CN112051825B - Multi-target production scheduling method considering employee operation capacity in automobile trial production workshop - Google Patents
Multi-target production scheduling method considering employee operation capacity in automobile trial production workshop Download PDFInfo
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- 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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- 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], computer integrated manufacturing [CIM]
- G05B19/41865—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], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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Abstract
The invention discloses a multi-target production scheduling method taking staff operation capacity into consideration in an automobile trial-manufacture workshop, which comprises the following steps of: step 1, establishing a quantifiable staff operation capability evaluation system, evaluating staff in a workshop according to the staff operation capability evaluation system, and obtaining the operation grade of the staff; step 2, constructing a production scheduling model for efficient and balanced distribution of personnel-tasks and matching constraint conditions by taking maximum finishing time, staff skill utilization balance and labor cost as optimization targets; and 3, solving a production scheduling model based on a multi-objective genetic algorithm, and selecting an optimal production scheduling scheme through a TOPSIS method. The method obtains the optimal scheduling method by constructing a scheduling model with the maximum finishing time, staff skill utilization balance degree and labor cost as optimization targets, so that the purposes of improving production efficiency, reducing staff busy and idle non-uniformity and reducing labor cost can be achieved by the scheduling method.
Description
Technical Field
The invention belongs to the technical field of production and manufacturing, and particularly relates to a multi-target production scheduling method for an automobile trial-manufacture workshop by considering employee operation capability.
Background
The automobile industry is rapidly developed and competition is increasingly promoted, and enterprises need to push new products out of the market at high efficiency and low cost to occupy the market. The automobile sample trial production is a key link in the new product development process, has the characteristics of complex and changeable production process, high customization degree, low automation degree, high dependence on staff operation capability level and the like, and has higher requirements for managers and operators in a trial production workshop. Due to the fact that staff skill level is different and the staff's own working capacity is different under different tasks, efficiency and cost of the whole production link are greatly affected, and staff become factors which need to be considered seriously for trial workshop scheduling.
In the existing automobile manufacturing enterprise trial production workshops, production scheduling is often carried out by means of subjective experience, and the problems of personnel-task mismatch, staff busy and idle non-uniformity and the like are often caused by unquantifiable staff operation capacity, so that the production period is long, production planning is often delayed, the trial production cost is high, and the development of enterprises is restricted to a great extent. Therefore, the quantification of employee work capacity and the production scheduling taking into consideration the employee work capacity are problems worthy of intensive research.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to solve the technical problems that: how to provide a multi-target production scheduling method considering employee operation capability in an automobile trial-manufacture workshop, an optimal scheduling scheme can be selected based on the employee operation capability.
In order to solve the technical problems, the invention adopts the following technical scheme:
a multi-target production scheduling method for considering employee operation capability in an automobile trial-manufacture workshop comprises the following steps:
step 1, establishing a quantifiable staff operation capability evaluation system, evaluating staff in a workshop according to the staff operation capability evaluation system, and obtaining the operation level of the staff;
step 2, constructing a production scheduling model for efficient and balanced distribution of personnel-tasks and matching constraint conditions by taking maximum finishing time, staff skill utilization balance and labor cost as optimization targets;
step 3, solving a production scheduling model based on a multi-objective genetic algorithm, and selecting an optimal production scheduling scheme through a TOPSIS method;
and 4, matching and scheduling the automobile trial-manufacture workshop by using the obtained optimal production scheduling scheme.
As optimization, the staff work ability evaluation system comprises four primary evaluation indexes of task skills, operation efficiency, staff attitude and competence, which are respectively set as J1, J2, J3 and J4;
the task skills comprise five secondary evaluation indexes of personnel knowledge, employee multi-skill flexibility, actual operation capability, employee skill utilization rate and employee skill proficiency, which are respectively set as J11, J12, J13, J14 and J15;
the operation efficiency comprises five total secondary evaluation indexes of safety accident rate, 5S management, process quality complaint rate, on-time completion rate and attendance utilization rate, which are respectively J21, J22, J23, J24 and J25;
the employee attitude comprises three secondary evaluation indexes of the violation rate, the work responsibility and initiative, and the employee attendance total, which are respectively set as J31, J32 and J33;
competence comprises four secondary evaluation indexes of personal learning and development flexibility, team cooperation flexibility, continuous improvement and innovation capability and employee prominence contribution capability, which are respectively set as J41, J42, J43 and J44;
the employee work ability value is calculated by the following formula:
wherein G is k The operation ability evaluation index total score of the kth employee, X i Is the total number of first-level indexes, Y i Is the total number of the secondary indexes under the ith primary index, w ij The weight of the jth secondary index under the ith primary index is A ijk Scoring employee k by the jth secondary index under the ith primary index, A ijk The value range is [0,1 ]]The weight of each secondary index is calculated by a DEMATEL-ANP method;
dividing employee operation grades into primary, intermediate and high grades, and special grades according to the total scores of employee operation capability evaluation indexes, when G is more than or equal to 0 k The staff operation grade is less than 0.6, and G is more than or equal to 0.6 k The staff operation grade is medium grade less than 0.7, G is more than or equal to 0.7 k The staff operation grade is medium and high and is less than 0.8 and less than or equal to G k The staff operation grade is higher than 0.9, and G is more than or equal to 0.9 k The operation grade of staff is special and is less than or equal to 1.
As optimization, the constraint conditions in the step 2 include:
constraint one: constraint of task procedure processing sequence;
constraint II: tasks must be processed after the release time has been reached;
constraint three: only one skill operation is needed for each task sub-process;
constraint four: because of the team cooperation of the working procedures of the trial production workshop, working hours of a plurality of staff in the same working procedure are processed together, and working hours of each staff are calculated according to the maximum working hours of the staff involved in the working hours;
constraint five: one employee can only process one process at a time;
constraint six: at the same time, the total number of optional staff in the same working procedure is not less than the number of specified staff in the task working procedure;
constraint seven: 0-1 variable constraint.
As an optimization, the constraint one describes the formula:
E ijk ≥S ijk +x ijk ×t ijk ，s i(j+1) ≥e ij
s ij ≥0，e ij ≥0
the constraint II has a description formula as follows:
s i1 ≥R i
the constraint III describes the formula as follows:
A ij ＝1
the constraint four is described as the following formula:
the constraint five has a description formula as follows:
S pqk ≥E ijk +λ(1-y ijpqk )
E pqk ≥S ijk +λ(1-y ijpqk )
the constraint six has a description formula as follows:
NA ij ≥a ij
the constraint seven, its description formula is:
x ijk ，y ijpqk ∈{0，1}
wherein E is ijk Representing employee P k Processing O ij Is equal to the finishing time of O ij Represents the j-th process of task i (j.epsilon.1, 2, …, n) i )，P k Represent employee k (k.epsilon.1, 2, …, m), S ijk Representing employee P k Processing O ij Time of start-up of (t) ijk Representing employee P k Processing O ij E ij Indicating procedure O ij Time to finish the processing s ij Indicating procedure O ij Time to start processing, R i Represents the release time of task i, A ij Represents the skill number, NA, required by the j-th process of the task i ij The number of staff available in the j-th procedure of the task i is represented; s is S i (j+1) represents the start time of the next step of the j-th step of task i, S i1 The time of starting the process of the 1 st step of the task i is shown,representing the maximum man-hour of staff participating in the same process, S pqk Representing employee P k Processing O pq Time to start of operation E pqk Representing employee P k Processing O pq Is equal to the finishing time of O pq Represents O ij In the subsequent step of step O ij If staff k finishes processing, then x ijk =1, x in case of other cases ijk =0, if staff first processes procedure O ij Post-processing Process O pq Then y ijpqk =1, in case of other cases, y ijpqk ＝0。
As optimization, the description function formula of the production scheduling model in the step 2 is as follows:
f 1 ＝max{T i |i＝1，2，…，n}
wherein f 1 Indicating maximum finishing time, f 2 Representing employee skill utilization balance, f 3 Representing the cost of labor, L k Representing the k skill utilization rate of staff, T i The completion time of task i is indicated,mean value of skill utilization rate of m staff is represented, W k Representing employee P k The processing cost per unit time is determined according to the employee operation level.
The step 3 specifically comprises the following steps:
step 31, writing an algorithm program by utilizing MATLAB, and setting an initialization population NP, a maximum iteration number maxgen and a cross probability P in the algorithm according to the complexity of the production scheduling problem c Probability of variation P m Parameters of (2);
step 32, determining an optimization objective function of maximum finishing time, employee skill utilization balance and labor cost as an adaptability function of a genetic algorithm;
step 33, determining codes in a genetic algorithm, wherein the codes consist of two sections, the first section is codes based on working procedures, natural numbers are adopted to code the working procedure sequences so as to determine the processing sequence of the working procedures, and the second section is codes based on available processing staff so as to select the processing staff of each working procedure;
step 34, starting an algorithm program, and performing selection, crossing and mutation operations on the codes generated in step 33;
step 35, judging whether the iteration times reach a set maximum value, if so, terminating the program, outputting an optimal feasible scheduling solution set, otherwise, returning to the program to continue running;
step 36, selecting an optimal solution from the optimal feasible scheduling solution set generated in step 35 based on the TOPSIS method.
Compared with the prior art, the invention has the following beneficial effects: the method quantifies the working capacity of the working staff through the established quantifiable staff working capacity evaluation system, constructs a scheduling model taking the maximum finishing time, staff skill utilization balance degree and labor cost as optimization targets, and solves the scheduling model through a multi-target genetic algorithm to obtain an optimal scheduling method, so that the purposes of improving production efficiency, reducing staff busy and free non-uniformity and reducing labor cost can be achieved through the scheduling method.
Drawings
FIG. 1 is an optimal scheduling scheme in an embodiment of the present invention;
FIG. 2 is an empirical scheduling scheme in an embodiment of the present invention;
FIG. 3 is a task processing sequence of an optimal scheduling scheme in an embodiment of the present invention;
fig. 4 is an example of encoding in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below.
The specific implementation method comprises the following steps: a multi-target production scheduling method for considering employee operation capability in an automobile trial-manufacture workshop comprises the following steps:
step 1, establishing a quantifiable staff operation capability evaluation system, evaluating staff in a workshop according to the staff operation capability evaluation system, and obtaining the operation level of the staff;
step 2, constructing a production scheduling model for efficient and balanced distribution of personnel-tasks and matching constraint conditions by taking maximum finishing time, staff skill utilization balance and labor cost as optimization targets;
step 3, solving a production scheduling model based on a multi-objective genetic algorithm, and selecting an optimal production scheduling scheme through a TOPSIS method;
and 4, matching and scheduling the automobile trial-manufacture workshop by using the obtained optimal production scheduling scheme.
As optimization, the staff work ability evaluation system comprises four primary evaluation indexes of task skills, operation efficiency, staff attitude and competence, which are respectively set as J1, J2, J3 and J4;
the task skills comprise five secondary evaluation indexes of personnel knowledge, employee multi-skill flexibility, actual operation capability, employee skill utilization rate and employee skill proficiency, which are respectively set as J11, J12, J13, J14 and J15;
the operation efficiency comprises five total secondary evaluation indexes of safety accident rate, 5S management, process quality complaint rate, on-time completion rate and attendance utilization rate, which are respectively J21, J22, J23, J24 and J25;
the employee attitude comprises three secondary evaluation indexes of the violation rate, the work responsibility and initiative, and the employee attendance total, which are respectively set as J31, J32 and J33;
competence comprises four secondary evaluation indexes of personal learning and development flexibility, team cooperation flexibility, continuous improvement and innovation capability and employee prominence contribution capability, which are respectively set as J41, J42, J43 and J44;
table 1 below is an employee work ability evaluation index table:
TABLE 1
The employee work ability value is calculated by the following formula:
wherein G is k The operation ability evaluation index total score of the kth employee, X i Is the total number of first-level indexes, Y i Is the total number of the secondary indexes under the ith primary index, w ij The weight of the jth secondary index under the ith primary index is A ijk Scoring employee k by the jth secondary index under the ith primary index, A ijk The value range is [0,1 ]]The weight of each secondary index is calculated by a DEMATEL-ANP method.
In order to quantitatively evaluate the working capacity of staff, the weights of all secondary indexes in an evaluation system are required to be determined first. When determining the index weight, the traditional index analysis methods such as an analytic hierarchy process, a principal component analysis method and the like are suitable for the group with large sample size, and cannot process the influence degree among complex indexes. Aiming at the situation that the sample size of the expert group in the trial production workshop is smaller, the indexes of an evaluation system are more and more complex, the weight analysis is carried out by adopting a DEMATEL-ANP method, and the weights of all indexes determined by the DEMATEL-ANP method are shown in the following table 2:
TABLE 2
w ij The normalized weights in table two are selected.
Dividing employee operation grades into primary, intermediate and high grades, and special grades according to the total scores of employee operation capability evaluation indexes, when G is more than or equal to 0 k The staff operation grade is less than 0.6, and G is more than or equal to 0.6 k The staff operation grade is medium grade less than 0.7, G is more than or equal to 0.7 k The staff operation grade is medium and high and is less than 0.8 and less than or equal to G k The staff operation grade is higher than 0.9, and G is more than or equal to 0.9 k The operation grade of staff is special and is less than or equal to 1.
As optimization, the constraint conditions in the step 2 include:
constraint one: constraint of task procedure processing sequence;
constraint II: tasks must be processed after the release time has been reached;
constraint three: only one skill operation is needed for each task sub-process;
constraint four: because of the team cooperation of the working procedures of the trial production workshop, working hours of a plurality of staff in the same working procedure are processed together, and working hours of each staff are calculated according to the maximum working hours of the staff involved in the working hours;
constraint five: one employee can only process one process at a time;
constraint six: at the same time, the total number of optional staff in the same working procedure is not less than the number of specified staff in the task working procedure;
constraint seven: 0-1 variable constraint.
As an optimization, the constraint one describes the formula:
E ijk ≥S ijk +x ijk ×t ijk ，s i(j+1) ≥e ij
s ij ≥0，e ij ≥0
the constraint II has a description formula as follows:
s i1 ≥R i
the constraint III describes the formula as follows:
A ij ＝1
the constraint four is described as the following formula:
the constraint five has a description formula as follows:
S pqk ≥E ijk +λ(1-y ijpqk )
E pqk ≥S ijk +λ(1-y ijpqk )
the constraint six has a description formula as follows:
NA ij ≥a ij
the constraint seven, its description formula is:
x ijk ，y ijpqk ∈{0，1}
wherein E is ijk Representing employee P k Processing O ij Is equal to the finishing time of O ij Represents the j-th process of task i (j.epsilon.1, 2, …, n) i )，P k Represent employee k (k.epsilon.1, 2, …, m), S ijk Representing employee P k Processing O ij Time of start-up of (t) ijk Representing employee P k Processing O ij E ij Indicating procedure O ij Time to finish the processing s ij Indicating procedure O ij Time to start processing, R i Represents the release time of task i, A ij Represents the skill number, NA, required by the j-th process of the task i ij Indicating the number of staff available in the j-th process of the task i, a ij The number of staff needed by the jth procedure of the task i is represented; s is S i (j+1) represents the start time of the next step of the j-th step of task i, S i1 The time of starting the process of the 1 st step of the task i is shown,representing the maximum man-hour of staff participating in the same process, S pqk Representing employee P k Processing O pq Time to start of operation E pqk Representing employee P k Processing O pq Is equal to the finishing time of O pq Represents O ij In the subsequent step of step O ij If staff k finishes processing, then x ijk =1, x in case of other cases ijk =0, if staff first processes procedure O ij Post-processing Process O pq Then y ijpqk =1, in case of other cases, y ijpqk ＝0。
Table 3 below defines for each variable in the scheduling model:
TABLE 3 Table 3
In a manufacturing enterprise, different departments hold different expectations on the indicators of shop scheduling decisions. For example: for a production plant, it is desirable to use plant production efficiency as a main performance index; in the human resource department, the lowest labor cost, the balance of staff busy and idle, and the like are desired. Thus, seeking to maximize comprehensive benefits among departments of an enterprise is an important research focus for workshop production scheduling.
Aiming at an automobile trial production workshop, the invention provides the optimization of the following 3 objective functions from the viewpoints of improving the production efficiency, balancing and distributing personnel-tasks and reasonably controlling the labor cost:
as optimization, the description function formula of the production scheduling model in the step 2 is as follows:
f 1 ＝max{T i |i＝1，2，...，n}
wherein f 1 Indicating maximum finishing time, f 2 Representing employee skill utilization balance, f 3 Representing the cost of labor, L k Represents the utilization rate of the k skills of the staff,mean value of skill utilization rate of m staff is represented, W k Representing employee P k The processing cost per unit time is determined according to the employee operation level.
Is satisfied thatOn the premise of technology, staff constraint and machine availability, the maximum finishing time f is made 1 Minimum, thereby shorten trial production cycle, improve production efficiency.
As each sub-process only needs one skill operation, the skill number of the staff k is A according to the calculation of one skill corresponding to one sub-process k Let staff k skill utilization be L k Average skill utilization rate of m employees isTo better trade-off employee skill utilization balance, each employee skill utilization is described in terms of the degree of fluctuation of the overall average level, i.e., the standard variance of the set of data. The smaller the standard deviation value is, the smaller the fluctuation of the skill utilization rate of the staff is, the more uniform the skill utilization of the staff is, and the phenomenon of uneven busy and idle staff is reduced, namely the skill utilization degree f of the staff 2 The smaller the better.
Minimizing production costs associated with pilot plant based on employee work capacity, i.e., labor costs f 3 And the cost is reasonably controlled, so that the market competitiveness of the company is improved.
The step 3 specifically comprises the following steps:
step 31, writing an algorithm program by utilizing MATLAB, and setting an initialization population NP, a maximum iteration number maxgen and a cross probability P in the algorithm according to the complexity of the production scheduling problem c Probability of variation P m Parameters of (2);
step 32, determining an optimization objective function of maximum finishing time, employee skill utilization balance and labor cost as an adaptability function of a genetic algorithm;
step 33, determining codes in a genetic algorithm, wherein the codes consist of two sections, the first section is codes based on working procedures, natural numbers are adopted to code the working procedure sequences so as to determine the processing sequence of the working procedures, and the second section is codes based on available processing staff so as to select the processing staff of each working procedure;
referring to FIG. 4, an example of the implementation of the coding rule is shown, wherein the length of the first chromosome segment is the total number of processes, and 1,2,1 represent the processing processes O11, O21, O12, and the number of occurrences of the task numerical value represents the processing sequence of the task sub-processes. The length of the second section chromosome is the total number of people available, namely the total number of people available in each sub-process is summed up to obtain the total number of people available for processing; the second chromosome represents an ordered set of available staff based on a process, e.g., process 2 corresponds to 5 staff [ P1, P2, P3, P9, P10] being able to process the process, and the numbers are re-numbered in the order of staff work numbers, then P1 corresponds to 1, P2 corresponds to 2, P3 corresponds to 3, P9 corresponds to 4, P10 corresponds to 5; the sequence of the chromosome genes in the step 2 represents the processing sequence of staff available in the step, and if 2 persons are actually required to process in the step 2, P1 and P10 are selected. The order of process and actual process staff arrangement are determined based on the two segments of chromosomes.
Step 34, starting an algorithm program, and performing selection, crossing and mutation operations on the codes generated in step 33; in the implementation, chromosome (the length of which is D) segments are adopted for cross mutation respectively, the first segment of procedure codes cross-select the first 1-NO sequences for cross mutation, the second segment of procedure codes NO-D sequences can be used for processing the number NA of people according to the procedure ij And after segmentation and splitting, performing cross mutation, namely dividing the chromosome into segments for determining sequence of the working procedures and working staff segments for each working procedure, and adding NO+1 segments of chromosome (NO represents the total number of working procedures).
Step 35, judging whether the iteration times reach a set maximum value, if so, terminating the program, outputting an optimal feasible scheduling solution set, otherwise, returning to the program to continue running;
step 36, selecting an optimal solution from the optimal feasible scheduling solution set generated in step 35 based on the TOPSIS method.
For the proposed production scheduling multi-objective optimization problem of the trial production workshop considering the working capacity of staff, the optimization targets are more and have the possibility of mutual conflict, and the constraint condition is more complex than the traditional scheduling problem, so that the optimization solution is carried out by adopting a multi-objective genetic algorithm based on the good-bad solution distance (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS). The method reduces the complexity of the non-inferior sorting genetic algorithm, has the advantages of high running speed and good convergence of the solution set, and can obtain the pareto optimal solution set in a relatively short time. The TOPSIS method is a comprehensive evaluation decision method commonly used for multi-objective schemes. The method can fully reflect the gap between each evaluation scheme, and has small demand on samples and strong applicability. The method comprises the steps of respectively selecting an optimal value and an worst value corresponding to each index of a problem to be evaluated as a positive ideal solution and a negative ideal solution after an original decision matrix is standardized, then calculating the distances between each evaluation scheme and the positive ideal solution and the negative ideal solution to order, and if the evaluation scheme is closest to the positive ideal solution and farthest from the negative ideal solution, then the evaluation scheme is an optimal scheme; otherwise, the method is not the optimal scheme.
In order to verify the effectiveness of the invention, the actual production cases of a trial production workshop of an automobile manufacturing enterprise are selected for verification, and the workshop scheduling cases have 36 working procedures in total of 6 production tasks and are completed by 24 staff with different working capacities.
Table 4 below shows production task J i Release time and sub-process task and employee job time information table:
TABLE 4 Table 4
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Table 5 below shows staff work ability and processing man-hour cost information after quantitatively evaluating the staff work ability using a staff work ability evaluation system:
TABLE 5
And solving by adopting a TOPSIS-based multi-objective genetic algorithm, and programming an algorithm program by utilizing MATLAB to generate a plurality of groups of different feasible scheduling schemes for comparison, so that an optimal scheduling scheme is selected. Through super-parameter analysis, the initialization population NP is set to be 200, the maximum iteration number maxgen is set to be 200, and the cross probability P is set c 0.8, probability of variation P m When the solution set is 0.2, the solution sets of the algorithm are relatively scattered, so that the diversity of the population is ensured, and the algorithm convergence is facilitated.
The maximum finishing time, employee skill utilization balance and labor cost are smaller and better, and belong to negative phase indexes, and forward processing is needed.
In this case, according to the actual production situation of a trial production workshop of a certain automobile manufacturing enterprise, the decision attribute weight vector w= [0.4,0.2,0.4 ] is set by the discussion and decision of an expert group and a management decision layer]Then weighting and normalizing to obtain a positive ideal solution U + ＝[0.0486，0.0207，0.0442]Negative ideal solution U - ＝[0，0，0]. The weight vector can be further adjusted according to the variation of the strategic targets and development requirements of the company.
The distance from each pareto solution to the positive and negative ideal solutions and the composite score were calculated as follows in table 6:
TABLE 6
In actual production, a trial workshop of a certain automobile manufacturing enterprise carries out workshop task scheduling by virtue of experience of a manager, the manager divides 24 workers into 4 groups for processing according to experience, and in production task allocation, each task is allocated to a group; and then, the team leader allocates specific tasks to the team members and prioritizes the team members to process, and if the number of the team member working procedures is insufficient, the team members can assist in completing the team members with other groups of available staff. The maximum finishing time is 284 hours, the staff skill utilization balance degree is 0.143, and the labor cost is 33073 yuan through objective function calculation.
Referring to fig. 1, an optimal scheduling scheme obtained by a multi-objective genetic algorithm in this embodiment is shown, fig. 2 is an empirically derived scheduling scheme, and fig. 3 shows a task processing sequence in the optimal scheduling scheme.
The following table 7 is a task-person assignment schedule for the optimal scheduling scheme:
TABLE 7
According to the invention, 200 scheduling schemes are comprehensively scored and sequenced by a TOPSIS comprehensive evaluation method, the total score of the scheme 162 is 0.740, the comprehensive ranking is first, the maximum finishing time is 191h, the staff skill utilization balance degree is 0.130, the labor cost is 26786 yuan, compared with the maximum finishing time of an empirical scheduling scheme which is 284h, the optimized maximum finishing time is reduced by 32.75%, meanwhile, the staff skill utilization balance degree is reduced by 9.09%, the labor cost is reduced by 19.01%, the optimized scheme is shown to greatly improve the production efficiency of a test workshop, the task delay rate is reduced, meanwhile, staff task allocation is more balanced, staff busy and idle non-uniformity is considered, the labor cost of the test workshop is reduced, the control of research and development cost under a competitive market is facilitated, and the core competitiveness of the company is improved.
While embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made herein without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents, and thus the embodiments of the invention are to be considered as illustrative only and not limiting of the invention in any way.
Claims (3)
1. A multi-target production scheduling method for considering employee operation capability in an automobile trial-manufacture workshop is characterized by comprising the following steps:
step 1, establishing a quantifiable staff operation capability evaluation system, evaluating staff in a workshop according to the staff operation capability evaluation system, and obtaining the operation grade of the staff; the staff operation capability evaluation system comprises four first-level evaluation indexes of task skills, operation efficiency, staff attitude and competence, which are respectively set as J1, J2, J3 and J4;
the task skills comprise five secondary evaluation indexes of personnel knowledge, employee multi-skill flexibility, actual operation capability, employee skill utilization rate and employee skill proficiency, which are respectively set as J11, J12, J13, J14 and J15;
the operation efficiency comprises five total secondary evaluation indexes of safety accident rate, 5S management, process quality complaint rate, on-time completion rate and attendance utilization rate, which are respectively J21, J22, J23, J24 and J25;
the employee attitude comprises three secondary evaluation indexes of the violation rate, the work responsibility and initiative, and the employee attendance total, which are respectively set as J31, J32 and J33;
competence comprises four secondary evaluation indexes of personal learning and development flexibility, team cooperation flexibility, continuous improvement and innovation capability and employee prominence contribution capability, which are respectively set as J41, J42, J43 and J44;
the employee work ability value is calculated by the following formula:
wherein G is k The operation ability evaluation index total score of the kth employee, X i Is a first-level fingerNumber of marks total, Y i Is the total number of the secondary indexes under the ith primary index, w ij The weight of the jth secondary index under the ith primary index is A ijk Scoring employee k by the jth secondary index under the ith primary index, A ijk The value range is [0,1 ]]The weight of each secondary index is calculated by a DEMATEL-ANP method;
dividing employee operation grades into primary, intermediate and high grades, and special grades according to the total scores of employee operation capability evaluation indexes, when G is more than or equal to 0 k The staff operation grade is less than 0.6, and G is more than or equal to 0.6 k The staff operation grade is medium grade less than 0.7, G is more than or equal to 0.7 k The staff operation grade is medium and high and is less than 0.8 and less than or equal to G k The staff operation grade is higher than 0.9, and G is more than or equal to 0.9 k The operation grade of staff is special grade and is less than or equal to 1;
step 2, constructing a production scheduling model for efficient and balanced distribution of personnel-tasks and matching constraint conditions by taking maximum finishing time, staff skill utilization balance and labor cost as optimization targets; wherein the constraint conditions include:
constraint one: constraint of task procedure processing sequence;
constraint II: tasks must be processed after the release time has been reached;
constraint three: only one skill operation is needed for each task sub-process;
constraint four: because of the team cooperation of the working procedures of the trial production workshop, working hours of a plurality of staff in the same working procedure are processed together, and working hours of each staff are calculated according to the maximum working hours of the staff involved in the working hours;
constraint five: one employee can only process one process at a time;
constraint six: at the same time, the total number of optional staff in the same working procedure is not less than the number of specified staff in the task working procedure;
constraint seven: 0-1 variable constraint;
the description function formula of the production scheduling model is as follows:
f 1 ＝max{T i |i＝1，2，…，n}
wherein f 1 Indicating maximum finishing time, f 2 Representing employee skill utilization balance, f 3 Representing the cost of labor, L k Representing the k skill utilization rate of staff, T i The completion time of task i is indicated,mean value of skill utilization rate of m staff is represented, W k Representing employee P k The processing cost in unit time is determined according to the operation level of staff; a is that k The number of skills possessed by employee k, wherein A k ∈A 1 ，A 2 ，A 3 ...A m ；/>t ijk Representing employee P k Processing O ij Is used for the time of the following period;
step 3, solving a production scheduling model based on a multi-objective genetic algorithm, and selecting an optimal production scheduling scheme through a TOPSIS method;
and step 4, scheduling the automobile trial-manufacture workshop by using the obtained optimal production scheduling scheme.
2. The multi-objective production scheduling method considering employee work ability for an automobile manufacturing shop according to claim 1, wherein: the constraint one, its description formula is:
E ijk ≥S ijk +x ijk ×t ijk ，s i(j+1) ≥e ij
s ij ≥0，e ij ≥0
the constraint II has a description formula as follows:
s i1 ≥R i
the constraint III describes the formula as follows:
A ij ＝1
the constraint four is described as the following formula:
the constraint five has a description formula as follows:
S pqk ≥E ijk +λ(1-y ijpqk )
E pqk ≥S ijk +λ(1-y ijpqk )
the constraint six has a description formula as follows:
NA ij ≥a ij
the constraint seven, its description formula is:
x ijk ，y ijpqk ∈{0，1}
wherein E is ijk Representing employee P k Processing O ij Is equal to the finishing time of O ij The j-th process of task i is represented, wherein j is 1,2,3,..n i ，P k Represents employee k, where k.epsilon.1, 2,3,. M, S ijk Representing employee P k Processing O ij Time of start-up of (t) ijk Representing employee P k Processing O ij E ij Indicating procedure O ij Time to finish the processing s ij Indicating procedure O ij Time to start processing, R i Representation ofRelease time of task i, A ij Represents the skill number, NA, required by the j-th process of the task i ij Indicating the number of staff available in the j-th process of the task i, a ij The number of staff needed by the jth procedure of the task i is represented; s is S i (j+1) represents the start time of the next step of the j-th step of task i, S i1 The time of starting the process of the 1 st step of the task i is shown,representing the maximum man-hour of staff participating in the same process, S pqk Representing employee P k Processing O pq Time to start of operation E pqk Representing employee P k Processing O pq Is equal to the finishing time of O pq Represents O ij In the subsequent step of step O ij If staff k finishes processing, then x ijk =1, x in case of other cases ijk =0, if staff first processes procedure O ij Post-processing Process O pq Then y ijpqk =1, in case of other cases, y ijpqk ＝0。
3. The multi-objective production scheduling method considering employee work ability for an automobile manufacturing shop according to claim 1, wherein: the step 3 specifically comprises the following steps:
step 31, writing an algorithm program by utilizing MATLAB, and setting an initialization population NP, a maximum iteration number max gen and a crossover probability P in the algorithm according to the complexity of the production scheduling problem c Probability of variation P m Parameters of (2);
step 32, determining an optimization objective function of maximum finishing time, employee skill utilization balance and labor cost as an adaptability function of a genetic algorithm;
step 33, determining codes in a genetic algorithm, wherein the codes consist of two sections, the first section is codes based on working procedures, natural numbers are adopted to code the working procedure sequences so as to determine the processing sequence of the working procedures, and the second section is codes based on available processing staff so as to select the processing staff of each working procedure;
step 34, starting an algorithm program, and performing selection, crossing and mutation operations on the codes generated in step 33;
step 35, judging whether the iteration times reach a set maximum value, if so, terminating the program, outputting an optimal feasible scheduling solution set, otherwise, returning to the program to continue running;
step 36, selecting an optimal solution from the optimal feasible scheduling solution set generated in step 35 based on the TOPSIS method.
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