CN112486107A - Multi-process flow product production scheduling method - Google Patents

Multi-process flow product production scheduling method Download PDF

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CN112486107A
CN112486107A CN201910870021.1A CN201910870021A CN112486107A CN 112486107 A CN112486107 A CN 112486107A CN 201910870021 A CN201910870021 A CN 201910870021A CN 112486107 A CN112486107 A CN 112486107A
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许新居
陈科甫
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Weibo Holding Co ltd
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    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

The product production scheduling method is executed by a product scheduling application program, firstly, receiving product type information, reading the information including process sequence number information and process type information, generating a directed network model according to the process sequence number information and the process type information, and generating a group of flow limiting formulas according to the sequence of a production line; receiving a manpower quantity information and generating a manpower limitation type according to the manpower quantity information; reading a target function related to the optimal productivity, and calculating an optimal solution of the target function and corresponding manpower configuration according to the group flow limiting formula, the manpower limiting formula and the process cycle time information; by generating a directed network model and a corresponding limited number of flow limiting formulas, the number of operations required to be performed is greatly reduced, so that the best manpower configuration can be obtained in a limited time when the product is arranged into an application program for execution.

Description

Multi-process flow product production scheduling method
[ technical field ] A method for producing a semiconductor device
A scheduling method, and more particularly, to a multi-process flow product production scheduling method.
[ background of the invention ]
In the labor-intensive industries such as textile and shoe manufacturing, a production and assembly operation for one product includes a plurality of processes, and a continuous production line composed of a large amount of labor is used for processing and assembling the product from raw materials to finished products. In order to achieve good production efficiency, how to properly distribute manpower to each process is a problem that each factory must face when producing products. In the process of manual allocation, conditions and relative relationships between the processes in the production line are also considered. For example, the average time required for each process is different, i.e., the efficiency of each process performed by the same human power in a fixed time is different. Generally, the production efficiency of a plant is defined as the number of finished products produced in a fixed time, and the production of one finished product must be complete through all the processes. Therefore, the number of finished products is limited by the throughput of the process with the lowest throughput. In other words, in order to achieve the best production efficiency, the labor allocation of each process must increase the output of the process with the lowest output in each process, so as to increase the output of the finished product. For example, a production line is configured by manpower on the same day, wherein the 3 rd process can only complete 500 products in one hour, and the other processes can complete 800 products in one hour, so that the production line can only produce 500 finished products in one hour, and the production efficiency is 500 pieces/hour. In same production on-line, through changing manpower configuration, 550 products can be produced to 3 rd process one hour, 700 processing of products can be accomplished to other processes one hour, then 550 finished products can be produced to this production line one hour, and production efficiency improves to 550/hour.
In each of these steps, the same kind of step may be performed in combination with adjacent steps. In the case where the amount of labor, the number of production processes, and the kinds of the processes are known, by combining the possibility of merging all the processes with the possibility of labor allocation and ranking them, and listing them all in an exhaustive manner and calculating their production efficiency, it is inevitable that only the optimum configuration having the optimum production efficiency can be obtained. However, the complexity of the problem for optimal production efficiency production scheduling is characterized by the NP-hard problem, and the number of calculations required to obtain the optimal placement increases exponentially with the amount of labor or the number of processes.
Furthermore, each working day has variables such as temporary change of manpower quantity and early completion of part of working procedures due to temporary leave-asking of personnel, so that the optimal efficiency can be achieved every day, and the optimal manpower configuration needs to be calculated once before working every day. For example, assuming that the number of working procedures of the product produced today is 15 and 25 people are working, the number of calculation times is 196 ten thousand to 96 hundred million times according to the different combinable working procedures; if the number of working procedures of the product produced today is 20 and 25 people perform the work, the number of required calculations is between 4.2 ten thousand and 8 billion times; if the number of processes for the product produced today is 20, and the number of workers increases to 30, the number of calculations required is between 2 million and 11 million. That is, the number of processes or the total number of manpower in the current day may be varied, which may significantly increase the number of calculations required to obtain the optimal method for manpower allocation through the exhaustion method. In fact, the number of processes for most products is often greater than 30, while the number of manpower in a single plant is often greater than 1000, and the amount of calculation to obtain the optimal configuration by the exhaustive method described above is such that any typical computer cannot obtain the optimal configuration for the limited time before the start of the day. In summary, the conventional production scheduling method is required to be further improved.
[ summary of the invention ]
In view of the huge amount of calculation in the conventional product production scheduling method, it is difficult to obtain the process merging and labor allocation scheduling meeting the efficiency standard within a limited time according to the daily labor amount and process variation in the actual execution. The product production scheduling method comprises the following steps:
receiving product type information, reading a product basic information database according to the product type information, wherein the product basic information database comprises process sequence number information, and process cycle time information and process type information corresponding to the process sequence number information;
generating a directed network model according to the process sequence number information and the process type information;
generating a set of traffic constraints based on the directed network model;
receiving manpower quantity information, and generating a manpower limitation formula according to the manpower quantity information;
reading an object function, calculating an optimal solution of the object function based on the directed network model according to the set of flow limiting formulas and the process cycle time information, and outputting a manual configuration corresponding to the optimal solution.
The directed network model is generated according to the process number and the corresponding process type, and the number of the set of flow limiting formulas generated according to the directed network model is limited by the number of the process number and is not affected by the amount of manpower. The calculation times required for obtaining the optimal configuration are greatly reduced by a limited restriction formula generated according to the directed network model and the production line sequence restriction, so that the computer can obtain the optimal configuration in real time according to the manpower and process calculation of the day.
[ description of the drawings ]
FIG. 1 is a flow chart illustrating a multi-process flow product scheduling method according to the present invention.
FIG. 2 is a schematic diagram of a transit node of a directed network model of the scheduling method for multi-process flow product production according to the present invention.
FIG. 3 is a schematic diagram of a directed network model of the scheduling method for multi-process flow product production according to the present invention.
FIG. 4 is a schematic diagram of a directed network model according to a first preferred embodiment of the scheduling method for multi-process flow product production.
FIG. 5 is a schematic diagram of an optimized manpower allocation directed network model according to a first preferred embodiment of the scheduling method for multi-process flow product production.
[ detailed description ] embodiments
In view of the huge amount of calculation in the conventional product production scheduling method, it is difficult to obtain the process merging and the process allocation scheduling meeting the efficiency standard within a limited time according to the daily total labor and process variation in actual execution.
Referring to fig. 1, the product production scheduling method includes the following steps:
receiving a product type information, reading a product basic information database according to the product type information, wherein the product basic information database comprises a process sequence number information, and a process cycle time information and a process type information corresponding to the process sequence number information (S101);
generating a directed network model according to the process sequence number information and the process type information (S102);
generating a set of traffic restriction equations (S103) based on the directed network model;
receiving a manpower quantity information, and generating a manpower limit formula according to the manpower quantity information (S104);
reading a target function, calculating an optimal solution of the target function based on the directed network model according to the set of flow restriction formula and the process cycle time information, and outputting a human configuration corresponding to the optimal solution (S105).
The manpower quantity information comprises the total manpower of the job, the process serial number information comprises a plurality of process serial numbers, each process serial number corresponds to one process of the product in sequence, the process type information comprises the process type corresponding to each process serial number, and the process cycle time information comprises the cycle time corresponding to each process serial number, namely the average time required for the product to pass and complete the process.
To describe the multi-process flow production scheduling method of the present invention in detail, each of the variables in the production flow of a product is described first. Wherein the total manpower number of the product is q, the number of the working procedures is m, and the total number of the product is J1～JnN total steps, each step JiRespectively belonging to one of M process classes, MkIs a set of k-th class processes. One person on average performs one process JiAnd a cycle time P required to produce a semi-finished product that passes through the processiIf it is the combination step JijThe required cycle time is Pij. Wherein, step Ji～JjNeed to belong to the same kind of process and form a combined process for the adjacent processSequence JijThat is, JijThe condition is satisfied is Ji～Jj∈Mk. In addition, preferably, M is set1Are a collection of manual process types, and a process belonging to manual can be combined with any other process type, that is, JijThe condition is satisfied is Ji～Jj∈(Mk∪M1)。
In the present invention, a human configuration is generated by the production scheduling method, which represents that the merging manner of a process is determined under the condition of merging the processes, and the human amount of the day is allocated to each process or merged process.
The following lists each of these variables and their definitions above:
n: total number of steps
q: total amount of manpower
m: total number of process types
Ji: the ith step;
Mk: a set of k-th type processes;
Jij：Ji～Jji is 1 to n, j is 1 to n, i is less than or equal to j;
Pi: cycle time of the ith process, representing that the process is performed by an average person;
Pij: merging step JijThe cycle time of (a) is,
Figure BDA0002200787610000051
based on the above production schedule variables, a capacity strain function can be derived:
Ci: single-person single-station capacity, single-person capacity of the ith procedure Ji,
Figure BDA0002200787610000052
where T is the unit time, thus CiNamely, the process J is performed by 1 personiThe number of products that can be completed in a unit time;
Cij: merging the processesJijThe single-person capacity of the electric power generating device,
Figure BDA0002200787610000053
under a certain manual configuration, if the process JijIf a plurality of persons are allocated to carry out the process simultaneously, the capacity of the process is in direct proportion to the number of allocated persons, namely the capacity of the process is the product of the number of persons and the capacity of a single person.
Each of the steps of the production scheduling method of the present invention will be described.
Referring to fig. 2, in order to store each process as the directed network model, a set of nodes in the directed network model is defined according to the process number of the product:
N＝{ai，bi1, i is 1 to n; wherein, ai、biIs a transfer node of a process Ji;
v ═ s, t } UN, s is the origin of the supply node, i.e. the production line; t is the end point of the production line.
In the present directed network model, aiIs a process JiIn transit node of biIs a process JiThe outbound transit node of (1).
Referring to fig. 3, then, according to the process type, an arc vector set a ═ a in the directed network model is defined1∪A2∪A3∪A4Wherein:
A1＝{(s，a1)}；
A2＝{(ai，bj)}，ai，bje is N, j is larger than or equal to i; wherein, when j is more than i; arc (a)i，bj) Set of process types to be considered, that is, if arc (a)I，bj) To be true, then JI，Jj∈Mk∪M1K 2, 3, … m, and arc (a)i+x，bj+x) All must be true, x ═ 1, 2 …, (j-i);
A3＝{(bi，ai+1)}，bi，ai+1∈N，i＝1，2，…，n-1；(bi，ai+1) stands for one of the processes JiIs connected to the next process Ji+1；
A4＝{(bn，t)}。
Next, a flow function f (x, y) is defined, and f (u, v) e {0, 1 }.
f (x, y) ═ 1, meaning that there is a flow rate from x to y;
f (x, y) ═ 0, meaning that there is no flow from x to y, where:
f(s，a1) 1, represents J1A first step after the start of supply;
f(bnt) is 1 and represents JnThe last procedure before the end point of the requirement;
f(ai，bj) 1 and J-i, meaning arrangement JiThe process is a process performed separately;
f(ai，bj) 1, and J > i, represents arrangement Ji～JjTo merge the working procedure Jij；
f(bi，ai+1) 1, denotes arrangement JiJoining J after completion of the processjAnd (5) working procedures.
For example, taking FIG. 3 as an example, in a manual configuration, when the flow function f (a)1，b2) 1 represents a setting step J1～J2Merging into a merging process J12Execution when f (a)3，b3) 1 represents a setting step J3Is a separately performed process. Further, f (s, a)1) And f (b)nT) is preset to 1, ensuring the beginning of the production process to enter the end of the first process and the last process.
Then define the manual function w (a)i，bj)。w(ai，bj) Is a process JijThe number of allocated manpower is in accordance with
Figure BDA0002200787610000071
That is, the total number of human power in each step is the total number of human power. For example, when f (a)1，b2) 1, and w (a)i，bj) (iii) 5 represents a combining step J125 persons were assigned to perform the merging process.
According to the flow and manpower function definition, the productivity of a single process can be further confirmed to be the product of the single-person productivity of the process and the manpower allocated to the station, namely:
Figure BDA0002200787610000072
further, the number of finished products produced by a production line is limited by the process having the lowest capacity at the particular labor configuration. That is, the production line produces the same number of finished products as the process with the lowest production capacity. For the above reasons, the man-machine efficiency (POH) of the manpower allocation can be defined as the single-station capacity of the process with the lowest single-station capacity divided by the total number of the manpower in the day, i.e., the number of finished products produced by one person on average in a unit time.
In addition, whether the process or the combined process is selected or not must be considered when determining the minimum single-station capacity. In the calculation, if the process or the merging process is not selected at all in the manpower configuration, the flow function value f (a) corresponding to the processi，bj) Making its corresponding single-station capacity 0 results in the process being determined to have the lowest single-station capacity, which is an unreasonable configuration and should not be present in any performable human configuration. To avoid the possible defects, in the present invention, a single-station capacity function C (f (a)) is further definedi，bj)，w(ai，bj))：
Figure BDA0002200787610000081
Wherein α is an abnormal alarm constant, preferably a very large number, C (a)i，bj)，w(ai，bj) Is the single-station capacity function under the manpower configuration.
Therefore, the man-averaged efficiency POH of this human configuration can be expressed as:
Figure BDA0002200787610000082
next, a target function, i.e., the best human-average efficiency Max (POH), is defined. From the above definition formula of the man-average efficiency POH, the best man-average efficiency can be listed as:
Figure BDA0002200787610000083
in addition, in a preferred embodiment, in order to avoid the situation that the configuration with the flow function value of 1 and the human function value of 0 occurs, i.e. in order to avoid the situation that the error can not be implemented when one process or the merging process is arranged but no human power is allocated, a convergence function is further generated:
Figure BDA0002200787610000084
wherein, when f (a) is definedi，bj) 1, but w (a)i，bj) When equal to 0, m (a)i，bj) 1. Namely:
Figure BDA0002200787610000085
further, the optimal per-person efficiency representing the target function is further corrected to be:
Figure BDA0002200787610000086
the corrected target function is such that if f (a) occurs when calculating the man-average efficiency of a certain human configurationi，bj) 1, but w (a)i，bj) If the configuration is 0, then:
m(ai，bj)＝1
Figure BDA0002200787610000091
that is, the modified objective function includes f (a)i，bj) 1 and w (a)i，bj) Irrational allocation of manpower of 0
Figure BDA0002200787610000092
Not likely to be a maximum. Thus, the purpose of avoiding the human configuration containing unreasonable human allocation from being selected as the optimal human configuration according to the operation result is achieved.
In the directed network model, the first step J is set from the supply start point s1With an input flow of 1, and from the last to process JnThe output flow rate of the input flow rate entering the required terminal point t is 1 and is according to the input flow rate a of each transfer nodei、bjMust equal the output flow limit, the following flow limit equation can be obtained:
to a1In terms of:
Figure BDA0002200787610000093
to aiIn terms of:
Figure BDA0002200787610000094
to b isjIn terms of:
Figure BDA0002200787610000095
to b isnIn terms of:
Figure BDA0002200787610000096
according to the input manpower quantity information, the total number of the daily manpower is judged to be the number q of the daily attendance, and a manpower limit formula can be obtained:
Figure BDA0002200787610000097
finally, according to the directed network model and the generated flow limit formula and manpower limit formula, an optimal solution of the objective function Max (POH) is calculated through an integer programming solving algorithm of software, and a flow function value and a manpower function value corresponding to the optimal solution are output, namely, a process merging method and a manpower distribution method under the optimal manpower configuration are output.
The operation of the product scheduling method of the present invention will be further illustrated below. For example, the product to be produced is a sock, so that a product category information input into the production scheduling application by the manager includes representative information of the sock product, such as a product code. The production scheduling application reads a product basic information database according to the product type information. The product basic information database of the socks is shown in table 1 below.
Sequence number of procedure (i) Description of the procedures Cycle time (P)i) Kind of procedure Procedure type number (k)
1 Sock cover cloth label sewing 10.2s Computer flatcar 2
2 Sock cover sewing 3.4s Eight-needle vehicle 3
3 Needle head with eight scissors 5.7s Manual operation 1
4 Sock turning sleeve 2.9s Manual operation 1
TABLE 1
The basic information database of the product comprises the serial number of each process in the production line of the sock product, the cycle time of the process, the kind of the process and the serial number. From the above table, it is known that the variables of the production line of the product on the current day are as follows:
the total number of the working procedures is 4;
process type set: m1＝{J3，J4}、M2＝{J1}、M3＝{J2}。
Wherein the 3 rd step J3And the 4 th step J4For manual working, the 1 st working procedure J1And 2. the step J2Different types of sewing processes. That is, according to the condition that the same kind of processes can be merged and a manual process can be merged with any other kind of processes, it is arrangedThe process combination method (2) and (3) are combined into J, for example23And the 3 rd and 4 th processes are combined into J34And 2 nd to 4 th working procedures are combined into J24And the like.
Referring to fig. 4, fig. 4 is a diagram illustrating a directed network model established according to the process-related information. Firstly, according to the information that the total number of processes is 4, the following node sets are generated:
the set of transit nodes N ═ a1，a2，a3，a4，b1，b2，b3，b4}；
V＝{s，t}∪N＝{s，t，a1，a2，a3，a4，b1，b2，b3，b4}。
Then defining arc vector set A ═ A1∪A2∪A3∪A4Wherein:
A1＝{(s，a1)}；
A2＝{(a1，b1)，(a2，b2)，(a2，b3)，(a2，b4)，(a3，b3)，(a4，b4)，(a3，b4)}；
A3＝{(b1，a2)，(b2，a3)，(b3，a4)}；
A4＝{(b4，t)}。
after the directed network model is generated, further according to the arc vector set, the following flow function can be generated:
f(s，a1)、f(a1，b1)、f(α2，b2)、f(a2，b3)、f(a2，b4)、f(a3，b3)、
f(a4，b4)、f(a3，b4)、f(b1，a2)、f(b2，a3)、f(b3，a4)、f(b4，t)。
from the above flow function, the following flow limit equation is generated:
to a1In terms of: f (s, a)1)＝f(a1，b1)＝1；
To a2In terms of: f (b)1，a2)＝f(a2，b2)+f(a2，b3)+f(a2，b3)+f(a2，b4)；
To a3In terms of: f (b)2，a3)＝f(a3，b3)+f(a3，b4)；
To a4In terms of: f (b)3，a4)＝f(a4，b4)；
To b is1In terms of: f (a)1，b1)＝f(b1，a2)；
To b is2In terms of: f (a)2，b2)＝f(a2，b3)；
To b is3In terms of: f (a)2，b3)+f(a3，b3)＝f(b3，a4)；
To b is4In terms of: f (a)2，b4)+f(a3，b4)+f(a4，b4)＝f(b4，t)＝1。
The single station capacity function considering the manpower allocation is as follows:
Figure BDA0002200787610000111
Figure BDA0002200787610000112
Figure BDA0002200787610000121
Figure BDA0002200787610000122
Figure BDA0002200787610000123
Figure BDA0002200787610000124
Figure BDA0002200787610000125
and generating a convergence function m (a) for avoiding misarrangementi，bj)：
Figure BDA0002200787610000126
Figure BDA0002200787610000127
Figure BDA0002200787610000128
Figure BDA0002200787610000129
Figure BDA00022007876100001210
Figure BDA00022007876100001211
Figure BDA00022007876100001212
Further, the production scheduling application receives the manpower amount information input by the manager, for example, if the number of people arriving on the day is 10, the total number q of the manpower in the manpower amount information is 10, so as to generate a manpower limitation formula:
Figure BDA00022007876100001213
the total number of the manpower allocated to each process or the combined process is the total number of the manpower in the day.
Based on the flow function and the manpower function in the directed network model, a manpower configuration table to be calculated can be generated, as shown in the following table 2.
Figure BDA0002200787610000131
TABLE 2
In the last step, the manpower allocation with the highest best solution of the lowest single-station capacity is obtained by the algorithm of planning and solving under the limiting conditions of the flow limiting equation and the manpower limiting equation according to the target function max (poh), as shown in table 3 below, wherein the flow function value and the manpower function value represent the merging manner of the processes, and the manpower function value represents the allocation of the manpower amount. Wherein the unit time T is set to be 1 hour, i.e. 3600 seconds, so the single-person single-station capacity and the single-station capacity are expressed by the number of products produced by 3600.
Figure BDA0002200787610000132
Figure BDA0002200787610000141
TABLE 3
Referring to FIG. 5 and Table 3, the best manpower formula for the production of the productIs set as a process J2～J4Merging into a merging process J24In the 1 st step J15 persons are distributed, and the merging process J245 persons were assigned. Under this manual configuration, the merging process J24Has the lowest single station capacity of all the procedures, namely 1500 pieces. That is, the production line produced 1500 finished products in 1 hour with this optimal manpower configuration.
In the above illustration, through the planning solution operation according to the directed network model and the flow restriction equation thereof, the product production scheduling method of the embodiment only needs to calculate the planning solution with 9 restriction equations for 1 time, and obtains the optimal manpower configuration. If the conventional permutation and combination exhaustive method is used, 35 operations are required.
That is, the computation complexity of the present invention will affect the computation time according to the number of the restricted expressions, which must be smaller than
Figure BDA0002200787610000142
Is not affected by manpower q, but only by the number of processes n, thus enabling the problem to be solved in a limited time. Therefore, the product production scheduling method greatly reduces the operation amount and solves the problem that the optimal manpower allocation cannot be obtained in a limited time through an exhaustion method.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A multi-process flow product production scheduling method executed by a production scheduling application program includes the following steps:
receiving product type information, reading a product basic information database according to the product type information, wherein the product basic information database comprises process sequence number information, and process cycle time information and process type information corresponding to the process sequence number information;
generating a directed network model according to the process sequence number information and the process type information;
generating a set of traffic constraints based on the directed network model;
receiving manpower quantity information, and generating a manpower limitation formula according to the manpower quantity information;
reading a target function, calculating an optimal solution of the target function based on the directed network model according to the set of flow limit formulas, the manpower limit formula and the process cycle time information, and outputting a manpower configuration corresponding to the optimal solution.
2. The method according to claim 1, wherein the step of generating a directed network model based on the process sequence number information and the process type information comprises the steps of,
generating a transit node set and an arc vector set; wherein the content of the first and second substances,
the directed network model includes the set of transit nodes and the set of arc vectors,
the transit node set comprises a plurality of inbound transit nodes and a plurality of outbound transit nodes, and a process sequence number in the process sequence number information corresponds to one of the inbound transit nodes and one of the outbound transit nodes;
the set of arc vectors includes a plurality of arc vectors generated based on the set of transfer nodes and the process type information.
3. The method of claim 2, wherein the step of generating a set of traffic constraints based on the directed network model comprises
Generating a plurality of flow functions, each of which corresponds to one of the arc vectors;
generating the set of traffic constraints for each of the traffic functions according to a constraint rule that an incoming traffic of each of the inbound transit nodes and each of the outbound transit nodes equals an outgoing traffic.
4. The multi-process flow product production scheduling method of claim 1, wherein the step of reading an objective function and calculating an optimal solution of the objective function based on the directed network model according to the set of flow constraints and process cycle time information is performed by a planning solution algorithm.
5. The method of claim 1, wherein the step of receiving a human quantity information and generating a human limit formula according to the human quantity information comprises the steps of:
generating a plurality of human functions, each of which corresponds to one of the arc vectors;
and generating a manpower limitation formula of which the sum of the values of the manpower functions is equal to the information of the manpower quantity according to the manpower quantity of each process and the merging process and a limitation rule of which the sum is equal to the total manpower quantity.
6. The method of claim 5, wherein the manpower constraint is as follows:
Figure FDA0002200787600000021
wherein, aiFor an inbound transit node corresponding to one of the process sequence numbers i, bjFor an outbound transit node corresponding to one of the process numbers j, w (a)i，bj) Is from an inbound transit node aiTo an outbound transit node bjQ is the total number of manpower.
7. The method of claim 3, wherein the set of flow restrictions is as follows:
a1：
Figure FDA0002200787600000022
ai：
Figure FDA0002200787600000023
bj：
Figure FDA0002200787600000024
wherein the content of the first and second substances,
aifor an inbound transit node corresponding to one of the process sequence numbers i, bjFor an outbound transit node corresponding to one of the process numbers j, f (a)i，bj) Is from an inbound transit node aiTo an outbound transit node bjA is the set of all arc vectors.
8. The method for multi-process flow product production scheduling of claim 7, further comprising the steps of:
generating a plurality of single station capacity functions; the single station capacity letter is as follows:
Figure FDA0002200787600000031
wherein, w (a)ι，bj) To be sent to an inbound transit node aiTransfer node b to outboundjA function of a human power, CijA single-person single-station productivity generated according to the process cycle time information, wherein alpha is an abnormal warning constant, A2According to all inbound transit nodes aiTransfer node b to outboundjA set of defined arc vectors.
9. The method of claim 8, wherein the objective function is as follows:
Figure FDA0002200787600000032
wherein the content of the first and second substances,
q is a total number of the human powers included in the human power amount information.
10. The method of claim 8, wherein the objective function is as follows:
Figure FDA0002200787600000033
wherein q is a total number of human powers contained in the information of the number of human powers, m (a)ι，bj) Is a convergence function, which is shown as follows:
Figure FDA0002200787600000034
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114219501A (en) * 2022-02-22 2022-03-22 杭州衡泰技术股份有限公司 Sample labeling resource allocation method, device and application

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006085213A (en) * 2004-09-14 2006-03-30 Inter Db:Kk Management system for work schedule
US20070067201A1 (en) * 2005-07-15 2007-03-22 Grzegorz Malewicz Method and system for parallel scheduling of complex dags under uncertainty
TW201142721A (en) * 2010-05-24 2011-12-01 Nat Univ Chin Yi Technology Project scheduling system based on man-work evaluation and method thereof
CN102621887A (en) * 2012-03-22 2012-08-01 苏州大学 Garment sewing process analysis and combination method
CN108376315A (en) * 2018-02-11 2018-08-07 重庆大学 A kind of uncertain steel smelting-continuous casting flexibility Optimization Scheduling and system based on graph model
CN109345060A (en) * 2018-08-21 2019-02-15 北京航空航天大学 A kind of product quality characteristics Error Tracing & analysis method based on multi-source perception
CN109685386A (en) * 2018-12-29 2019-04-26 齐鲁工业大学 The complex network model construction method of multi-objective Job Shop based on data information
TW201928800A (en) * 2017-12-14 2019-07-16 大陸商北京嘀嘀無限科技發展有限公司 Systems and methods for optimizing order allocation

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006085213A (en) * 2004-09-14 2006-03-30 Inter Db:Kk Management system for work schedule
US20070067201A1 (en) * 2005-07-15 2007-03-22 Grzegorz Malewicz Method and system for parallel scheduling of complex dags under uncertainty
TW201142721A (en) * 2010-05-24 2011-12-01 Nat Univ Chin Yi Technology Project scheduling system based on man-work evaluation and method thereof
CN102621887A (en) * 2012-03-22 2012-08-01 苏州大学 Garment sewing process analysis and combination method
TW201928800A (en) * 2017-12-14 2019-07-16 大陸商北京嘀嘀無限科技發展有限公司 Systems and methods for optimizing order allocation
CN108376315A (en) * 2018-02-11 2018-08-07 重庆大学 A kind of uncertain steel smelting-continuous casting flexibility Optimization Scheduling and system based on graph model
CN109345060A (en) * 2018-08-21 2019-02-15 北京航空航天大学 A kind of product quality characteristics Error Tracing & analysis method based on multi-source perception
CN109685386A (en) * 2018-12-29 2019-04-26 齐鲁工业大学 The complex network model construction method of multi-objective Job Shop based on data information

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
周群阳;杨煜俊;丁炼;: "面向模具车间机器人制造单元的任务调度策略", 机电工程技术, vol. 40, no. 09, 30 September 2011 (2011-09-30) *
李玉玲, 胡洛燕, 王东云, 凌德麟: "计算机辅助服装缝纫流水线的负荷优化调度", 中原工学院学报, no. 04, 25 December 2002 (2002-12-25) *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114219501A (en) * 2022-02-22 2022-03-22 杭州衡泰技术股份有限公司 Sample labeling resource allocation method, device and application
CN114219501B (en) * 2022-02-22 2022-06-28 杭州衡泰技术股份有限公司 Sample labeling resource allocation method, device and application

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