CN112396214B - Smelting workshop scheduling method and system based on improved teaching optimization algorithm - Google Patents
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Abstract
The application 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 application provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, an original teaching optimization algorithm is innovated for solving the model, class initialization, teaching links, self-adaptive learning links, bernoulli self-learning links and other aspects are respectively improved and innovated, the local innovation sub-process is incorporated into the framework of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching links, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning ability of individual class students is improved, the approximate 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 application 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.), raw materials of common metals such as aluminum, iron, and some rare earth elements may not be fully ready for completion at the beginning of the production cycle. In most cases, the raw material may arrive at the smelting plant at different times. Through information technologies such as the internet of things, the dynamic arrival time of raw materials can be known by a workshop management system at the beginning of a production cycle. At the same time, the sales system also provides the delivery time of the smelting work order to the shop management system. The operation of a smelting workshop is a key link of energy consumption in the precision casting process of the whole 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 smelting workshop operation is reduced, so that the energy consumption cost of the investment casting production line can be effectively reduced. The operation of a smelting workshop is carried out 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 a specific melting temperature profile, it was found that after casting was completed, the furnace remained at a certain temperature and cooled down to the initial temperature over a period of time. How to solve the energy-saving production scheduling problem in the operation of a smelting workshop is a problem to be solved urgently.
The existing energy-saving-oriented production scheduling problem mainly utilizes a machine closing and restarting strategy to reduce the energy consumption in an idle period of a production process, does not consider the possible residual heat utilization phenomenon in the precision investment casting process of high-end equipment, and is difficult to perform effective energy efficiency balance optimization management by utilizing the existing Internet of things technology.
The present application is directed to an improved teaching optimization algorithm for solving the problem of energy balance optimization of precision furnace casting with dynamic arrival time of raw materials and machine dormancy strategy. The existing teaching optimization algorithm comprises the following steps:
(1) Initializing the number of students and the number of disciplines;
(2) The score information of each subject of each chemical student is randomly initialized, and the comprehensive score of each student is calculated;
(3) Taking the student with the best comprehensive score as a teacher and setting the student individuals with average score as equal student individuals;
(4) Carrying out iteration of teaching links on each student individual by using the equal student individual information and teacher individual information;
(5) Carrying out iteration of learning links on each student by using information of any other student in the class;
(6) Updating the achievements and comprehensive achievements of various departments of teachers and even students;
(7) And (4) judging whether the iteration condition is met, if so, turning to the step (4), otherwise, stopping iteration, and outputting the current optimal solution.
The existing teaching optimization has the defects in the aspects of randomizing initial population, teaching links, learning links and the like, so that the searching efficiency is low, and the relationship between the diversity and the convergence of algorithm population cannot be well balanced.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a smelting workshop scheduling method and system based on an improved teaching optimization algorithm, which solve the problem that the existing scheduling algorithm cannot utilize waste heat possibly existing in the precision investment casting process of high-end equipment.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme:
in a first aspect, a smelting shop scheduling method based on an improved teaching optimization algorithm is provided, the method comprising:
s1, acquiring scheduling information; setting total iteration times, and initializing the iteration times it=0;
s2, randomly initializing 80% of the student individual achievements in the class, randomly initializing the rest 20% of the student individual achievements based on a furnace body waste heat full utilization principle, and constructing initial student individual achievements in the class, wherein the number of disciplines is the same as that of parts;
s3, based on initial various grades of the student individuals, respectively calculating comprehensive grades of the student individuals in the class, and constructing initial class grades;
s4, taking the student individuals with the best comprehensive performance in the current class as teacher individuals;
taking student individuals with comprehensive achievements as average numbers of various departments in the current class as equal student individuals;
taking a student individual with the comprehensive score of the current class as a median student individual;
s5, executing 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 current class score;
s6, performing self-adaptive learning links for the students in the class by using the score of each student in the class, and updating the current class score again;
s7, executing a self-learning link of the student individuals in the current class, and updating the current class score to form an it-th iterative class score;
s8, judging whether the iteration times it reach the total iteration times 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 achievements and the iteration times of each family of the current teacher individual, the equal student individual and the median student individual, and returning to the step S5.
Further, the scheduling information includes:
component assembly Ω= { J 1 ,…,J j ,…,J N }；
Aggregation of component dynamic arrival times
Preheating time t required for parts p ；
Smelting time pi= { s 1 ,…,s j ,…,s N }；
Refining time ω= { p 1 ,…,p j ,…p N }。
Further, the step S2 of randomly initializing the various scores of 80% of the student individuals in the class, and randomly initializing the various scores of the rest 20% of the student individuals based on the furnace body waste heat full utilization principle, wherein the specific steps of constructing the initial various scores of the student individuals in the class are as follows:
s201, randomly generating random numbers between [0,1] as student individual random achievements;
s202, decoding 20% of individual student random results by using a decoding algorithm to obtain corresponding scheduling sequences, and recording the scheduling sequences asN is the number of parts;
s203, j=1 is set and at max { t p ,r j Time start part J j Is described as part J j The finishing time of the refining operation is C j ；
t p Representing the preheating time required by the parts, r j Representing the dynamic arrival time of the jth component;
s204, judging whether the arrival time of the residual parts is not more than C j If true, arrange for all arrival times not to exceed C j Starting the melting operation of the part having the largest sum of the melting time and the refining time, and recording the finishing time of the refining operation as C j+1 ；
S205, judging whether j is smaller than N-1, if so, enabling j to be=j+1 and returning to S204; otherwise, the decoding algorithm is utilized again to reversely calculate the current scheduling sequence, initial various grades of the student individuals corresponding to 20% of the random grades of the student individuals are obtained, and the initial various grades of the student individuals in the class are formed together with 80% of the random grades of the student individuals.
Further, S5, the step of executing 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 current class score specifically includes the following steps:
s501, initializing i=1, it=0; i.e. when the 1 st iteration is performed, iterating based on the initial class score;
the various scores and the comprehensive scores of the class student individuals of the first generation are as follows:
wherein f (X) i [it]) Representing the initial comprehensive performance of the ith student individual in the class of the ith generation; i=1, …, NP;
X NP [it]represents the generation itNP students individuals in class;
representing the initial performance of the family d of the individual NP students in the class of the generation it;
the various achievements and comprehensive achievements of the teacher individual are as follows:
the various scores and the comprehensive scores of the median student individuals are as follows:
the score and the comprehensive score of each department of the individual students are equal:
s502, let j=1;
s503, command rand i ＝rand(0,1)，TF i ＝1+rand(0,1)，
And construct a first new class of student individual j-th family achievements:
s504, command rand i =rand (0, 1) and TF i ＝1+rand(0,1)，
And constructing the achievement of the j family of the second new student individuals; the construction formula is as follows:
s505, judging whether j is less than or equal to N, if soIf true, j=j+1 is turned to S503; otherwise, respectively calculating New1X of two New types of student individuals i [it]And New2X i [it]And will X i [it]、New1X i [it]And New2X i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S506, judging whether i is less than or equal to NP, if so, enabling i=i+1 to update a teacher individual, an equally born individual and a median student individual, and turning to S502; otherwise, the teaching link is terminated.
Further, the step S6 of executing an adaptive learning link for each student individual in the class by using the score of each student individual in the class, and updating the current class score again, specifically includes the following steps:
s601, setting i=1;
s602, selecting any one different from X i [it]The student individual is recorded as X k [it]Setting j=1;
s603, orderWhere θ > 1, and construct new student individual j-th family achievements:
s604, judging whether j is less than or equal to N, if so, enabling j to be equal to j+1, and turning to S603; otherwise, calculate new student individual NewX i [it]And will X i [it]And NewX i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S605, judging whether i is less than or equal to NP, if so, making i=i+1, and turning to S602; otherwise, the adaptive learning link is terminated.
Further, the step S7 is to execute a self-learning link of the student individuals in the current class, update the current class score, and form the class score after the ith iteration, and specifically includes the steps of:
s701: the setting of i=1 is that,
s702, setting j=1;
s703, order BR i =rand (0, 1), and construct new student individual achievement of family j:
s704, judging whether j is less than or equal to N, if so, enabling j=j+1 and turning to S703; otherwise, calculate new student individual newBX i [it]And will X i [it]And NewBX i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S705: judging whether i is less than or equal to NP, if so, making i=i+1, and turning to S702; otherwise, the Bernoulli self-learning link is terminated.
Further, the calculation formula of the comprehensive score of the student individual is as follows:
wherein C is max Representing the manufacturing span of the casting system, Σe representing the total energy consumption of the casting system, LB C And LB E Lower bound of manufacturing span and total energy consumption of casting system, w C And w E Respectively the weight of manufacturing span and total energy consumption;
wherein s is j The smelting time of the jth component is shown;
p j the refining time of the j-th part is represented;
alpha is the utilization rate of the waste heat, and represents that 0 is more than alpha and less than 1;
Δt j,j+1 representing the interval time of processing two parts;
t p indicating the preheating time required for the parts
χ represents a cool timeout threshold;
P s representing smelting power;
P p represents refining power;
P pre representing the preheat power.
Further, the decoding algorithm is used for converting the result of the ith generation of the student individual into a scheduling sequence, and the specific steps are as follows:
step 1: the achievements of each student are in one-to-one correspondence with the serial numbers of the parts;
step 2: and (3) sorting the serial numbers of the parts in ascending order according to the sizes of the scores of the students, obtaining a new serial number sorting of the parts, and taking the sorting as a scheduling sequence.
In a second aspect, there is provided a smelting plant scheduling 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) beneficial effects
The application 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 application provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, an original teaching optimization algorithm is innovated for solving the model, class initialization, teaching links, self-adaptive learning links, bernoulli cross self-learning links and other improvements and innovations are respectively carried out, the local innovation sub-process is incorporated into the framework of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching links, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning ability of individual class students is improved, the approximate 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 initialization population mode in the traditional teaching optimization algorithm, in the improved teaching optimization algorithm, the characteristic of the energy efficiency balance optimization problem is combined except the traditional random initialization population mode, an initial population quality improvement mechanism with a waste heat full utilization principle is introduced, and the searching precision of the initial population can be well enhanced.
(3) The teaching link of the traditional teaching optimization algorithm only relates to teacher individuals and equal generation individuals, but in consideration of the complexity of the solution of the comprehensive score value, the equal generation with the average score of each family cannot be used as an individual with the average value of the comprehensive score value, so that the concept of a median student is introduced into the improved teaching optimization algorithm, and the student individuals with the comprehensive score value at the middle level can be well expressed.
(4) In order to further reduce the possibility of the algorithm falling into local optimum, in the improved teaching optimization algorithm, based on the concept of Bernoulli intersection in a biased random key algorithm, a self-learning link of students based on the Bernoulli intersection is innovatively provided, and the link can well balance the depth and breadth of search.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application solves the problem that the existing scheduling algorithm cannot utilize the possible waste heat in the precision investment casting process of high-end equipment by providing the smelting workshop scheduling method and system based on the improved teaching optimization algorithm, and realizes energy-saving-oriented production scheduling in the operation of the smelting workshop.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows: the energy efficiency balance optimization model of the dynamic arrival time of the raw materials is considered, an original teaching optimization algorithm is innovated for solving the model, class initialization, teaching links, self-adaptive learning links, bernoulli self-learning links and other aspects are improved and innovated respectively, the local innovation sub-process is brought into the framework of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can describe the teaching links more objectively, the learning step length of students in the class can be adjusted in the later stage of the algorithm in a self-adaptive manner, the self-learning ability of individual students in the class is improved, and the approximate optimal solution of the energy efficiency balance optimization model can be solved in reasonable solving time, so that certain theoretical guidance and support are provided for the production practice of enterprises.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Example 1:
as shown in fig. 1, the present application provides a smelting shop scheduling method based on an improved teaching optimization algorithm, the method being executed by a computer, the method comprising:
s1, acquiring scheduling information; setting total iteration times Mit and initializing the iteration times it=0;
s2, randomly initializing the scores of 80% of student individuals in the class, randomly initializing the scores of the rest 20% of student individuals based on a furnace body waste heat full utilization principle, and constructing initial scores of the students in the class;
s3, based on initial various grades of the student individuals, respectively calculating comprehensive grades of the student individuals in the class, and constructing initial class grades;
s4, taking the student individuals with the best comprehensive performance in the current class as teacher individuals;
taking student individuals with comprehensive achievements as average numbers of various departments in the current class as equal student individuals;
taking a student individual with the comprehensive score of the current class as a median student individual;
s5, executing 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 current class score;
s6, performing self-adaptive learning links for the students in the class by using the score of each student in the class, and updating the current class score again;
s7, executing a self-learning link of the student individuals in the current class, and updating the current class score to form an it-th iterative class score;
s8, judging whether the iteration times it reach the total iteration times 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 achievements and the iteration times of each family of the current teacher individual, the equal student individual and the median student individual, and returning to the step S5.
The beneficial effects of this embodiment are:
the application provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, an original teaching optimization algorithm is innovated for solving the model, class initialization, teaching links, self-adaptive learning links, bernoulli self-learning links and other aspects are respectively improved and innovated, the local innovation sub-process is incorporated into the framework of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching links, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning ability of individual class students is improved, the approximate 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 implementation process of the embodiment of the present application is described in detail below:
s1, acquiring scheduling information; setting total iteration times Mit and initializing the iteration times it=0;
the scheduling information includes:
the casting operation of the precision casting workshop with a single smelting furnace is required to be carried out by N high-end equipment precision parts, and the high-end equipment parts are collectively named as omega= { J 1 ,…,J j ,…,J N -a }; the serial numbers of the parts are {1,2, …, N };
the time of the raw materials of all parts reaching a smelting workshop is inconsistent, but the time of the raw materials reaching the smelting workshop 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 directed to the crucible and the furnace body system in the furnace, 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 denoted as t p ；
In the smelting and refining stages, because the weight and volume of raw materials required for each part are different, the required smelting and refining times are different, respectively denoted as:
smelting time pi= { s 1 ,…,s j ,…,s N }；
Refining time ω= { p 1 ,…,p j ,…p N }；
S2, randomly initializing 80% of the student individual achievements in the class, randomly initializing the rest 20% of the student individual achievements based on the furnace body waste heat full utilization principle, and constructing the initial student individual achievements in the class, wherein the number d of disciplines is the same as the number of parts.
The method specifically comprises the following steps:
s201, randomly generating random numbers between [0,1] as student individual random achievements;
s202, decoding 20% of individual student random results by using a decoding algorithm to obtain corresponding scheduling sequences, and recording the scheduling sequences as
S203, j=1 is set and at max { t p ,r j Time start part J j Is described as part J j The finishing time of the refining operation is C j ；
S204, judging whether the arrival time of the residual parts is not more than C j If true, arrange for all arrival times not to exceed C j Starting the melting operation of the part having the largest sum of the melting time and the refining time, and recording the finishing time of the refining operation as C j+1 ；
S205, judging whether j is smaller than N-1, if so, enabling j to be=j+1 and returning to S204; otherwise, the decoding algorithm is utilized again to reversely calculate the current scheduling sequence, initial various grades of the student individuals corresponding to 20% of the random grades of the student individuals are obtained, and the initial various grades of the student individuals in the class are formed together with 80% of the random grades of the student individuals.
The decoding algorithm is used for converting the result of the ith generation of the student individual into a scheduling sequence, and specifically comprises the following steps:
step 1: score of each department of student individualCorresponds 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 set according to the results of each studentAnd (3) the size ascending order of the parts is used for obtaining a new part sequence number order, and the order is used as a scheduling sequence.
S3, based on initial scores of the students, respectively calculating comprehensive scores of the students in the class, namely energy efficiency balance indexes of a scheduling sequence; constructing an initial class score;
according to the characteristics of the actual smelting process, the method has the following typical characteristics:
(1) all parts are cast according to a non-preemptive processing mode, namely, once casting operation of a certain part is started, the process can not be interrupted, and casting of the subsequent part is required to be finished after the last part is processed.
(2) Assuming that the melting temperature and refining temperature of each part tend to be consistent, once the crucible has been treated with the last part J j-1 Immediately after the next component J is processed j In the latter case, the melting time is αs j Wherein alpha is the utilization rate of waste heat, and alpha is more than 0 and less than 1.
(3) Once the cooling time of the crucible and furnace system exceeds the threshold χ, additional preheating time t is required for the casting operation of the next component p 。
(4) Assuming that the preheating power of the crucible and the furnace body is P pre Smelting power P s Refining power is P p . The objective of the above energy balance optimization is on the one hand to minimize the manufacturing span required for casting all the parts and on the other hand to minimize the total energy consumption of the crucible and furnace system.
Assume that the processing sequence of the parts isAdjacent J j And J j+1 Idle time between two componentsIs delta t i,i+1 Then the manufacturing span can be calculated as:
the total energy consumption of the casting system can be calculated as:
the manufacturing span and the lower bound of the total energy consumption of the casting system, respectively, can be obtained from the nature of the problem:
combining the manufacturing span and the energy consumption lower bound information, the double-target problem can be converted into a single-target problem, and the energy efficiency balance index (i.e. the comprehensive performance of the student individual) of the scheduling sequence can be expressed as follows:
w C and w E The weight of manufacturing span and total energy consumption, respectively.
Thus, the specific step of calculating the overall performance of each individual student in the class may be the following:
s301, converting initial achievements of student individuals into scheduling sequences by using a decoding algorithm, and recording the scheduling sequences as
S302, j=1 is set, and at max { t } p ,r j Time of onsetComponent J j Is described as part J j The finishing time of the refining operation is C j ；
S303, at max { C j ,r j+1 Time start part J j+1 Is described as part J j+1 The finishing time of the refining operation is C j+1 Recording Δt j,j+1 ＝max{C j ,r j+1 }-C j Is a value of (2);
s304, judging whether j is smaller than N-1, if so, enabling j=j+1 and turning to the step 2; otherwise, calculating and outputting the energy efficiency balance index, and taking the energy efficiency balance index as the comprehensive achievement of the student individuals.
Based on the comprehensive results and the scores of each student, an initial class result is constructed and recorded as follows:
wherein f (X) i [0]) Representing an initial composite score for an ith student individual in the class; i=1, …, NP;
X NP [0]represents the NP-th student individual in the class;
representing an initial performance of the family d for the NP student individuals in the class;
s4, taking the student individual with the best comprehensive performance in the current class as a teacher individual X best [0]The method comprises the following steps:
arg min{f(X i [0]),i＝1,…,NP}
the student individuals with the comprehensive score of each department in the current class are taken as equal student individuals X avg [0]The method comprises the following steps:
taking a student individual with the comprehensive achievement of the current class as a median student individual X med [0]The method comprises the following steps:
s5, executing 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 current class score;
the teaching link mainly uses the results of teacher individuals, equally born individuals and median student individuals to iterate the current class, and the specific steps are as follows:
s501, initializing i=1, it=0; i.e., iterating based on the initial class score when iterating 1 st.
The various scores and the comprehensive scores of the class student individuals of the first generation are specifically expressed as follows:
the various achievements and the comprehensive achievements of the teacher individual are recorded as:
the various scores and the comprehensive scores of the median student individuals are recorded as:
the score of each department and the comprehensive score of the equal student individuals are recorded as:
s502, let j=1;
s503, command rand i ＝rand(0,1)，TF i ＝1+rand(0,1)，
And constructing the performance of the j th family of the first new student individuals according to the following formula;
s504, command rand i =rand (0, 1) and TF i ＝1+rand(0,1)，
And constructing the achievement of the j family of the second new student individual according to the following formula;
s505, judging whether j is less than or equal to N, if so, enabling j=j+1 and turning to S503; otherwise, respectively calculating New1X of two New types of student individuals i [it]And New2X i [it]And will X i [it]、New1X i [it]And New2X i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S506, judging whether i is less than or equal to NP, if so, enabling i=i+1 to update a teacher individual, an equally born individual and a median student individual, and turning to S502; otherwise, the teaching link is terminated.
S6, performing self-adaptive learning links for the students in the class by using the score of each student in the class, and updating the current class score again;
the self-adaptive link mainly simulates the mutual learning of any two students in a class, and the learning step length is increased along with the increase of the iteration times, so that the diversity of class achievements is increased in the later period of the iteration. The method comprises the following specific steps:
s601, set i=1,
s602, selecting any one different from X i [it]The student individual is recorded as X k [it]Setting j=1;
s603, orderWherein θ > 1, andand constructing new student individual j-th score according to the following formula;
s604, judging whether j is less than or equal to N, if so, enabling j to be equal to j+1, and turning to S603; otherwise, calculate new student individual NewX i [it]And will X i [it]And NewX i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S605, judging whether i is less than or equal to NP, if so, making i=i+1, and turning to S602; otherwise, the adaptive learning link is terminated.
S7, executing a self-learning link of the student individuals in the current class, and updating the current class score to form an it-th iterative class score;
the Bernoulli self-learning link mainly simulates the self-learning situation of a single student individual in a class, and the self-updating of each student individual score is carried out in a Bernoulli cross variation mode, so that the aim of updating is to achieve each student individual score of a teacher individual. The method comprises the following specific steps:
s701: the setting of i=1 is that,
s702, setting j=1;
s703, order BR i =rand (0, 1), and construct the performance of the j-th family of new student individuals according to the following formula;
s704, judging whether j is less than or equal to N, if so, enabling j=j+1 and turning to S703; otherwise, calculate new student individual newBX i [it]And will X i [it]And NewBX i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S705: judging whether i is less than or equal to NP, if so, making i=i+1, and turning to S702; otherwise, the Bernoulli self-learning link is terminated.
S8, judging whether the iteration times it reach the total iteration times 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 achievements and the iteration times of each family of the current teacher individual, the equal student individual and the median student individual, and returning to the step S5.
Example 2
The application 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 may be understood that the smelting shop scheduling system based on the improved teaching optimization algorithm provided by the embodiment of the present application corresponds to the smelting shop scheduling method based on the improved teaching optimization algorithm, and the explanation, the examples, the beneficial effects and other parts of the relevant content may refer to the corresponding content in the smelting shop scheduling method based on the improved teaching optimization algorithm, which is not repeated herein.
In summary, compared with the prior art, the application has the following beneficial effects:
the application provides an energy efficiency balance optimization model considering the dynamic arrival time of raw materials, an original teaching optimization algorithm is innovated for solving the model, class initialization, teaching links, self-adaptive learning links, bernoulli self-learning links and other aspects are respectively improved and innovated, the local innovation sub-process is incorporated into the framework of the improved teaching optimization algorithm by combining the structural characteristics of the model, the improved algorithm can more objectively describe the teaching links, the learning step length of class students is adaptively adjusted at the later stage of the algorithm, the self-learning ability of individual class students is improved, the approximate 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, from the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by means of software plus necessary general hardware platform. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
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 total iteration times, and initializing the iteration times it=0;
s2, randomly initializing 80% of the student individual achievements in the class, randomly initializing the rest 20% of the student individual achievements based on a furnace body waste heat full utilization principle, and constructing initial student individual achievements in the class, wherein the number of disciplines is the same as that of parts;
s3, based on initial various grades of the student individuals, respectively calculating comprehensive grades of the student individuals in the class, and constructing initial class grades;
s4, taking the student individuals with the best comprehensive performance in the current class as teacher individuals;
taking student individuals with comprehensive achievements as average numbers of various departments in the current class as equal student individuals;
taking a student individual with the comprehensive score of the current class as a median student individual;
s5, executing 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 current class score;
s6, performing self-adaptive learning links for the students in the class by using the score of each student in the class, and updating the current class score again;
s7, executing a self-learning link of the student individuals in the current class, and updating the current class score to form an it-th iterative class score;
s8, judging whether the iteration times it reach the total iteration times 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 achievements and the iteration times of each family of the current teacher individual, the equal student individual and the median student individual, and returning to the step S5.
2. The smelting shop scheduling method based on the improved teaching optimization algorithm according to claim 1, wherein the scheduling information includes:
component assembly Ω= { J 1 ,…,J j ,…,J N }；
Aggregation of component dynamic arrival times
Preheating time t required for parts p ；
Smelting time pi= { s 1 ,…,s j ,…,s N }；
Refining time ω= { p 1 ,…,p j ,…p N }。
3. The smelting workshop scheduling method based on the improved teaching optimization algorithm as claimed in claim 1, wherein the step of randomly initializing the scores of 80% of the students in the class and randomly initializing the scores of the remaining 20% of the students based on the furnace waste heat full utilization principle, 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 student individual random achievements;
s202, decoding 20% of individual student random results by using a decoding algorithm to obtain corresponding scheduling sequences, and recording the scheduling sequences asN is the number of parts;
s203, j=1 is set and at max { t p ,r j Time start part J j Is described as part J j The finishing time of the refining operation is C j ；
t p Representing the preheating time required by the parts, r j Representing the dynamic arrival time of the jth component;
s204, judging whether the arrival time of the residual parts is not more than C j If true, arrange for all arrival times not to exceed C j Starting the melting operation of the part having the largest sum of the melting time and the refining time, and recording the finishing time of the refining operation as C j+1 ；
S205, judging whether j is smaller than N-1, if so, enabling j to be=j+1 and returning to S204; otherwise, the decoding algorithm is utilized again to reversely calculate the current scheduling sequence, initial various grades of the student individuals corresponding to 20% of the random grades of the student individuals are obtained, and the initial various grades of the student individuals in the class are formed together with 80% of the random grades of the student individuals.
4. The smelting shop scheduling method based on the improved teaching optimization algorithm according to claim 3, wherein S5, the teaching links are performed on the current class based on the current teacher individual, the equal student individual and the median student individual, and the current class score is updated, specifically comprising the following steps:
s501, initializing i=1, it=0; i.e. when the 1 st iteration is performed, iterating based on the initial class score;
the various scores and the comprehensive scores of the class student individuals of the first generation are as follows:
wherein f (X) i [it]) Representing the initial comprehensive performance of the ith student individual in the class of the ith generation; i=1, …, NP;
X NP [it]represents the NP-th student individual in the class of the first generation;
representing the initial performance of the family d of the individual NP students in the class of the generation it;
the various achievements and comprehensive achievements of the teacher individual are as follows:
the various scores and the comprehensive scores of the median student individuals are as follows:
the score and the comprehensive score of each department of the individual students are equal:
s502, let j=1;
s503, command rand i ＝rand(0,1)，TF i ＝1+rand(0,1)，
And construct a first new class of student individual j-th family achievements:
s504, command rand i =rand (0, 1) and TF i ＝1+rand(0,1)，
And constructing the achievement of the j family of the second new student individuals; the construction formula is as follows:
s505, judging whether j is less than or equal to N, if so, enabling j=j+1 and turning to S503; otherwise, respectively calculating New1X of two New types of student individuals i [it]And New2X i [it]And will X i [it]、New1X i [it]And New2X i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S506, judging whether i is less than or equal to NP, if so, enabling i=i+1 to update a teacher individual, an equally born individual and a median student individual, and turning to S502; otherwise, the teaching link is terminated.
5. The smelting shop scheduling method based on the improved teaching optimization algorithm according to claim 4, wherein S6, the self-adaptive learning link is executed for the students in the class by using the score of each student in the class, and the current class score is updated again, specifically comprising the following steps:
s601, setting i=1;
s602, selecting any one different from X i [it]The student individual is recorded as X k [it]Setting j=1;
s603, orderWhere θ > 1, and construct new student individual j-th family achievements:
s604, judging whether j is less than or equal to N, if so, enabling j to be equal to j+1, and turning to S603; otherwise, calculate new student individual NewX i [it]And will X i [it]And NewX i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S605, judging whether i is less than or equal to NP, if so, making i=i+1, and turning to S602; otherwise, the adaptive learning link is terminated.
6. The smelting workshop scheduling method based on the improved teaching optimization algorithm according to claim 5, wherein the step S7 is to execute the self-learning link of the student individual in the current class and update the current class score to form the class score after the ith iteration, and the specific steps are as follows:
s701: the setting of i=1 is that,
s702, setting j=1;
s703, order BR i =rand (0, 1), and construct new student individual achievement of family j:
s704, judging whether j is less than or equal to N, if so, enabling j=j+1 and turning to S703; otherwise, calculate new student individual newBX i [it]And will X i [it]And NewBX i [it]The best person of the comprehensive achievement in the middle is reserved as X i [it+1]；
S705: judging whether i is less than or equal to NP, if so, making i=i+1, and turning to S702; otherwise, the Bernoulli self-learning link is terminated.
7. The smelting shop scheduling method based on an improved teaching optimization algorithm according to any one of claims 1-6, wherein the calculation formula of the comprehensive performance of the student individuals is:
wherein C is max Representing the manufacturing span of the casting system, Σe representing the total energy consumption of the casting system, LB C And LB E Lower bound of manufacturing span and total energy consumption of casting system, w C And w E Respectively the weight of manufacturing span and total energy consumption;
wherein s is j The smelting time of the jth component is shown;
p j the refining time of the j-th part is represented;
alpha is the utilization rate of the waste heat, and represents that 0 is more than alpha and less than 1;
Δt j,j+1 representing the interval time of processing two parts;
t p indicating the preheating time required for the parts
χ represents a cool timeout threshold;
P s representing smelting power;
P p represents refining power;
P pre representing the preheat power.
8. The smelting shop scheduling method based on an improved teaching optimization algorithm according to any one of claims 1-6, wherein the decoding algorithm is used for converting the student individual's it's performance into a scheduling sequence, and the specific steps are:
step 1: the achievements of each student are in one-to-one correspondence with the serial numbers of the parts;
step 2: and (3) sorting the serial numbers of the parts in ascending order according to the sizes of the scores of the students, obtaining a new serial number sorting of the parts, and taking the sorting as a scheduling sequence.
9. A smelting plant scheduling system based on an improved teaching optimization algorithm, said system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1 to 8 when executing said computer program.
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