CN103093311A - Open shop scheduling problem key operation identification method based on varing-amount dichotomy - Google Patents
Open shop scheduling problem key operation identification method based on varing-amount dichotomy Download PDFInfo
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Abstract
The invention provides an open shop scheduling problem key operation identification method based on varying-amount dichotomy. The open shop scheduling problem key operation identification method based on the varying-amount dichotomy includes the steps of dividing coding sequence into a left part and a right part at first according to the principle of varying-amount allocation, wherein the length of the left part and the length of the right part are different, then separately adjusting coding order of the left part and the right part according to a shuffle algorithm, recalculating evaluation index of adjusted coding sequence, updating position parameter according to the evaluation index and continuously narrowing searching zone until an end condition is satisfied. The open shop scheduling problem key operation identification method based on the varying-amount dichotomy has the advantages of rapidly narrowing range, and being simple in operation and specific in direction. In addition, the open shop scheduling problem (OSP) key operation identification method based on the varying-amount dichotomy changes passive search of an optimum proposal into active utilization, rapidly and effectively seeks out key operations (including a key machine, a key workpiece and a key process) which restrain the whole proposal, can rapidly find out a better production proposal and improve enterprise benefits.
Description
Technical field
The present invention relates to key operation identification problem in open workshop, be specially a kind of open solve job shop scheduling problems key operation recognition methods based on the inequality dichotomy.
Background technology
The definition in open workshop can be described as: the workpiece set J={1 of a given n workpiece, and 2 ..., n}, collection of machines M={1 with m machine, 2 ..., m}, the definition workpiece is processed on a machine and is called a procedure, out-of-order constraint between the operation of workpiece.Any time, workpiece i ∈ J can only process at a machine at most simultaneously arbitrarily, and the same time of machine j ∈ M can only be processed a workpiece arbitrarily, and problems belongs to open solve job shop scheduling problems (Open-shop Scheduling Problem, OSP).
Key operation refers to the operation that whole open Job-Shop scheme is had the greatest impact, and key operation comprises crucial workpiece, critical process and critical machine, and mark key operation set is O
*, crucial workpiece set is J
*, the critical machine set is M
*, the critical process set is P
*, O is arranged
*={ J
*, M
*, P
*.
The distinguishing feature of OSP is out-of-order constraint between workpiece, machine, operation, and is namely independent of one another.This makes problem that larger optimization space is arranged on the one hand, also makes on the other hand the solution space scale be geometric index and increases, and gives to find the solution and brings difficulty.The way of current main-stream is that problem is encoded, and then utilizes the optimized algorithm iteration to seek optimum solution.But, as previously mentioned, huge due to solution space, " the passive searching " of " purposelessly ", inefficiency not only, and also the quality of separating does not ensure.
Summary of the invention
The technical matters that solves
For solving classic method to the weak point of prioritization scheme " passive searching ", the present invention proposes a kind of open solve job shop scheduling problems key operation recognition methods based on the inequality dichotomy, the method " is initiatively utilized " characteristics of OSP out-of-order constraint, deeply excavate the behind information of prioritization scheme, can identify fast the key operation in OSP, accelerated problem solving speed, helped to improve and find the solution quality.
Technical scheme
Key step of the present invention comprises: representation; The initial code scheme is decoded; Parameter initialization; Dichotomy " mid point " location positioning; Neighborhood search; The scheme decoding; Parameter is upgraded; Obtain crucial workpiece and critical process; The output net result.
Technical scheme of the present invention is:
Described a kind of open solve job shop scheduling problems key operation recognition methods based on the inequality dichotomy is characterized in that: adopt following steps:
Step 1: representation: open solve job shop scheduling problems is adopted based on the integer coding that operates, obtain coded sequence I
0={ g
1, g
2..., g
N, wherein N is I
0In the gene number that comprises, g
jI
0J gene, j is that gene is at coded sequence I
0In Position Number, a workpiece numbering in the corresponding actual schedule problem of the represented round values of gene, certain round values is at I
0In the number of times that occurs by order from left to right represent the process number of the corresponding workpiece of this round values;
Step 2: to coded sequence I
0Carry out activity decoding computing, obtain coded sequence I
0Corresponding total completion date V
0With coded sequence I
0Corresponding production decision S
0
Step 3: parameter initialization: iterations sd initial value is 1, neighborhood search left margin pos
LInitial value is 1, neighborhood search right margin pos
RInitial value is N;
Step 4: adopt following steps to produce interval [pos
L+ 1, pos
R-1] the random integers mid in:
Step 4.1: produce the random real number r in interval [0,1];
Step 4.2: produce interval [pos
L+ 1, pos
R-1] the random real number tmid in:
tmid＝r×[(pos
R-1)-(pos
L+1)]+(pos
L+1)；
Step 4.3: the random real number tmid that step 4.2 is obtained carries out rounding operation, obtains interval [pos
L+ 1, pos
R-1] the random integers mid in;
Step 5: to coded sequence I
0Corresponding interval [the pos of middle Position Number
L, mid] all genes utilize the shuffling algorithm random rearrangement, the coded sequence I after being reset
LTo coded sequence I
0Middle Position Number corresponding interval [mid, pos
R] all genes utilize the shuffling algorithm random rearrangement, the coded sequence I after being reset
R
Step 6: to coded sequence I
L, I
RCarry out activity decoding computing, obtain coded sequence I
L, I
RCorresponding total completion date V
L, V
R
Step 7: the coded sequence I that obtains according to step 6
L, I
RCorresponding total completion date V
L, V
R, contrast coded sequence I
0Corresponding total completion date V
0, upgrade neighborhood search left margin pos
LWith neighborhood search right margin pos
R, update rule is:
Step 8: if | pos
R-pos
L| 〉=2, carry out step 9, otherwise carry out step 10;
Step 9: if iterations sd less than maximum iteration time SD, iterations adds 1, and returns to step 4, otherwise carry out step 10;
Step 10: according to [pos between the gene location numbering area that obtains
L, pos
R], between this gene location numbering area in all corresponding workpiece form crucial workpiece set J
*, crucial workpiece between this gene location numbering area in corresponding operation form critical process set P
*, crucial workpiece and critical process are according to production decision S
0Obtain critical machine set M
*, J
*, P
*And M
*Form key operation set O
*
Beneficial effect
The present invention is according to the inequality distribution principle, a kind of production decision of OSP is encoded, coded sequence is divided into left and right two parts that length does not wait, then adjust respectively left and right two parts coded sequence according to shuffling algorithm, coded sequence after adjusting is recomputated the evaluation indexes such as deadline (Makespan), upgrade location parameter according to evaluation index, constantly continue to reduce the scope, until satisfy stopping criterion for iteration, that finally find is exactly crucial workpiece set J
*With the critical process set be P
*, obtain critical machine set M by decoding
*, can obtain key operation is O
*={ J
*, M
*, P
*.And may find new more excellent encoding scheme in seeking the key operation process, help to find more eugenic product scheme.
Description of drawings
Fig. 1: method flow diagram of the present invention;
Fig. 2: the growing method figure in embodiment.
Embodiment
Below in conjunction with specific embodiment, the present invention is described:
The open solve job shop scheduling problems key operation recognition methods based on the inequality dichotomy in the present embodiment, adopt following steps:
Step 1: representation: open solve job shop scheduling problems is adopted based on the integer coding that operates, obtain coded sequence I
0={ g
1, g
2..., g
N, wherein N is I
0In the gene number that comprises, g
jI
0J gene, j is that gene is at coded sequence I
0In Position Number, a workpiece numbering in the corresponding actual schedule problem of the represented round values of gene, certain round values is at I
0In the number of times that occurs by order from left to right represent the process number of the corresponding workpiece of this round values.
Integer coding mode based on operation represents with m * n the genomic constitution that operates with coded sequence, is an arrangement of all operations, and wherein n work piece number all occurs m time.The example code I={1 of 3 workpiece of 4 machines based on the integer coding of operation of OSP problem, 2,2,1,3,2,1,3,3,2,1,3}, each digitized representation workpiece numbering in set, and which operation of which time this workpiece of appearance expression of each numeral (Wang Ling, Job-Shop and genetic algorithm .2003 thereof, Beijing: publishing house of Tsing-Hua University. the 69th page).
Step 2: to coded sequence I
0Carry out activity decoding computing, obtain coded sequence I
0Corresponding total completion date V
0With coded sequence I
0Corresponding production decision S
0Movable decoding computing reference (Wang Ling, Job-Shop and genetic algorithm .2003 thereof, Beijing: publishing house of Tsing-Hua University. the 82nd page).
Step 3: parameter initialization: iterations sd initial value is 1, neighborhood search left margin pos
LInitial value is 1, neighborhood search right margin pos
RInitial value is N;
Step 4: inequality dichotomy point midway is determined: adopt following steps to produce interval [pos
L+ 1, pos
R-1] the random integers mid in:
Step 4.1: produce the random real number r in interval [0,1]; Be exactly specifically to adopt the rand () function in Matlab software to generate the interior random real number r in an interval [0,1];
Step 4.2: produce interval [pos
L+ 1, pos
R-1] the random real number tmid in:
tmid＝r×[(pos
R-1)-(pos
L+1)]+(pos
L+1)；
Step 4.3: the random real number tmid that step 4.2 is obtained carries out rounding operation, obtains interval [pos
L+ 1, pos
R-1] the random integers mid in;
Step 5: to coded sequence I
0Corresponding interval [the pos of middle Position Number
L, mid] all genes utilize the shuffling algorithm random rearrangement, the coded sequence I after being reset
LTo coded sequence I
0Middle Position Number corresponding interval [mid, pos
R] all genes utilize the shuffling algorithm random rearrangement, the coded sequence I after being reset
R
Wherein shuffling algorithm is known method, is described further here: following employing shuffling algorithm all gene random rearrangements to Position Number corresponding interval [1, M]:
Step 5.1: parameter initialization: i=1, i ∈ [1, M], i is loop variable;
Step 5.2: produce random number: adopt the method in step 4 to produce interval [i, M] interior random integers x;
Step 5.3: exchange base because of: namely exchange i gene and x gene,, simultaneously loop variable increases by 1;
Step 5.4: loop iteration: if i≤M returns to step 5.2, otherwise the gene sets after output process adjustment order.
Step 6: to coded sequence I
L, I
RCarry out activity decoding computing, obtain coded sequence I
L, I
RCorresponding total completion date V
L, V
R
Step 7: the coded sequence I that obtains according to step 6
L, I
RCorresponding total completion date V
L, V
R, contrast coded sequence I
0Corresponding total completion date V
0, upgrade neighborhood search left margin pos
LWith neighborhood search right margin pos
R, update rule is:
Here, if V
L＞V
0And V
R≤ V
0, show that key operation is positioned at interval [pos
L, mid] and corresponding certain gene or genetic fragment, pos when upgrading
LConstant, pos
R=mid; If V
R＞V
0And V
L≤ V
0Show that key operation is positioned at interval [mid, pos
R] corresponding certain gene or genetic fragment, pos when upgrading
L=mid, pos
RConstant.
Step 8: judge whether gene travels through complete: if pos
R-pos
L〉=2, carry out step 9, otherwise carry out step 10;
Step 9: judge whether to stop search: if iterations sd less than maximum iteration time SD, iterations adds 1, and returns to step 4, otherwise carry out step 10;
Step 10: Output rusults: neighborhood search finally obtains [pos between the gene location numbering area
L, pos
R], between this gene location numbering area in all corresponding workpiece form crucial workpiece set J
*, crucial workpiece between this gene location numbering area in corresponding operation form critical process set P
*, crucial workpiece and critical process are according to production decision S
0Obtain critical machine set M
*, J
*, P
*And M
*Form key operation set O
*
Collection of machines M={1 in the present embodiment, 2,3,4,5,6}, workpiece set N={1,2,3,4,5,6}, SD=5, time matrix
Wherein the row of T represents workpiece, and row represent machine, and its numerical value represents the corresponding time.Coded sequence
I
0={ 6,1,5,3,6,3,1,5,4,4,5,2,2,2,3,2,4,5,4,3,6,5,1,1,5,4,1,1,3,6,3,6,2,6,2,4}, the result of decoding is as shown in Figure 2.Through this method obtain neighborhood search finally obtain between the gene location numbering area in pos
L=15, pos
R=19, obtain
J
*＝{3,2,4,5}，M
*＝{5,4,6,1}，P
*＝{3,4,{3,4},4}。
The present invention is at first according to the inequality distribution principle, coded sequence is divided into left and right two parts that length does not wait, then adjust respectively left and right two parts coded sequence according to shuffling algorithm, coded sequence after adjusting is recomputated evaluation index, upgrade location parameter according to evaluation index, constantly dwindle the hunting zone, until satisfy end condition, what finally look for is exactly that crucial workpiece set and critical process show that this section is that chromosome comprises key gene or key gene fragment; With the initial boundary of this portion gene boundary position as next iteration, continue to reduce the scope, until satisfy stopping criterion for iteration, that finally find is exactly crucial workpiece set J
*With the critical process set be P
*, obtain critical machine set M by decoding
*, can obtain key operation is O
*={ J
*, M
*, P
*.And may find new more excellent encoding scheme in seeking the key operation process, help to find more eugenic product scheme.The method of key operation in quick identification OSP provided by the present invention, the characteristics that dichotomy can reduce the scope fast, simple to operate, direction is clear and definite have been utilized, " the passive searching " that become prioritization scheme is " initiatively utilizing ", find quickly and efficiently the key operation (comprising critical machine, crucial workpiece and critical process) of the whole scheme of restriction, can find fast more excellent production decision, improve the performance of enterprises.
Claims (1)
1. open solve job shop scheduling problems key operation recognition methods based on the inequality dichotomy is characterized in that: adopt following steps:
Step 1: representation: open solve job shop scheduling problems is adopted based on the integer coding that operates, obtain coded sequence I
0={ g
1, g
2..., g
N, wherein N is I
0In the gene number that comprises, g
jI
0J gene, j is that gene is at coded sequence I
0In Position Number, a workpiece numbering in the corresponding actual schedule problem of the represented round values of gene, certain round values is at I
0In the number of times that occurs by order from left to right represent the process number of the corresponding workpiece of this round values;
Step 2: to coded sequence I
0Carry out activity decoding computing, obtain coded sequence I
0Corresponding total completion date V
0With coded sequence I
0Corresponding production decision S
0
Step 3: parameter initialization: iterations sd initial value is 1, neighborhood search left margin pos
LInitial value is 1, neighborhood search right margin pos
RInitial value is N;
Step 4: adopt following steps to produce interval [pos
L+ 1, pos
R-1] the random integers mid in:
Step 4.1: produce the random real number r in interval [0,1];
Step 4.2: produce interval [pos
L+ 1, pos
R-1] the random real number tmid in:
tmid＝r×[(pos
R-1)-(pos
L+1)]+(pos
L+1)；
Step 4.3: the random real number tmid that step 4.2 is obtained carries out rounding operation, obtains interval [pos
L+ 1, pos
R-1] the random integers mid in;
Step 5: to coded sequence I
0Corresponding interval [the pos of middle Position Number
L, mid] all genes utilize the shuffling algorithm random rearrangement, the coded sequence I after being reset
LTo coded sequence I
0Middle Position Number corresponding interval [mid, pos
R] all genes utilize the shuffling algorithm random rearrangement, the coded sequence I after being reset
R
Step 6: to coded sequence I
L, I
RCarry out activity decoding computing, obtain coded sequence I
L, I
RCorresponding total completion date V
L, V
R
Step 7: the coded sequence I that obtains according to step 6
L, I
RCorresponding total completion date V
L, V
R, contrast coded sequence I
0Corresponding total completion date V
0, upgrade the neighborhood search left margin
pos
LWith the neighborhood search right margin
pos
R, update rule is:
Step 8: if | pos
R-pos
L〉=2, carry out step 9, otherwise carry out step 10;
Step 9: if iterations sd less than maximum iteration time SD, iterations adds 1, and returns to step 4, otherwise carry out step 10;
Step 10: according to [pos between the gene location numbering area that obtains
L, pos
R], between this gene location numbering area in all corresponding workpiece form crucial workpiece set J
*, crucial workpiece between this gene location numbering area in corresponding operation form critical process set P
*, crucial workpiece and critical process are according to production decision S
0Obtain critical machine set M
*, J
*, P
*And M
*Form key operation set O
*
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110751411A (en) * | 2019-10-28 | 2020-02-04 | 河海大学常州校区 | Cloud manufacturing task oriented manufacturing resource matching method |
| CN113467401A (en) * | 2021-07-19 | 2021-10-01 | 江苏天芯微半导体设备有限公司 | Scheduling method of multi-cavity plasma reaction equipment, computing equipment and medium |
| CN117787476A (en) * | 2023-12-07 | 2024-03-29 | 聊城大学 | Quick evaluation method for blocking flow shop scheduling based on key machine |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110751411A (en) * | 2019-10-28 | 2020-02-04 | 河海大学常州校区 | Cloud manufacturing task oriented manufacturing resource matching method |
| CN110751411B (en) * | 2019-10-28 | 2023-04-07 | 河海大学常州校区 | Cloud manufacturing task oriented manufacturing resource matching method |
| CN113467401A (en) * | 2021-07-19 | 2021-10-01 | 江苏天芯微半导体设备有限公司 | Scheduling method of multi-cavity plasma reaction equipment, computing equipment and medium |
| CN113467401B (en) * | 2021-07-19 | 2022-09-09 | 江苏天芯微半导体设备有限公司 | Scheduling method of multi-cavity plasma reaction equipment, computing equipment and medium |
| CN117787476A (en) * | 2023-12-07 | 2024-03-29 | 聊城大学 | Quick evaluation method for blocking flow shop scheduling based on key machine |
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