CN111325424A - Intelligent scheduling method and system based on improved ant colony algorithm - Google Patents
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Abstract
The embodiment of the invention provides an intelligent scheduling method and system based on an improved ant colony algorithm. The method comprises the following steps: scheduling optimization is carried out by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises route time and maintenance time, and the output comprises tasks distributed by each maintenance worker, task execution sequence, task predicted processing time and task predicted completion time; when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, the algorithm is triggered to recalculate to generate a new task allocation scheme; and if the last order returned by the maintenance personnel is not received before the predicted completion time of the last order distributed by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to the work order pool, triggering the algorithm to recalculate, and generating a new task distribution scheme. The embodiment of the invention can realize efficient scheduling between assembly and maintenance personnel and an assembly and maintenance work order.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to an intelligent scheduling method and system based on an improved ant colony algorithm.
Background
At present, the mobile broadband service is developed at a high speed, and the contradiction between home wide equipment maintenance and personnel scheduling is increasingly prominent. How to realize efficient and accurate assembly and maintenance service, reduce meaningless assembly and maintenance consumption and maximize the assembly capacity is a difficult problem in the front.
At present, most of the whole broadband maintenance service still stays in the original manual scheduling stage. However, the manual scheduling obviously cannot take the situations of busy and idle conditions, distance, road conditions and the like of the maintenance personnel into consideration, and cannot perform accurate and efficient scheduling, so that not only is the labor cost wasted, but also extra cost waste is caused by inaccurate scheduling. In addition, the existing scheduling mode is not flexible enough, the working condition of assembly and maintenance personnel cannot be obtained in time, and if the current work has influence on the execution of subsequent dispatching, the work order cannot be scheduled flexibly.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides an intelligent scheduling method and system based on an improved ant colony algorithm.
In a first aspect, an embodiment of the present invention provides an intelligent scheduling method based on an improved ant colony algorithm, where the method includes:
scheduling optimization is carried out by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, triggering the improved ant colony algorithm to recalculate, and generating a new task allocation scheme;
and if the last single receipt of the maintenance personnel is not received before the predicted completion time of the last single task allocated by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to a work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate after the predicted completion time of the last single task is increased by a specified time length to generate a new task allocation scheme.
In a second aspect, an embodiment of the present invention provides an intelligent scheduling system based on an improved ant colony algorithm, where the system includes:
the first scheduling unit is used for performing scheduling optimization by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
the second scheduling unit is used for triggering the improved ant colony algorithm to recalculate when a new work order is added into the work order pool and the appointment time is the same day or the work order appointment time in the work order pool is changed in the same day, so as to generate a new task allocation scheme;
and the third scheduling unit is used for recovering the current task work order of the maintenance personnel to the work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate to generate a new task allocation scheme after the predicted completion time of the previous task is increased by a specified time length if the previous list return list of the maintenance personnel is not received before the predicted completion time of the previous list task allocated by the maintenance personnel is overtime.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method provided in the first aspect is implemented.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method provided in the first aspect.
The embodiment of the invention realizes the integral optimized scheduling between the assembly and maintenance personnel and the assembly and maintenance work order through the improved ant colony algorithm, introduces the reservation time limit and the busy and idle limit of the assembly and maintenance personnel, and realizes flexible scheduling. The method can flexibly adapt to the conditions of loading and maintenance burst problem, temporary order adding, user time change and the like, realizes efficient scheduling, and improves the loading and maintenance efficiency and quality.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of an intelligent scheduling method based on an improved ant colony algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an execution sequence of an nth person assigned three tasks in a certain assignment scheme according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent scheduling system based on an improved ant colony algorithm according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a flow chart of an intelligent scheduling method based on an improved ant colony algorithm according to an embodiment of the present invention.
As shown in fig. 1, the intelligent scheduling method based on the improved ant colony algorithm provided in the embodiment of the present invention specifically includes the following steps:
s11, performing scheduling optimization by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
specifically, the ant colony algorithm is one of artificial intelligence algorithms, and is particularly suitable for scenes such as task scheduling and path optimization. The embodiment of the invention realizes the overall optimized scheduling between assembly and maintenance personnel and an assembly and maintenance work order based on the scheduling platform of the improved ant colony algorithm. The input of the scheduling platform comprises a work order pool and maintenance personnel information, and the work order pool comprises a plurality of to-be-loaded task work orders, reservation time of each work order, geographical positions of each to-be-loaded task work order and the like. The maintenance and installation personnel information comprises the number of maintenance and installation personnel and the time consumed by each maintenance and installation personnel to complete each work order.
The required road time of each maintenance worker among tasks and the maintenance time of each maintenance worker for each task can be collected and calculated through related channels and used as known quantities.
For example, the route time is comprehensively determined according to the geographical position relationship between the previous task and the next task, the time period for which the tasks are arranged and the traffic mode of the current maintenance personnel, and the relevant information can be obtained from other navigation or map systems, namely the route time needed by the maintenance personnel among the tasks is reasonably predicted by combining the traffic condition. The business hall has detailed historical statistical data for the assembly and maintenance process of each assembly and maintenance person, and the assembly and maintenance time of each assembly and maintenance person for each task can be obtained based on the detailed historical statistical data. And obtaining the predicted processing time and the predicted completion time of each task according to the acquired road time and maintenance time.
S12, when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, triggering the improved ant colony algorithm to recalculate to generate a new task allocation scheme;
specifically, the platform has the condition of temporary work order addition, and after the business hall accepts the work order, the work order is pushed to the work order pool to be maintained, the system detects that a new work order is added into the work order pool, the appointment time is the same day, and the system triggers recalculation when the appointment time of the work order is changed in the same day.
And S13, if the last order of the maintenance personnel is not received before the predicted completion time of the last order task allocated by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to a work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate after the predicted completion time of the last order task is increased by a specified time length to generate a new task allocation scheme.
Specifically, for the dispatched work order, if the work order receiving personnel does not return the previous work order through the platform before the previous work order appointment time, which indicates that the personnel does not complete the assembly of the previous work order, the work order is recovered to the work order pool, meanwhile, the work order processing time pre-allocated by the personnel is cleared, the predicted completion time of the previous work order is increased by a specified time (such as 30 minutes), and rescheduling calculation is triggered.
The embodiment of the invention considers the problems that in the actual scheduling, emergencies exist, such as temporary leave-on of personnel, sudden increase of tasks, failure of timely completion of tasks by maintenance personnel to cause failure of getting on the door of the next maintenance task according to the reserved time and the like, and recalculation is needed, so as to ensure that each task can be completed.
The embodiment of the invention realizes the integral optimized scheduling between the assembly and maintenance personnel and the assembly and maintenance work order through the improved ant colony algorithm, introduces the reservation time limit and the busy and idle limit of the assembly and maintenance personnel, and realizes flexible scheduling. The method can flexibly adapt to the conditions of loading and maintenance burst problem, temporary order adding, user time change and the like, realizes efficient scheduling, and improves the loading and maintenance efficiency and quality.
On the basis of the above embodiment, when there are N installation and maintenance personnel, the number of the task work orders to be installed in the work order pool is M, the number of the ant colony iterations is K, the personnel consuming the longest time in the allocation scheme generated by each iteration is the final time of the allocation scheme, the shortest time for completing all the tasks to be installed is set as the objective function, and then the objective function is:
wherein ,
indicating that in the assignment generated in the kth iteration, the nth assembly personnel takes the longest time,
the final time spent on the k-th distribution scheme is obtained; wherein K is 1,2,3 … … K;
the objective function is:
wherein ,
the value of (A) includes two parts of the road-way time and the door-mounting dimension time, namely:
and the required road time of each maintenance and installation personnel among tasks is determined according to the geographical position relationship among the tasks in the work order pool, the reservation time of each task and the traffic mode of the current maintenance and installation personnel.
Specifically, assume that there are N staffs, M job orders to be assembled, and K ant colony iterations. In order to maximize the assembly and maintenance efficiency, all assembly and maintenance work orders need to be completed in the shortest time, and because the assembly and maintenance personnel work in parallel, the personnel consuming the longest time in the distribution scheme is the final time of the distribution scheme. Thus, the objective function is set:
in summary, the objective function is set as:
wherein ,
the value of (A) includes two parts of the road-way time and the door-mounting dimension time, namely:
troad course and tClothes and maintenanceCan be collected and calculated as known quantities through relevant channels.
wherein tRoad courseAccording to the geographical position relation of the former task and the latter task, the time period for which the tasks are arranged and the traffic mode of the current maintenance personnel, the method can obtain relevant information from other navigation or map systems, namely reasonably predicting the required road time of the maintenance personnel among the tasks by combining the traffic condition.
The ant colony algorithm has three layers of circulation when a program is executed, wherein the first layer is iteration times K, the second layer is the number of ants, and the third layer is a dimension task. Each iteration produces an allocation scheme that is no worse than the previous one.
The embodiment of the invention completes the installation and maintenance intelligent scheduling based on the ant colony algorithm, reasonably introduces the route time when setting the target function, and more reasonably provides a scheduling scheme.
On the basis of the above embodiment, in each distribution scheme, the predicted completion time of each task is the sum of the order dispatching time and the predicted time consumed by the task;
the estimated completion time for the nth person to complete the mth task in the kth allocation scenario is:
the scheduling time is the executing ending time of the ant colony algorithm distributed to the task;
and the predicted time consumption is the road time plus the installation dimension time.
Fig. 2 shows a schematic diagram of the execution sequence of three tasks assigned by the nth person in a certain assignment scheme.
As shown in FIG. 2, the expected completion time for the serviceman to complete the first task is:
ttime of sending orderNamely, the task allocates the ant colony algorithm execution ending time, and the server can acquire the time.
On the basis of the above embodiment, when the improved ant colony algorithm is triggered to perform recalculation, the method further includes:
judging whether each maintenance worker is in a busy state or not, wherein the judgment conditions are as follows:
tpredicted completion time>tCurrent time
Marking maintenance personnel meeting the judgment condition as busy;
for the assembly and maintenance personnel marked as busy, judging whether the assembly and maintenance personnel can arrive at the assembly and maintenance site before the reserved time when the first task is distributed, wherein the judgment conditions are as follows:
Δt＝tappointment time-tRoad course-tPredicted completion time>0
Δ t >0 indicates that the serviceman has completed the last task and can reach the servicing location before the appointment time;
if Δ t <0, the allocation scheme is marked as not feasible for solution, while added to the corresponding tabu matrix.
Specifically, when a recalculation is triggered by an emergency, since some maintenance personnel are busy (tasks assigned in the last calculation are being executed), the time for the maintenance personnel to process the tasks needs to be eliminated when the maintenance personnel participate in the recalculation.
During the third layer of circulation, all the assembly and maintenance personnel participating in the calculation are selected, the tasks are all the assembly and maintenance worksheets which are not executed, and when each ant distributes the tasks, the following judgment is added:
(1) judging whether the assembly and maintenance personnel are in a busy state, and judging the conditions:
tpredicted completion time>tCurrent time
(2) For the assembly maintenance personnel marked as busy, judging whether the personnel can arrive at the assembly maintenance site before the appointment time when the personnel first distribute tasks, namely:
Δt＝tappointment time-tRoad course-tPredicted completion time>0
Δ t >0 indicates that the person has completed the last task and can reach the build site before the appointment time. The ant distribution scheme is a feasible solution. If Δ t <0, the allocation scheme is marked as not feasible for solution, while added to the corresponding tabu matrix.
The embodiment of the invention considers the problem of busy and idle installation and maintenance personnel, and realizes unified scheduling and integral optimization.
On the basis of the above embodiment, when the improved ant colony algorithm is triggered to perform recalculation, the method further includes:
judging whether the assembly and maintenance personnel can arrive at the assembly and maintenance place before the appointment time, wherein the judgment conditions are as follows:
tappointment time>tPredicted completion time+tRoad course
If the above formula is satisfied, the maintenance and installation site can be reached before the reserved time, and the distribution scheme is a feasible solution;
if the above formula is not satisfied, it cannot be reached before the reserved time, and the allocation scheme is an infeasible solution and is added to the corresponding tabu matrix.
In particular, embodiments of the present invention also consider the issue of getting on before the appointment time. As described above, the time allocated to each task, that is, the time taken by the maintenance worker to travel and the time taken to perform maintenance are calculated, so that the time that the maintenance worker expects to reach the maintenance location can be determined. Therefore, the addition at the third cycle is judged as follows:
tappointment time>tPredicted completion time+tRoad course
If the formula is satisfied, the solution can be considered to be a feasible solution when the solution arrives before the reserved time, and if the formula is not satisfied, the solution can not be reached before the reserved time, the solution is considered to be an infeasible solution, and meanwhile, the corresponding taboo matrix is arranged.
The more the iteration times of the ant colony algorithm are, the higher the accuracy is, the improved ant colony algorithm provided by the invention adds a screening link in the process, and greatly reduces the operation amount, so that the iteration times can be improved on the premise of the same calculation amount, and the goal of more optimizing the distribution scheme is reached.
The embodiment of the invention can effectively respond to the home-wide quality improvement activity of 'loading and handling immediately, reporting and repairing immediately', flexibly adapt to the conditions of loading and maintaining burst problem, temporary order adding, user time change and the like, solve the problem of limitation of reservation time and busy and idle loading and maintaining personnel, realize efficient scheduling and improve the loading and maintaining efficiency and quality.
It should be noted that, the embodiment of the present invention takes assembly and maintenance work order scheduling as an example, and is actually applicable to various service scheduling scenarios, such as logistics transportation, production distribution, and workshop scheduling.
Fig. 3 is a schematic structural diagram illustrating an intelligent scheduling system based on an improved ant colony algorithm according to an embodiment of the present invention.
As shown in fig. 3, the intelligent scheduling system based on the improved ant colony algorithm provided in the embodiment of the present invention includes a first scheduling unit 11, a second scheduling unit 12, and a third scheduling unit 13, where:
the first scheduling unit 11 is configured to perform scheduling optimization by using an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
specifically, the ant colony algorithm is one of artificial intelligence algorithms, and is particularly suitable for scenes such as task scheduling and path optimization. The embodiment of the invention realizes the overall optimized scheduling between assembly and maintenance personnel and an assembly and maintenance work order based on the scheduling platform of the improved ant colony algorithm. The input of the scheduling platform comprises a work order pool and maintenance personnel information, and the work order pool comprises a plurality of to-be-loaded task work orders, reservation time of each work order, geographical positions of each to-be-loaded task work order and the like. The maintenance and installation personnel information comprises the number of maintenance and installation personnel and the time consumed by each maintenance and installation personnel to complete each work order.
The required road time of each maintenance worker among tasks and the maintenance time of each maintenance worker for each task can be collected and calculated through related channels and used as known quantities.
For example, the route time is comprehensively determined according to the geographical position relationship between the previous task and the next task, the time period for which the tasks are arranged and the traffic mode of the current maintenance personnel, and the relevant information can be obtained from other navigation or map systems, namely the route time needed by the maintenance personnel among the tasks is reasonably predicted by combining the traffic condition. The business hall has detailed historical statistical data for the assembly and maintenance process of each assembly and maintenance person, and the assembly and maintenance time of each assembly and maintenance person for each task can be obtained based on the detailed historical statistical data. And obtaining the predicted processing time and the predicted completion time of each task according to the acquired road time and maintenance time.
The second scheduling unit 12 is configured to trigger the improved ant colony algorithm to perform recalculation to generate a new task allocation scheme when a new work order is added to the work order pool and the appointment time is the same day, or when the work order appointment time is changed in the work order pool on the same day;
specifically, the platform has the condition of temporary work order addition, and after the business hall accepts the work order, the work order is pushed to the work order pool to be maintained, the system detects that a new work order is added into the work order pool, the appointment time is the same day, and the system triggers recalculation when the appointment time of the work order is changed in the same day.
The third scheduling unit 13 is configured to, if the last single receipt of the maintenance worker is not received before the predicted completion time of the last single task allocated by the maintenance worker is overtime, recycle the work order of the current task of the maintenance worker to the work order pool, clear the predicted processing time of the current task, and increase the predicted completion time of the last single task by a specified time length, and then trigger the improved ant colony algorithm to perform recalculation to generate a new task allocation scheme.
Specifically, for the dispatched work order, if the work order receiving personnel does not return the previous work order through the platform before the previous work order appointment time, which indicates that the personnel does not complete the assembly of the previous work order, the work order is recovered to the work order pool, meanwhile, the work order processing time pre-allocated by the personnel is cleared, the predicted completion time of the previous work order is increased by a specified time (such as 30 minutes), and rescheduling calculation is triggered.
The embodiment of the invention considers the problems that in the actual scheduling, emergencies exist, such as temporary leave-on of personnel, sudden increase of tasks, failure of timely completion of tasks by maintenance personnel to cause failure of getting on the door of the next maintenance task according to the reserved time and the like, and recalculation is needed, so as to ensure that each task can be completed.
The embodiment of the invention realizes the integral optimized scheduling between the assembly and maintenance personnel and the assembly and maintenance work order through the improved ant colony algorithm, introduces the reservation time limit and the busy and idle limit of the assembly and maintenance personnel, and realizes flexible scheduling. The method can flexibly adapt to the conditions of loading and maintenance burst problem, temporary order adding, user time change and the like, realizes efficient scheduling, and improves the loading and maintenance efficiency and quality.
On the basis of the above embodiment, when there are N installation and maintenance personnel, the number of the task work orders to be installed in the work order pool is M, the number of the ant colony iterations is K, the personnel consuming the longest time in the allocation scheme generated by each iteration is the final time of the allocation scheme, the shortest time for completing all the tasks to be installed is set as the objective function, and then the objective function is:
wherein ,
indicating that in the assignment generated in the kth iteration, the nth assembly personnel takes the longest time,
the final time spent on the k-th distribution scheme is obtained; wherein K is 1,2,3 … … K;
the objective function is:
wherein ,
the value of (A) includes two parts of the road-way time and the door-mounting dimension time, namely:
and the required road time of each maintenance and installation personnel among tasks is determined according to the geographical position relationship among the tasks in the work order pool, the reservation time of each task and the traffic mode of the current maintenance and installation personnel.
Specifically, assume that there are N staffs, M job orders to be assembled, and K ant colony iterations. In order to maximize the assembly and maintenance efficiency, all assembly and maintenance work orders need to be completed in the shortest time, and because the assembly and maintenance personnel work in parallel, the personnel consuming the longest time in the distribution scheme is the final time of the distribution scheme. Thus, the objective function is set:
in summary, the objective function is set as:
wherein ,
the value of (A) includes two parts of the road-way time and the door-mounting dimension time, namely:
troad course and tClothes and maintenanceCan be collected and calculated as known quantities through relevant channels.
wherein tRoad courseAccording to the geographical position relation of the former task and the latter task, the time period for which the tasks are arranged and the traffic mode of the current maintenance personnel, the method can obtain relevant information from other navigation or map systems, namely reasonably predicting the required road time of the maintenance personnel among the tasks by combining the traffic condition.
The ant colony algorithm has three layers of circulation when a program is executed, wherein the first layer is iteration times K, the second layer is the number of ants, and the third layer is a dimension task. Each iteration produces an allocation scheme that is no worse than the previous one.
The embodiment of the invention completes the installation and maintenance intelligent scheduling based on the ant colony algorithm, reasonably introduces the route time when setting the target function, and more reasonably provides a scheduling scheme.
On the basis of the above embodiment, in each distribution scheme, the predicted completion time of each task is the sum of the order dispatching time and the predicted time consumed by the task;
the estimated completion time for the nth person to complete the mth task in the kth allocation scenario is:
the scheduling time is the executing ending time of the ant colony algorithm distributed to the task;
and the predicted time consumption is the road time plus the installation dimension time.
As shown in FIG. 2, the expected completion time for the serviceman to complete the first task is:
ttime of sending orderNamely, the task allocates the ant colony algorithm execution ending time, and the server can acquire the time.
On the basis of the above embodiment, the system further includes:
the first judgment unit is used for judging whether each maintenance worker is in a busy state or not, and the judgment conditions are as follows:
tpredicted completion time>tCurrent time
The marking unit is used for marking the maintenance personnel meeting the judgment condition as busy;
the second judgment unit is used for judging whether the assembly and maintenance personnel can arrive at the assembly and maintenance site before the reserved time when the assembly and maintenance personnel first task is distributed for the assembly and maintenance personnel marked as busy, and the judgment conditions are as follows:
Δt＝tappointment time-tRoad course-tPredicted completion time>0
Δ t >0 indicates that the serviceman has completed the last task and can reach the servicing location before the appointment time;
a first processing unit for marking the allocation scheme as not feasible if at <0, while adding to the corresponding tabu matrix.
Specifically, when a recalculation is triggered by an emergency, since some maintenance personnel are busy (tasks assigned in the last calculation are being executed), the time for the maintenance personnel to process the tasks needs to be eliminated when the maintenance personnel participate in the recalculation.
During the third layer of circulation, all the assembly and maintenance personnel participating in the calculation are selected, the tasks are all the assembly and maintenance worksheets which are not executed, and when each ant distributes the tasks, the following judgment is added:
(1) judging whether the assembly and maintenance personnel are in a busy state, and judging the conditions:
tpredicted completion time>tCurrent time
(2) For the assembly maintenance personnel marked as busy, judging whether the personnel can arrive at the assembly maintenance site before the appointment time when the personnel first distribute tasks, namely:
Δt＝tappointment time-tRoad course-tPredicted completion time>0
Δ t >0 indicates that the person has completed the last task and can reach the build site before the appointment time. The ant distribution scheme is a feasible solution. If Δ t <0, the allocation scheme is marked as not feasible for solution, while added to the corresponding tabu matrix.
The embodiment of the invention considers the problem of busy and idle installation and maintenance personnel, and realizes unified scheduling and integral optimization.
On the basis of the above embodiment, the system further includes:
a third judging unit, for judging whether the assembly and maintenance personnel can arrive at the assembly and maintenance place before the reserved time, the judging conditions are:
tappointment time>tPredicted completion time+tRoad course
If the above formula is satisfied, the maintenance and installation site can be reached before the reserved time, and the distribution scheme is a feasible solution;
and the second processing unit is used for failing to reach the reserved time if the formula is not satisfied, and the allocation scheme is an infeasible solution and is added to the corresponding taboo matrix.
In particular, embodiments of the present invention also consider the issue of getting on before the appointment time. As described above, the time allocated to each task, that is, the time taken by the maintenance worker to travel and the time taken to perform maintenance are calculated, so that the time that the maintenance worker expects to reach the maintenance location can be determined. Therefore, the addition at the third cycle is judged as follows:
tappointment time>tPredicted completion time+tRoad course
If the formula is satisfied, the solution can be considered to be a feasible solution when the solution arrives before the reserved time, and if the formula is not satisfied, the solution can not be reached before the reserved time, the solution is considered to be an infeasible solution, and meanwhile, the corresponding taboo matrix is arranged.
The more the iteration times of the ant colony algorithm are, the higher the accuracy is, the improved ant colony algorithm provided by the invention adds a screening link in the process, and greatly reduces the operation amount, so that the iteration times can be improved on the premise of the same calculation amount, and the goal of more optimizing the distribution scheme is reached.
The embodiment of the invention can effectively respond to the home-wide quality improvement activity of 'loading and handling immediately, reporting and repairing immediately', flexibly adapt to the conditions of loading and maintaining burst problem, temporary order adding, user time change and the like, solve the problem of limitation of reservation time and busy and idle loading and maintaining personnel, realize efficient scheduling and improve the loading and maintaining efficiency and quality.
It should be noted that, the embodiment of the present invention takes assembly and maintenance work order scheduling as an example, and is actually applicable to various service scheduling scenarios, such as logistics transportation, production distribution, and workshop scheduling.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method shown in fig. 1 is implemented.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
As shown in fig. 4, the electronic device provided by the embodiment of the present invention includes a memory 21, a processor 22, a bus 23, and a computer program stored on the memory 21 and executable on the processor 22. The memory 21 and the processor 22 complete communication with each other through the bus 23.
The processor 22 is used to call the program instructions in the memory 21 to implement the method of fig. 1 when executing the program.
For example, the processor implements the following method when executing the program:
scheduling optimization is carried out by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, triggering the improved ant colony algorithm to recalculate, and generating a new task allocation scheme;
and if the last single receipt of the maintenance personnel is not received before the predicted completion time of the last single task allocated by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to a work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate after the predicted completion time of the last single task is increased by a specified time length to generate a new task allocation scheme.
According to the electronic equipment provided by the embodiment of the invention, the integral optimized scheduling between the assembly and maintenance personnel and the assembly and maintenance work order is realized through the improved ant colony algorithm, and the reservation time limit and the busy and idle limit of the assembly and maintenance personnel are introduced, so that the flexible scheduling is realized. The method can flexibly adapt to the conditions of loading and maintenance burst problem, temporary order adding, user time change and the like, realizes efficient scheduling, and improves the loading and maintenance efficiency and quality.
Embodiments of the present invention also provide a non-transitory computer readable storage medium, on which a computer program is stored, and the program, when executed by a processor, implements the steps of fig. 1.
For example, the processor implements the following method when executing the program:
scheduling optimization is carried out by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, triggering the improved ant colony algorithm to recalculate, and generating a new task allocation scheme;
and if the last single receipt of the maintenance personnel is not received before the predicted completion time of the last single task allocated by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to a work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate after the predicted completion time of the last single task is increased by a specified time length to generate a new task allocation scheme.
The non-transitory computer readable storage medium provided by the embodiment of the invention realizes the overall optimized scheduling between the assembly and maintenance staff and the assembly and maintenance work order through the improved ant colony algorithm, and introduces the reservation time limit and the busy and idle limit of the assembly and maintenance staff to realize flexible scheduling. The method can flexibly adapt to the conditions of loading and maintenance burst problem, temporary order adding, user time change and the like, realizes efficient scheduling, and improves the loading and maintenance efficiency and quality.
An embodiment of the present invention discloses a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform the methods provided by the above-mentioned method embodiments, for example, including:
scheduling optimization is carried out by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, triggering the improved ant colony algorithm to recalculate, and generating a new task allocation scheme;
and if the last single receipt of the maintenance personnel is not received before the predicted completion time of the last single task allocated by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to a work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate after the predicted completion time of the last single task is increased by a specified time length to generate a new task allocation scheme.
The functional modules in the embodiments of the present invention may be implemented by a hardware processor (hardware processor), and the embodiments of the present invention are not described again.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: 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 will 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. An intelligent scheduling method based on an improved ant colony algorithm is characterized by comprising the following steps:
scheduling optimization is carried out by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
when a new work order is added into the work order pool and the appointment time is the same day, or the work order appointment time in the work order pool is changed, triggering the improved ant colony algorithm to recalculate, and generating a new task allocation scheme;
and if the last single receipt of the maintenance personnel is not received before the predicted completion time of the last single task allocated by the maintenance personnel is overtime, recovering the current task work order of the maintenance personnel to a work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate after the predicted completion time of the last single task is increased by a specified time length to generate a new task allocation scheme.
2. The method according to claim 1, wherein when the number of the maintenance personnel is N, the number of the work orders to be loaded in the work order pool is M, the number of the ant colony iterations is K, the personnel consuming the longest time in the distribution scheme generated by each iteration is the final time of the distribution scheme, and the shortest time for completing all the tasks to be loaded is set as an objective function, and the objective function is:
wherein ,
indicated in the k-th iterationIn the distribution scheme generated by the generation, the nth maintenance personnel takes the longest time,
the final time spent on the k-th distribution scheme is obtained; wherein K is 1,2,3 … … K;
the objective function is:
wherein ,
the value of (A) includes two parts of the road-way time and the door-mounting dimension time, namely:
and the required road time of each maintenance and installation personnel among tasks is determined according to the geographical position relationship among the tasks in the work order pool, the reservation time of each task and the traffic mode of the current maintenance and installation personnel.
3. The method of claim 2, wherein in each allocation scenario, the predicted completion time for each task is the order dispatch time plus the predicted elapsed time for that task;
the estimated completion time for the nth person to complete the mth task in the kth allocation scenario is:
the scheduling time is the executing ending time of the ant colony algorithm distributed to the task;
and the predicted time consumption is the road time plus the installation dimension time.
4. The method of claim 3, wherein when the improved ant colony algorithm is triggered to recalculate, the method further comprises:
judging whether each maintenance worker is in a busy state or not, wherein the judgment conditions are as follows:
tpredicted completion time>tCurrent time
Marking maintenance personnel meeting the judgment condition as busy;
for the assembly and maintenance personnel marked as busy, judging whether the assembly and maintenance personnel can arrive at the assembly and maintenance site before the reserved time when the first task is distributed, wherein the judgment conditions are as follows:
Δt＝tappointment time-tRoad course-tPredicted completion time>0
Δ t >0 indicates that the serviceman has completed the last task and can reach the servicing location before the appointment time;
if Δ t <0, the allocation scheme is marked as not feasible for solution, while added to the corresponding tabu matrix.
5. The method of claim 3, wherein when the improved ant colony algorithm is triggered to recalculate, the method further comprises:
judging whether the assembly and maintenance personnel can arrive at the assembly and maintenance place before the appointment time, wherein the judgment conditions are as follows:
tappointment time>tPredicted completion time+tRoad course
If the above formula is satisfied, the maintenance and installation site can be reached before the reserved time, and the distribution scheme is a feasible solution;
if the above formula is not satisfied, it cannot be reached before the reserved time, and the allocation scheme is an infeasible solution and is added to the corresponding tabu matrix.
6. An intelligent scheduling system based on an improved ant colony algorithm, the system comprising:
the first scheduling unit is used for performing scheduling optimization by adopting an improved ant colony algorithm to generate a task allocation scheme; the input of the improved ant colony algorithm comprises the required route time of each maintenance worker among each task and the maintenance time of each maintenance worker for each task, and the output comprises the tasks distributed by each maintenance worker, the task execution sequence, the task predicted processing time and the task predicted completion time;
the second scheduling unit is used for triggering the improved ant colony algorithm to recalculate when a new work order is added into the work order pool and the appointment time is the same day or the work order appointment time in the work order pool is changed in the same day, so as to generate a new task allocation scheme;
and the third scheduling unit is used for recovering the current task work order of the maintenance personnel to the work order pool, clearing the predicted processing time of the current task, and triggering the improved ant colony algorithm to recalculate to generate a new task allocation scheme after the predicted completion time of the previous task is increased by a specified time length if the previous list return list of the maintenance personnel is not received before the predicted completion time of the previous list task allocated by the maintenance personnel is overtime.
7. The system according to claim 6, wherein when the number of the maintenance personnel is N, the number of the work orders to be loaded in the work order pool is M, the number of the ant colony iterations is K, the personnel consuming the longest time in the distribution scheme generated by each iteration is the final time of the distribution scheme, and the shortest time for completing all the tasks to be loaded is set as the objective function, and then the objective function is:
wherein ,
indicating that in the assignment generated in the kth iteration, the nth assembly personnel takes the longest time,
the final time spent on the k-th distribution scheme is obtained; wherein K is 1,2,3 … … K;
the objective function is:
wherein ,
the value of (A) includes two parts of the road-way time and the door-mounting dimension time, namely:
and the required road time of each maintenance and installation personnel among tasks is determined according to the geographical position relationship among the tasks in the work order pool, the reservation time of each task and the traffic mode of the current maintenance and installation personnel.
8. The system of claim 7, wherein in each allocation plan, the predicted completion time for each task is the order dispatch time plus the predicted elapsed time for that task;
the estimated completion time for the nth person to complete the mth task in the kth allocation scenario is:
the scheduling time is the executing ending time of the ant colony algorithm distributed to the task;
and the predicted time consumption is the road time plus the installation dimension time.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the improved ant colony algorithm based intelligent scheduling method according to any one of claims 1 to 5.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the improved ant colony algorithm based intelligent scheduling method according to any one of claims 1 to 5.
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