CN113240176B - Unit type assembly workshop intelligent scheduling method based on limited personnel instant station - Google Patents
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Abstract
The invention discloses an intelligent scheduling method of a unit type assembly workshop based on the instant station of limited personnel, which aims at factors such as workshop scheduling tasks and workshop personnel production and the like to construct an intelligent scheduling problem mathematical model of the unit type assembly workshop; combining rules, solving a production scheduling mathematical model by utilizing an improved genetic algorithm, wherein the production scheduling mathematical model comprises the steps of constructing a task matrix, constructing an initial population, coding genes of task and personnel stations, cross variation of population genes, calculating population fitness, selecting excellent genes and the like, so as to obtain an optimal immediate station production scheduling scheme; the invention combines the instant station problem with the production scheduling problem, integrates rules and improved genetic algorithm, limits the constraint of the problem through mathematical abstraction, ensures the corresponding relation between the responsible person and the task by means of coding, and obtains the optimal production scheduling scheme of the instant station of the unit assembly workshop on the whole by the production scheduling algorithm, thereby improving the production efficiency of the assembly workshop, balancing the workload of staff, improving the utilization rate of equipment, reducing the production cost and improving the comprehensive competitiveness of enterprises.
Description
Technical Field
The invention belongs to the technical field of operation research, and particularly relates to an intelligent scheduling method for a unit assembly workshop.
Background
At present, many enterprises still use a static scheduling method, and a production workshop can normally run, but once a scheduling plan is determined, the production is generally carried out according to the plan, resources cannot be effectively configured, events such as machine faults, personnel lack of sentry and the like are difficult to deal with, scheduling is not timely, and production efficiency of the workshop is low. How to allocate limited resources within a certain time, overall arrangement of production tasks, shortening of production period and maximum utilization of resources are key to influencing comprehensive strength of enterprises.
Dynamic scheduling is a dynamic process that treats the entire manufacturing system as a dynamic process, requiring the entire system to respond in real time to dynamic events such as component arrival uncertainty, machine failure, etc., with good flexibility, but also increasing the complexity of the entire system and the difficulty of scheduling.
Most of the research on intelligent production technology is mainly based on an approximation method. For the intelligent scheduling problem of an assembly workshop, the scheduling process is complex, the on-site operator factors are not considered in the current research, and a model aiming at the self-adaptive scheduling problem in a unit assembly mode is lacking, so that human resources are wasted, the equipment utilization rate is low, and the production efficiency of the assembly workshop is low. Meanwhile, the automatic scheduling method provided in the prior art is low in efficiency and poor in application effect in enterprises. Therefore, the corresponding scheduling strategy and the structure of a design system are further determined, a mathematical model and a high-efficiency solving algorithm are established, so that an optimal intelligent scheduling scheme of the instant station of the unit assembly workshop is obtained, workshop production is guided, and enterprise benefit is improved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent scheduling method of a unit type assembly workshop based on the real-time station of limited personnel, and a mathematical model of the intelligent scheduling problem of the unit type assembly workshop is constructed aiming at factors such as workshop scheduling tasks, workshop personnel production and the like; combining rules, solving a production scheduling mathematical model by utilizing an improved genetic algorithm, wherein the production scheduling mathematical model comprises the steps of constructing a task matrix, constructing an initial population, coding genes of task and personnel stations, cross variation of population genes, calculating population fitness, selecting excellent genes and the like, so as to obtain an optimal immediate station production scheduling scheme; the invention combines the instant station problem with the production scheduling problem, integrates rules and improved genetic algorithm, limits the constraint of the problem through mathematical abstraction, ensures the corresponding relation between the responsible person and the task by means of coding, and obtains the optimal production scheduling scheme of the instant station of the unit assembly workshop on the whole by the production scheduling algorithm, thereby improving the production efficiency of the assembly workshop, balancing the workload of staff, improving the utilization rate of equipment, reducing the production cost and improving the comprehensive competitiveness of enterprises.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
step 1: acquiring a unit type assembly workshop production element data set;
step 2: aiming at the production scheduling task and production element data of the unit assembly workshop, constructing a mathematical model of the production scheduling problem of the instant station of the unit assembly workshop;
step 3: determining a task scheduling optimization strategy according to a task scheduling rule, a personnel station rule and a scheduling optimization target;
step 4: solving a mathematical model of the scheduling problem by utilizing an improved genetic algorithm to obtain an optimal unit assembly workshop instant station intelligent scheduling scheme;
step 5: and (3) judging whether the scheduling task of the unit assembly workshop has update change, and if so, returning to the step (1) to perform scheduling again.
Further, the unit assembly shop production element dataset comprises: on Shift worker data, scheduling task data, scheduling date data;
on Shift worker data, i.e., the number of workers in the unit that are on Shift and can engage in assembly production; scheduling task data, namely, the number of scheduled tasks, the number of working procedures, the assembly and adjustment time length of each working procedure, the task issuing time, the task delivery time and the total task processing period; the date of production is the time of the first work procedure of the first task of the batch of tasks to start assembly work.
Further, the mathematical model of the scheduling problem of the instant station of the unit assembly workshop is as follows:
wherein f represents the minimum maximum finishing time, i.e. the optimization objective of scheduling; t (T) ij The finishing time of the j-th process of the i-th component is represented, n represents the number of tasks to be produced at this time, and m represents the total number of assembly processes for each task, that is, the number of stations in the unit assembly shop.
Further, constraints of the mathematical model of the production problem at the instant station of the unit assembly shop include:
constraint 1: the components are assembled in sequence according to a process route which is established in advance:
S ij +x ijk y ikl t ij ≤S i(j+1) ，i∈{0,1,2,...n}j∈{0,1,2,...m}k∈{0,1,2,...h}
wherein S is ij Indicating the start assembly time of the jth process of the ith component, S i(j+1) Indicating the start assembly time, t, of the (j+1) th process of the (i) th component ij The processing time length of the jth procedure of the ith part is represented, and h represents the number of workers;
constraint 2: the same procedure for different components can only be assembled one at a time:
S ij ≠S i′j ，i、i’∈{0,1,2,...n}，j∈{0,1,2,...m}
constraint 3: an assembler can only assemble one procedure at a time:
∑x ijk ＝1，i∈{0,1,2,...n}，j∈{0,1,2,...m}，k∈{0,1,2,...l}
in which x is ijk Indicating the number of working marks of the staff, wherein the value is 0 or 1;
constraint 4: only one working procedure can be assembled at one station at the same time:
∑y ikl ＝1，i∈{0,1,2,...n}，k∈{0,1,2,...m}，l∈{0,1,2,...m}
in which y ikl Indicating the working mark number of the station, wherein the value is 0 or 1;
constraint 5: the process cannot be terminated once assembly begins until completion:
T ij ＝S ij +t ij 。
further, the task scheduling rules comprise task arrival time, task delivery time, task assembly period, task duration time and task urgency rules; the personnel station rules comprise personnel station positioning, personnel task positioning, idle station, and random station rules; the optimization strategy comprises genetic algorithm optimization based on rules and genetic algorithm optimization of global optimization.
Further, the improved genetic algorithm is utilized to solve the mathematical model of the production scheduling problem, so as to obtain an optimal intelligent production scheme of the real-time station of the unit assembly workshop, and the method specifically comprises the following steps:
step 4-1: constructing an initial condition matrix;
an n multiplied by m matrix t is adopted to represent the assembly time length of each procedure of each component, and the arrangement of each row element in the matrix obeys the task scheduling rule, namely, the priority of the task is increased or decreased along with the increase of the number of rows;
an n multiplied by m matrix p is adopted to represent the station situation of each station worker, and the arrangement of each element in the matrix obeys the worker station rule;
step 4-2: coding and decoding population individual genes:
step 4-3: creating an initial population;
generating a plurality of initial population sequences in a random generation mode; forming an initial population g= (G) 1 ,g 2 ,…,g q ) Wherein q is the number of individuals in the initial population set;
step 4-4: adopting a two-point corresponding method to carry out individual gene sequence cross variation in the population;
step 4-5: constructing a fitness function, and solving individual fitness value f in the population fitness ：
Step 4-6: selecting individuals in the population using a method that combines optimal individual retention and tournament selection;
step 4-7: judging whether a stopping criterion of a genetic algorithm is met, if so, outputting an optimal unit type assembly workshop instant station intelligent production scheduling scheme, and if not, continuing to execute;
step 4-8: and generating a Gantt chart for production scheduling according to the output optimal production scheduling scheme, and generating a production scheme downwards to a workshop.
Further, the population individual gene codes and decodes are described as follows:
the population individual genes comprise working procedures and worker stations, the total length of the gene sequences is set to be 2 multiplied by m multiplied by n, the first multiplied by n elements are used for carrying out gene arrangement for each working procedure of each component, meanwhile, the elements in the matrix p are arranged to form a post multiplied by n gene sequence of the genes by the index value of the working procedure arrangement, and the decoding process is the inverse process of the encoding process.
Further, the method adopting two points to correspond to carry out the cross mutation of the individual gene sequences in the population comprises the following specific steps:
step 4-4-1: rule-based genetic algorithm optimization:
A. the crossing operation adopts the work-position corresponding POX crossing and comprises the following steps:
1) Randomly selecting a numbered component R and a worker station corresponding to each procedure of the component R from all product types as fixed position genes;
2) Copying the sequence of the working procedures R of the parts of the two father generations P1 and P2 into the offspring C1 and C2 respectively, wherein the positions of the station genes of the working staff corresponding to the working procedures are kept unchanged;
3) Copying the rest products in the product sequences in P1 and P2 into C2 and C1 in sequence;
B. gene sequence variation:
the mutation operation adopts the interchange mutation corresponding to the work-position: randomly selecting two positions on a product sequence, then exchanging genes at the two positions, and simultaneously exchanging the worker station genes corresponding to the two position procedures;
step 4-4-2: optimizing a globally optimal genetic algorithm;
step 4-4-2-1: crossing gene sequences;
A. the crossing operation adopts segment crossing, the sequence crossing of process segment gene sequences adopts POX crossing, and the method comprises the following steps:
1) Randomly selecting a numbered component R and a worker station corresponding to each procedure of the component R from all product types as fixed position genes;
2) Copying the sequence of the working procedures R of the parts of the two father generations P1 and P2 into the offspring C1 and C2 respectively, wherein the positions of the station genes of the working staff corresponding to the working procedures are kept unchanged;
3) Copying the rest products in the product sequences in P1 and P2 into C2 and C1 in sequence;
B. the worker station section gene adopts even crossing, includes:
1) Randomly selecting a plurality of integers in a sequence length interval [1, M ] selected by the robot;
2) Generating integers according to the step 1, and respectively copying the gene holding positions and sequences at the corresponding positions on the selection sequences of the parent P1 and P2 robots into the offspring C1 and C2;
3) Copying the rest genes in the robot selection sequences in P1 and P2 into C2 and C1 in sequence, and keeping the positions and the sequences unchanged;
step 4-4-2-2: variation of the gene sequence;
1) The mutation operation adopts sectional mutation, and the part procedure sequence segment genes adopt interchangeable mutation: randomly selecting two positions on a product sequence, then exchanging genes at the two positions, and simultaneously exchanging genes at the worker stations corresponding to the two positions;
2) The worker station gene adopts heterologous mutation: and randomly selecting a plurality of positions on the personnel station segment genes, and changing the personnel station positions on the positions into codes of different personnel at present.
A unit type assembly workshop intelligent scheduling system based on limited personnel instant station comprises:
the model construction module is used for constructing a mathematical model of the scheduling problem of the instant station of the unit assembly workshop aiming at the workshop scheduling task;
the solving module is used for solving the mathematical model of the scheduling problem by utilizing an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and the judging module is used for judging whether the scheduling task is updated or not, and if so, executing the model building module.
Further, the judging module judges the process as follows:
step 8-1: judging whether a new component enters a waiting component queue according to the production element data of the unit assembly workshop obtained in the step 1, if no new component enters, executing the step 4-8, otherwise, executing the step 4-1;
step 8-2: judging whether the number of workers in a workshop is changed according to the production element data of the unit assembly workshop obtained in the step 1, if the number of workers is changed, executing the step 4-1, otherwise, executing the step 4-8;
step 8-3: judging whether the number of the waiting parts in the waiting part queue exceeds the preset queue length according to the production element data of the unit assembly workshop obtained in the step 1, if yes, executing the step 4-1, otherwise, returning to execute the step 4-8;
step 8-4: reconstructing the fitness function and executing the step 2.
The beneficial effects of the invention are as follows:
1. on the basis of describing the real-time station intelligent scheduling problem of the unit assembly workshop, the invention fully considers each production element and constraint, and simultaneously considers the on-duty worker condition of the scene ignored by the former, establishes a mathematical model of the problem, avoids the waste of human resources and improves the utilization rate of equipment;
2. the invention combines the genetic algorithm optimization of the rules and combines the rules and the heuristic algorithm, thereby being capable of efficiently and rapidly solving and optimizing the production scheduling problem under various rules;
3. according to the invention, an improved genetic algorithm is adopted to solve the model, the coding of the genetic algorithm is improved, the coding efficiency is improved, an immediate station intelligent production scheduling optimization scheme of a unit type assembly workshop with good performance can be effectively obtained, the production efficiency of the assembly workshop is improved, the production cost is reduced, and the like;
4. the method has simple operation process, reduces unnecessary resource waste in the process while obtaining better optimization effect, reduces production cost and improves comprehensive competitiveness of manufacturing enterprises.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart of an improved genetic algorithm of the present invention.
FIG. 3 is a schematic diagram of genetic algorithm gene crossover variation based on rules in the method of the present invention
FIG. 4 is a diagram of the global optimization genetic algorithm gene crossover variation of the method of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
As shown in FIG. 1, the intelligent scheduling method for the unit assembly workshops based on the real-time station of the limited personnel comprises the following steps:
step 1: acquiring a unit type assembly workshop production element data set;
step 2: aiming at the production scheduling task and production element data of the unit assembly workshop, constructing a mathematical model of the production scheduling problem of the instant station of the unit assembly workshop;
step 3: determining a task scheduling optimization strategy according to a task scheduling rule, a personnel station rule and a scheduling optimization target;
step 4: solving a mathematical model of the scheduling problem by utilizing an improved genetic algorithm to obtain an optimal unit assembly workshop instant station intelligent scheduling scheme;
step 5: and (3) judging whether the scheduling task of the unit assembly workshop has update change, and if so, returning to the step (1) to perform scheduling again.
Further, the unit assembly shop production element dataset comprises: on Shift worker data, scheduling task data, scheduling date data;
on Shift worker data, i.e., the number of workers in the unit that are on Shift and can engage in assembly production; scheduling task data, namely, the number of scheduled tasks, the number of working procedures, the assembly and adjustment time length of each working procedure, the task issuing time, the task delivery time and the total task processing period; the date of production is the time of the first work procedure of the first task of the batch of tasks to start assembly work.
Further, the mathematical model of the scheduling problem of the instant station of the unit assembly workshop is as follows:
wherein f represents the minimum maximum finishing time, i.e. the optimization objective of scheduling; t (T) ij The finishing time of the j-th process of the i-th component is represented, n represents the number of tasks to be produced at this time, and m represents the total number of assembly processes for each task, that is, the number of stations in the unit assembly shop.
Further, constraints of the mathematical model of the production problem at the instant station of the unit assembly shop include:
constraint 1: the components are assembled in sequence according to a process route which is established in advance:
S ij +x ijk y ikl t ij ≤S i(j+1) ，i∈{0,1,2,...n}j∈{0,1,2,...m}k∈{0,1,2,...h}
wherein S is ij Indicating the start assembly time of the jth process of the ith component, S i(j+1) Indicating the start assembly time, t, of the (j+1) th process of the (i) th component ij The processing time length of the jth procedure of the ith part is represented, and h represents the number of workers;
constraint 2: the same procedure for different components can only be assembled one at a time:
S ij ≠S i′j ，i、i’∈{0,1,2,...n}，j∈{0,1,2,...m}
constraint 3: an assembler can only assemble one procedure at a time:
∑x ijk ＝1，i∈{0,1,2,...n}，j∈{0,1,2,...m}，k∈{0,1,2,...l}
in which x is ijk Indicating the number of working marks of the staff, wherein the value is 0 or 1;
constraint 4: only one working procedure can be assembled at one station at the same time:
∑y ikl ＝1，i∈{0,1,2,...n}，k∈{0,1,2,...m}，l∈{0,1,2,...m}
in which y ikl Indicating the working mark number of the station, wherein the value is 0 or 1;
constraint 5: the process cannot be terminated once assembly begins until completion:
T ij ＝S ij +t ij 。
further, the task scheduling rules comprise task arrival time, task delivery time, task assembly period, task duration time and task urgency rules; the personnel station rules comprise personnel station positioning, personnel task positioning, idle station, and random station rules; formulating an assembly priority and a personnel station position scheme of the task according to the rules; the optimization targets include: 1. rule-based genetic algorithm optimization, namely, optimizing a scheme formulated by obeying rules to a certain extent by using an improved genetic algorithm to obtain an optimal scheduling scheme obeying the rules; 2. optimizing a global optimal genetic algorithm, namely optimizing by using an improved genetic algorithm under the condition of meeting limited personnel and timely stations in a global scope without considering the constraint of rules so as to obtain a global optimal scheduling scheme;
further, the improved genetic algorithm is utilized to solve the mathematical model of the production scheduling problem, so as to obtain an optimal intelligent production scheme of the real-time station of the unit assembly workshop, and the method specifically comprises the following steps:
step 4-1: constructing an initial condition matrix;
an n multiplied by m matrix t is adopted to represent the assembly time length of each procedure of each component, and the arrangement of each row element in the matrix obeys the task scheduling rule, namely, the priority of the task is increased or decreased along with the increase of the number of rows;
an n multiplied by m matrix p is adopted to represent the station situation of each station worker, and the arrangement of each element in the matrix obeys the worker station rule;
step 4-2: coding and decoding population individual genes:
the coding of genes is divided into two cases according to the objective of optimization: 1. the genetic algorithm optimization based on rules is carried out, order sequence and procedure sequence are nearly considered based on regular task scheduling, and meanwhile, worker station positions corresponding to all procedures are considered, so that the coding of genes is subject to the task scheduling rules and personnel station position rules, the coding of individual gene sequences is carried out in a front-to-back mode, and the tasks with high priority are arranged to the sequence in front of the genes according to a certain probability q; 2. the genetic algorithm of global optimum is optimized, rule constraint is not considered, and the gene coding adopts a front-end and back-end form to obtain a global optimum production scheduling scheme; the decoding process is the inverse of the encoding process;
step 4-3: creating an initial population;
generating a plurality of initial population sequences in a random generation mode; forming an initial population g= (G) 1 ,g 2 ,…,g p ) Wherein p is the number of individuals in the initial population set;
step 4-4: adopting a two-point corresponding method to carry out individual gene sequence cross variation in the population;
step 4-5: constructing a fitness function, and solving individual fitness value f in the population fitness ：
Step 4-6: selecting individuals in the population using a method that combines optimal individual retention and tournament selection;
step 4-7: judging whether a stopping criterion of a genetic algorithm is met, if so, outputting an optimal unit type assembly workshop instant station intelligent production scheduling scheme, and if not, continuing to execute;
step 4-8: and generating a Gantt chart for production scheduling according to the output optimal production scheduling scheme, and generating a production scheme downwards to a workshop.
Further, the population individual gene codes and decodes are described as follows:
the population individual genes comprise working procedures and worker stations, the total length of the gene sequences is set to be 2 multiplied by m multiplied by n, the first multiplied by n elements are used for carrying out gene arrangement for each working procedure of each component, meanwhile, the elements in the matrix p are arranged to form a post multiplied by n gene sequence of the genes by the index value of the working procedure arrangement, and the decoding process is the inverse process of the encoding process.
Further, the method adopting two points to correspond to carry out the cross mutation of the individual gene sequences in the population comprises the following specific steps:
step 4-4-1: rule-based genetic algorithm optimization:
A. the crossing operation adopts the work-position corresponding POX crossing and comprises the following steps:
1) Randomly selecting a numbered component R and a worker station corresponding to each procedure of the component R from all product types as fixed position genes;
2) Copying the sequence of the working procedures R of the parts of the two father generations P1 and P2 into the offspring C1 and C2 respectively, wherein the positions of the station genes of the working staff corresponding to the working procedures are kept unchanged;
3) Copying the rest products in the product sequences in P1 and P2 into C2 and C1 in sequence;
B. gene sequence variation:
the mutation operation adopts the interchange mutation corresponding to the work-position: randomly selecting two positions on a product sequence, then exchanging genes at the two positions, and simultaneously exchanging the worker station genes corresponding to the two position procedures;
step 4-4-2: optimizing a globally optimal genetic algorithm;
step 4-4-2-1: crossing gene sequences;
A. the crossing operation adopts segment crossing, the sequence crossing of process segment gene sequences adopts POX crossing, and the method comprises the following steps:
1) Randomly selecting a numbered component R and a worker station corresponding to each procedure of the component R from all product types as fixed position genes;
2) Copying the sequence of the working procedures R of the parts of the two father generations P1 and P2 into the offspring C1 and C2 respectively, wherein the positions of the station genes of the working staff corresponding to the working procedures are kept unchanged;
3) Copying the rest products in the product sequences in P1 and P2 into C2 and C1 in sequence;
B. the worker station section gene adopts even crossing, includes:
1) Randomly selecting a plurality of integers in a sequence length interval [1, M ] selected by the robot;
2) Generating integers according to the step 1, and respectively copying the gene holding positions and sequences at the corresponding positions on the selection sequences of the parent P1 and P2 robots into the offspring C1 and C2;
3) Copying the rest genes in the robot selection sequences in P1 and P2 into C2 and C1 in sequence, and keeping the positions and the sequences unchanged;
step 4-4-2-2: variation of the gene sequence;
1) The mutation operation adopts sectional mutation, and the part procedure sequence segment genes adopt interchangeable mutation: randomly selecting two positions on a product sequence, then exchanging genes at the two positions, and simultaneously exchanging genes at the worker stations corresponding to the two positions;
2) The worker station gene adopts heterologous mutation: and randomly selecting a plurality of positions on the personnel station segment genes, and changing the personnel station positions on the positions into codes of different personnel at present.
A unit type assembly workshop intelligent scheduling system based on limited personnel instant station comprises:
the model construction module is used for constructing a mathematical model of the scheduling problem of the instant station of the unit assembly workshop aiming at the workshop scheduling task;
the solving module is used for solving the mathematical model of the scheduling problem by utilizing an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and the judging module is used for judging whether the scheduling task is updated or not, and if so, executing the model building module.
Further, the judging module judges the process as follows:
step 8-1: judging whether a new component enters a waiting component queue according to the production element data of the unit assembly workshop obtained in the step 1, if no new component enters, executing the step 4-8, otherwise, executing the step 4-1;
step 8-2: judging whether the number of workers in a workshop is changed according to the production element data of the unit assembly workshop obtained in the step 1, if the number of workers is changed, executing the step 4-1, otherwise, executing the step 4-8;
step 8-3: judging whether the number of the waiting parts in the waiting part queue exceeds the preset queue length according to the production element data of the unit assembly workshop obtained in the step 1, if yes, executing the step 4-1, otherwise, returning to execute the step 4-8;
step 8-4: reconstructing the fitness function and executing the step 2.
Specific examples:
1. acquiring a unit type assembly workshop production element data set;
the unit assembly shop production element dataset comprises: on duty worker data, scheduling task data, scheduling date data, etc.; on Shift worker data, i.e., the number of workers on Shift that can engage in assembly production in the workshop; scheduling task data, namely scheduling data information such as the number of scheduled tasks, the number of working procedures, the assembly and adjustment time of each working procedure, the task issuing time, the task delivery time, the total task processing period and the like; the date of production is the time of the first work procedure of the first task of the batch of tasks to start assembly work. For example, the existing on-duty personnel [ a, b ] are responsible for three 3-procedure parts to be assembled [0,1,2], and the production date is 20210203; the process duration and other information data sets may be represented as [0,4,6,2,20210103,20220103,365,12],0 for order number 0,4,6,2 for process duration, 20210103,20220103 for task issue time and delivery time, 365 for task duration, and 12 for total task assembly duration, respectively.
2. Aiming at the production scheduling task and production element data of the unit assembly workshop, constructing a mathematical model and constraint conditions of the production scheduling problem of the instant station of the unit assembly workshop;
3. determining a task scheduling optimization strategy according to the task scheduling rules and the personnel station rules; the optimization targets include: 1. rule-based genetic algorithm optimization, namely, optimizing a scheme formulated by obeying rules to a certain extent by using an improved genetic algorithm to obtain an optimal scheduling scheme obeying the rules; 2. optimizing a global optimal genetic algorithm, namely optimizing by using an improved genetic algorithm under the condition of meeting limited personnel and timely stations in a global scope without considering the constraint of rules so as to obtain a global optimal scheduling scheme; and according to the optimization target, selecting rule-based scheduling optimization or global scheduling optimization, and determining a scheduling optimization scheme.
4. Solving the mathematical model by utilizing an improved genetic algorithm, wherein the flow is shown in figure 2, and obtaining and outputting an optimal intelligent scheduling scheme of the instant station of the unit assembly workshop;
4.1, constructing an initial condition matrix;
4.2 coding and decoding individual genes of population:
taking 3 orders 3 procedure 2 workers as an example, a task set {0,1,2}, a worker set { a, b };
the genetic algorithm optimization based on rules is carried out, the task priority is from high to low 0,1 and 2, the personnel station rule is that personnel station location is defined, a is responsible for 1 station and 3 station, b is responsible for 2 station, tasks with high priority are arranged before tasks with priority level at the probability of 70%, one of the gene sequences of the batch of tasks is '001012122 abaabaabaaba', and the total gene length is 3×3×2=18; from left to right in the gene sequence, the first "0" represents the 1 st procedure of order number "0", the second "0" represents the 2 nd procedure of order number "0", and so on; the first a of the second half is used for numbering the first order of the first half of the drink with a number of 0, which means that the worker a is responsible for the work station with a number of 1, and also means that the worker a is responsible for the work station with a number of 1 of the order with a number of 0, and the third a also means that the worker a is responsible for the work station with a number of 3, and also means that the worker a is responsible for the work station with a number of 3 of the order with a number of 0, the first b means that the worker b is responsible for the work station with a number of 1, and also means that the worker b is responsible for the work station with a number of 1 of the order with a number of 1, and so on; the decoding process is the inverse of the encoding process;
the genetic algorithm of global optimum is optimized, and one gene sequence of the batch of tasks is: "100120122abbabaaba", is as defined above.
4.3 creating an initial population;
4.4, carrying out individual gene sequence cross variation in the population by adopting a two-point corresponding method;
4.4.1 rule-based genetic algorithm optimization (taking personalized localization as an example), as in fig. 3:
A. the crossover operation employs a work-position corresponding POX crossover (as shown in FIG. 3 (a));
B. genetic sequence variation (as shown in FIG. 3 (b));
4.4.2 genetic algorithm optimization for global optimization as in fig. 4:
(1) Crossing of gene sequences (as shown in FIG. 4 (a));
(2) Genetic sequence variation (as shown in FIG. 4 (b));
4.5, constructing a fitness function, and solving individual fitness values in the population:
4.6 selecting individuals in the population using a method that combines optimal individual retention and tournament selection; the specific process comprises the following steps:
(1) Calculating the fitness of all individuals, and marking as F i ，0<i≤p；
(2) Adopting the best individual retention, and selecting the previous (p/2) individual with the largest adaptation value to enter the next generation population;
(3) The number of individuals N selected each time is determined using the competitive game method. (binary tournament selection, i.e., selecting 2 individuals);
(4) N individuals are randomly selected from the rest population (the probability of each individual being selected is the same), and the individual with the best fitness value is selected to enter the next generation population according to the fitness value of each individual.
(5) Repeating the step (4) for a plurality of times (the number of times is the size of the population) until the new population size reaches the original population size.
4.7 judging whether the stopping criterion of the genetic algorithm is met or not by the judging module, if yes, outputting an optimal scheduling scheme, and if not, continuing to execute;
and 4.8, generating a Gantt chart for production scheduling according to the output optimal production scheduling scheme, and generating a production scheme downwards to a workshop.
5. And (3) judging whether the task data are updated and changed, if so, triggering a rearrangement production algorithm, and returning to the step (1).
Claims (7)
1. The intelligent scheduling method for the unit assembly workshop based on the real-time station of the limited personnel is characterized by comprising the following steps of:
step 1: acquiring a unit type assembly workshop production element data set;
step 2: aiming at the production scheduling task and production element data of the unit assembly workshop, constructing a mathematical model of the production scheduling problem of the instant station of the unit assembly workshop;
the mathematical model of the production problem of the instant station of the unit assembly workshop is as follows:
wherein f represents the minimum maximum finishing time, i.e. the optimization objective of scheduling; t (T) ij The finishing time of the j-th working procedure of the i-th component is represented, n represents the number of tasks of the production scheduling, and m represents the total number of assembly working procedures of each task, namely the number of stations of a unit assembly workshop;
constraint conditions of the mathematical model of the production problem of the instant station of the unit assembly workshop comprise:
constraint 1: the components are assembled in sequence according to a process route which is established in advance:
S ij +x ijk y ikl t ij ≤S i(j+1) ，i∈{0,1,2,...n}j∈{0,1,2,...m}k∈{0,1,2,...h}
wherein S is ij Indicating the start assembly time of the jth process of the ith component, S i(j+1) Indicating the start assembly time, t, of the (j+1) th process of the (i) th component ij The processing time length of the jth procedure of the ith part is represented, and h represents the number of workers;
constraint 2: the same procedure for different components can only be assembled one at a time:
S ij ≠S i′j ，i、i’∈{0,1,2,...n}，j∈{0,1,2,...m}
constraint 3: an assembler can only assemble one procedure at a time:
Σx ijk ＝1，i∈{0,1,2,...n}，j∈{0,1,2,...m}，k∈{0,1,2,...l}
in which x is ijk Indicating the number of working marks of the staff, wherein the value is 0 or 1;
constraint 4: only one working procedure can be assembled at one station at the same time:
Σy ikl ＝1，i∈{0,1,2,...n}，k∈{0,1,2,...m}，l∈{0,1,2,...m}
in which y ikl Indicating the working mark number of the station, wherein the value is 0 or 1;
constraint 5: the process cannot be terminated once assembly begins until completion:
T ij ＝S ij +t ij ；
step 3: determining a task scheduling optimization strategy according to a task scheduling rule, a personnel station rule and a scheduling optimization target;
step 4: solving a mathematical model of the scheduling problem by utilizing an improved genetic algorithm to obtain an optimal unit assembly workshop instant station intelligent scheduling scheme;
the improved genetic algorithm is utilized to solve a mathematical model of the production scheduling problem, and an optimal unit type assembly workshop instant station intelligent production scheduling scheme is obtained, and the method specifically comprises the following steps:
step 4-1: constructing an initial condition matrix;
an n multiplied by m matrix t is adopted to represent the assembly time length of each procedure of each component, and the arrangement of each row element in the matrix obeys the task scheduling rule, namely, the priority of the task is increased or decreased along with the increase of the number of rows;
an n multiplied by m matrix p is adopted to represent the station situation of each station worker, and the arrangement of each element in the matrix obeys the worker station rule;
step 4-2: coding and decoding population individual genes:
step 4-3: creating an initial population;
generating a plurality of initial population sequences in a random generation mode; forming an initial population g= (G) 1 ,g 2 ,…,g q ) Wherein q is the number of individuals in the initial population set;
step 4-4: adopting a two-point corresponding method to carry out individual gene sequence cross variation in the population;
step 4-5: constructing a fitness function, and solving individual fitness value f in the population fitness ：
Step 4-6: selecting individuals in the population using a method that combines optimal individual retention and tournament selection;
step 4-7: judging whether a stopping criterion of a genetic algorithm is met, if so, outputting an optimal unit type assembly workshop instant station intelligent production scheduling scheme, and if not, continuing to execute;
step 4-8: generating a Gantt chart of production scheduling according to the output optimal production scheduling scheme, and generating a production scheme downwards to a workshop;
step 5: and (3) judging whether the scheduling task of the unit assembly workshop has update change, and if so, returning to the step (1) to perform scheduling again.
2. The intelligent scheduling method for a unit assembly shop based on the real-time stop of a limited person according to claim 1, wherein the unit assembly shop production element dataset comprises: on Shift worker data, scheduling task data, scheduling date data;
on Shift worker data, i.e., the number of workers in the unit that are on Shift and can engage in assembly production; scheduling task data, namely, the number of scheduled tasks, the number of working procedures, the assembly and adjustment time length of each working procedure, the task issuing time, the task delivery time and the total task processing period; the date of production is the time of the first work procedure of the first task of the batch of tasks to start assembly work.
3. The intelligent scheduling method for the unit assembly shop based on the limited personnel instant stop position according to claim 2, wherein the task scheduling rules comprise task arrival time, task delivery time, task assembly period, task duration and task urgency rules; the personnel station rules comprise personnel station positioning, personnel task positioning, idle station, and random station rules; the optimization strategy comprises genetic algorithm optimization based on rules and genetic algorithm optimization of global optimization.
4. The intelligent production scheduling method for a unit assembly shop based on the immediate station of limited personnel according to claim 3, wherein the individual population gene codes and decodes are described as follows:
the population individual genes comprise working procedures and worker stations, the total length of the gene sequences is set to be 2 multiplied by m multiplied by n, the first multiplied by n elements are used for carrying out gene arrangement for each working procedure of each component, meanwhile, the elements in the matrix p are arranged to form a post multiplied by n gene sequence of the genes by the index value of the working procedure arrangement, and the decoding process is the inverse process of the encoding process.
5. The intelligent scheduling method for the unit assembly workshops based on the real-time stop of limited personnel according to claim 4, wherein the method adopting two points for correspondence is used for carrying out the cross mutation of individual gene sequences in the population, and comprises the following specific steps:
step 4-4-1: rule-based genetic algorithm optimization:
A. the crossing operation adopts the work-position corresponding POX crossing and comprises the following steps:
1) Randomly selecting a numbered component R and a worker station corresponding to each procedure of the component R from all product types as fixed position genes;
2) Copying the sequence of the working procedures R of the parts of the two father generations P1 and P2 into the offspring C1 and C2 respectively, wherein the positions of the station genes of the working staff corresponding to the working procedures are kept unchanged;
3) Copying the rest products in the product sequences in P1 and P2 into C2 and C1 in sequence;
B. gene sequence variation:
the mutation operation adopts the interchange mutation corresponding to the work-position: randomly selecting two positions on a product sequence, then exchanging genes at the two positions, and simultaneously exchanging the worker station genes corresponding to the two position procedures;
step 4-4-2: optimizing a globally optimal genetic algorithm;
step 4-4-2-1: crossing gene sequences;
A. the crossing operation adopts segment crossing, the sequence crossing of process segment gene sequences adopts POX crossing, and the method comprises the following steps:
1) Randomly selecting a numbered component R and a worker station corresponding to each procedure of the component R from all product types as fixed position genes;
2) Copying the sequence of the working procedures R of the parts of the two father generations P1 and P2 into the offspring C1 and C2 respectively, wherein the positions of the station genes of the working staff corresponding to the working procedures are kept unchanged;
3) Copying the rest products in the product sequences in P1 and P2 into C2 and C1 in sequence;
B. the worker station section gene adopts even crossing, includes:
1) Randomly selecting a plurality of integers in a sequence length interval [1, M ] selected by the robot;
2) Generating integers according to the step 1, and respectively copying the gene holding positions and sequences at the corresponding positions on the selection sequences of the parent P1 and P2 robots into the offspring C1 and C2;
3) Copying the rest genes in the robot selection sequences in P1 and P2 into C2 and C1 in sequence, and keeping the positions and the sequences unchanged;
step 4-4-2-2: variation of the gene sequence;
1) The mutation operation adopts sectional mutation, and the part procedure sequence segment genes adopt interchangeable mutation: randomly selecting two positions on a product sequence, then exchanging genes at the two positions, and simultaneously exchanging genes at the worker stations corresponding to the two positions;
2) The worker station gene adopts heterologous mutation: and randomly selecting a plurality of positions on the personnel station segment genes, and changing the personnel station positions on the positions into codes of different personnel at present.
6. A modular assembly plant intelligent scheduling system for implementing limited personnel immediate station based using the method of claim 1, comprising:
the model construction module is used for constructing a mathematical model of the scheduling problem of the instant station of the unit assembly workshop aiming at the workshop scheduling task;
the solving module is used for solving the mathematical model of the scheduling problem by utilizing an improved genetic algorithm to obtain an optimal unit type assembly workshop instant station intelligent scheduling scheme;
and the judging module is used for judging whether the scheduling task is updated or not, and if so, executing the model building module.
7. The system of claim 6, wherein the determining module determines the following:
step 8-1: judging whether a new component enters a waiting component queue according to the production element data of the unit assembly workshop obtained in the step 1, if no new component enters, executing the step 4-8, otherwise, executing the step 4-1;
step 8-2: judging whether the number of workers in a workshop is changed according to the production element data of the unit assembly workshop obtained in the step 1, if the number of workers is changed, executing the step 4-1, otherwise, executing the step 4-8;
step 8-3: judging whether the number of the waiting parts in the waiting part queue exceeds the preset queue length according to the production element data of the unit assembly workshop obtained in the step 1, if yes, executing the step 4-1, otherwise, returning to execute the step 4-8;
step 8-4: reconstructing the fitness function and executing the step 2.
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