CN116011723A - Intelligent dispatching method and application of coking and coking mixed flow shop based on Harris eagle algorithm - Google Patents

Intelligent dispatching method and application of coking and coking mixed flow shop based on Harris eagle algorithm Download PDF

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CN116011723A
CN116011723A CN202211164848.9A CN202211164848A CN116011723A CN 116011723 A CN116011723 A CN 116011723A CN 202211164848 A CN202211164848 A CN 202211164848A CN 116011723 A CN116011723 A CN 116011723A
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position information
population
coking
harris
formula
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朱旭辉
张子路
倪志伟
王相山
薛庆峰
刘强
夏平凡
倪丽萍
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Hefei University of Technology
Linhuan Coking and Chemical Co Ltd
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Hefei University of Technology
Linhuan Coking and Chemical Co Ltd
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Abstract

The invention discloses an intelligent dispatching method and application of a coking and coking mixed flow shop based on a Harris eagle algorithm, and the method comprises the following steps: 1, establishing a coking process intelligent scheduling model for a coking mixed flow shop scheduling problem by acquiring production information of a coke production line of a coking workshop; 2, coding the Harisk eagle individuals by adopting an arrangement coding mode, and forming a coking workshop scheduling scheme based on a workpiece first-to-first machining rule and an earliest idle machine rule as a decoding mode; and 3, performing iterative optimization on the coking mixed flow shop scheduling model by utilizing an improved Harris eagle algorithm, outputting a global optimal scheduling scheme and arranging coke production. According to the invention, by using an intelligent method, the dispatching optimization efficiency and accuracy of the coking and coking mixed flow shop are improved, the intellectualization of dispatching is realized, and scientific decision is made by assisting dispatching personnel.

Description

Intelligent dispatching method and application of coking and coking mixed flow shop based on Harris eagle algorithm
Technical Field
The invention belongs to the technical field of computer integrated manufacturing, and particularly relates to an intelligent dispatching method for a coking and coking mixed flow shop based on a Harris eagle algorithm and application thereof.
Background
Hybrid flow shop scheduling refers to a production shop, also known as a flexible flow shop, that includes multiple processes and one or more parallel machines per process, arranged in a continuous line. The workshop structure can effectively eliminate the influence of a bottleneck machine on production continuity, improve the efficiency of the whole production line, and effectively balance the utilization rate of the machine and increase the productivity. In a hybrid flow shop scheduling problem, a single process may have multiple available machines in parallel, and thus a processing machine may be selected from the multiple parallel machines. The goal of mixed flow shop scheduling is to consider not only the proper machine arrangement for each process, but also how to arrange the processing sequence of the processing tasks, which is a more difficult NP-hard problem.
Coke is a pulse of energy economy in China, and has higher economic value and strategic significance. Coking is an important process for preparing coke, and although the coking process is complicated in the type of coking intermediate products and processing technology, the coking process adopts a streamline processing mode, the process is clear, and a plurality of parallel processing devices exist in each process, so that key coking steps can be extracted, and the method belongs to the scheduling problem of a mixed flow shop. There is currently no study on the scheduling problem of coking workshops for two main reasons: firstly, a coking and coking process belongs to the traditional chemical product manufacturing industry, has dangers in the production process, has low automation and standardization degrees and has limited application scenes; second, compared with conventional mixed flow shop scheduling, the problem of coke product production shop scheduling has the characteristics of more constraint conditions and great complexity. Therefore, the research on intelligent production of coke has higher practical value and theoretical significance.
The method for solving the scheduling problem of the mixed flow shop is mainly divided into an accurate solving method and an approximate method. The accurate solving method is to build an optimal solution which completely accords with the actual problem model of production and finally solves the problem aiming at the specific problem, but the scheduling problem of the mixed flow shop is proved to be an NP-hard problem, so that the method is difficult to be suitable for the scheduling problem of the mixed flow shop in a large scale.
Coking production is used as an important prop industry of energy economy, and indexes such as production efficiency and production cost of enterprises are directly influenced by how to scientifically arrange processing tasks of coking workshops and reasonably allocate workshop resources to realize scheduling optimization of a production system. In addition, because the coking production process flow is complex and the quantity of intermediate products is large, the scheduling problem of the coking production workshop is researched by fresh students.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent dispatching method and application of a coking and coking mixed flow shop based on a Harris eagle algorithm, so that an intelligent method can be adopted to replace or assist dispatching personnel to make decisions, and therefore the dispatching efficiency and accuracy of the shop can be improved.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to an intelligent dispatching method for a coking and coking mixed flow shop based on a Harris eagle algorithm, which is characterized by being applied to the production of M mixed coals with different proportions which are processed into coke after passing through S procedures of the coking shop, and comprising the following steps:
step 1: obtaining production information of a coke production line of a coking workshop, establishing an objective function of a coking mixed flow shop scheduling problem by using a formula (1), and establishing constraint conditions by using formulas (2) - (7), thereby forming a coking mixed flow shop scheduling model:
Figure BDA0003860885250000021
Figure BDA0003860885250000022
X i,i-1,m,k +X i-1,i,m,k ≤1 (3)
Figure BDA0003860885250000023
Figure BDA0003860885250000024
C ij ≥C i,j+1 +p ij (6)
X i,j,k 、X i,i-1,m,k ∈{0,1} (7)
in the formulas (1) - (7), X i,j,k Indicating whether the jth step of the ith blended coal is processed on the kth machine, if X i,j,k By 1, we mean that the workpiece is processed on the kth machine, if X i,j,k =0, then means not machined on the kth machine; x is X i,i-1,m,k Indicating whether the jth step of processing the ith-1 th mixed coal is preferred for the jth step of processing the ith mixed coal on the kth machine, and if so, letting X i,i-1,m,k 1, otherwise, let X i,i-1,m,k Is 0; s represents the number of steps; p is p ij The processing time of the ith mixed coal in the jth procedure is shown; m is m j Representing the number of parallel machines in the jth machining process; c (C) ij Indicating the finishing time of the ith mixed coal in the jth processing procedure; c (C) i,j+1 Indicating the finishing time of the ith mixed coal in the (j+1) th process; y represents a relaxation variable;
Figure BDA0003860885250000025
representing the maximum finishing time of the ith blended coal; i=1, 2,; j=1, 2,. -%, S; k=1, 2, m j ；
Step 2: determining an initial scheduling scheme consisting of the processing sequence and the machine distribution sequence of the mixed coal in each procedure according to the historical experience of coke production in a coking workshop;
defining the maximum iteration termination time as T, and enabling the current iteration time t=0; making the current initial scheduling scheme be the optimal scheduling scheme of the t-1 th iteration
Figure BDA0003860885250000031
I.e., the prey location of the t-1 th iteration;
step 3: solving the coking and coking mixed flow shop scheduling model by utilizing an improved Harris eagle algorithm;
step 3.1: defining the population scale as N; constructing Harris eagle set of the t th generation population as
Figure BDA0003860885250000032
Figure BDA0003860885250000033
The position information of the v th Harris hawk representing the t generation population is recorded as { pi }, and the position information of each Harris hawk is recorded as M mixed coals processing sequence codes v,1 ,π v,2 ,...,π v,i ,...,π v,M -a }; wherein pi v,i Representing the processing sequence of the ith mixed coal in the v-th Harris eagle;
distributing each mixed coal to the earliest available machine for processing according to the finishing sequence of the last procedure of the mixed coal, and randomly selecting one machine for processing if a plurality of machines are available at the same time, so that the position information of each Harisch eagle is decoded into the machine distribution sequence of M mixed coal processing;
the processing sequence of M mixed coals represented by the position information of the v-th Harris hawk in the t-th generation population and the machine distribution sequence of M mixed coals are used for forming a v-th coking workshop scheduling scheme of the t-th iteration, wherein v=1, 2, … and N; n is population scale;
step 3.2: taking the objective function F as the fitness function of the population of the t th generation
Figure BDA0003860885250000034
Wherein (1)>
Figure BDA0003860885250000035
Representing the fitness function of the v-th Harris eagle in the t-th generation population;
step 3.3: calculating the fitness value of each halisk in the t generation population according to the formula (1), and setting the position information of the halisk with the optimal fitness value as the position of the prey in the t iteration
Figure BDA0003860885250000036
Step 3.4: the method comprises the steps of updating the escaping energy of the hunting, and then executing a corresponding position updating strategy in searching or developing behaviors according to the escaping energy of the hunting and the generated random number;
step 3.4.1: calculating the escape energy E of the hunting object in the t iteration by using the formula (8) t ：
E t ＝2E t,0 ×((1-t/T) 1/3 ) 1/2 (8)
In the formula (8), E t,0 Random numbers within interval (-1, 1) at the t-th iteration;
step 3.4.2: if |E t The I is more than or equal to 1, then the random exploration stage is entered, and the v hawk of the t generation population is updated by using the formula (9)Position information of (a)
Figure BDA0003860885250000037
Thereby obtaining the position information of the v th Harriscral of the t+1st generation population +.>
Figure BDA0003860885250000038
Thus obtaining the population of the t+1st generation;
Figure BDA0003860885250000039
Figure BDA0003860885250000041
in the formulas (9) and (10),
Figure BDA0003860885250000042
position information representing individuals randomly selected by the t generation population; r is (r) 1 、r 2 、r 3 Three random numbers in the expression interval (0, 1,)>
Figure BDA0003860885250000043
Representing the average position of harris eagles of the t-th generation population, LB representing the upper bound of the algorithm search space, UB representing the lower bound of the algorithm search space;
if |E|<1, the development behavior is executed, and one strategy is selected from the four strategies to obtain the position information of the v Harris hawk of the t+1st generation population
Figure BDA0003860885250000044
Thus obtaining the population of the t+1st generation;
step 3.4.3: dividing the t+1th generation population into a plurality of sub-populations, and carrying out sub-population information exchange operation on any two sub-populations by adopting a crossing method to obtain an updated t+1th generation population;
step 3.5: calculating the position information of the v Harris eagle in the updated t+1st generation population
Figure BDA0003860885250000045
Is adapted to (a)
Figure BDA0003860885250000046
And with the prey position of the t-th iteration +.>
Figure BDA0003860885250000047
Is>
Figure BDA0003860885250000048
Comparing if->
Figure BDA0003860885250000049
Position information of the v th Harris eagle->
Figure BDA00038608852500000410
The position of the prey as the t+1st iteration, i.e. the optimal scheduling scheme of the t+1st iteration +.>
Figure BDA00038608852500000411
Otherwise, the prey position of the t-th iteration +.>
Figure BDA00038608852500000412
As a prey location for the t+1st iteration;
step 3.6: after t+1 is assigned to T, the sequence is returned to step 3.3 until T > T, so that the position of the prey of the T-th iteration is output as a globally optimal scheduling scheme and production is scheduled.
The coking and coking mixed flow shop scheduling method of the invention is also characterized in that the four strategies in the step 3.4.2 comprise:
strategy 1: when |E t The I is more than or equal to alpha and the r is more than or equal to beta, alpha, beta and r are three random numbers in the interval (0, 1), and the formula (11) is utilized to update the position information of the v th Harris hawk of the t-th generation population
Figure BDA00038608852500000413
Thereby obtaining the firstPositional information of the v th Harris eagle of the t+1 generation population +.>
Figure BDA00038608852500000414
/>
Figure BDA00038608852500000415
Figure BDA00038608852500000416
In the formulas (11) and (12),
Figure BDA00038608852500000417
representing the difference between the position of the prey in the t iteration population and the position information of the v Harris eagle, J represents the random jump intensity in the whole escape process of the prey, and J is a random number in the interval (0, 2);
strategy 2: when |E t The I is more than or equal to alpha and r is more than or equal to beta, and the position information of the v hawk of the t+1st generation population is obtained by utilizing the formula (13)
Figure BDA00038608852500000418
Figure BDA00038608852500000419
Strategy 3: when |E t The I is more than or equal to alpha and r<Beta, using the formula (14) and the formula (15), obtaining the position information Y or Z of the v-th Harriscrant of the t+1st generation population:
Figure BDA00038608852500000420
Z＝Y+S×LF(D) (15)
in the formulas (14) and (15), r4 represents a random number in a section (0, 1), D represents a dimension of a scheduling problem of the coking mixed flow workshop, S represents a random vector with a size of 1 xD, and LF is a Levy flight function;
if the fitness value
Figure BDA0003860885250000051
Make->
Figure BDA0003860885250000052
Thereby obtaining the position information of the v-th Harris eagle of the t+1st generation population by using the formula (14)>
Figure BDA0003860885250000053
If the fitness value
Figure BDA0003860885250000054
Make->
Figure BDA0003860885250000055
Thereby obtaining the position information of the v-th Harris hawk of the t+1st generation population by using the formula (15)>
Figure BDA0003860885250000056
Figure BDA0003860885250000057
The fitness value of the v-th Harris eagle of the t+1st generation population obtained by the formula (14) is represented by +.>
Figure BDA0003860885250000058
A fitness value of a v-th Harris hawk of the t+1st generation population obtained by the formula (15);
strategy 4: when |E t |<Alpha and r<Beta, using the formula (16) and the formula (17), obtaining the position information Y or Z of the v-th Harriset of the t+1st generation population:
Figure BDA0003860885250000059
Z＝Y+S×LF(D) (17)
if the fitness value
Figure BDA00038608852500000510
Make->
Figure BDA00038608852500000511
Thereby obtaining positional information of the v-th Harris eagle of the t+1st population +.>
Figure BDA00038608852500000512
If the fitness value
Figure BDA00038608852500000513
Make->
Figure BDA00038608852500000514
Whereby the positional information of the v-th Harriset hawk of the t+1th population of formula (17) is used +>
Figure BDA00038608852500000515
The sub-population information exchange in step 3.4.3 includes a random selection operation and an elite population operation:
step 3.4.3.1: the fitness values of all harris hawks in the t+1st generation of population are sorted in ascending order and divided into e sub-populations of the t+1st generation, wherein each sub-population has N/e harris hawks;
step 3.4.3.2: position information of randomly selected ith Harris eagle in any two sub-populations of the t+1st generation using formula (18) and formula (19)
Figure BDA00038608852500000516
And position information of jth Harriset eagle->
Figure BDA00038608852500000517
Performing crossover operation to obtain position information +.>
Figure BDA00038608852500000518
Figure BDA00038608852500000519
/>
Figure BDA00038608852500000520
In the formula (18) and the formula (19), gamma is a random number between [0,1 ];
if it is
Figure BDA00038608852500000521
Then the position information of the new Harris hawk after crossing is +.>
Figure BDA00038608852500000522
Position information giving the ith Harriset eagle +.>
Figure BDA0003860885250000061
Otherwise, the position information of the ith Harris eagle +.>
Figure BDA0003860885250000062
Remain unchanged; wherein (1)>
Figure BDA0003860885250000063
Position information representing new harris eagle after crossing +.>
Figure BDA0003860885250000064
Adaptation value of F i ′t Position information indicating the ith Harriset eagle->
Figure BDA0003860885250000065
Is a fitness value of (a);
if it is
Figure BDA0003860885250000066
Then the position information of the new Harris hawk after crossing is +.>
Figure BDA0003860885250000067
Position information giving the j-th Harris eagle +.>
Figure BDA0003860885250000068
Otherwise, the position information of the jth Harris eagle +.>
Figure BDA0003860885250000069
Remain unchanged, wherein->
Figure BDA00038608852500000610
Position information representing new harris eagle after crossing +.>
Figure BDA00038608852500000611
F j ′ t represents the position information of the jth Harris eagle +.>
Figure BDA00038608852500000612
Is a fitness value of (a);
step 3.4.3.3: processing all the sub-populations according to the process of the step 3.4.3.2 to obtain updated e sub-populations of the t+1st generation;
step 3.4.3.4: ascending and sorting fitness values of haustics in the updated t+1th generation e sub-populations to obtain optimal individual information in each sub-population, and recording the optimal individual information as elite individuals;
step 3.4.3.5: positional information of elite individuals in any two sub-populations of the updated t+1st generation using formulas (20) and (21)
Figure BDA00038608852500000613
And->
Figure BDA00038608852500000614
Performing crossover operation to obtain position information of two new elite individuals after crossover
Figure BDA00038608852500000615
Figure BDA00038608852500000616
Figure BDA00038608852500000617
In the formula (20) and the formula (21), μ is a random number between [0,1 ];
if it is
Figure BDA00038608852500000618
Then the position information of the new elite individual after crossing is +.>
Figure BDA00038608852500000619
Giving elite individuals position information +.>
Figure BDA00038608852500000620
Otherwise, elite individual position information +.>
Figure BDA00038608852500000621
Remain unchanged, wherein->
Figure BDA00038608852500000622
Position information representing new elite individual after crossing +.>
Figure BDA00038608852500000623
Adaptation value of F a ′t Position information representing elite harris eagle in a sub-population +.>
Figure BDA00038608852500000624
Is a fitness value of (a);
if it is
Figure BDA00038608852500000625
Then the position information of the new elite individual after crossing is +.>
Figure BDA00038608852500000626
Giving elite individuals position information +.>
Figure BDA00038608852500000627
Otherwise, elite individual position information +.>
Figure BDA00038608852500000628
Remain unchanged, wherein->
Figure BDA00038608852500000629
Position information representing new elite individual after crossing +.>
Figure BDA00038608852500000630
Adaptation value of F b ′t Position information representing elite individuals in sub-population +.>
Figure BDA00038608852500000631
Is a fitness value of (a);
the invention provides an electronic device comprising a memory and a processor, characterized in that the memory is used for storing a program for supporting the processor to execute any one of the methods, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, on which a computer program is stored, characterized in that the computer program when being run by a processor performs the steps of any one of the methods.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the actual coking workshop demand, the invention provides a haustilago-based optimization method for solving the coking workshop scheduling scheme, and the haustilago-based optimization method is applied to an actual industrial scene, so that the application field of a group intelligent optimization algorithm is widened, the defect of lack of intelligent pain points in the traditional industrial production scheduling field is practically solved, the error caused by manual reasons in scheduling is avoided, the flexible adjustment of a production plan is ensured, and the utilization rate of a production machine is improved.
2. The Harris hawk optimization algorithm has the characteristics of simple structure, rapid convergence and the like, and is very suitable for solving the problem of scheduling of a large-scale mixed flow shop. The coking mixed running water scheduling problem tends to be high-dimensional in dimension, the performance of a standard Harris eagle optimization algorithm for solving the problem is still to be improved, and meanwhile, the algorithm is easy to sink into local optimization, so that an optimal scheduling scheme cannot be obtained. Therefore, the algorithm is purposefully designed and improved on the basis of considering the Harris eagle optimization algorithm, so that the scheduling problem of the coking mixed flow shop is solved more effectively.
Drawings
FIG. 1 is a flow chart for solving an intelligent scheduling problem of a coking mixed flow shop based on a Harris eagle algorithm;
FIG. 2 is a flow chart of a coking process from a coking plant according to the present invention;
FIG. 3 is a layout of a coking and mixing flow shop according to the present invention;
FIG. 4 is a Gantt chart of a four-workpiece two-stage hybrid flow shop schedule obtained by the prior art decoding method.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
In this embodiment, as shown in fig. 1, an intelligent dispatching optimization method for a coking and coking mixed flow shop based on a haustilago algorithm is applied to the production of M kinds of mixed coals with different proportions processed into coke after passing through S procedures of the coking shop, and includes the following steps:
step 1: referring to fig. 2, process information of a coking plant coke production line is acquired. The key working procedures are extracted from the coking process flow, and the scheduling problem of the coking mixing flow shop is constructed. The coking process flow mainly comprises four parts of coal preparation, coking, coke quenching and coke screening. The key processes are coal blending, iron removal, crushing by a reversible counter-hammer crusher, coal taking by a coal tower, high-temperature carbonization by a carbonization chamber, coke blocking machine conveying, coke quenching Jiao Daxi, coke cooling by a coke cooling table and coke cooling and screening. In the coking process, the mixed coal needs to go through the same processing procedures to obtain coke products, at least one parallel machine exists in each procedure, the working hours in each step are different due to the different quality of the mixed coal, and the problems of infinite buffer interval, neglecting equipment faults, arrival delay and the like are assumed to exist between different stages. The coking mixed flow shop scheduling is to optimize the production plan decision of the coking shop, and the production time of coke is minimized by reasonably arranging the processing sequence of mixed coal and efficiently configuring the production resources of the shop.
Referring to fig. 3, a layout diagram of equipment in a coking mixed flow shop is shown, although the types of intermediate products of coking products and the processing technology are complex, the main coking process adopts a flow line processing mode, the technological process is clear, a plurality of parallel processing equipment exist in each process, and the problem of scheduling in the mixed flow shop is solved; establishing an objective function of the coking mixing flow shop scheduling problem by using the formula (1), and establishing constraint conditions by using the formulas (2) - (7), thereby forming a coking mixing flow shop scheduling model:
Figure BDA0003860885250000071
Figure BDA0003860885250000081
X i,i-1,m,k +X i-1,i,m,k ≤1 (3)
Figure BDA0003860885250000082
Figure BDA0003860885250000083
C ij ≥C i,j+1 +p ij (6)
X i,j,k 、X i,i-1,m,k ∈{0,1} (7)
in the formulas (1) - (7), X i,j,k Indicating whether the jth step of the ith blended coal is processed on the kth machine, if X i,j,k By 1, we mean that the workpiece is processed on the kth machine, if X i,j,k =0, then means not machined on the kth machine; x is X i,i-1,m,k Indicating whether the jth step of processing the ith-1 th mixed coal is preferred for the jth step of processing the ith mixed coal on the kth machine, and if so, letting X i,i-1,m,k 1, otherwise, let X i,i-1,m,k Is 0; s represents the number of steps; p is p ij The processing time of the ith mixed coal in the jth procedure is shown; m is m j Representing the number of parallel machines in the jth machining process; c (C) ij Indicating the finishing time of the ith mixed coal in the jth processing procedure; c (C) i,j+1 Indicating the finishing time of the ith mixed coal in the (j+1) th process; y represents a relaxation variable;
Figure BDA0003860885250000084
representing the maximum finishing time of the ith blended coal; i=1, 2,; j=1, 2,. -%, S; k=1, 2, m j ；
Step 2: determining an initial scheduling scheme consisting of the processing sequence and the machine distribution sequence of the mixed coal in each procedure according to the historical experience of coke production in a coking workshop;
defining the maximum iteration termination time as T, and enabling the current iteration time t=0; making the current initial scheduling scheme be the optimal scheduling scheme of the t-1 th iteration
Figure BDA0003860885250000085
I.e., the prey location of the t-1 th iteration;
step 3: because the original Harris hawk optimization algorithm has the problem that population diversity is easy to lose in the later iteration stage, the method falls into the locally optimal congenital defect, and the improved Harris hawk algorithm is used for solving the coking mixed flow shop scheduling model, and the specific steps are as follows:
step 3.1: defining the population scale as N; constructing Harris eagle set of the t th generation population as
Figure BDA0003860885250000086
Figure BDA0003860885250000087
The position information of the v th Harris hawk representing the t generation population is recorded as { pi }, and the position information of each Harris hawk is recorded as M mixed coals processing sequence codes v,1 ,π v,2 ,...,π v,i ,...,π v,M And }, where pi v,i Representing the processing sequence of the ith mixed coal in the v-th Harris eagle, wherein the specific operation is to assume that 4 mixed coals exist in a coking workshop to be coked, numbering the mixed coals according to the sequence, and then randomly and fully arranging the mixed coals, wherein the number represents the processed priority of the mixed coals; when the idle machine exists and the machining conditions are met, preferentially considering to machine the workpiece with the front coding position; for example, the mixed coal sequence 4,1,3,2, shows that in the first pass, a machine is arranged for mixed coal 4, and then for mixed coals 1,3,2 in sequence.
Distributing each mixed coal to the earliest available machine for processing according to the finishing sequence of the last procedure of the mixed coal, and randomly selecting one machine for processing if a plurality of machines are available at the same time, so that the position information of each Harisch eagle is decoded into the machine distribution sequence of M mixed coal processing; referring to fig. 4, a schematic diagram of two processes of four kinds of mixed coal using the above decoding method is shown;
the processing sequence of M mixed coals represented by the position information of the v-th Harris hawk in the t-th generation population and the machine distribution sequence of M mixed coals are used for forming a v-th coking workshop scheduling scheme of the t-th iteration, wherein v=1, 2, … and N; n is population scale;
step 3.2: taking the objective function F as the fitness function of the population of the t th generation
Figure BDA0003860885250000091
Wherein (1)>
Figure BDA0003860885250000092
Represent the firstthe fitness function of the v-th Harris eagle in the t-generation population; />
Step 3.3: calculating the fitness value of each Harisch eagle in the t generation population according to the formula (1); and setting the position information of the Harris hawk with the optimal fitness value as the position of the prey in the t-th iteration
Figure BDA0003860885250000093
Step 3.4: the method comprises the steps of updating the escaping energy of the hunting, and then executing a corresponding position updating strategy in searching or developing behaviors according to the escaping energy of the hunting and the generated random number;
step 3.4.1: because the energy changes to be nonlinear when the prey escapes, a brand new prey energy updating formula is provided, and the global search and the local search of the algorithm are balanced. Calculating the escape energy E of the hunting object in the t iteration by using the formula (8) t ：
E t ＝2E t,0 ×((1-t/T) 1/3 ) 1/2 (8)
In the formula (8), E t,0 Random numbers within interval (-1, 1) at the t-th iteration;
step 3.4.2: if |E t The I is more than or equal to 1, the random exploration stage is entered, and the position information of the v-th Harris hawk of the t-th generation population is updated by using the formula (9)
Figure BDA0003860885250000094
Thereby obtaining the position information of the v th Harriscral of the t+1st generation population +.>
Figure BDA0003860885250000095
Thus obtaining the population of the t+1st generation;
Figure BDA0003860885250000096
Figure BDA0003860885250000101
in the formulas (9) and (10),
Figure BDA0003860885250000102
position information representing individuals randomly selected by the t generation population; r is (r) 1 、r 2 、r 3 Representing random numbers in interval (0, 1, ">
Figure BDA0003860885250000103
Representing the average position of the harris eagle of the t-th generation population, LB representing the upper bound of the algorithm search space and UB representing the lower bound of the algorithm search space.
If |E|<1, the development behavior is executed, and one strategy is selected from the four strategies to obtain the position information of the v Harris hawk of the t+1st generation population
Figure BDA0003860885250000104
Thus obtaining the population of the t+1st generation;
strategy 1: when |E t The I is more than or equal to alpha and the r is more than or equal to beta, alpha, beta and r are random numbers in the interval (0, 1), and the formula (11) is utilized to update the position information of the v-th Harris hawk of the t-th generation population
Figure BDA0003860885250000105
Thereby obtaining the position information of the v-th Harriset eagle of the t+1st generation population
Figure BDA0003860885250000106
Figure BDA0003860885250000107
Figure BDA0003860885250000108
In the formulas (11) and (12),
Figure BDA0003860885250000109
representing the position of a prey in a t-th iteration populationThe difference in current position of the eagle, J, represents the random jump intensity throughout the life of the game, J being the random number of interval (0, 2).
Strategy 2: when |E t The I is more than or equal to alpha and r is more than or equal to beta, and the position information of the v hawk of the t+1st generation population is obtained by utilizing the formula (13)
Figure BDA00038608852500001010
Figure BDA00038608852500001011
Strategy 3: when |E t The I is more than or equal to alpha and r<Beta, since the prey still has a large jump capacity J, the harris eagle enables a fast dive attack, if the attack fails (the Y fitness is not improved), then a random walk Z is performed, if the walk fails, then the return to the home is made. The sine and cosine optimization algorithm (Sine Cosine Algorithm, SCA) realizes an optimizing process by utilizing the periodical change of a sine and cosine function, the position updating mode enables the algorithm to explore a larger area during exploration, the algorithm can search accurately in a smaller range during local development, the position updating mode of the SCA is comprehensively utilized and applied to the local search position updating of the Harris eagle algorithm, the Harris eagle algorithm is improved, and the overall optimizing level of the Harris eagle algorithm is improved. Obtaining position information Y or Z of the v-th Harris eagle of the t+1st generation population by using the formula (14) and the formula (15):
Figure BDA00038608852500001012
Z＝Y+S×LF(D) (15)
in the formulas (14) and (15), r4 represents a random number in a section (0, 1), D represents a dimension of a scheduling problem of the coking mixed flow workshop, S represents a random vector with a size of 1 xD, and LF is a Levy flight function;
if the fitness value
Figure BDA00038608852500001013
Make->
Figure BDA00038608852500001014
Thereby obtaining the position information of the v-th Harris eagle of the t+1st generation population by using the formula (14)>
Figure BDA00038608852500001015
If the fitness value
Figure BDA00038608852500001016
Make->
Figure BDA00038608852500001017
Thereby obtaining the position information of the v-th Harris hawk of the t+1st generation population by using the formula (15)>
Figure BDA0003860885250000111
Figure BDA0003860885250000112
The fitness value of the v-th Harris eagle of the t+1st generation population obtained by the formula (14) is represented by +.>
Figure BDA0003860885250000113
A fitness value of a v-th Harris hawk of the t+1st generation population obtained by the formula (15);
strategy 4: when |E t |<Alpha and r<Beta, using the formula (16) and the formula (17), obtaining the position information Y or Z of the v-th Harriset of the t+1st generation population:
Figure BDA0003860885250000114
Z＝Y+S×LF(D) (17)
if the fitness value
Figure BDA0003860885250000115
Make->
Figure BDA0003860885250000116
Thereby obtaining the position information of the v-th Harris hawk of the t+1st generation population by using the formula (16)>
Figure BDA0003860885250000117
If the fitness value
Figure BDA0003860885250000118
Make->
Figure BDA0003860885250000119
Thereby utilizing the positional information of the v-th Harriset eagle of the t+1th generation population of formula (17)>
Figure BDA00038608852500001110
Step 3.4.3: because the Harris eagle algorithm easily loses population diversity in the later iteration period, a population information communication mechanism is provided for the defect, the specific operation is that the t+1th generation population is divided into a plurality of sub-populations, the sub-population information communication operation is carried out on any two sub-populations by adopting a crossing method, and the updated t+1th generation population is obtained; the main purpose of adopting the population information communication mechanism is that after repeated iterative position updating, harris eagle individuals are gathered, the position information is single, and the algorithm is difficult to achieve global optimum, so that attention is paid to randomly selecting individuals to be crossed, and the population diversity is enriched;
step 3.4.3.1: the fitness values of all harris hawks in the t+1st generation of population are sorted in ascending order and divided into e sub-populations of the t+1st generation, wherein each sub-population has N/e harris hawks;
step 3.4.3.2: position information of randomly selected ith Harris eagle in any two sub-populations of the t+1st generation using formula (18) and formula (19)
Figure BDA00038608852500001111
And position information of jth Harriset eagle->
Figure BDA00038608852500001112
Performing crossover operation to obtain position information +.>
Figure BDA00038608852500001113
Figure BDA00038608852500001114
Figure BDA00038608852500001115
In the formula (18) and the formula (19), gamma is a random number between [0,1 ];
if it is
Figure BDA00038608852500001116
Then the position information of the new Harris hawk after crossing is +.>
Figure BDA00038608852500001117
Position information giving the ith Harriset eagle +.>
Figure BDA00038608852500001118
Otherwise, keeping the original position information unchanged; wherein (1)>
Figure BDA00038608852500001119
Position information representing new harris eagle after crossing +.>
Figure BDA00038608852500001120
Adaptation value of F i ′t Position information indicating the ith Harriset eagle->
Figure BDA00038608852500001121
Is a fitness value of (a);
Figure BDA0003860885250000121
then using (19) willPosition information of new harris eagle after crossing +.>
Figure BDA0003860885250000122
Position information giving the j-th Harris eagle +.>
Figure BDA0003860885250000123
Otherwise, the original position information is kept unchanged, wherein +.>
Figure BDA0003860885250000124
Position information representing new harris eagle after crossing +.>
Figure BDA0003860885250000125
F j ′ t Position information +.>
Figure BDA0003860885250000126
Is a fitness value of (a);
step 3.4.3.3: processing all the sub-populations according to the process of the step 3.4.3.2 to obtain updated e sub-populations of the t+1st generation;
step 3.4.3.4: ascending and sorting fitness values of haustics in the updated t+1th generation e sub-populations to obtain optimal individual information in each sub-population, and recording the optimal individual information as elite individuals;
step 3.4.3.5: positional information of elite individuals in any two sub-populations of the updated t+1st generation using formulas (20) and (21)
Figure BDA0003860885250000127
And->
Figure BDA0003860885250000128
Performing crossover operation to obtain position information of two new elite individuals after crossover
Figure BDA0003860885250000129
Figure BDA00038608852500001210
Figure BDA00038608852500001211
In the formulas (20) and (21), μ is a random number between [0,1]
If it is
Figure BDA00038608852500001212
Then the position information of the new elite individual after crossing is +.>
Figure BDA00038608852500001213
Giving elite individuals position information +.>
Figure BDA00038608852500001214
Otherwise, hold location information +.>
Figure BDA00038608852500001215
Unchanged; wherein (1)>
Figure BDA00038608852500001216
Representing position information of new elite individual after crossing
Figure BDA00038608852500001217
Adaptation value of F a ′t Position information representing elite individuals in sub-population +.>
Figure BDA00038608852500001218
Is a fitness value of (a);
if it is
Figure BDA00038608852500001219
Then the position information of the new elite individual after crossing is +.>
Figure BDA00038608852500001220
ImpartingPosition information of elite individual->
Figure BDA00038608852500001221
Otherwise, hold location information +.>
Figure BDA00038608852500001222
Unchanged; wherein (1)>
Figure BDA00038608852500001223
Representing position information of new elite individual after crossing
Figure BDA00038608852500001224
Adaptation value of F a ′ t Position information representing elite individuals in sub-population +.>
Figure BDA00038608852500001225
Is used for the adaptation value of the (c).
Step 3.5: calculating the position information of the v Harris eagle in the updated t+1st generation population
Figure BDA00038608852500001226
Is adapted to (a)
Figure BDA00038608852500001227
And with the prey position of the t-th iteration +.>
Figure BDA00038608852500001228
Is>
Figure BDA00038608852500001229
Comparing if->
Figure BDA00038608852500001230
Position information of the v th Harris eagle->
Figure BDA00038608852500001231
The position of the prey as the t+1st iteration, i.e. the optimal scheduling scheme of the t+1st iteration +.>
Figure BDA00038608852500001232
Otherwise, the prey position of the t-th iteration +.>
Figure BDA00038608852500001233
As a prey location for the t+1st iteration;
step 3.6: after t+1 is assigned to T, the sequence is returned to step 3.3 until T > T, so that the position of the prey of the T-th iteration is output as a globally optimal scheduling scheme and production is scheduled.

Claims (5)

1. The intelligent dispatching method for the coking and coking mixed flow shop based on the Harris eagle algorithm is characterized by being applied to the production of M kinds of mixed coals with different proportions which are processed into coke after passing through S procedures of the coking shop, and comprises the following steps:
step 1: obtaining production information of a coke production line of a coking workshop, establishing an objective function of a coking mixed flow shop scheduling problem by using a formula (1), and establishing constraint conditions by using formulas (2) - (7), thereby forming a coking mixed flow shop scheduling model:
Figure FDA0003860885240000011
Figure FDA0003860885240000012
Figure FDA0003860885240000013
Figure FDA0003860885240000014
Figure FDA0003860885240000015
C ij ≥C i，j+1 +p ij (6)
X i，j，k 、X i，i-1，m，k ∈{0，1} (7)
in the formulas (1) - (7), X i,j,k Indicating whether the jth step of the ith blended coal is processed on the kth machine, if X i,j,k By 1, we mean that the workpiece is processed on the kth machine, if X i,j,k =0, then means not machined on the kth machine; x is X i,i-1,m,k Indicating whether the jth step of processing the ith-1 th mixed coal is preferred for the jth step of processing the ith mixed coal on the kth machine, and if so, letting X i,i-1,m,k 1, otherwise, let X i,i-1,m,k Is 0; s represents the number of steps; p is p ij The processing time of the ith mixed coal in the jth procedure is shown; m is m j Representing the number of parallel machines in the jth machining process; c (C) ij Indicating the finishing time of the ith mixed coal in the jth processing procedure; c (C) i,j+1 Indicating the finishing time of the ith mixed coal in the (j+1) th process; y represents a relaxation variable;
Figure FDA0003860885240000016
representing the maximum finishing time of the ith blended coal; i=1, 2,; j=1, 2,. -%, S; k=1, 2, m j ；
Step 2: determining an initial scheduling scheme consisting of the processing sequence and the machine distribution sequence of the mixed coal in each procedure according to the historical experience of coke production in a coking workshop;
defining the maximum iteration termination time as T, and enabling the current iteration time t=0; making the current initial scheduling scheme be the optimal scheduling scheme of the t-1 th iteration
Figure FDA0003860885240000017
I.e., the prey location of the t-1 th iteration;
step 3: solving the coking and coking mixed flow shop scheduling model by utilizing an improved Harris eagle algorithm;
step 3.1: defining the population scale as N; constructing Harris eagle set of the t th generation population as
Figure FDA0003860885240000021
Figure FDA0003860885240000022
The position information of the v th Harris hawk representing the t generation population is recorded as { pi }, and the position information of each Harris hawk is recorded as M mixed coals processing sequence codes v,1 ,π v,2 ,...,π v,i ,...,π v,M -a }; wherein pi v,i Representing the processing sequence of the ith mixed coal in the v-th Harris eagle;
distributing each mixed coal to the earliest available machine for processing according to the finishing sequence of the last procedure of the mixed coal, and randomly selecting one machine for processing if a plurality of machines are available at the same time, so that the position information of each Harisch eagle is decoded into the machine distribution sequence of M mixed coal processing;
the processing sequence of M mixed coals represented by the position information of the v-th Harris hawk in the t-th generation population and the machine distribution sequence of M mixed coals are used for forming a v-th coking workshop scheduling scheme of the t-th iteration, wherein v=1, 2, … and N; n is population scale;
step 3.2: taking the objective function F as the fitness function of the population of the t th generation
Figure FDA0003860885240000023
Wherein (1)>
Figure FDA0003860885240000024
Representing the fitness function of the v-th Harris eagle in the t-th generation population;
step 3.3: calculating the fitness value of each halisk in the t generation population according to the formula (1), and setting the position information of the halisk with the optimal fitness value as the position of the prey in the t iteration
Figure FDA0003860885240000025
Step 3.4: the method comprises the steps of updating the escaping energy of the hunting, and then executing a corresponding position updating strategy in searching or developing behaviors according to the escaping energy of the hunting and the generated random number;
step 3.4.1: calculating the escape energy E of the hunting object in the t iteration by using the formula (8) t ：
E t ＝2E t,0 ×((1-t/T) 1/3 ) 1/2 (8)
In the formula (8), E t,0 Random numbers within interval (-1, 1) at the t-th iteration;
step 3.4.2: if |E t The I is more than or equal to 1, the random exploration stage is entered, and the position information of the v-th Harris hawk of the t-th generation population is updated by using the formula (9)
Figure FDA0003860885240000026
Thereby obtaining the position information of the v th Harriscral of the t+1st generation population +.>
Figure FDA0003860885240000027
Thus obtaining the population of the t+1st generation;
Figure FDA0003860885240000028
Figure FDA0003860885240000029
in the formulas (9) and (10),
Figure FDA00038608852400000210
position information representing individuals randomly selected by the t generation population; r is (r) 1 、r 2 、r 3 Three random numbers in the expression interval (0, 1,)>
Figure FDA00038608852400000211
Representing the average position of harris eagles of the t-th generation population, LB representing the upper bound of the algorithm search space, UB representing the lower bound of the algorithm search space;
if |E|<1, the development behavior is executed, and one strategy is selected from the four strategies to obtain the position information of the v Harris hawk of the t+1st generation population
Figure FDA0003860885240000031
Thus obtaining the population of the t+1st generation;
step 3.4.3: dividing the t+1th generation population into a plurality of sub-populations, and carrying out sub-population information exchange operation on any two sub-populations by adopting a crossing method to obtain an updated t+1th generation population;
step 3.5: calculating the position information of the v Harris eagle in the updated t+1st generation population
Figure FDA0003860885240000032
Is->
Figure FDA0003860885240000033
And with the prey position of the t-th iteration +.>
Figure FDA0003860885240000034
Is>
Figure FDA0003860885240000035
Comparing if->
Figure FDA0003860885240000036
Position information of the v th Harris eagle->
Figure FDA0003860885240000037
The position of the prey as the t+1st iteration, i.e. the optimal scheduling scheme of the t+1st iteration +.>
Figure FDA0003860885240000038
Otherwise, the prey position of the t-th iteration +.>
Figure FDA0003860885240000039
As a prey location for the t+1st iteration;
step 3.6: after t+1 is assigned to T, the sequence is returned to step 3.3 until T > T, so that the position of the prey of the T-th iteration is output as a globally optimal scheduling scheme and production is scheduled.
2. The coking and coking mixing flow shop scheduling method according to claim 1, wherein the four strategies in step 3.4.2 include:
strategy 1: when |E t The I is more than or equal to alpha and the r is more than or equal to beta, alpha, beta and r are three random numbers in the interval (0, 1), and the formula (11) is utilized to update the position information of the v th Harris hawk of the t-th generation population
Figure FDA00038608852400000310
Thereby obtaining the position information of the v th Harriscral of the t+1st generation population +.>
Figure FDA00038608852400000311
Figure FDA00038608852400000312
Figure FDA00038608852400000313
In the formulas (11) and (12),
Figure FDA00038608852400000314
representing the difference between the position of the prey in the t-th iteration population and the position information of the v-th Harris eagle, J represents the randomness during the whole prey escape processJump intensity, and J is a random number of interval (0, 2);
strategy 2: when |E t The I is more than or equal to alpha and r is more than or equal to beta, and the position information of the v hawk of the t+1st generation population is obtained by utilizing the formula (13)
Figure FDA00038608852400000315
Figure FDA00038608852400000316
Strategy 3: when |E t The I is more than or equal to alpha and r<Beta, using the formula (14) and the formula (15), obtaining the position information Y or Z of the v-th Harriscrant of the t+1st generation population:
Figure FDA00038608852400000317
Z＝Y+S×LF(D) (15)
in the formulas (14) and (15), r4 represents a random number in a section (0, 1), D represents a dimension of a scheduling problem of the coking mixed flow workshop, S represents a random vector with a size of 1 xD, and LF is a Levy flight function;
if the fitness value
Figure FDA00038608852400000318
Make->
Figure FDA00038608852400000319
Thereby obtaining the position information of the v-th Harris eagle of the t+1st generation population by using the formula (14)>
Figure FDA0003860885240000041
If the fitness value
Figure FDA0003860885240000042
Make->
Figure FDA0003860885240000043
Thereby obtaining the position information of the v-th Harris hawk of the t+1st generation population by using the formula (15)>
Figure FDA0003860885240000044
Figure FDA0003860885240000045
The fitness value of the v-th Harris eagle of the t+1st generation population obtained by the formula (14) is represented by +.>
Figure FDA0003860885240000046
A fitness value of a v-th Harris hawk of the t+1st generation population obtained by the formula (15);
strategy 4: when |E t |<Alpha and r<Beta, using the formula (16) and the formula (17), obtaining the position information Y or Z of the v-th Harriset of the t+1st generation population:
Figure FDA0003860885240000047
Z＝Y+S×LF(D) (17)
if the fitness value
Figure FDA0003860885240000048
Make->
Figure FDA0003860885240000049
Thereby obtaining positional information of the v-th Harris eagle of the t+1st population +.>
Figure FDA00038608852400000410
If the fitness value
Figure FDA00038608852400000411
Make->
Figure FDA00038608852400000412
Whereby the positional information of the v-th Harriset hawk of the t+1th population of formula (17) is used +>
Figure FDA00038608852400000413
3. The coking and coking mixed flow shop scheduling method according to claim 2, wherein the sub-population information exchange in the step 3.4.3 includes a random selection operation and an elite population operation:
step 3.4.3.1: the fitness values of all harris hawks in the t+1st generation of population are sorted in ascending order and divided into e sub-populations of the t+1st generation, wherein each sub-population has N/e harris hawks;
step 3.4.3.2: position information of randomly selected ith Harris eagle in any two sub-populations of the t+1st generation using formula (18) and formula (19)
Figure FDA00038608852400000414
And position information of jth Harriset eagle->
Figure FDA00038608852400000415
Performing crossover operation to obtain position information +.>
Figure FDA00038608852400000416
/>
Figure FDA00038608852400000417
Figure FDA00038608852400000418
In the formula (18) and the formula (19), gamma is a random number between [0,1 ];
if it is
Figure FDA00038608852400000419
Then the position information of the new Harris hawk after crossing is +.>
Figure FDA00038608852400000420
Position information giving the ith Harriset eagle +.>
Figure FDA00038608852400000421
Otherwise, the position information of the ith Harris eagle +.>
Figure FDA00038608852400000422
Remain unchanged; wherein (1)>
Figure FDA00038608852400000423
Position information representing new harris eagle after crossing +.>
Figure FDA00038608852400000424
Adaptation value of F i ′ t Position information indicating the ith Harriset eagle->
Figure FDA00038608852400000425
Is a fitness value of (a);
if it is
Figure FDA00038608852400000426
Then the position information of the new Harris hawk after crossing is +.>
Figure FDA00038608852400000427
Position information giving the j-th Harris eagle +.>
Figure FDA0003860885240000051
Otherwise, the position information of the jth Harris eagle +.>
Figure FDA0003860885240000052
Remain unchanged, wherein->
Figure FDA0003860885240000053
Position information representing new harris eagle after crossing +.>
Figure FDA0003860885240000054
Figure FDA0003860885240000055
Position information +.>
Figure FDA0003860885240000056
Is a fitness value of (a);
step 3.4.3.3: processing all the sub-populations according to the process of the step 3.4.3.2 to obtain updated e sub-populations of the t+1st generation;
step 3.4.3.4: ascending and sorting fitness values of haustics in the updated t+1th generation e sub-populations to obtain optimal individual information in each sub-population, and recording the optimal individual information as elite individuals;
step 3.4.3.5: positional information of elite individuals in any two sub-populations of the updated t+1st generation using formulas (20) and (21)
Figure FDA0003860885240000057
And->
Figure FDA0003860885240000058
Performing crossover operation to obtain position information of two new elite individuals after crossover
Figure FDA0003860885240000059
Figure FDA00038608852400000510
Figure FDA00038608852400000511
In the formula (20) and the formula (21), μ is a random number between [0,1 ];
if it is
Figure FDA00038608852400000512
Then the position information of the new elite individual after crossing is +.>
Figure FDA00038608852400000513
Giving elite individuals position information +.>
Figure FDA00038608852400000514
Otherwise, elite individual position information +.>
Figure FDA00038608852400000515
Remain unchanged, wherein->
Figure FDA00038608852400000516
Position information representing new elite individual after crossing +.>
Figure FDA00038608852400000517
Is adapted to the value of->
Figure FDA00038608852400000518
Position information representing elite harris eagle in a sub-population +.>
Figure FDA00038608852400000519
Is a fitness value of (a);
if it is
Figure FDA00038608852400000520
Then use is made of (21)) Position information of new elite individual after crossing +.>
Figure FDA00038608852400000521
Giving elite individuals position information +.>
Figure FDA00038608852400000522
Otherwise, elite individual position information +.>
Figure FDA00038608852400000523
Remain unchanged, wherein->
Figure FDA00038608852400000524
Position information representing new elite individual after crossing +.>
Figure FDA00038608852400000525
Is adapted to the value of->
Figure FDA00038608852400000526
Position information representing elite individuals in sub-population +.>
Figure FDA00038608852400000527
Is used for the adaptation value of the (c).
4. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the method of any of claims 1-3, the processor being configured to execute the program stored in the memory.
5. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when run by a processor performs the steps of the method according to any of claims 1-3.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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