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CN112836928A - An optimization method for manpower scheduling in an assembly shop - Google Patents

An optimization method for manpower scheduling in an assembly shop Download PDF

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CN112836928A
CN112836928A CN202011606759.6A CN202011606759A CN112836928A CN 112836928 A CN112836928 A CN 112836928A CN 202011606759 A CN202011606759 A CN 202011606759A CN 112836928 A CN112836928 A CN 112836928A
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骆淑云
王无双
王成群
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Zhejiang University of Technology ZJUT
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Abstract

The invention discloses a flow shop manpower scheduling optimization method, which comprises the following steps: s1, acquiring the total number of products of a certain style, basic information of the process and the total number of employees; s2, determining specific targets and constraint conditions of workshop scheduling, and establishing a flow workshop manpower scheduling model; and S3, solving the flow shop manpower scheduling model established in the S2 by using a greedy algorithm to obtain an optimal solution of the scheduling model, and outputting a manpower scheduling scheme. In the invention, a process is regarded as a node in the overall consideration, and the more workers are arranged, the stronger the processing capacity of the process is; in order to minimize the total completion time, a flow shop labor force scheduling model with the optimized target of approximately equal processing capacity of each procedure is established, and related constraint conditions are established; meanwhile, a greedy algorithm is utilized to obtain an optimal flow shop manpower scheduling scheme.

Description

Flow shop manpower scheduling optimization method
Technical Field
The invention belongs to the technical field of workshop production scheduling control, and particularly relates to a flow shop manpower scheduling optimization method.
Background
Production scheduling is the work of organizing and executing a production scheduling plan. This is a decision-making process that plays an important role in manufacturing and production systems. The effective production scheduling can not only improve the production capacity, but also improve the customer satisfaction, reduce the energy consumption, the environmental pollution and the like. The first work in this regard was that in the 1950 s, Johnson solved the flow shop problem. Since then, people have developed a lot of research aiming at the production scheduling problem, and a series of meaningful scheduling optimization problems are solved. However, the work of Conway et al on production scheduling problems is currently beginning as a formal approach to scheduling problem theory. Since then, many scholars' research efforts have been developed around their research.
The clothing industry is one of the pillar type industries in China as a typical labor-intensive industry. In recent years, the consumption level of people is continuously improved, and the requirements on the aspects of clothes styles, fashion trends and the like are higher and higher. In order to remain competitive in the consumer market, garment enterprises must meet these needs of consumers. In addition, in the fast fashion era, clothing enterprises have to change from the original large-scale stock-type production mode to the small-order, multi-style, multi-batch order-based production mode. Driven by such a large environment, apparel enterprises have become deeply aware that changes in management patterns and scientific and technical forces will have a tremendous impact on productivity and labor costs.
The clothing industry is labor-intensive and has high professional requirements, so that in recent years, some new labor force is not willing to enter the clothing industry, and human resources become the most important wealth in the clothing industry. If the human resources are unreasonably utilized, great waste is caused to enterprises.
At present, some flow shop production scheduling research achievements which take human resources and machine resources into consideration exist. Fatemeh Bozorgnezhad et al studied the flexible flow shop scheduling problem including finding the best worker allocation. M.k. Marichelvam et al studied the multi-stage hybrid flow shop scheduling problem with the same parallel machine at each stage, taking into account the different skill levels of the employees and the impact of the forgetting effect. Ming Liu et al investigated the hybrid flow shop scheduling problem with the goal of minimizing manufacturing time and total flow time as optimization, taking into account the skills that workers possess and the varying degrees of familiarity with the skills.
However, the above researches are difficult to find an optimal solution, and the scheme provided by the invention can obtain an optimal scheduling strategy. According to the scheme, an optimal production and work arrangement strategy can be obtained through a greedy algorithm, the problem of manpower resource waste in the existing flow shop is solved, the labor cost is effectively reduced, and the intelligent work arrangement effect of the flow shop is achieved in a certain sense.
Disclosure of Invention
Based on the problems in the prior art, the technical problem to be solved by the invention is to provide an optimization scheme capable of helping the labor scheduling of the garment enterprise flow shop based on fast fashion so as to achieve intelligent production scheduling.
The invention adopts the following technical scheme: a flow shop manpower scheduling optimization method comprises the following steps:
s1, acquiring the total number of products of a certain style, basic information of the process and the total number of employees;
s2, determining specific targets and constraint conditions of workshop scheduling, and establishing a flow workshop manpower scheduling model;
and S3, solving the flow shop manpower scheduling model established in the S2 by using a greedy algorithm to obtain an optimal solution of the scheduling model, and outputting a manpower scheduling scheme.
Preferably, the basic information of the procedure comprises the total number of procedures required by the style product and the basic time required by the style product on each procedure.
Preferably, in step S2, the objective function optimization objective of the flow shop human scheduling model is: the processing capacity of each process is approximately equal, so that the completion time is minimized, and the objective function of the scheduling problem is as follows:
Figure BDA0002866093850000031
wherein, tnThe base time, R, required for the product in the nth stepnThe number of workers scheduled in the nth process.
Preferably, step S3 includes the following steps:
s3.1, according to t1:t2:...:tN＝R1:R2:...:RnFinding R1,R2,...,RnAnd R is1+R2+...+RnR denotes total number of employees;
s3.2, calculating R obtained in the step S3.11,R2,...,RnEliminating the decimal place;
s3.3, judging R obtained after eliminating decimal place in the step S3.21,R2,...,RnIf the number is 0, changing 0 to 1, namely allocating 1 person to the corresponding process, if not, the obtained integer number is the number of the allocated persons corresponding to each process, and after the allocation is finished, obtaining the first scheduling result.
Preferably, step S3.3 is followed by the step of:
s3.4, subtracting the distributed number from the total number R to obtain the number R capable of being redistributedZ；
S3.5, dividing the basic time of each procedure by the corresponding distributed number of people, and sequencing the number of the rest people from large to smallZAnd 1, allocating the working procedures at the front of the sequence according to the working procedure 1, and obtaining a second scheduling result after the allocation is finished, namely an optimal manpower scheduling result.
Preferably, the total completion time for this style of product is:
Figure BDA0002866093850000041
wherein D is the total number of the products, and n is the total number of the processes for producing the products.
Preferably, the total number of products D is much larger than the total number of processes n.
Preferably, the proficiency of each employee is consistent.
Preferably, the total number of pieces of the design product is 300, and a total of 15 processes are required to produce the design product.
Preferably, the basic time required by 15 procedures is respectively as follows: 10. 8, 15, 5, 20, 10, 3, 12, 15, 3, 8, 10, 12 minutes, the total number of employees is 70.
The invention has the beneficial effects that: the flow shop is specifically analyzed, and different from the traditional modeling mode, one procedure is regarded as a node and considered from the whole; in order to minimize the total completion time, a flow shop labor force scheduling model with the optimized target of approximately equal processing capacity of each procedure is established, and related constraint conditions are established; meanwhile, a greedy algorithm is utilized to obtain an optimal flow shop manpower scheduling scheme.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without any creative effort.
FIG. 1 is a flow chart of a method for flow shop human scheduling optimization;
FIG. 2 is a schematic view of a flow shop process and manpower allocation;
FIG. 3 is a schematic flow chart of a greedy algorithm-based human scheduling optimization algorithm;
Detailed Description
The following description of the embodiments of the present invention is provided by way of specific examples, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1, the embodiment provides a method for optimizing flow shop manpower scheduling, and is described by way of example when a product is a garment, and includes the steps of:
s1, acquiring the total number of the produced clothes of a certain style, basic information of the process and the total number of employees;
s2, determining specific targets and constraint conditions of workshop scheduling, and establishing a flow workshop manpower scheduling model;
and S3, solving the flow shop manpower scheduling model established in the S2 by using a greedy algorithm to obtain an optimal solution of the scheduling model, and outputting a manpower scheduling scheme.
Specifically, the method comprises the following steps:
in step S1, the basic information of the process includes the total number of processes required for the style of clothes, and the basic time required for each process for the style of clothes.
The garment factory receives a single piece and produces D pieces of clothes of a certain style, n serial processes are needed for producing the clothes of the style, the sequence of the processes is known, and the basic time of each process is known. Assuming that the plant personnel are familiar with all the processes and have consistent proficiency, there is at least one employee per process for a total of R people. The number of staff on each process needs to be scheduled to minimize the time to complete the order.
The parameters and symbolic representations are shown in table 1, as follows:
TABLE 1 symbols and their meanings
Figure BDA0002866093850000061
As described with reference to fig. 2, considering each process as a node as a whole, the process is performed by using a single node
Figure BDA0002866093850000062
I.e. the processing capacity of the j-th step, the processing capacity of each step should be as nearly equal as possible in order to allow almost unimpeded circulation of the garments on the production line.
Therefore, in step S2, the objective function optimization goal of the flow shop human scheduling model is: the processing capacity of each process is approximately equal, so that the completion time is minimized, and the objective function of the scheduling problem is as follows:
Figure BDA0002866093850000063
wherein, tnBasic time required for the clothing in the nth process, RnThe number of workers scheduled in the nth process.
The total finishing time for this style of garment is:
Figure BDA0002866093850000064
wherein D is the total number of the clothes, n is the total number of the working procedures for producing the clothes, and the character constraint conditions in the formula are as follows:
D＞＞n (3)
Figure BDA0002866093850000065
Rj≥1，j＝1,2,...,n (5)
tj≥0，j＝1,2,...,n (6)
Rj∈Z+ (7)
the objective function (1) in the above formula indicates that the processing capacity of each process is expected to be approximately equal; formula (2) represents the total processing time of the D-piece clothes; the constraint condition (3) indicates that the number of clothes is far larger than the number of processes, the constraint condition (4) indicates that the number of workers is limited, the constraint condition (5) indicates that at least one worker is arranged in each process, the constraint condition (6) indicates that the basic time of all the processes must be positive, and the constraint condition (7) indicates that the number of workers arranged in each process must be a positive integer.
Referring to fig. 3, step S3 includes the following steps:
s3.1, according to t1:t2:...:tN＝R1:R2:...:RnFinding R1,R2,...,RnAnd R is1+R2+...+RnR denotes total number of employees;
s3.2, calculating R obtained in the step S3.11,R2,...,RnEliminating the decimal place;
s3.3, judging R obtained after eliminating decimal place in the step S3.21,R2,...,RnIf the number is 0, changing 0 to 1, namely allocating 1 person to the corresponding process, if not, the obtained integer number is the number of the allocated persons corresponding to each process, and after the allocation is finished, obtaining the first scheduling result.
Step S3.3 is followed by the step of:
s3.4, subtracting the distributed number from the total number R to obtain the number R capable of being redistributedZ；
S3.5, dividing the basic time of each procedure by the corresponding distributed number of people, and sequencing the number of the rest people from large to smallZAnd 1, allocating the working procedures at the front of the sequence according to the number of 1 in each working procedure, and obtaining a second scheduling result, namely an optimal manpower scheduling result after the allocation is finished.
As a verification, the following experiment was also performed in this example. Suppose that a single person is received in a factory at present to produce 300 clothes of a certain style, 15 processes are needed for producing the clothes, and the basic time needed by each process is as follows: 10. 8, 15, 5, 20, 10, 3, 12, 15, 3, 8, 10, 12 minutes, and a total number of staff members to be scheduled is 70. According to the formula:
10:8:15:5:5:20:10:10:10:3:12:15:3:8:10:12＝R1:R2:...:Rnand the number of workers arranged on each procedure for the first time is respectively: 3.2, 6, 1, 8, 3, 0, 4, 6, 0, 2, 3, 4, 9 workers remain to be allocated. Dividing the basic time of each process by the number of the first distributed workers, sequencing the basic time and the number of the first distributed workers, distributing the remaining 9 workers to be distributed to the first 9 processes, distributing each process to one worker, and outputting the final optimal result of manpower scheduling, wherein the number of the workers in each process is respectively: 4. 4, 7, 3, 9, 5, 2, 5, 7, 2, 4, 5, the total time-out taken was 782.5 minutes.
In summary, the embodiment specifically analyzes the flow shop, and different from the previous modeling mode, the method of the embodiment considers a process as a node as a whole, and the more workers are arranged, the stronger the processing capacity of the process is; in order to minimize the total completion time, a flow shop labor force scheduling model with the optimized target of approximately equal processing capacity of each procedure is established, and related constraint conditions are established; meanwhile, a greedy algorithm is utilized to obtain an optimal flow shop manpower scheduling scheme.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope of the present invention.

Claims (10)

1.一种流水车间人力调度优化方法，其特征在于，包括步骤：1. a method for optimizing manpower scheduling in a flow shop, is characterized in that, comprises the steps: S1、获取生产某一款式产品的总件数、工序的基本信息以及员工总数；S1. Obtain the total number of products produced in a certain style, the basic information of the process and the total number of employees; S2、确定车间调度的具体目标、约束条件，建立流水车间人力调度模型；S2. Determine the specific goals and constraints of workshop scheduling, and establish a manpower scheduling model for the flow workshop; S3、使用贪心算法对S2中建立好的流水车间人力调度模型进行求解，获得调度模型的最优解，输出人力调度方案。S3. Use the greedy algorithm to solve the manpower scheduling model of the assembly line established in S2, obtain the optimal solution of the scheduling model, and output the manpower scheduling scheme. 2.根据权利要求1所述的一种流水车间人力调度优化方法，其特征在于，工序的基本信息包括该款式产品所需的工序总数、该款式产品在每道工序上所需的基础时间。2 . The method for optimizing manpower scheduling in an assembly shop according to claim 1 , wherein the basic information of the process includes the total number of processes required for the product of this style and the basic time required for each process of the product of the style. 3 . 3.根据权利要求2所述的一种流水车间人力调度优化方法，其特征在于，步骤S2中，流水车间人力调度模型的目标函数优化目标为：各道工序的处理能力近似相等使得完工时间最小化，该调度问题的目标函数为：3. a kind of flow shop manpower scheduling optimization method according to claim 2, is characterized in that, in step S2, the objective function optimization target of flow shop manpower scheduling model is: the processing capacity of each process is approximately equal to make the completion time minimum The objective function of this scheduling problem is:
Figure FDA0002866093840000011
Figure FDA0002866093840000011
 其中，tn为该种产品在第n道工序上所需的基础时间，Rn为在第n道工序上所安排的员工数。Among them, t n is the basic time required for the product in the nth process, and Rn is the number of employees arranged in the nth process. 4.根据权利要求3所述的一种流水车间人力调度优化方法，其特征在于，步骤S3中，包括以下步骤：4. a kind of flow workshop manpower scheduling optimization method according to claim 3, is characterized in that, in step S3, comprises the following steps: S3.1、根据t1:t2:...:tN＝R1:R2:...:Rn求出R1,R2,...,Rn，且R1+R2+...+Rn＝R，R表示员工总数；S3.1. Calculate R 1 , R 2 ,..., R n according to t 1 :t 2 :...:t N =R 1 :R 2 :...:R n , and R 1 +R 2 +...+R n =R, R represents the total number of employees; S3.2、将步骤S3.1中计算得到的R1,R2,...,Rn的小数位剔除；S3.2, remove the decimal places of R 1 , R 2 ,..., R n calculated in step S3.1; S3.3、判断步骤S3.2中剔除小数位后得到的R1,R2,...,Rn是否为0，若为0，则将0改为1，即分配1人至相应工序，若不为0，则得到的整数位即各工序对应的分配人数，分配完成后得到第一次调度结果。 S3.3 . Determine whether R 1 , R 2 , . , if it is not 0, the obtained integer bits are the assigned number of people corresponding to each process, and the first scheduling result is obtained after the assignment is completed. 5.根据权利要求4所述的一种流水车间人力调度优化方法，其特征在于，步骤S3.3之后还包括步骤：5. a kind of manpower dispatching optimization method of assembly-flow workshop according to claim 4, is characterized in that, after step S3.3 also comprises step: S3.4、将总人数R减去已分配人数得到可再分配人数RZ；S3.4, the total number of people R is deducted from the allocated number of people to obtain the redistributed number of people R Z ; S3.5、将各道工序基础时间除以相应的已分配人数，并从大到小排序，将剩下人数RZ按每道工序1人分配给排序靠前的工序，分配完成后得到第二次调度结果，即最优人力调度结果。S3.5. Divide the basic time of each process by the corresponding assigned number of people, and sort them from large to small, and assign the remaining number of people R Z to the process with the highest ranking according to 1 person in each process. After the allocation is completed, the first The secondary scheduling result is the optimal manpower scheduling result. 6.根据权利要求4所述的一种流水车间人力调度优化方法，其特征在于，针对该款式产品的总完工时间为：6. a kind of flow shop manpower scheduling optimization method according to claim 4 is characterized in that, the total completion time for this style product is:
Figure FDA0002866093840000021
Figure FDA0002866093840000021
 其中D为该款产品总件数，n为生产该款产品的工序总数。Among them, D is the total number of pieces of the product, and n is the total number of processes to produce the product. 7.根据权利要求6所述的一种流水车间人力调度优化方法，其特征在于，产品总件数D远大于工序总数n。7 . The method for optimizing manpower scheduling in an assembly shop according to claim 6 , wherein the total number of products D is much larger than the total number of processes n. 8 . 8.根据权利要求1-7任一项所述的一种流水车间人力调度优化方法，其特征在于，每个员工的熟练程度一致。8 . The method for optimizing manpower scheduling in an assembly water workshop according to claim 1 , wherein the proficiency of each employee is the same. 9 . 9.根据权利要求1-7任一项所述的一种流水车间人力调度优化方法，其特征在于，该款式产品的总件数为300件，生产该款式的产品总共需要15道工序。9 . The method for optimizing manpower scheduling in an assembly line according to claim 1 , wherein the total number of products of this style is 300, and a total of 15 processes are required to produce products of this style. 10 . 10.根据权利要求9所述的一种流水车间人力调度优化方法，其特征在于，15道工序相应所需的基础时间分别为：10、8、15、5、5、20、10、10、3、12、15、3、8、10、12分钟，员工总数为70人。10. A method for optimizing manpower scheduling in an assembly-flow workshop according to claim 9, wherein the corresponding basic time required for the 15 processes is: 10, 8, 15, 5, 5, 20, 10, 10, 3, 12, 15, 3, 8, 10, 12 minutes, the total number of employees is 70.
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CN113554225B (en) * 2021-07-22 2023-09-26 浙江理工大学 Flow shop manpower scheduling optimization method based on barnacle mating algorithm
CN115730792A (en) * 2022-11-16 2023-03-03 浙江理工大学 Personalized clothing production line personnel scheduling method and system based on genetic algorithm

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