which comprises:
the encoding module is used for acquiring data of N tasks to be scheduled in the project, encoding the tasks to be scheduled and generating a task list A L ＝[a 0 ,a 1 ...a N+1 ]Each element in the list represents the number of the task and the position of the element represents the priority order of the task scheduling, wherein a 0 And a N+1 Is a virtual task, representing the beginning and end of a project;
the initial population generation module is used for processing the task list based on heuristic rules and a probability selection method to generate an initial population;
the optimization module is used for taking the initial population as an initial bird nest, optimizing the initial bird nest by an improved discrete cuckoo search algorithm by taking the maximum completion time and the minimum delay cost as targets to obtain an optimal solution;
wherein the heuristic rules comprise:
heuristic rule 1: prioritizing hard milestone tasks of the tasks to be scheduled, without regard to resource conflicts, the hard milestone tasks being tasks whose completion time may not exceed a specified expiration date;
heuristic rule 2: each task is scheduled according to the earliest starting time rule, namely the task is scheduled as early as possible in the period when available resources exist;
heuristic rule 3: when two soft milestone tasks conflict and either task is independently scheduled not to exceed the expiration date, a milestone task with an earlier expiration date is prioritized, which is a task whose completion time may exceed the specified expiration date, but which, once exceeded, incurs a delay cost.
It can be understood that the system for randomly scheduling a ship maintenance task under consideration of the milestone constraints provided in the embodiment of the present invention corresponds to the method for randomly scheduling a ship maintenance task under consideration of the milestone constraints, and for explanation, exemplification, and beneficial effects of the above-mentioned system, reference may be made to corresponding contents in the method for randomly scheduling a ship maintenance task under consideration of the milestone constraints, and details are not described here again.
Embodiments of the present invention also provide a computer readable storage medium storing a computer program for random scheduling of a ship repair task under consideration of a milestone constraint, wherein the computer program causes a computer to execute the random scheduling method of a ship repair task under consideration of a milestone constraint as described above.
An embodiment of the present invention further provides an electronic device, including:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a stochastic scheduling method for maintenance tasks on a vessel under consideration of milestone constraints as described above.
In summary, compared with the prior art, the method has the following beneficial effects:
1. the embodiment of the invention aims at minimizing the maximum completion time and the minimum delay cost of the task, researches the task scheduling method of the time-varying task construction period and the resource demand under the milestone task, and provides a plurality of heuristic rules based on the characteristics of the milestone task and random scheduling. The method fills the gap of the current research, and obtains a task scheduling scheme of high-end equipment manufacture which is more in line with the realization of the production condition.
2. The problem model solved by the invention can be applied to similar problems after certain problem assumptions or constraints are added, and has strong applicability.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of stochastic scheduling of marine repair missions under consideration of milestone constraints, comprising:
s1, acquiring data of a task to be scheduled in a project, coding the task to be scheduled, and generating a task list;
s2, processing the task list based on heuristic rules and a probability selection method to generate an initial population;
s3, taking the initial population as an initial bird nest, and carrying out optimization processing on the initial bird nest through an improved discrete cuckoo search algorithm by taking the maximum completion time and the minimum delay cost as targets to obtain an optimal solution;
wherein the heuristic rules comprise:
heuristic rule 1: prioritizing hard milestone tasks of the tasks to be scheduled, without regard to resource conflicts, the hard milestone tasks being tasks whose completion time may not exceed a specified expiration date;
heuristic rule 2: each task is scheduled according to the earliest starting time rule, namely the task is scheduled as early as possible in the period when available resources exist;
heuristic rule 3: when two soft milestone tasks conflict and any one task is independently arranged not to exceed the deadline, the milestone task with the earlier deadline is preferentially arranged, and the soft milestone task has the completion time which can exceed the specified deadline but can generate the delay cost if the completion time exceeds the specified completion time.
2. The method of stochastic scheduling of marine repair missions under consideration of milestone constraints of claim 1, wherein the probabilistic selection method comprises:
randomly sampling by using the regret preference value, calculating the selection probability of all the optional tasks by combining the latest task completion time, and randomly selecting the next task according to the probability, wherein the calculation formula is as follows:
where ρ is j Is the regret preference value, LE, of the task i And LE j Represents the latest ending time of the task i and the task j, v is an optional task set, eta j Is the probability of selection for task j.
3. The method for stochastic scheduling of marine repair missions under consideration of milestone constraints according to claim 1, wherein S2 comprises in particular:
s201, sequentially arranging tasks according to a probability selection method and a heuristic rule 1 until a feasible scheduling scheme is generated, and repeating M times to generate an initial population;
s202, decoding individuals in the population by combining the heuristic rule 2, the heuristic rule 3 and the random serial scheduling generation scheme, selecting corresponding tasks according to the coded task sequence by the random serial scheduling generation scheme, arranging the tasks into a plan on the basis of considering the priority, resource constraint and milestone constraint limit of the tasks, and generating the starting time and the ending time of the tasks by changing the positions of the tasks compared with the codes.
4. A method for random scheduling of maintenance tasks to a vessel under consideration of milestone constraints according to any of claims 1 to 3, wherein said S3 comprises in particular:
s301, taking the initial population as an initial bird nest, and initializing the current iteration times T and the maximum iteration times T max Generating an initial external archive Q of capacity N out ；
S302, calculating the fitness value f of each individual in the current population m1 And f m2 Wherein:
f m1 ＝E(S n+1 (λ))
in the formula (I), the compound is shown in the specification,
f m1 a desired completion time for individual m; s n+1 The starting time of the last virtual task, namely the ending time of the last actual task; f. of m2 A deferred cost for an individual m, m 2 For soft milestone tasks, B m To complete a set of soft milestone tasks beyond a defined time,
in order to be a penalty factor,
for task m 2 The start time of (c) is set,
for task m 2 During the duration of the time instant t,
for task m 2 Saving the non-inferior solution in the limited completion time into an external archive Q;
s303, updating the current population individual by using improved discrete Levis flight;
s304, calculating the fitness value of the individuals in the population after the updating is finished, and storing the non-inferior solution in the population into an external archive Q;
s305, judging the number N of non-inferior solutions in Q par And capacity N out If N is the size of par ＜N out Then all individuals in the current Q are saved and N is regenerated by using the method in S1-S2 out -N par Supplementing for individual; otherwise, calculating the congestion degree of all individuals, and storing the top N with larger congestion degree out Individuals are used as next generation population;
s306, let T = T +1, judge T ≦ T max And if so, returning to the S302, otherwise, ending the algorithm and outputting the current external file Q as an optimal solution.
5. The method according to claim 4, wherein the step S303 specifically comprises:
s303a, firstly, calculating the Euclidean distance of each bird nest in the population according to the following formula, wherein the bird nest with the minimum Euclidean distance is the optimal bird nest, and the calculation formula is as follows:
wherein f is lm For the 1 st objective function value of the mth individual,
representing the Euclidean distance of each bird nest;
s303b, calculating the individual distance between the current bird nest and the optimal bird nest, and taking the individual distance as the crossing probability of the individual, wherein the calculation formula of the individual distance is as follows:
wherein HD(s) 1 ，s 2 ) Is an individual (bird nest in the present embodiment) s 1 And s 2 Hamming distance of, N max The upper limit of the hamming distance, i.e. the length of the individual,
representing individual distances between the front bird nest and the optimal bird nest;
s303c, generating a random number matrix of 1 x (N + 1), wherein the range of each number is 0 to 1; comparing the number of each position in the matrix with the cross probability, if the number is greater than the cross probability, reserving the task sequence number of the position, and otherwise deleting the task sequence number of the position;
s303d, deleting the task serial numbers of the optimal bird nest, which are the same as the task serial numbers reserved by the current bird nest, and sequentially placing the rest task serial numbers of the optimal bird nest into the deleted task serial numbers of the current bird nest;
s303e, sorting and grading the population through rapid non-dominated sorting, dividing the interval into subsets which are equally spaced and have the same length as the number of the levels, wherein the probability that the individual at the best level is discovered is 0, and the probability that the individual at the worst level is discovered is P max And carrying out local search operation on the discovered individuals.
6. The method of stochastic scheduling of marine repair missions under consideration of milestone constraints of claim 5, wherein the local search operation comprises:
each non-milestone task in an individual is selected with a certain probability, the selected task will move in the neighborhood between the tasks immediately before and after the closest task based on the principle of the maximum hamming distance, and the moving mode comprises the following steps:
when no milestone task exists in the neighborhood, calculating the Hamming distance between the sequence after the selected task moves to each position and the sequence before the movement, and moving the task to the position with the maximum Hamming distance;
when a milestone task exists on the left side of the selected task in the neighborhood, the movable position is changed into an immediately-after task closest to the selected task from the left-side milestone, the Hamming distance before and after movement is calculated, and the task is moved to the position with the largest Hamming distance.
7. The method of stochastic scheduling of repair missions to ships under consideration of milestone constraints according to claim 4, wherein the crowdedness is calculated as follows:
stratify each solution according to their dominated case: firstly, find out the non-dominant solution set in the population, and mark it as the first non-dominant layer FR 1 And removed from the entire population; then, continuously finding out the non-domination solution set in the rest population, and marking as a second non-domination sorting layer FR 2 (ii) a Proceeding as such until the entire population is stratified;
the congestion degree is calculated for solutions in the same layer: make the degree of crowding d m N, and sorting the individuals in ascending order according to a 1 st objective function, and giving d to the left and right individuals 1 ＝d n = infinity, calculating intermediate
Wherein f is l (m + 1) and f l (m-1) the objective function values of the next and previous individuals to individual m,
and
the maximum value and the minimum value of the 1 st objective function in the current level are obtained; updating the crowdedness degree for each objective function according to the mode until the final crowdedness degree is calculated; the dominance condition refers to that when each target function index of the solution A is different from the target function index of the solution B, the solution A is named as dominated by the solution B.
8. A stochastic dispatch system for maintenance tasks on a vessel under milestone constraints, comprising:
the encoding module is used for acquiring data of tasks to be scheduled in the project, encoding the tasks to be scheduled and generating a task list;
the initial population generation module is used for processing the task list based on heuristic rules and a probability selection method to generate an initial population;
the optimization module is used for taking the initial population as an initial bird nest, optimizing the initial bird nest by an improved discrete cuckoo search algorithm by taking the maximum completion time and the minimum delay cost as targets to obtain an optimal solution;
wherein the heuristic rules comprise:
heuristic rule 1: prioritizing hard milestone tasks of the tasks to be scheduled, without regard to resource conflicts, the hard milestone tasks being tasks whose completion time may not exceed a specified expiration date;
heuristic rule 2: each task is scheduled according to the earliest starting time rule, namely the task is scheduled as early as possible in the period when available resources exist;
heuristic rule 3: when two soft milestone tasks conflict and any one task is independently arranged not to exceed the deadline, the milestone task with the earlier deadline is preferentially arranged, and the soft milestone task has the completion time which can exceed the specified deadline but can generate the delay cost if the completion time exceeds the specified completion time.
9. A computer-readable storage medium, characterized in that it stores a computer program for random scheduling of maintenance tasks to a vessel under consideration of milestone constraints, wherein the computer program causes a computer to execute the method of random scheduling of maintenance tasks to a vessel under consideration of milestone constraints according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a stochastic scheduling method for maintenance tasks to a vessel under consideration of milestone constraints as recited in any of claims 1-7.
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