CN111582531A - Model creation method, distribution optimization method, device, equipment and storage medium - Google Patents
Model creation method, distribution optimization method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a model creating method, a distribution optimizing method, a device, equipment and a storage medium thereof. The method comprises the following steps: constructing a decision variable group, wherein the decision variable group comprises decision variables related to first-class equipment, decision variables related to second-class equipment, decision variables of workers required for assisting the equipment and decision variables related to a processing result of a task to be processed, and the first-class equipment, the second-class equipment and the workers cooperatively process the task to be processed; constructing a set of constraints, the set of constraints comprising constraints relating to processing limitations of the first type of device; constructing an objective function set, wherein the objective function set comprises a minimized objective function related to the usage amount of the first type of equipment and a minimized objective function related to staff; and constructing a multi-target mixed integer programming model by utilizing the constraint condition set and the target function set. According to the embodiment of the application, tedious manual calculation is avoided, and the resource processing efficiency is improved.
Description
Technical Field
The present application relates generally to the field of data processing technology, and more particularly, to a model creation method and an allocation optimization method, apparatus, device, and storage medium thereof.
Background
The development of related technologies of each production link of the logistics industry is promoted by the automatic development of the logistics industry. For example, the introduction of end equipment (such as a gun) and transfer equipment (such as sorting equipment) into a logistics operation field greatly promotes the development of systematization and automation.
In the prior art, sorting equipment capable of reading in a sorting plan is configured in a transfer site, express mail sorting can be performed according to the sorting plan, but after the sorting plan is obtained, the using condition of the equipment and the requirement condition of workers of auxiliary equipment in each time period are still in a stage of manually correcting and adjusting according to experience, and a large amount of time is required for calculating and checking.
Moreover, in a business processing scenario where the sorted items fluctuate greatly, it is difficult to perform calculation adjustment in a manual manner in time and efficiently, for example, at peak stages of twenty-one and twenty-two.
Disclosure of Invention
In view of the above-mentioned drawbacks and deficiencies of the prior art, it is desirable to provide a solution for optimizing task resources by applying a task resource allocation optimization model to reduce labor costs and time costs of service providers.
In a first aspect, an embodiment of the present application provides a multi-objective planning model creating method, including:
constructing a decision variable group, wherein the decision variable group comprises decision variables related to first-class equipment, decision variables related to second-class equipment, decision variables of workers required for assisting the equipment and decision variables related to a processing result of a task to be processed, and the first-class equipment, the second-class equipment and the workers cooperatively process the task to be processed;
constructing a set of constraints, the set of constraints comprising constraints relating to processing limitations of the first type of device;
constructing an objective function set, wherein the objective function set comprises a minimized objective function related to the usage amount of the first type of equipment and a minimized objective function related to staff;
and constructing a multi-target mixed integer programming model by utilizing the constraint condition set and the target function set.
In a second aspect, an embodiment of the present application provides a method for optimizing task resource allocation, where the method includes:
acquiring a data set of tasks to be processed, wherein the data set comprises a task wave number w and the task number of a task type j in each time period t corresponding to the task wave number;
acquiring a first quantity and a first efficiency index of a first type of equipment and a second quantity and a second efficiency index of a second type of equipment of a task processing site;
inputting the data set and the first quantity, the first efficiency index, the second quantity and the second efficiency index into a multi-target mixed integer programming model created according to the method described in the first aspect, and solving the multi-target mixed integer programming model to obtain a first optimization result of the first type of equipment, a second optimization result of the second type of equipment and an optimization result of a worker required for assisting the equipment;
and allocating resources required by the tasks to be processed by utilizing the optimization result, wherein t and j are integers.
In a third aspect, an embodiment of the present application provides a multi-objective planning model creating apparatus, where the apparatus includes:
the system comprises a first construction unit, a second construction unit and a task processing unit, wherein the first construction unit is used for constructing a decision variable group, the decision variable group comprises decision variables related to first-class equipment, decision variables related to second-class equipment, decision variables of workers required for assisting the equipment and decision variables related to a processing result of a task to be processed, and the first-class equipment, the second-class equipment and the workers cooperatively process the task to be processed;
a second construction unit for constructing a set of constraints, the set of constraints including constraints relating to processing limitations of the first type of device;
the third construction unit is used for constructing an objective function set, and the objective function set comprises a minimized objective function related to the usage amount of the first type of equipment and a minimized objective function related to staff;
and the fourth construction unit is used for constructing the multi-target mixed integer programming model by utilizing the constraint condition set and the target function set.
In a fourth aspect, an embodiment of the present application provides a task resource allocation optimization apparatus, where the apparatus includes:
the system comprises a first acquisition unit, a second acquisition unit and a processing unit, wherein the first acquisition unit is used for acquiring a data set of tasks to be processed, and the data set comprises a task wave number w and the task number of a task type j in each time period t corresponding to the task wave number;
the second acquisition unit is used for acquiring a first quantity and a first efficiency index of the first type of equipment and a second quantity and a second efficiency index of the second type of equipment of the task processing site;
the model solving unit is used for inputting the data set and the first quantity, the first efficiency index, the second quantity and the second efficiency index into the multi-target mixed integer programming model created according to the method described in the first aspect, and solving the multi-target mixed integer programming model to obtain a first optimization result of the first type of equipment, a second optimization result of the second type of equipment and an optimization result of a worker required by the auxiliary equipment;
a resource allocation unit for allocating the resources required by the task to be processed by using the optimization result, wherein t, j is an integer
In a fifth aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method as described in the embodiments of the present application when executing the program.
In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, the computer program being configured to:
which when executed by a processor implements a method as described in embodiments of the present application.
According to the multi-target planning model establishing method, the multi-target mixed integer planning model is established by establishing the decision variable group, the constraint condition set and the target function set, so that the problems that the equipment i processes the resource distribution of the task j at the moment t and the number of people in the process a of the task j at the moment t of the equipment i are solved, the tedious manual calculation is avoided, and the efficiency of resource scheduling management is effectively improved.
Furthermore, the embodiment of the application also provides a method for optimizing resource scheduling based on the multi-objective integer scheduling model, and the model is utilized to effectively reduce the labor cost and improve the utilization rate of equipment.
Further, the method and the device for processing the task in the first time period reduce the fluctuation times of the device for processing the different task types in the first time period by constructing the objective function which minimizes the times of executing the different task types by switching each device.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart diagram illustrating a method for creating a multi-objective planning model according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a task resource allocation optimization method according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of a multi-objective planning model creation apparatus 300 according to an embodiment of the present application;
fig. 4 is a schematic structural diagram illustrating a task resource allocation optimizing device 400 according to another embodiment of the present application;
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use in implementing the computer device of an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In a logistics terminal, a plurality of sorting devices can be included. The sorting equipment can realize the sorting work of the express according to the sorting plan, but different sorting equipment needs to be configured with corresponding amount of staff to assist the sorting equipment in different business processing link processes. The sorting equipment can be, for example, cross belt sorting equipment, sorting cabinets, etc.
For example, when a cross-belt sorting facility is processing fast goods, it may involve a number of process steps depending on the type of service being processed, for example, a first sort of a shipment may involve a number of process steps such as a decoration, a bag building, a return piece processing, a bag changing, a bag building, etc. Wherein each process step may require a certain number of personnel to be deployed to assist the sorting equipment in completing the work.
At present, the using quantity of a plurality of sorting devices, the quantity of workers of each sorting device in each process step and the like are adjusted and corrected mainly through manual calculation, the tedious manual calculation is adopted, a large amount of time is divided, and the effect of a resource scheduling scheme is not ideal.
Therefore, in the embodiment of the present application, the method for scheduling resources is summarized as a mathematical model, that is, a mathematical model that the device i processes the resource allocation of the task j at the time t and the device i processes the process a of the task j at the time t needs to allocate the number of workers.
In the embodiment of the application, the mathematical model is realized by adopting a mixed integer programming method.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a multi-objective planning model creation method according to an embodiment of the present application. The method may be performed by a processing device.
As shown in fig. 1, the method includes:
In the embodiment of the application, the decision variable group includes decision variables related to the first type of equipment, decision variables related to the second type of equipment, decision variables of workers required for assisting the first type of equipment and the second type of equipment, and decision variables related to a processing result of a task to be processed. The first equipment, the second equipment and the staff cooperate to process the tasks to be processed.
The first type of equipment may be, for example, cross-belt sorting equipment. The second type of equipment, for example, may be sorting cabinets. When the first type of equipment collects or sorts bulk goods, a certain number of workers are required to be configured in corresponding process steps to assist the operation of the cross belt type sorting equipment and the sorting cabinet according to different requirements of primary sorting or subdivision. For example, in the process of initial distribution of the collected goods, process steps such as decoration, package building and the like may be required, and each process step arranges corresponding staff according to the requirement of the quantity of the goods. For the second type of equipment, a facility may also need to have a staff member arranged to assist the second type of equipment in sorting.
The decision variable associated with the first type of device may be, for example
First decision variable ei，j，tAnd indicating whether the ith first-class device processes the task to be processed with the task type j in the tth time period, wherein i, t and j are integers.
Second decision variable ehi，j，tAnd the number of tasks of the ith first-class device processing the task type j in the tth time period is represented.
Third decision variable beui，j，wAnd indicates whether the ith first-type device is used within the task wave time w in the task type j.
Fourth decision variable deui，jIndicating whether the task type j uses the ith first type device during the first time period.
Decision variables associated with the second class of devices include:
a fifth decision variable cj，tAnd indicates whether the task type j uses the second type device in the t-th time period.
Sixth decision variable chj，tAnd represents the number of task types j processed in the t-th time period, wherein t, j is an integer.
The decision variables of the staff needed to assist the first and second type of equipment include:
seventh decision variable cnj，tIndicating the number of devices of the second type that the task type j needs to use during the t-th time period.
Considering that the number of workers required by each second type of equipment is equal to the number of second type of equipment, the number of second type of equipment can be used as a decision variable to represent when a decision variable of the number of workers required by the second type of equipment is constructed.
Eighth decision variable hni，j，t，aThe number of the workers needed for processing the process step a corresponding to the task type j by the ith first-type equipment in the tth time period is represented, wherein i, t, j and a are integers.
The decision variables related to the processing result of the task to be processed include:
ninth decision variable qljwAnd indicating the residual quantity of the tasks to be processed of the task type j in the w-th task wave, wherein w and j are integers.
Note that the first and second embodiments described in the present application are only used to define decision variables, constraints, or objective functions, and devices, and the like, and are not to be construed as sequential limitations.
And step 120, constructing a constraint condition set.
In the embodiment of the application, in order to solve the problem that the equipment i processes the resource allocation of the task j at the moment t and the problem that the number of workers needs to be configured in the process a of processing the task j at the moment t, the mathematical model is realized by using the multi-target mixed integer programming model, and the multi-target mixed integer programming model is solved to obtain the optimal solution as the optimization scheme of resource scheduling.
In an embodiment of the present application, the set of constraints may include constraints related to processing limitations of the first type of device.
The first constraint may be, for example, a constraint on the number of devices of the first type, which may be understood as the number of devices of the first type used per time period t must not exceed the number of devices of the first type at the task processing site. The first constraint may be expressed as the sum of the number of devices of the first type used per time period t being less than or equal to the first number. The first number represents a number of devices of a first type of device of the task processing site. Specifically, as shown in formula (1):
the value range of I is 1 to TTBN, wherein the TTBN represents the number of a type of equipment of a task processing site. The value range of J can be 0, 1, 2, 3, and J is used to represent the task type. For example, j takes 0 to indicate a bulk cargo initial classification type, j takes 1 to indicate an aggregate initial classification type, j takes 2 to indicate a bulk cargo subdivision type, and j takes 3 to indicate an aggregate subdivision type. T may range from a first time period, such as 24 hours a day, and 72 hours a 3 day. t is used to indicate the division position of the time period over the first time period. For example, 24 hours of the whole day may be divided at 10 minute intervals to obtain 144 time periods. For example, the 0 th time period may be represented as 10, corresponding to a time of 0:01-0:10, the 1 st time period may be represented as 20, corresponding to a time of 0:11-0: 20; by analogy, a 143 th time period, which may be denoted as 1430, may be obtained, corresponding to 23:51-0: 00.
The second constraint may be understood as a constraint on the minimum time within each task pass that the first type of device needs to operate. In view of the operability and rationality of the first type devices in a task processing site, it is necessary to constrain at least the time threshold of operation of a certain first type device if it is turned on at the w-th task pass. The second constraint condition may be expressed as that the work time required for the ith first-type device to process the task type j in each task wave time w is greater than or equal to a time threshold required for the ith first-type device to work. As shown in the formula (2) in detail,
wherein, beui，j，wMeans whether the ith first-class device processes the task type j, T in the w-th task wavewIndicating the time range corresponding to the w-th task wave. w represents the number of task waves and is determined according to the departure time period. For example, a 24 hour day may be divided into 12 task waves, where each task wave is in the range of 2 hours, i.e., 120 minutes. Each task wave corresponds to 12 time segments. It is also possible to divide 24 hours a day into a plurality of wave times according to the service situation, for example, divide 0:00-6:00 of a day into the first task wave time, and the corresponding time period may be 36 time periods. 7:00-9:00 are the second task wave, after which the waves are divided at intervals of 3 hours, or otherwise.
J is a set of J, J is used to represent the task type, and the value range of J can be {0, 1, 2, 3}, etc. For example, j takes 0 to indicate a bulk cargo rough classification type, j takes 1 to indicate an aggregate rough classification type, j takes 2 to indicate a bulk cargo subdivision type, and j takes 3 to indicate an aggregate subdivision type. t represents a time period, which may be 144 time periods divided by 10 minute intervals for 24 hours of the day. For example, the 0 th time period may be represented as 10, corresponding to a time of 0:01-0:10, the 1 st time period may be represented as 20, corresponding to a time of 0:11-0: 20; by analogy, a 143 th time period, which may be denoted as 1430, may be obtained, corresponding to 23:51-0: 00.
And the third constraint condition is used for constraining the tasks to be processed within the range from the corresponding starting time period to the ending time period in each task wave to be processed and completed. The third constraint may be expressed as the sum of the number of tasks of the task type j processed within each task wave w plus the number of unprocessed tasks of all the first type devices within each task wave w being equal to or greater than the sum of the number of tasks of the task type j to be processed within each task wave w. Specifically, as shown in formula (3):
wherein, the values of the related variables are the same as those of the variables in the constraint condition; q. q.sj，tRepresenting the amount of task type j at time t.
And a fourth constraint for constraining the first type device to process the task from the task type j to the task type j', wherein the first type device does not allow continuous work and needs to be prohibited from using for a period of time, wherein the period of time can be a switching penalty time threshold or a penalty time threshold, and the threshold can be set to 10 minutes or other time values. The fourth constraint may be that the i-th first-class device executes the task type j in the t-th time period, and the penalty time threshold is required to be satisfied when executing the task type j' in t +1 time periods. Specifically, as shown in formula (4)
Wherein the content of the first and second substances,
wherein, the values of the related variables are the same as those of the variables in the constraint conditions.
And a fifth constraint condition, namely, the constraint that the same task type j in each task wave number w can only adopt the same equipment, such as cross-belt type sorting equipment or a sorting cabinet. The fifth preset condition may be expressed as that only the same type of device is allowed to process the task of the same task type within each task time w. As shown in the formula (5) in detail,
wherein, the values of the related variables are the same as those of the variables in the constraint conditions.
A sixth constraint, the number of staff members who are required to restrict a certain process a to assist the equipment of the first type, is determined by the number of task types j that the equipment of the first type handles during a certain period of time and the performance of the process. The sixth constraint may be expressed as the number of workers needed to assist the ith first-type device in processing the task of the task type j in the w-th task wave multiplied by the performance of the step a of the process, which is within a redundant range of the number of tasks of the ith first-type device in processing the task type j in the w-th task wave, where w, i, t, j is an integer.
Specifically, as shown in formula (6)
Wherein u istRepresenting the length of time of each time segment, otherwise known as the time granularity, such as 10 minutes for the model.
paIndicating a processing performance corresponding to each process step; the value of which is related to the process step, e.g. p of the pendulumaMay be 1500 pieces/hour.
raThe reduction coefficient corresponding to each process step is expressed, and the value of the reduction coefficient is between 0 and 1.
The values of the other related variables are the same as the values of the variables in the constraint condition.
And step 130, constructing an objective function set.
The objective function set in the embodiment of the application may include a minimization objective function related to the usage amount of the first type device and a minimization objective function related to the staff.
The minimized objective function related to the usage of the first type of device comprises at least one of:
a first optimization objective expressed as minimizing the sum of the remaining quantities of the tasks to be processed of all the task types j within the first time period w, as shown in formula (7),
where w represents the number of task waves, determined from the departure time period. W represents the number of task orders contained in the first time period. For example, a 24 hour day may be divided into 12 task waves, where each task wave is in the range of 2 hours, i.e., 120 minutes. Each task wave corresponds to 12 time periods, and the end of each time period is the departure time point. It is also possible to divide 24 hours a day into a plurality of wave times according to the service situation, for example, divide 0:00-6:00 of a day into the first task wave time, and the corresponding time period may be 36 time periods. 7:00-9:00 are the second task wave, after which the waves are divided at intervals of 3 hours, or otherwise.
J is a set of J, J is used to represent the task type, and the value range of J can be {0, 1, 2, 3}, etc. For example, j takes 0 to indicate a bulk cargo rough separation type, j takes 1 to indicate a cargo rough separation type, j takes 2 to indicate a bulk cargo subdivision type, and j takes 3 to indicate a cargo subdivision type. t represents a time period, which may be 144 time periods divided by 10 minute intervals for 24 hours of the day. For example, the 0 th time period may be represented as 10, corresponding to a time of 0:01-0:10, the 1 st time period may be represented as 20, corresponding to a time of 0:11-0: 20; by analogy, a 143 th time period, which may be denoted as 1430, may be obtained, corresponding to 23:51-0: 00.
A second optimization objective expressed as minimizing a peak of the number of first class devices used by each task type j at the w-th task wave within the first time period, as shown in equation (8),
a third optimization objective expressed as minimizing the sum of the first devices used by all task types j in the first time period, wherein w, j is an integer, as shown in formula (9):
the minimum objective function associated with the staff member includes at least one of:
a fourth optimization objective expressed as a peak to minimize the number of workers required by all time slots to assist all devices in processing tasks of all task types j during the first time period is specified as shown in equation (10),
the value of a refers to the number of process steps required by the first type of equipment when the first type of equipment executes the task type j. For example, the procedure of collecting the first goods includes unpacking, decoration, bag building, reflux treatment, mixed separation and bag pulling, frame pushing, bag conveying and on-line, special-shaped piece treatment, bag changing, empty bag arrangement and the like
A is a set of processes, which may be represented in numerical form, such as {0, 1, 2, … … 10 }.
A fifth optimization objective to minimize the sum of the working hours of the staff members needed to assist all devices in processing tasks of all task types j during all time periods within the first time period, wherein j is an integer, as shown in equation (11),
and 140, constructing a multi-target mixed integer programming model by using the constraint condition set and the target function set.
In the embodiment of the application, the multi-objective mixed integer programming model is realized by defining decision variables, setting objective functions and constraint conditions. The multi-target mixed integer programming model can be solved by adopting a widely used solver at present.
According to the method, the total number of the workers assisting the first-class equipment and the second-class equipment is set to be minimum by adopting a mixed integer programming method, optimization objectives such as the minimum total number of the first-class equipment and the second-class equipment are used, time effect constraints and equipment operation constraints are set according to a service scene, then a model is operated, a resource scheduling scheme of the first-class equipment and the second-class equipment and a worker allocation scheduling scheme corresponding to the first-class equipment and the second-class equipment are obtained, the labor cost of the transfer is effectively saved, and the calculation time is saved.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a task resource allocation optimization method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes:
In the embodiment of the present application, the data set for acquiring the to-be-processed task may include a task frequency w and a task number q of task types j in each time period t corresponding to the task frequency wj，t，qj，tAnd indicating the number of tasks corresponding to the task type j in the t-th time period.
For example, in the application scenario of the transition, the data set may be a data set associated with waybill information, and when the original waybill data is acquired, the quantity q of the elements in a time period is obtained by aggregating scattered waybill quantities into the time periodj，t. Considering that the departure of the transfer station affects the working time period of the sorting equipment, the information of the departure time is used as the task frequency to establish an association relation with the waybill data. For example, different values of the task wave number correspond to different departure times. The value of the task order may be, for example, an integer. The time period can be understood as time granularity, namely statistical time, and is used for counting waybill data in a time range. The relationship between the task wave number and the time period may be, for example, one task wave number corresponding to a plurality of time periods, and each time period may be bounded by 10 minutes. Assume that the time corresponding to the 2 nd task wave may be from 360 minutes to 389 minutes. The time period may be a time subset obtained by dividing the time corresponding to the task wave number into a certain number of time subsets according to the requirement, where one time subset represents one time period, the time interval corresponding to the first time period is 360-. Until the whole task wave is finished.
In the embodiment of the application, the first type of equipment may be cross-belt type sorting equipment, the second type of equipment may be a sorting cabinet, and in an application scenario of a transfer, the first number and the first performance index of the first type of equipment and the second number and the second performance index of the second type of equipment may be acquired from a site server. The first number may be denoted TTBN, which indicates the number of devices of the first type in which the transfer actually exists. A first performance index representing the amount of parts per hour that each of the first type of equipment is capable of handling. The second number represents the number of devices of the second type that actually exist in the transition. A second performance indicator representing an amount of parts per hour that can be processed by each of the second type of equipment.
Step 203, inputting the data set and the first quantity, the first performance index, the second quantity, and the second performance index into the multi-objective mixed integer programming model created according to the method described in fig. 1, and solving the multi-objective mixed integer programming model to obtain a first optimization result of the first type of equipment, a second optimization result of the second type of equipment, and an optimization result of a worker required by the auxiliary equipment.
And step 204, distributing resources required by the tasks to be processed by using the optimization results.
According to the embodiment of the application, the resource scheduling scheme of the transfer station human equipment with optimal manpower and minimum equipment occupation is obtained through mixed integer programming, the calculation time is saved, and the resource utilization rate is improved.
It should be noted that while the operations of the method of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
With further reference to FIG. 3, FIG. 3 illustrates an exemplary block diagram of a multi-objective planning model creation apparatus 300, according to one embodiment of the present application.
The apparatus 300 comprises:
a first construction unit 301, configured to construct a decision variable group.
In the embodiment of the application, the decision variable group includes decision variables related to the first type of equipment, decision variables related to the second type of equipment, decision variables of workers required for assisting the first type of equipment and the second type of equipment, and decision variables related to a processing result of a task to be processed. The first equipment, the second equipment and the staff cooperate to process the tasks to be processed.
The first type of equipment may be, for example, cross-belt sorting equipment. The second type of equipment, for example, may be sorting cabinets. When the first type of equipment collects or sorts bulk goods, a certain number of workers are required to be configured in corresponding process steps to assist the operation of the cross belt type sorting equipment and the sorting cabinet according to different requirements of primary sorting or subdivision. For example, in the process of initial distribution of the collected goods, process steps such as decoration, package building and the like may be required, and each process step arranges corresponding staff according to the requirement of the quantity of the goods. For the second type of equipment, a facility may also need to have a staff member arranged to assist the second type of equipment in sorting.
The decision variables associated with the first type of device may be, for example:
first decision variable ei，j，tAnd indicating whether the ith first-class device processes the task to be processed with the task type j in the tth time period, wherein i, t and j are integers.
Second decision variable ehi，j，tAnd the number of tasks of the ith first-class device processing the task type j in the tth time period is represented.
Third decision variable beui，j，wAnd indicates whether the ith first-type device is used within the task wave time w in the task type j.
Fourth decision variable deui，jIndicating whether the task type j uses the ith first type device during the first time period.
Decision variables associated with the second class of devices include:
a fifth decision variable cj，tWatch, watchShowing whether the task type j uses the second type device at the t-th time period.
Sixth decision variable chj，tAnd the method represents the number of tasks needing to be processed in the t-th time period, wherein j and t are integers.
The decision variables of the staff needed to assist the first and second type of equipment include:
seventh decision variable cnj，tIndicating the number of devices of the second type that the task type j needs to use during the t-th time period.
Considering that the number of workers required by each second type of equipment is equal to the number of second type of equipment, the number of second type of equipment can be used as a decision variable to represent when a decision variable of the number of workers required by the second type of equipment is constructed.
Eighth decision variable hni，j，t，aThe number of the workers needed for processing the process step a corresponding to the task type j by the ith first-type equipment in the tth time period is represented, wherein i, t, j and a are integers.
The decision variables related to the processing result of the task to be processed include:
ninth decision variable qljwAnd indicating the residual quantity of the tasks to be processed of the task type j in the w-th task wave, wherein w and j are integers.
Note that the first and second embodiments described in the present application are only used to define decision variables, constraints, or objective functions, and devices, and the like, and are not to be construed as sequential limitations.
A second constructing unit 302, configured to construct a constraint condition set.
In the embodiment of the application, in order to solve the problem that the equipment i processes the task j at the moment t and the process a of the equipment i processes the task j at the moment t needs to be configured with the number of workers, a multi-objective mixed integer programming model is used for realizing a mathematical model, and the multi-objective mixed integer programming model is solved to obtain an optimal solution as an optimization scheme of resource scheduling.
In an embodiment of the present application, the set of constraints may include constraints related to processing limitations of the first type of device. The same as described above with respect to the constraint set is specifically referred to the description of fig. 1.
The first constraint may be, for example, a constraint on the number of devices of the first type, which may be understood as the number of devices of the first type used per time period t must not exceed the number of devices of the first type at the task processing site. The first constraint may be expressed as the sum of the number of devices of the first type used per time period t being less than or equal to the first number. The first number represents a number of devices of a first type of device of the task processing site.
The second constraint may be understood as a constraint on the minimum time within each task pass that the first type of device needs to operate. In view of the operability and rationality of the first type devices in a task processing site, it is necessary to constrain at least the time threshold of operation of a certain first type device if it is turned on at the w-th task pass. The second constraint condition may be that the work time required for the ith first-type device to process the task type j in each task wave time w is greater than or equal to a time threshold required for the ith first-type device to work.
And the third constraint condition is used for constraining the tasks to be processed within the range from the corresponding starting time period to the ending time period in each task wave to be processed and completed. The third constraint may be expressed as the sum of the number of tasks of the task type j processed within each task wave w plus the number of unprocessed tasks of all the first type devices within each task wave w being equal to or greater than the sum of the number of tasks of the task type j to be processed within each task wave w.
And a fourth constraint for constraining the first type device to process the task from the task type j to the task type j', wherein the first type device does not allow continuous work and needs to be prohibited from using for a period of time, wherein the period of time can be a switching penalty time threshold or a penalty time threshold, and the threshold can be set to 10 minutes or other time values. The fourth constraint may be that the i-th first-class device executes the task type j in the t-th time period, and the penalty time threshold is required to be satisfied when executing the task type j' in t +1 time periods.
And a fifth constraint condition, namely, the constraint that the same task type j in each task wave number w can only adopt the same equipment, such as cross-belt type sorting equipment or a sorting cabinet. The fifth preset condition may be expressed as that only the same type of device is allowed to process the task of the same task type within each task time w.
A sixth constraint that assists the first type of equipment in the number of configuration staff required at a certain process step a is determined by the number of task types j that the first type of equipment handles within a certain time period and the performance of that process step. The sixth constraint may be expressed as the number of workers needed to assist the ith first-type device in processing the task of the task type j in the w-th task wave multiplied by the performance of the step a of the process, which is within a redundant range of the number of tasks of the ith first-type device in processing the task type j in the w-th task wave, where w, i, t, j is an integer.
A third constructing unit 303, configured to construct an objective function set.
The objective function set in the embodiment of the application may include a minimization objective function related to the usage amount of the first type device and a minimization objective function related to the staff.
The minimized objective function related to the usage of the first type of device comprises at least one of:
the first optimization objective is expressed as minimizing the sum of the remaining amount of the tasks to be processed of all the task types j in all the task waves w in the first time period.
A second optimization objective expressed as minimizing a peak in the number of uses of the first type of device per task type j at the w-th task wave during the first time period.
A third optimization objective, expressed as minimizing the sum of the first class devices used by all task types j during a first time period, where w, j are integers.
The minimum objective function associated with the staff member includes at least one of:
a fourth optimization objective expressed as minimizing a peak in the number of staff required for all time periods to assist all devices in processing tasks of all task types j during the first time period.
A fifth optimization objective expressed as minimizing a sum of the working hours of the staff required by all time periods to assist all devices in processing tasks of all task types j within the first time period, wherein j is an integer.
A fourth constructing unit 304, configured to construct a multi-target mixed integer programming model by using the constraint condition set and the objective function set.
In the embodiment of the application, the multi-objective mixed integer programming model is realized by defining decision variables, setting objective functions and constraint conditions. The multi-target mixed integer programming model can be solved by adopting a widely used solver at present.
According to the method, the number of workers assisting the first-class equipment and the second-class equipment is set to be minimum, optimization targets such as the minimum number of the first-class equipment and the second-class equipment are used, timeliness constraints and equipment operation constraints are set according to a service scene, then the model is operated, a resource scheduling scheme of the first-class equipment and the second-class equipment and a worker allocation scheduling scheme corresponding to the first-class equipment and the second-class equipment are obtained, the labor cost of a transfer station is effectively saved, and the calculation time is saved.
Referring to fig. 4, fig. 4 is a block diagram illustrating an exemplary structure of a task resource allocation optimizing device 400 according to an embodiment of the present application.
The device includes:
a first obtaining unit 401, configured to obtain a data set of a task to be processed.
The data set comprises a task wave number w and the task number of the task type j in each time period t corresponding to the task wave number;
a second obtaining unit 402, configured to obtain a first number and a first performance index of the first type of equipment and a second number and a second performance index of the second type of equipment of the task processing site.
A model solving unit 403, configured to input the data set and the first number, the first performance index, the second number, and the second performance index into the multi-objective mixed integer programming model created by the method described in fig. 1, and solve the multi-objective mixed integer programming model to obtain a first optimization result of the first type of device, a second optimization result of the second type of device, and an optimization result of a worker required by the auxiliary device;
and a resource allocation unit 404, configured to allocate resources required by the task to be processed by using the optimization result, where t, j is an integer.
It should be understood that the units or modules described in the apparatus 300-400 correspond to the various steps in the method described with reference to fig. 1-2. Thus, the operations and features described above with respect to the method are equally applicable to the apparatus 300-400 and the units included therein and will not be described again here. The apparatus 400 may be implemented in a browser or other security applications of the electronic device in advance, or may be loaded into the browser or other security applications of the electronic device by downloading or the like. The corresponding units in the apparatus 300-400 can cooperate with units in the electronic device to implement the solution of the embodiment of the present application.
Referring now to FIG. 5, a block diagram of a computer system 500 suitable for use in implementing a terminal device or server of an embodiment of the present application is shown.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, the processes described above with reference to the flow diagrams of fig. 1-2 may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software or hardware. The described units or modules may also be provided in a processor, and may be described as: a processor includes a first building element, a second building element, a third building element, and a fourth building element. Where the names of these units or modules do not in some cases constitute a limitation of the unit or module itself, for example, the first building unit may also be described as a "unit for building a decision variable set".
As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may be separate and not incorporated into the electronic device. The computer readable storage medium stores one or more programs which, when executed by one or more processors, perform the multi-objective planning model creation method and the task resource allocation optimization method described herein.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention as defined above. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (13)
1. A multi-objective planning model creation method is characterized by comprising the following steps:
constructing a decision variable group, wherein the decision variable group comprises decision variables related to first-class equipment, decision variables related to second-class equipment, decision variables of workers required for assisting the equipment and decision variables related to a processing result of a task to be processed, and the first-class equipment, the second-class equipment and the workers cooperatively process the task to be processed;
constructing a set of constraints, the set of constraints comprising constraints relating to processing limitations of the first class of devices;
constructing an objective function set, wherein the objective function set comprises a minimized objective function related to the usage amount of the first type of equipment and a minimized objective function related to the staff;
and constructing the multi-target mixed integer programming model by using the constraint condition set and the target function set.
2. The multi-objective planning model creation method of claim 1, wherein the decision variables associated with the first class of equipment comprise:
first decision variable ei，j，tIndicating whether the ith equipment processes the task to be processed with the task type j in the tth time period, wherein i, t, j are integers;
second decision variable ehi，j，tIndicating the task quantity of the ith device processing task type j in the tth time period;
third decision variable beui，j，wIndicating whether the ith first-type device is used in the task wave number w or not in the task type j;
fourth decision variable deui，jIndicating whether the task type j uses the ith device of the first type in the first time period.
3. The multi-objective planning model creation method of claim 1, wherein the decision variables associated with the second class of equipment comprise:
a fifth decision variable cj，tIndicating whether the task type j uses the second type device in the t time period;
sixth decision variable chj，tIndicating the number of task types j that need to be processed during the t-th time period, where t, j is an integer.
4. The multi-objective planning model creation method of claim 1, wherein the decision variables of the staff required to assist the equipment comprise:
seventh decision variable cnj，tThe number of the task type j needing to use the second type of equipment in the t-th time period is represented;
eighth decision variable hni，j，t，aAt the ith time period, iThe number of the working personnel required by the process step a corresponding to the task type j, wherein i, t, j and a are integers.
5. The multi-objective planning model creation method of claim 1, wherein decision variables related to processing results of the tasks to be processed comprise:
ninth decision variable qljwAnd indicating the residual quantity of the tasks to be processed in the w-th task wave task type j, wherein w and j are integers.
6. The multi-objective planning model creation method of claim 1 wherein the constraints associated with the process limitations of the first type of equipment include:
the sum of the number of the first type of equipment used in each time period t is less than or equal to a first number;
the working time required by the ith first-class device to process the task type j in each task wave number w is greater than or equal to the time threshold required by the ith first-class device to work;
the sum of the number of tasks of the task type j processed in each task wave w by all the first-type devices and the number of unprocessed tasks is greater than or equal to the sum of the number of tasks of the task type j to be processed in each task wave w;
the ith first-class device executes a task type j in a t-th time period, and executes a task type j' in t +1 time periods to meet a penalty time threshold, wherein the task type j and the task type j are determined;
only the same type of equipment is allowed to process the tasks of the same task type in each task wave number w;
and multiplying the number of workers required for assisting the ith first-class equipment in processing the task of the task type j in the w-th task wave by the efficiency of the step a of the working procedure, wherein the number is within a redundant range of the number of tasks of the ith first-class equipment in processing the task type j in the w-th task wave, and w, i, t and j are integers.
7. The multi-objective planning model creation method of claim 1, wherein the minimization objective function related to the usage of the first type of equipment comprises at least one of:
minimizing the sum of the residual quantities of the tasks to be processed of all the task types j in all the task wave times w in the first time period;
minimizing a peak in a number of uses of the first type of device per task type j per task turn over a first time period;
minimizing a sum of devices of the first type used by all task types j during a first time period, wherein w, j is an integer.
8. The multi-objective planning model creation method of claim 1, wherein the minimization objective function associated with the staff member comprises at least one of:
minimizing a peak in a number of workers required by all time periods to assist all of the devices in processing tasks of all task types j within a first time period;
minimizing a sum of the work hours of the staff required by all the time periods to assist all the devices in processing tasks of all task types j within the first time period, wherein j is an integer.
9. A task resource allocation optimization method is characterized by comprising the following steps:
acquiring a data set of tasks to be processed, wherein the data set comprises a task wave number w and the task number of a task type j in each time period t corresponding to the task wave number;
acquiring a first quantity and a first efficiency index of a first type of equipment and a second quantity and a second efficiency index of a second type of equipment of a task processing site;
inputting the data set and the first number, the first performance index, the second number and the second performance index into a multi-objective mixed integer programming model created according to the method of any one of claims 1 to 8, and solving the multi-objective mixed integer programming model to obtain a first optimization result of the first type of equipment, a second optimization result of the second type of equipment and an optimization result of a worker required for assisting the equipment;
and distributing resources required by the task to be processed by using the optimization result, wherein t and j are integers.
10. An apparatus for multi-objective planning model creation, the apparatus comprising:
the system comprises a first construction unit and a second construction unit, wherein the first construction unit is used for constructing a decision variable group, the decision variable group comprises decision variables related to first-class equipment, decision variables related to second-class equipment, decision variables of workers required for assisting the equipment and decision variables related to a processing result of a task to be processed, and the first-class equipment, the second-class equipment and the workers cooperatively process the task to be processed;
a second construction unit for constructing a set of constraints, the set of constraints comprising constraints relating to processing limitations of the first type of device;
the third construction unit is used for constructing an objective function set, and the objective function set comprises a minimized objective function related to the usage amount of the first type of equipment and a minimized objective function related to the staff;
and the fourth construction unit is used for constructing the multi-target mixed integer programming model by utilizing the constraint condition set and the target function set.
11. An apparatus for optimizing task resource allocation, the apparatus comprising:
the system comprises a first acquisition unit, a second acquisition unit and a processing unit, wherein the first acquisition unit is used for acquiring a data set of tasks to be processed, and the data set comprises a task wave number w and the task number of a task type j in each time period t corresponding to the task wave number;
the second acquisition unit is used for acquiring a first quantity and a first efficiency index of the first type of equipment and a second quantity and a second efficiency index of the second type of equipment of the task processing site;
a model solving unit, configured to input the data set and the first number, the first performance index, the second number, and the second performance index into a multi-objective mixed integer programming model created according to any one of claims 1 to 8, and solve the multi-objective mixed integer programming model to obtain a first optimization result of the first type of equipment, a second optimization result of the second type of equipment, and an optimization result of a worker required to assist the equipment;
and the resource allocation unit is used for allocating the resources required by the task to be processed by utilizing the optimization result, wherein t, j is an integer.
12. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a method of task resource allocation optimization model creation as claimed in any one of claims 1 to 8 or a method of task resource optimization as claimed in claim 9 when executing the program.
13. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of task resource allocation optimization model creation according to any one of claims 1 to 8 or a method of task resource optimization according to claim 9.
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