CN112396214A - Improved teaching optimization algorithm-based smelting workshop scheduling method and system - Google Patents
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Abstract
The invention provides a smelting workshop scheduling method and system based on an improved teaching optimization algorithm, and relates to the technical field of workshop scheduling optimization. The invention provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, in order to solve the model, an original teaching optimization algorithm is innovated, class initialization, a teaching link, a self-adaptive learning link, Bernoulli self-learning link and other aspects of improvement and innovation are respectively carried out, the local innovation subprocess is brought under the frame of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching link, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning capability of class students is improved, the approximately optimal solution of the energy efficiency balance optimization model can be solved within reasonable solving time, and certain theoretical guidance and support are provided for the production practice of enterprises.
Description
Technical Field
The invention relates to the technical field of workshop scheduling optimization, in particular to a smelting workshop scheduling method and system based on an improved teaching optimization algorithm.
Background
In high-end equipment precision casting lines (e.g., precision aerospace components, heavy equipment structures, etc.), the raw materials of common metals such as aluminum, iron, and some rare earth elements may not be fully prepared at the beginning of the production cycle. In most cases, the raw materials may arrive at the smelting plant at different times. Through information technologies such as the Internet of things, the dynamic arrival time of the raw materials can be known by a workshop management system at the beginning of a production cycle. At the same time, the sales system will also provide the shop management system with the delivery times of the order for the melted workpieces. The smelting workshop operation is a key link of energy consumption in the whole precision casting process of high-end equipment, and has the typical characteristics of long energy consumption period, complex energy consumption composition, huge total energy consumption and the like. The energy consumption level of the operation of the smelting workshop is reduced, so that the energy consumption cost of the investment casting production line can be effectively reduced. The operation of the smelting workshop is developed around various smelting furnaces, and the operation flow of the smelting furnaces is generally divided into the steps of preheating, vibration feeding, smelting, refining, casting, cooling and the like. After analysis of the specific melting temperature profile, it was found that after casting was completed, the temperature in the furnace was maintained and decreased to the initial temperature after a period of cooling. How to solve the problem of energy-saving production scheduling in the operation of a smelting workshop is a problem which needs to be solved urgently.
The existing energy-saving-oriented production scheduling problem mainly utilizes a machine closing and restarting strategy to reduce energy consumption in idle time periods in a production process, does not consider a possible waste heat utilization phenomenon in a high-end equipment precision investment casting process, and is difficult to utilize the existing internet of things technology to carry out effective energy efficiency balance optimization management.
The invention aims to provide an improved teaching optimization algorithm for solving the problem of casting energy efficiency balance optimization of a precision smelting furnace under the conditions of raw material dynamic arrival time and machine dormancy strategies. The existing teaching optimization algorithm comprises the following steps:
(1) initializing the number of students and the number of disciplines;
(2) randomly initializing score information of each subject of each student individual and calculating the comprehensive score of each student individual;
(3) the student with the best comprehensive performance is used as a teacher, and the student individuals with the average performance are set as equal student individuals;
(4) carrying out iteration of a teaching link on each student individual by utilizing the equal student individual and the teacher individual information;
(5) carrying out iteration of a learning link on each student individual by using the information of any other student individual in the classroom;
(6) updating the scores and the comprehensive scores of the teacher and the equal students;
(7) and (4) judging whether the iteration condition is met, if so, turning to the step (4), otherwise, stopping the iteration and outputting the current optimal solution.
The existing teaching optimization has the defects of randomizing an initial population, a teaching link, a learning link and the like, so that the searching efficiency is low, and the relationship between the diversity and the convergence of an algorithm population cannot be well balanced.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method and a system for scheduling a smelting workshop based on an improved teaching optimization algorithm, and solves the problem that the existing scheduling algorithm cannot utilize the possible residual heat in the process of precision investment casting of high-end equipment.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, a method for scheduling a smelting workshop based on an improved teaching optimization algorithm is provided, and the method comprises the following steps:
s1, acquiring scheduling information; setting the total iteration times, and setting the initialization iteration time it to be 0;
s2, randomly initializing the scores of 80% of the students in the class, randomly initializing the scores of the rest 20% of the students based on the principle of full utilization of the waste heat of the furnace body, and constructing the initial scores of the students in the class, wherein the number of the disciplines is the same as that of the parts;
s3, respectively calculating the comprehensive scores of all student individuals in the class based on the initial scores of all the departments of the student individuals, and constructing initial class scores;
s4, taking the student individual with the best comprehensive performance in the current class as a teacher individual;
taking the student individuals with the comprehensive scores of all families in the current class as the equivalent student individuals;
taking the student individuals with the comprehensive scores of the current class being the median as the median student individuals;
s5, performing a teaching link on the current class based on the current teacher individuals, the equal student individuals and the middle student individuals, and updating the score of the current class;
s6, performing a self-adaptive learning link for each student individual in the class by using the score of each student individual in the class, and updating the score of the current class again;
s7, executing a self-learning link of the individual students in the current class, and updating the score of the current class to form the score of the class after the it iteration;
s8, judging whether the iteration number it reaches the total iteration number Mit, if so, outputting the current optimal student individual score, and converting the current optimal student individual score into a scheduling sequence through a decoding algorithm; otherwise, updating the scores and the iteration times of the current teacher individual, the equal student individual and the middle student individual, and returning to S5.
Further, the scheduling information includes:
set of parts Ω ═ J1,…,Jj,…,JN}；
Aggregation of part dynamic arrival times
Preheating time t required by partsp；
Melting time pi ═ s1,…,sj,…,sN}；
Refining time ω ═ p1,…,pj,…pN}。
Further, the step S2 of randomly initializing scores of 80% of the students in the class, and randomly initializing scores of the remaining 20% of the students based on the principle of full utilization of the waste heat of the furnace body, and the specific steps of constructing the initial scores of the students in the class include:
s201, randomly generating random numbers between [0,1] as individual random scores of students;
s202, decoding 20% of student individual random results by using a decoding algorithm to obtain a corresponding scheduling sequence, and recording the scheduling sequence as
N is the number of parts;
s203, set j to 1, and at max { tp,rjTime start part JjMelting operation of (1), component JjRefining operation completion time is Cj；
tpIndicates the required preheating time of the component, rjRepresenting the dynamic arrival time of the jth part;
s204, judging whether the arrival time of the residual parts does not exceed CjIf true, then all arrival times are scheduled not to exceed CjStarting the smelting operation of the part with the largest sum of the smelting time and the refining time, wherein the finishing time of the refining operation is marked as Cj+1；
S205, judging whether j is smaller than N-1, if so, making j equal to j +1 and returning to S204; otherwise, the decoding algorithm is used again to reversely deduce the current scheduling sequence, so as to obtain initial scores of all departments of the student individuals corresponding to 20% of the random scores of the student individuals, and the initial scores of all departments of the student individuals in the class are formed together with 80% of the random scores of the student individuals.
Further, S5, performing a teaching link on the current class based on the current teacher individual, the equal student individual and the median student individual, and updating the score of the current class, specifically includes the following steps:
s501, initializing i to 1 and it to 0; when 1 st iteration is carried out, iteration is carried out based on the initial class score;
the individual scores and comprehensive scores of the ith generation of class students are as follows:
wherein, f (X)i[it]) Representing the initial comprehensive achievement of the ith student individual in the ith generation class; i 1, …, NP;
XNP[it]representing the NP-th individual student in the ith generation class;
the individual subject scores and comprehensive scores of the teachers are as follows:
the scores and the comprehensive scores of the individual middle students are as follows:
the scores and the comprehensive scores of each department of the equal student individuals are as follows:
s502, let j equal 1;
s503, ordering randi＝rand(0,1)，TFi＝1+rand(0,1)，
And constructing the score of the jth family of the first kind of new student individuals:
s504, ordering randiRan (0,1) and TFi＝1+rand(0,1)，
And constructing the scores of the jth family of the second class of new student individuals; the construction formula is as follows:
s505, determining whether j is equal to or less than N, and if so, turning j to j +1 and going to S503; otherwise, two New classes of student individuals New1X are calculated respectivelyi[it]And New2Xi[it]And will be Xi[it]、New1Xi[it]And New2Xi[it]The person with the best overall performance is reserved as Xi[it+1]；
S506, judging whether the NP is not more than i, if so, enabling i to be i +1, updating the teacher individual, the equal student individual and the middle student individual, and going to S502; otherwise, the teaching link is terminated.
Further, the step S6 of performing an adaptive learning link for the student individuals in the class by using the scores of any student individual in the class, and updating the current class score again specifically includes the following steps:
s601, setting i to be 1;
s602, selecting any one of the Xi[it]The student of (1) is recorded as Xk[it]Setting j to 1;
s603, order
Where θ > 1, and constructing new student individual performance of family j:
s604, determining whether j is equal to or less than N, if so, making j equal to j +1, and going to S603; otherwise, calculating new individual NewX of studenti[it]And will be Xi[it]And NewXi[it]The person with the best overall performance is reserved as Xi[it+1]；
S605, determining whether i is equal to or less than NP, if so, changing i to i +1, and going to S602; otherwise, the adaptive learning link is terminated.
Further, in step S7, a self-learning link of the individual students in the current class is executed, and the score of the current class is updated to form the score of the class after the it iteration, which includes the specific steps of:
s701: the setting of i to 1 is made,
s702, setting j to 1;
s703, order BRiRand (0,1), and construct the score of the j family of new student individuals:
s704, determining whether j is equal to or less than N, and if so, turning to S703 if j is equal to j + 1; otherwise, calculating new individual NewBX of studenti[it]And will be Xi[it]And NewBXi[it]The person with the best overall performance is reserved as Xi[it+1]；
S705: judging whether the i is not more than NP, if so, making i equal to i +1, and going to S702; otherwise, the Bernoulli self-learning link is terminated.
Further, the calculation formula of the comprehensive achievements of the individual students is as follows:
wherein, CmaxRepresenting the manufacturing span of the casting system, E representing the total energy consumption of the casting system, LBCAnd LBELower bound of manufacturing span and total energy consumption, w, of casting systemsCAnd wEWeights for manufacturing span and total energy consumption, respectively;
wherein s isjThe smelting time of the jth part is shown;
pjrepresenting the refining time of the jth part;
alpha is the utilization rate of waste heat and represents that alpha is more than 0 and less than 1;
Δtj,j+1representing the interval time between the processing of the two parts;
tpindicating the required preheating time of the component
χ represents a cooling timeout threshold;
Psrepresents the smelting power;
Pprepresents the refining power;
Ppreindicating the preheating power.
Further, the decoding algorithm is used for converting the achievement of the unit generation of the student into a scheduling sequence, and the specific steps are as follows:
step 1: corresponding the scores of each department of the individual students to the serial numbers of the parts one by one;
step 2: and sequencing the serial numbers of the parts in an ascending order according to the sizes of the scores of the departments of the individual students to obtain a new serial number sequence of the parts, and taking the sequence as a scheduling sequence.
In a second aspect, there is provided a smelt plant dispatch system based on an improved teaching optimization algorithm, the system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
(III) advantageous effects
The invention provides a smelting workshop scheduling method and system based on an improved teaching optimization algorithm. Compared with the prior art, the method has the following beneficial effects:
(1) the invention provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, for solving the model, an original teaching optimization algorithm is innovated, class initialization, a teaching link, a self-adaptive learning link, a Bernoulli cross self-learning link and other aspects of improvement and innovation are respectively carried out, the local innovation subprocess is brought under the frame of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching link, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning capability of class students is improved, the approximately optimal solution of the energy efficiency balance optimization model can be solved within reasonable solving time, and certain theoretical guidance and support are provided for the production practice of enterprises.
(2) Compared with a single random population initializing mode in the traditional teaching optimization algorithm, the improved teaching optimization algorithm combines the characteristics of an energy efficiency balance optimization problem in addition to the traditional random population initializing mode, introduces an initial population quality improving mechanism with a waste heat full utilization principle, and can well enhance the searching precision of the initial population.
(3) The teaching links of the traditional teaching optimization algorithm only relate to teacher individuals and equal student individuals, but considering the complexity of the solution of the comprehensive performance values, equal students with average performances of all departments cannot be taken as individuals with the average comprehensive performance values, so that the concept of a middle-number student is introduced into the improved teaching optimization algorithm, and the individual students with the intermediate comprehensive performance values can be well expressed.
(4) In order to further reduce the possibility that the algorithm is trapped into local optimization, a Bernoulli cross concept in a partial random key algorithm is used in an improved teaching optimization algorithm, a student self-learning link based on Bernoulli cross is innovatively provided, and the depth and the breadth of searching can be well balanced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a method and a system for scheduling a smelting workshop based on an improved teaching optimization algorithm, solves the problem that the existing scheduling algorithm cannot utilize the possible residual heat in the process of high-end equipment precision investment casting, and realizes energy-saving production scheduling in the operation of the smelting workshop.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows: the method comprises the steps of considering an energy efficiency balance optimization model of raw material dynamic arrival time, creating an original teaching optimization algorithm for solving the model, improving and creating class initialization, a teaching link, a self-adaptive learning link, a Bernoulli self-learning link and the like, bringing the local innovation subprocess into the frame of the improved teaching optimization algorithm by combining the structural characteristics of the model, enabling the improved algorithm to describe the teaching link more objectively, adjusting the learning step length of class students in the later stage of the algorithm in a self-adaptive manner, improving the self-learning capability of individual class students, solving the approximately optimal solution of the energy efficiency balance optimization model in reasonable solving time, and providing certain theoretical guidance and support for the production practice of enterprises.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
as shown in fig. 1, the present invention provides a method for scheduling a smelting plant based on an improved teaching optimization algorithm, the method being executed by a computer, the method comprising:
s1, acquiring scheduling information; setting a total iteration time Mit, and setting an initialization iteration time it to be 0;
s2, randomly initializing scores of 80% of students in the class, randomly initializing scores of the rest 20% of students in the class based on the principle of full utilization of waste heat of the furnace body, and constructing initial scores of the students in the class;
s3, respectively calculating the comprehensive scores of all student individuals in the class based on the initial scores of all the departments of the student individuals, and constructing initial class scores;
s4, taking the student individual with the best comprehensive performance in the current class as a teacher individual;
taking the student individuals with the comprehensive scores of all families in the current class as the equivalent student individuals;
taking the student individuals with the comprehensive scores of the current class being the median as the median student individuals;
s5, performing a teaching link on the current class based on the current teacher individuals, the equal student individuals and the middle student individuals, and updating the score of the current class;
s6, performing a self-adaptive learning link for each student individual in the class by using the score of each student individual in the class, and updating the score of the current class again;
s7, executing a self-learning link of the individual students in the current class, and updating the score of the current class to form the score of the class after the it iteration;
s8, judging whether the iteration number it reaches the total iteration number Mit, if so, outputting the current optimal student individual score, and converting the current optimal student individual score into a scheduling sequence through a decoding algorithm; otherwise, updating the scores and the iteration times of the current teacher individual, the equal student individual and the middle student individual, and returning to S5.
The beneficial effect of this embodiment does:
the invention provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, in order to solve the model, an original teaching optimization algorithm is innovated, class initialization, a teaching link, a self-adaptive learning link, Bernoulli self-learning link and other aspects of improvement and innovation are respectively carried out, the local innovation subprocess is brought under the frame of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching link, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning capability of class students is improved, the approximately optimal solution of the energy efficiency balance optimization model can be solved within reasonable solving time, and certain theoretical guidance and support are provided for the production practice of enterprises.
The following describes the implementation process of the embodiment of the present invention in detail:
s1, acquiring scheduling information; setting a total iteration time Mit, and setting an initialization iteration time it to be 0;
the scheduling information includes:
there are N high-end equipment precision parts that require casting operations in a precision foundry with a single melting furnace, and the high-end equipment parts are collectively designated as Ω ═ J1,…,Jj,…,JN}; the serial numbers of the parts are {1,2, …, N };
all detailsThe time of the raw materials arriving at the smelting workshop is inconsistent, but can be known in advance through the technology of the Internet of things, and the collection of the dynamic arrival time of the parts is recorded as
Since the preheating operation is mainly performed on the crucible in the furnace and the furnace body system, the preheating time is almost the same when each part is processed, and therefore, the preheating time required for processing each part is the same and is marked as tp；
In the smelting and refining stages, because the weight and volume of raw materials required by each part are different, the required smelting and refining time is different and is respectively marked as:
melting time pi ═ s1,…,sj,…,sN}；
Refining time ω ═ p1,…,pj,…pN}；
S2, randomly initializing the scores of 80% of the students in the class, randomly initializing the scores of the rest 20% of the students based on the principle of full utilization of the waste heat of the furnace body, and constructing the initial scores of the students in the class, wherein the number d of the disciplines is the same as the number of the parts.
The method specifically comprises the following steps:
s201, randomly generating random numbers between [0,1] as individual random scores of students;
s202, decoding 20% of student individual random results by using a decoding algorithm to obtain a corresponding scheduling sequence, and recording the scheduling sequence as
S203, set j to 1, and at max { tp,rjTime start part JjMelting operation of (1), component JjRefining operation completion time is Cj；
S204, judging whether the arrival time of the residual parts does not exceed CjIf true, then all arrival times are scheduled not to exceed CjComponent (2)Starting the smelting operation of the part with the maximum sum of the medium smelting time and the refining time, wherein the finishing time of the refining operation is marked as Cj+1；
S205, judging whether j is smaller than N-1, if so, making j equal to j +1 and returning to S204; otherwise, the decoding algorithm is used again to reversely deduce the current scheduling sequence, so as to obtain initial scores of all departments of the student individuals corresponding to 20% of the random scores of the student individuals, and the initial scores of all departments of the student individuals in the class are formed together with 80% of the random scores of the student individuals.
The decoding algorithm is used for converting the achievement of the unit generation of the student into a scheduling sequence, and comprises the following specific steps:
step 1: the scores of each department of the individual students
Corresponding to the serial numbers {1,2, …, N } of the parts one by one;
step 2: the serial numbers {1,2, …, N } of the parts are matched according to the scores of each department of the individual students
The new sequence number of the parts is obtained by ascending sequencing the sizes of the parts, and the sequence is used as a scheduling sequence.
S3, respectively calculating the comprehensive scores of all student individuals in the class, namely the energy efficiency balance index of the scheduling sequence, based on the initial scores of all the students; constructing an initial class score;
according to the characteristics of the actual smelting process, the method has the following typical characteristics:
all parts are cast according to a non-preemptive processing mode, namely once the casting operation of a certain part is started, the process cannot be interrupted, and the casting of the subsequent part must be completed after the processing of the previous part is completed.
Secondly, assuming that the smelting temperature and the refining temperature of each part tend to be consistent, once the crucible is processed, the last part J is processedj-1Processing the next part JjWhen the melting time of the latter is alpha sjWherein alpha is more thanThe heat utilization rate is more than 0 and less than 1.
Thirdly, once the cooling time of the crucible and the furnace body system exceeds a threshold value X, the next part needs additional preheating time t for casting operationp。
Supposing that the preheating power of the crucible and the furnace body is PpreWith a smelting power of PsRefining power of Pp. The above energy efficiency balance optimization aims at minimizing the manufacturing span required to cast all the parts on the one hand and at minimizing the total energy consumption of the crucible and furnace system on the other hand.
Suppose the parts processing sequence is
Adjacent JjAnd Jj+1Idle time between two parts is Δ ti,i+1Then the manufacturing span can be calculated as:
the total casting system energy consumption can then be calculated as:
the lower bounds on the manufacturing span and total energy consumption of a casting system that can be derived from the nature of the problem are:
by combining the manufacturing span and the energy consumption lower bound information, the above two-target problem can be converted into a single-target problem, and the energy efficiency balance index (i.e. the comprehensive performance of the student individuals) of the scheduling sequence can be expressed as follows:
wCand wERespectively, the manufacturing span and the total power consumption.
Therefore, the specific steps of calculating the comprehensive performances of each student individual in the class can adopt the following steps:
s301, converting initial scores of each department of the student into a scheduling sequence by using a decoding algorithm, and recording the scheduling sequence as a scheduling sequence
S302, setting j to 1, and setting at max { tp,rjTime start part JjMelting operation of (1), component JjRefining operation completion time is Cj；
S303, at max { Cj,rj+1Time start part Jj+1Melting operation of (1), component Jj+1Refining operation completion time is Cj+1Record Δ tj,j+1＝max{Cj,rj+1}-CjA value of (d);
s304, judging whether j is smaller than N-1, if so, changing j to j +1 and returning to the step 2; otherwise, calculating and outputting an energy efficiency balance index, and taking the energy efficiency balance index as the comprehensive score of the individual student.
Based on the comprehensive scores and the scores of each department of the student individuals, the initial class score is constructed and recorded as:
wherein, f (X)i[0]) Representing the initial comprehensive achievement of the ith student individual in the class; i 1, …, NP;
XNP[0]representing the NP-th individual student in the class;
s4, taking the student individual with the best comprehensive performance in the current class as a teacher individual Xbest[0]Namely:
arg min{f(Xi[0]),i＝1,…,NP}
taking the student individuals with the comprehensive scores of all departments in the current class as the equal student individuals Xavg[0]Namely:
taking the student individuals with the comprehensive scores of the current class as the median student individuals Xmed[0]Namely:
s5, performing a teaching link on the current class based on the current teacher individuals, the equal student individuals and the middle student individuals, and updating the score of the current class;
the teaching link mainly utilizes the scores of individual teachers, equal students and median students to carry out iteration of the current class, and comprises the following specific steps:
s501, initializing i to 1 and it to 0; i.e., iteration 1, iteration is performed based on the initial class score.
The individual scores and comprehensive scores of the ith generation of class students are specifically expressed as follows:
recording the individual subject scores and comprehensive scores of the teachers:
recording the scores of each department and the comprehensive scores of the individual middle students:
recording the scores of each department and the comprehensive scores of the equal student individuals:
s502, let j equal 1;
s503, ordering randi＝rand(0,1)，TFi＝1+rand(0,1)，
And constructing the score of the jth family of the first kind of new student individuals according to the following formula;
s504, ordering randiRan (0,1) and TFi＝1+rand(0,1)，
And the score of the jth family of the second kind of new student individuals is constructed according to the following formula;
s505, determining whether j is equal to or less than N, and if so, turning j to j +1 and going to S503; otherwise, two New classes of student individuals New1X are calculated respectivelyi[it]And New2Xi[it]And will be Xi[it]、New1Xi[it]And New2Xi[it]The person with the best overall performance is reserved as Xi[it+1]；
S506, judging whether the NP is not more than i, if so, enabling i to be i +1, updating the teacher individual, the equal student individual and the middle student individual, and going to S502; otherwise, the teaching link is terminated.
S6, performing a self-adaptive learning link for each student individual in the class by using the score of each student individual in the class, and updating the score of the current class again;
the self-adaptive link mainly simulates the mutual learning of any two student individuals in the class, and the learning step length is increased along with the increase of the iteration times, so that the diversity of class scores is increased in the later period of the iteration. The method comprises the following specific steps:
s601, setting i to 1,
s602, selecting any one of the Xi[it]The student of (1) is recorded as Xk[it]Setting j to 1;
s603, order
Wherein theta is larger than 1, and the achievement of the jth family of the new student individual is constructed according to the following formula;
s604, determining whether j is equal to or less than N, if so, making j equal to j +1, and going to S603; otherwise, calculating new individual NewX of studenti[it]And will be Xi[it]And NewXi[it]The person with the best overall performance is reserved as Xi[it+1]；
S605, determining whether i is equal to or less than NP, if so, changing i to i +1, and going to S602; otherwise, the adaptive learning link is terminated.
S7, executing a self-learning link of the individual students in the current class, and updating the score of the current class to form the score of the class after the it iteration;
the Bernoulli self-learning link mainly simulates the self-learning situation of a single student in a class, self-updating of each department score of the student is carried out in a Bernoulli cross mutation mode, and the updating aim is to achieve each department score of the teacher individual. The method comprises the following specific steps:
s701: the setting of i to 1 is made,
s702, setting j to 1;
s703, order BRiRand (0,1), and constructing the achievement of the jth family of new student individuals according to the following formula;
s704, determining whether j is equal to or less than N, and if so, turning to S703 if j is equal to j + 1; otherwise, calculating new individual NewBX of studenti[it]And will be Xi[it]And NewBXi[it]The person with the best overall performance is reserved as Xi[it+1]；
S705: judging whether the i is not more than NP, if so, making i equal to i +1, and going to S702; otherwise, the Bernoulli self-learning link is terminated.
S8, judging whether the iteration number it reaches the total iteration number Mit, if so, outputting the current optimal student individual score, and converting the current optimal student individual score into a scheduling sequence through a decoding algorithm; otherwise, updating the scores and the iteration times of the current teacher individual, the equal student individual and the middle student individual, and returning to S5.
Example 2
The invention also provides a smelting shop scheduling system based on the improved teaching optimization algorithm, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the method when executing the computer program.
It can be understood that the improved teaching optimization algorithm-based molten shop scheduling system provided in the embodiment of the present invention corresponds to the improved teaching optimization algorithm-based molten shop scheduling method, and the explanations, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the improved teaching optimization algorithm-based molten shop scheduling method, and are not described herein again.
In summary, compared with the prior art, the invention has the following beneficial effects:
the invention provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, in order to solve the model, an original teaching optimization algorithm is innovated, class initialization, a teaching link, a self-adaptive learning link, Bernoulli self-learning link and other aspects of improvement and innovation are respectively carried out, the local innovation subprocess is brought under the frame of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching link, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning capability of class students is improved, the approximately optimal solution of the energy efficiency balance optimization model can be solved within reasonable solving time, and certain theoretical guidance and support are provided for the production practice of enterprises.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments. In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A smelting workshop scheduling method based on an improved teaching optimization algorithm is characterized by comprising the following steps:
s1, acquiring scheduling information; setting the total iteration times, and setting the initialization iteration time it to be 0;
s2, randomly initializing the scores of 80% of the students in the class, randomly initializing the scores of the rest 20% of the students based on the principle of full utilization of the waste heat of the furnace body, and constructing the initial scores of the students in the class, wherein the number of the disciplines is the same as that of the parts;
s3, respectively calculating the comprehensive scores of all student individuals in the class based on the initial scores of all the departments of the student individuals, and constructing initial class scores;
s4, taking the student individual with the best comprehensive performance in the current class as a teacher individual;
taking the student individuals with the comprehensive scores of all families in the current class as the equivalent student individuals;
taking the student individuals with the comprehensive scores of the current class being the median as the median student individuals;
s5, performing a teaching link on the current class based on the current teacher individuals, the equal student individuals and the middle student individuals, and updating the score of the current class;
s6, performing a self-adaptive learning link for each student individual in the class by using the score of each student individual in the class, and updating the score of the current class again;
s7, executing a self-learning link of the individual students in the current class, and updating the score of the current class to form the score of the class after the it iteration;
s8, judging whether the iteration number it reaches the total iteration number Mit, if so, outputting the current optimal student individual score, and converting the current optimal student individual score into a scheduling sequence through a decoding algorithm; otherwise, updating the scores and the iteration times of the current teacher individual, the equal student individual and the middle student individual, and returning to S5.
2. The method of claim 1, wherein the scheduling information comprises:
set of parts Ω ═ J1,…,Jj,…,JN}；
Aggregation of part dynamic arrival times
Preheating time t required by partsp；
Melting time pi ═ s1,…,sj,…,sN}；
Refining time ω ═ p1,…,pj,…pN}。
3. The improved teaching optimization algorithm-based smelting workshop scheduling method according to claim 1, wherein the step of S2 randomly initializing the scores of 80% of the students in the class, the step of randomly initializing the scores of the rest 20% of the students in the class based on the principle of full utilization of the waste heat of the furnace body, and the specific steps of constructing the initial scores of the students in the class are as follows:
s201, randomly generating random numbers between [0,1] as individual random scores of students;
s202, decoding 20% of student individual random results by using a decoding algorithm to obtain a corresponding scheduling sequence, and recording the scheduling sequence as
N is the number of parts;
s203, set j to 1, and at max { tp,rjTime start part JjMelting operation of (1), component JjRefining operation completion time is Cj；
tpIndicates the required preheating time of the component, rjRepresenting the dynamic arrival time of the jth part;
s204, judging whether the arrival time of the residual parts does not exceed CjIf true, then all arrival times are scheduled not to exceed CjStarting the smelting operation of the part with the largest sum of the smelting time and the refining time, wherein the finishing time of the refining operation is marked as Cj+1；
S205, judging whether j is smaller than N-1, if so, making j equal to j +1 and returning to S204; otherwise, the decoding algorithm is used again to reversely deduce the current scheduling sequence, so as to obtain initial scores of all departments of the student individuals corresponding to 20% of the random scores of the student individuals, and the initial scores of all departments of the student individuals in the class are formed together with 80% of the random scores of the student individuals.
4. The improved teaching optimization algorithm-based smelt shop scheduling method according to claim 3, wherein the step S5 of performing a teaching link for the current class based on the current teacher individuals, equal student individuals and middle student individuals and updating the performance of the current class comprises the following steps:
s501, initializing i to 1 and it to 0; when 1 st iteration is carried out, iteration is carried out based on the initial class score;
the individual scores and comprehensive scores of the ith generation of class students are as follows:
wherein, f (X)i[it]) Representing the initial comprehensive achievement of the ith student individual in the ith generation class; i 1, …, NP;
XNP[it]representing the NP-th individual student in the ith generation class;
the individual subject scores and comprehensive scores of the teachers are as follows:
the scores and the comprehensive scores of the individual middle students are as follows:
the scores and the comprehensive scores of each department of the equal student individuals are as follows:
s502, let j equal 1;
s503, ordering randi＝rand(0,1)，TFi＝1+rand(0,1)，
And constructing the score of the jth family of the first kind of new student individuals:
s504, ordering randiRan (0,1) and TFi＝1+rand(0,1)，
And constructing the scores of the jth family of the second class of new student individuals; the construction formula is as follows:
s505, determining whether j is equal to or less than N, and if so, turning j to j +1 and going to S503; otherwise, two New classes of student individuals New1X are calculated respectivelyi[it]And New2Xi[it]And will be Xi[it]、New1Xi[it]And New2Xi[it]The person with the best overall performance is reserved as Xi[it+1]；
S506, judging whether the NP is not more than i, if so, enabling i to be i +1, updating the teacher individual, the equal student individual and the middle student individual, and going to S502; otherwise, the teaching link is terminated.
5. The improved teaching optimization algorithm-based smelt shop scheduling method according to claim 4, wherein said S6, utilizing the scientific achievements of any student in the class to perform an adaptive learning link for the student in the class, and updating the current class achievements again, specifically comprises the following steps:
s601, setting i to be 1;
s602, selecting any one of the Xi[it]The student of (1) is recorded as Xk[it]Setting j to 1;
s603, order
Where θ > 1, and constructing new student individual performance of family j:
s604, determining whether j is equal to or less than N, if so, making j equal to j +1, and going to S603; otherwise, calculating new individual NewX of studenti[it]And will be Xi[it]And NewXi[it]The person with the best overall performance is reserved as Xi[it+1]；
S605, determining whether i is equal to or less than NP, if so, changing i to i +1, and going to S602; otherwise, the adaptive learning link is terminated.
6. The improved teaching optimization algorithm-based smelt shop scheduling method according to claim 5, wherein said S7, performing a self-learning procedure of student individuals in the current class, and updating the current class score to form the class score after the it iteration, comprises the following specific steps:
s701: the setting of i to 1 is made,
s702, setting j to 1;
s703, order BRiRand (0,1), and construct the score of the j family of new student individuals:
s704, determining whether j is equal to or less than N, and if so, turning to S703 if j is equal to j + 1; otherwise, calculating new individual NewBX of studenti[it]And will be Xi[it]And NewBXi[it]The person with the best overall performance is reserved as Xi[it+1]；
S705: judging whether the i is not more than NP, if so, making i equal to i +1, and going to S702; otherwise, the Bernoulli self-learning link is terminated.
7. The improved teaching optimization algorithm-based smelt plant scheduling method according to any one of claims 1 to 6, wherein the calculation formula of the comprehensive performance of said student individuals is:
wherein, CmaxRepresenting the manufacturing span of the casting system, E representing the total energy consumption of the casting system, LBCAnd LBELower bound of manufacturing span and total energy consumption, w, of casting systemsCAnd wEWeights for manufacturing span and total energy consumption, respectively;
wherein s isjThe smelting time of the jth part is shown;
pjrepresenting the refining time of the jth part;
alpha is the utilization rate of waste heat and represents that alpha is more than 0 and less than 1;
Δtj,j+1representing the interval time between the processing of the two parts;
tpindicating the required preheating time of the component
χ represents a cooling timeout threshold;
Psrepresents the smelting power;
Pprepresents the refining power;
Ppreindicating the preheating power.
8. The method for scheduling the smelting shop based on the improved teaching optimization algorithm as claimed in any one of claims 1 to 6, wherein the decoding algorithm is used for converting the achievements of the ith generation of the student individuals into the scheduling sequence, and the specific steps are as follows:
step 1: corresponding the scores of each department of the individual students to the serial numbers of the parts one by one;
step 2: and sequencing the serial numbers of the parts in an ascending order according to the sizes of the scores of the departments of the individual students to obtain a new serial number sequence of the parts, and taking the sequence as a scheduling sequence.
9. A molten shop scheduling system based on a refined teaching optimization algorithm, the system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of any one of claims 1 to 8.
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