CN103676902A - Flow shop rescheduling method - Google Patents

Abstract

The invention provides a flow shop rescheduling method which belongs to the technical field of shop scheduling. The method comprises the steps of: acquiring unexpected events, rescheduling workpieces, time available for machinery and manufacturing process completion time of a current flow shop in real time; for different unexpected events, grouping processes which are started and processes which are not started; determining rescheduling objectives and constraints of the flow shop; generating a rescheduling scheme of the flow shop using the particle swarm optimization; sending the rescheduling scheme of the flow shop to each process of the flow shop for rescheduling. The rescheduling method uses a particle swarm optimization to solve the problem of flow shop rescheduling, takes account of a variety of unexpected events, to guarantee rapid response among the unexpected events, uses inserted or exchanged neighborhood search mechanisms to effectively guarantee that the rescheduling scheme is used for processing in time between two unexpected events, constantly transmits search information to the subsequent migratory birds, and constantly swaps the lead bird, thereby ensuring the diversity and stability of scheduling schemes.

Description

A kind of Flow Shop rescheduling method

Technical field

The invention belongs to Job-Shop technical field, be specifically related to a kind of Flow Shop rescheduling method.

Background technology

Flow Shop Scheduling is a kind of solve job shop scheduling problems, is extensively present in various production runes, as steel-making continuous casting, textile process etc.At present, Chinese scholars has been carried out large quantity research for the scheduling problem of Flow Shop scheduling, has obtained widely achievement in research and has been able to apply in production reality.Yet real production environment is uncertain often, there is multiple accident, as workpiece arrives at random, workpiece random revocation, workpiece variation process time, equipment random fault etc.Thereby the more realistic production run of the relative scheduling problem of Flow Shop reschedule problem, has become one of focus in production scheduling research, for actual Production Scheduling Problem and scheduling, there is important using value.How to consider various accidents, scientifically work out Flow Shop scheduling scheme, for shortening the product process-cycle and improving enterprise productivity, play vital effect.

The methods such as the initial main applies heuristic rules of research of Flow Shop reschedule technology, integer programming.The feature of these methods is that rule understands, easily realize, but because the uncertain and enchancement factor in actual production process is too many, said method cannot consider various accidents, has larger gap with practical application.In recent years, development along with computational intelligence method, many intelligent optimization methods are applied to solving in Flow Shop Scheduling, as methods such as tabu search, simulated annealing, particle group optimizing, genetic algorithm, neural network, meta-heuristic algorithm, expert system and multi-Agent technologies, above-mentioned algorithm is often or because convergence capabilities is not enough, or owing to cannot solving extensive problem, or owing to cannot jumping out the reasons such as local optimum, and can not be applied to solve the Flow Shop reschedule problem that considers multiple accident.

Migrating flock of birds algorithm (Migrating Birds Optimization, MBO) is that Duman equals a kind of new swarm intelligence algorithm proposing for 2012, originally in order effectively to solve the combinatorial optimization problems such as secondary appointment.Its basic thought is as follows:

(1) according to adopting the flight theory of " V " type queue, MBO to adopt " V " type topological structure to organize a group individual (being the solution of problem) in search procedure in migratory bird migration course.

(2) from initial population, each individuality is not only searched for the neighborhood of self, and can obtain useful information there from the individuality before it.Like this, the individuality of useful information from " V " type summit starts to transmit backward successively.

(3) as migrating, will change after flock of birds flight a period of time leading bird, through several times search iteration, MBO changes and comes the individuality on " V " type summit.Information starts to transmit backward from new " V " type summit.So just guarantee the diversity of colony, prevented MBO stagnation, constantly to the excellent solution direction approximation of the overall situation.By the unidirectional delivery mechanism of useful information, make good information of separating in colony pass to other solution, just accelerated the speed of convergence of MBO.

(4) MBO adopts discrete decision variable coding, utilizes neighborhood search mechanism and information transmission mechanism for discrete coding to produce new explanation, and MBO just has discrete essence like this, is more suitable for solving in combinatorial optimization problem.The research of Duman etc. shows, for quadratic assignment problem, MBO algorithm has obtained current better result of study.

Consider multiple accident, comprise that workpiece arrives at random, workpiece random revocation, workpiece variation process time, equipment random fault etc., in conjunction with steel-making continuous casting production actual conditions, the Flow Shop rescheduling method of design based on migrating flock of birds optimized algorithm can be produced the valuable reschedule scheme that provides for punctualization, can give full play to plant factor, reduce equipment free time, prevent that workpiece temperature from changing, improve productive capacity, for actual shop Planning and scheduling provide decision-making foundation.

Summary of the invention

The problem existing for prior art, the invention provides a kind of Flow Shop rescheduling method.

Technical scheme of the present invention is:

A Flow Shop rescheduling method, comprises the following steps:

Step 1: the accident of the current Flow Shop of Real-time Obtaining, reschedule workpiece, machine can complete constantly by the moment and positive manufacturing procedure;

Described accident comprises that workpiece arrives at random, workpiece random revocation, workpiece change and equipment random fault process time;

Described reschedule workpiece comprises workpiece to be processed and the new workpiece that inserts;

Described machine can obtain constantly with the idle machine that is constantly the reschedule moment, or the completion of the positive processing work of busy machine/fault machine constantly;

The original plan completion moment that described positive manufacturing procedure completion is constantly non-fault machine or the reschedule of fault machine are constantly;

Step 2: for different accidents, to going into operation, operation and the operation that do not go into operation are divided into groups: if accident is workpiece, arrive at random, the former dispatching sequence of the operation that maintenance has gone into operation on first lathe, to the workpiece execution step 3 that does not go into operation operation and newly add; If accident is workpiece random revocation, the former dispatching sequence of the operation that maintenance has gone into operation on first lathe performs step 3 to the operation that do not go into operation after deleting this to cancel workpiece; If accident is to change workpiece process time, calculate be subject to the positive manufacturing procedure that accident affects completion constantly, execution step 3; If accident is equipment random fault, calculate the available moment be subject to the lathe that accident affects, execution step 3;

Step 3: determine Flow Shop reschedule target and constraint condition: take to minimize maximum completion date and minimize the workpiece quantity changing on-stream time and set up Flow Shop reschedule objective function as target, the constraint condition of this function comprises:

The completion date constraint of workpiece: the completion date that under reschedule, workpiece is processed on lathe is not less than time and the machine failure time sum that under on-stream time that under reschedule, workpiece is processed on lathe, reschedule, workpiece is processed on lathe;

The on-stream time constraint of workpiece: under reschedule condition, be not less than its release time the on-stream time of workpiece;

The process sequence constraint of lathe: under reschedule condition, overlapping processing phenomenon can not appear in two workpiece of next-door neighbour of processing on same lathe;

The process sequence constraint of workpiece: under reschedule condition, the order relation that same workpiece is processed on two streamlines of next-door neighbour, workpiece is not less than it at the completion date of a upper streamline in the on-stream time of next streamline;

Step 4: for Flow Shop reschedule target and constraint condition, adopt the flock of birds optimization method generation Flow Shop reschedule scheme of migrating;

Step 4.1: produce one according to former operation plan and migrate bird, and add and migrate flock of birds, random several of circulation are initially migrated bird and added and migrate flock of birds;

Step 4.2: each is migrated to bird calculating target function value, and the bird that migrates of selecting to migrate target function value optimum in flock of birds migrates bird as leading, and all the other are migrated bird and arrange according to V-arrangement at random;

Step 4.3: arrange and to migrate flock of birds maximum iteration time and every and leadingly migrate bird and lead the maximum iteration time of migrating flock of birds;

Step 4.4: adopt to insert or the neighborhood search mechanism of exchange is migrated bird and carried out Local Search leading, obtain some random neighborhood solutions, these solutions are carried out to ascending order arrangement according to target function value, choose the neighborhood solution of target function value optimum and upgrade the current leading bird that migrates;

Step 4.5: migrate bird and select several untapped random neighborhood solutions leading, first migrates bird to pass to V-arrangement queue the right and left;

Step 4.6: for migrating the non-leading some neighborhood solutions of the random generation of bird of migrating in flock of birds, and these fields are separated and migrated with this upper strata of migrating bird the field solution that bird transmits and arrange according to target function value ascending order, choose that the neighborhood solution renewal of target function value optimum is current migrates bird;

Step 4.7: non-ly in flock of birds leadingly migrate bird and select several untapped random neighborhood solutions migrating, the lower one deck that passes to V-arrangement queue is migrated bird;

Step 4.8: leadingly migrate bird while leading the maximum iteration time of migrating flock of birds when reaching every, change the leading bird that migrates: adjust and leadingly migrate bird to V-arrangement queue rearmost position, in V-arrangement queue, first is migrated bird and is set to the leading bird that migrates;

Step 4.9: judge whether to meet and migrate flock of birds maximum iteration time, if do not meet, carry out next iteration; Otherwise, the current bird that migrates of choosing target function value optimum in migrating flock of birds, as Flow Shop reschedule scheme;

Step 5: Flow Shop reschedule scheme is issued to each operation of Flow Shop and carries out reschedule.

Beneficial effect:

(1) the present invention will migrate flock of birds algorithm for Flow Shop reschedule problem, consider multiple accident, guarantee to complete rapid reaction between accident; In reschedule process, adopt the neighborhood search mechanism of inserting or exchanging, can effectively guarantee the processing in time between two accidents of reschedule scheme; MBO migrates bird by continuous transmission search information to follow-up, can effectively improve performance; By the leading bird of continuous transposing, can guarantee diversity and the stability of scheduling scheme.

(2) the invention provides Bi-objective Optimization Mechanism, minimize the production efficiency that maximum completion date can improve scheduling scheme as far as possible, reduce production costs, improve plant factor.Minimize change of workpiece number, can effectively improve scheduling scheme and compare robustness in the original plan, reduce and change the uncertainty of bringing.Above-mentioned target has higher directiveness to actual Production Scheduling Problem and scheduling.

Accompanying drawing explanation

Fig. 1 is the Flow Shop rescheduling method process flow diagram of the specific embodiment of the invention;

Fig. 2 is that flock of birds optimization method generation Flow Shop reschedule scheme process flow diagram is migrated in the employing of the specific embodiment of the invention;

Fig. 3 is the original plan scheduling result Gantt chart of the specific embodiment of the invention;

Fig. 4 is the reschedule result Gantt chart of the specific embodiment of the invention.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is elaborated.

Present embodiment is Flow Shop rescheduling method to be applied to steel-making continuous casting produce in Flow Shop, and 5 workpiece of this Workshop Production, have 3 lathes, and process time is as shown in table 1, and wherein, workpiece is J ₁～J ₅, lathe is M ₁～M ₃, accident is at the 10th minute constantly, lathe M ₁on break down, need to repair 10 minutes.

Table 1 machining schedule

The Flow Shop rescheduling method of present embodiment, as shown in Figure 1, comprises the following steps:

Step 1: the accident of the current Flow Shop of Real-time Obtaining, reschedule workpiece, machine can complete constantly by the moment and positive manufacturing procedure;

Accident comprises that workpiece arrives at random, workpiece random revocation, workpiece change and equipment random fault process time;

Reschedule workpiece comprises workpiece to be processed and the new workpiece that inserts;

Machine can obtain constantly with the idle machine that is constantly the reschedule moment, or the completion of the positive processing work of busy machine/fault machine constantly;

The original plan completion moment that positive manufacturing procedure completion is constantly non-fault machine or the reschedule of fault machine are constantly;

In present embodiment, accident occurred constantly at the 10th minute, and the type of accident is equipment random fault, i.e. lathe M ₁on break down, need to repair 10 minutes.

Step 2: for different accidents, to going into operation, operation and the operation that do not go into operation are divided into groups: if accident is workpiece, arrive at random, the former dispatching sequence of the operation that maintenance has gone into operation on first lathe, to the workpiece execution step 3 that does not go into operation operation and newly add; If accident is workpiece random revocation, the former dispatching sequence of the operation that maintenance has gone into operation on first lathe performs step 3 to the operation that do not go into operation after deleting this to cancel workpiece; If accident is to change workpiece process time, calculate be subject to the positive manufacturing procedure that accident affects completion constantly, execution step 3; If accident is equipment random fault, calculate the available moment be subject to the lathe that accident affects, execution step 3;

Owing to only having workpiece J ₄at lathe M ₁on start processing, other workpiece are at lathe M ₁on also do not start processing, the principle (go into operation operation and the operation that do not go into operation being divided into groups) of therefore dividing according to workpiece, all workpiece are divided into two groups, that is: the operation group that gone into operation 1={J ₄, the operation that do not go into operation group 2={J ₁, J ₂, J ₅, J ₃.

Step 3: determine Flow Shop reschedule target and constraint condition: take to minimize maximum completion date and minimize the workpiece quantity changing on-stream time and set up Flow Shop reschedule objective function as target, the constraint condition of this function comprises:

The completion date constraint of workpiece: the completion date that under reschedule, workpiece is processed on lathe is not less than time and the machine failure time sum that under on-stream time that under reschedule, workpiece is processed on lathe, reschedule, workpiece is processed on lathe;

The on-stream time constraint of workpiece: under reschedule condition, be not less than its release time the on-stream time of workpiece;

The process sequence constraint of lathe: under reschedule condition, overlapping processing phenomenon can not appear in two workpiece of next-door neighbour of processing on same lathe;

The process sequence constraint of workpiece: under reschedule condition, the order relation that same workpiece is processed on two streamlines of next-door neighbour, workpiece is not less than it at the completion date of a upper streamline in the on-stream time of next streamline;

F is as follows for Flow Shop reschedule objective function:

f＝w ₁*f ₁+(1-w ₁)*f ₂ (1)

${\mathrm{<figure-callout\; id="1"\; label="f"\; filenames="HDA0000442802080000031.png,HDA0000442802080000041.png"\; state="\{\{state\}\}">f</figure-callout>}}_1=\min \left\{\underset{l\&le;i\&le;n}{\max }{c}_{i,m}\right\}---\left(2\right)$

${\mathrm{<figure-callout\; id="2"\; label="f"\; filenames="HDA0000442802080000031.png,HDA0000442802080000041.png"\; state="\{\{state\}\}">f</figure-callout>}}_2=\min \left\{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{X}_{\mathrm{ij}}\right\}---\left(3\right)$

$\begin{array}{c}s.t.\\ \stackrel{\&OverBar;}{c_{i,j}}\&GreaterEqual;\stackrel{\&OverBar;}{s_{i,j}}+\stackrel{\&OverBar;}{p_{i,j}}+Y_{\mathrm{ij}}(B_e-B_s)\end{array}---\left(4\right)$

$\stackrel{\&OverBar;}{s_{i,j}}\&GreaterEqual;r_{i,j}---\left(5\right)$

$\stackrel{\&OverBar;}{s_{i+1,j}}\&GreaterEqual;\stackrel{\&OverBar;}{s_{i,j}}+\stackrel{\&OverBar;}{p_{i,j}}---\left(6\right)$

$\stackrel{\&OverBar;}{s_{i+1,j+1}}\&GreaterEqual;\stackrel{\&OverBar;}{c_{i,j}}---\left(7\right)$

X _ij＝{0，1} (8)

Y _ij＝{0，1} (9)

0≤w ₁≤1 (10)

In above-mentioned function, formula (1) is objective function, and it comprises two target f ₁and f ₂, and these two targets are weighted to processing; Formula (2) represents first aim f ₁, minimize maximum completion date, the in the end maximal value of the completion date on a lathe m, the i.e. maximum completion date of current solution of all workpiece in formula; Formula (3) represents second target f ₂, i.e. system stability index, the on-stream time with all workpiece under reschedule and in the original plan under the summation of difference of on-stream time represent; Formula (4) represents under reschedule condition, the constraint of the completion date of workpiece; Formula (5) represents that under reschedule condition, should be not less than its release time the on-stream time of workpiece; Formula (6) represents that under reschedule condition, overlapping processing phenomenon can not appear in two workpiece of next-door neighbour of processing on same lathe; Formula (7) represents under reschedule condition, the order relation that same workpiece is processed on two streamlines of next-door neighbour, and workpiece is not less than it at the completion date of a upper streamline in the on-stream time of next streamline; Formula (8-10) represents under reschedule condition, the span of variable.

Objective function variable is as follows:

Step 4: for Flow Shop reschedule target and constraint condition, adopt and migrate flock of birds optimization method generation Flow Shop reschedule scheme, its flow process as shown in Figure 2;

Step 4.1: produce one according to former operation plan and migrate bird, and add and migrate flock of birds, the random P that generates of circulation _nindividually initially migrate bird and add and migrate flock of birds;

Initially migrate bird generation rule as follows: first, according to the coding of former scheduling optimum result, arrange and produce one and migrate bird, and add and migrate flock of birds; Secondly, the random P that produces of circulation _ninitially migrate bird for-1, for guaranteeing that the queue both sides that V-arrangement is arranged are symmetrical arranged P _nfor odd number, generation initially migrate flock of birds, select optimum solution to migrate bird as leading, all the other are migrated bird and according to V-arrangement, arrange at random.

In present embodiment, the process sequence of the operation group 1 that keeps having gone into operation is constant, and the process sequence of the operation that do not go into operation group 2 can arbitrarily be adjusted, and generate 51 and migrate bird, 51 solutions of correspondence problem, because problem scale is little, wherein some solution may be identical.

For example, wherein three solutions are as follows:

Migrate bird 1:{4,1,2,5,3}, corresponding implication is that, on any lathe, the process sequence of workpiece is J ₄, J ₁, J ₂, J ₅, J ₃.

Migrate bird 2:{4,1,3,5,2}, corresponding implication is that, on any lathe, the process sequence of workpiece is J ₄, J ₁, J ₃, J ₅, J ₂.

Migrate bird 3:{4,5,3,1,2}, corresponding implication is that, on any lathe, the process sequence of workpiece is J ₄, J ₅, J ₃, J ₁, J ₂.

According to two targets of problem, evaluate the above-mentioned bird that migrates, select the leading bird of optimum conduct, as common, migrate bird for other two.

Step 4.2: each is migrated to bird calculating target function value, and the bird that migrates of selecting to migrate target function value optimum in flock of birds migrates bird as leading, and all the other are migrated bird and arrange according to V-arrangement at random;

Step 4.3: arrange and to migrate flock of birds maximum iteration time and every and leadingly migrate bird and lead the maximum iteration time of migrating flock of birds;

Step 4.4: adopt to insert or the neighborhood search mechanism of exchange is migrated bird and carried out Local Search k time leading, obtain k random neighborhood solution, these solutions are carried out to ascending order arrangement according to target function value, choose the neighborhood solution of target function value optimum and upgrade the current leading bird that migrates;

Step 4.5: to leading x the untapped random neighborhood solution of bird selection of migrating, first migrates bird to pass to V-arrangement queue the right and left;

Step 4.6: for migrating the non-leading bird S that migrates in flock of birds _rrandom (k-x) the individual neighborhood solution that produces, deposits set N in _r, and these fields are separated and migrated bird S with this upper strata of migrating bird _r-1the field solution of transmitting is arranged according to target function value ascending order, forms set Nr,, choose the neighborhood solution of target function value optimum and upgrade the current bird that migrates;

Step 4.7: to migrating non-leading x untapped random neighborhood solution of bird selection of migrating in flock of birds, the lower one deck that passes to V-arrangement queue is migrated bird;

Step 4.8: leadingly migrate bird while leading the maximum iteration time w that migrates flock of birds when reaching every, change the leading bird that migrates: adjust and leadingly migrate bird to V-arrangement queue rearmost position, in V-arrangement queue, first is migrated bird and is set to the leading bird that migrates;

Step 4.9: judge whether to meet and migrate flock of birds maximum iteration time K, if do not meet, carry out next iteration; Otherwise, the current bird that migrates of choosing target function value optimum in migrating flock of birds, as Flow Shop reschedule scheme;

The current bird that migrates of choosing target function value optimum in migrating flock of birds, be that { two targets are for 4,1,2,3,5}, target function value=173.33 that it is corresponding: { 242,13}, i.e. maximum completion date=242, change of workpiece summation=13.

Step 5: Flow Shop reschedule scheme is issued to each operation of Flow Shop and carries out reschedule.

As shown in Figure 3, the scheduling scheme after reschedule as shown in Figure 4 in scheduling in the original plan.In figure, each square frame represents a workpiece, numeral workpiece in square frame numbering, and " * " symbol in square frame represents machine failure servicing time, the completion date of bottom-right this workpiece of numeral of square frame.

Adopt 450 standard instance collection to check the method validity of present embodiment.Test example problem scale is divided into following a few class: 20.vs.5,20.vs.10, and 20.vs.20,50.vs.5,50.vs.10,50.vs.20,100.vs.5,100.vs.10,100.vs.20, two numerals wherein represent respectively Number of Jobs and lathe number.

Experiment parameter arranges as follows: initially migrate flock of birds size P _n=51; Leadingly migrate bird and carry out Local Search k=7 time, obtain k=7 random neighborhood solution; To migrating non-leading x=3 untapped random neighborhood solution of bird selection of migrating in flock of birds, first migrates bird to pass to V-arrangement queue the right and left; Leading bird maximum iteration time w=20; Migrate flock of birds maximum iteration time K=500; The weight coefficient w of objective function ₁=0.8.

Experimental situation: adopt C Plus Plus programming to realize, program running environment is: DELL desktop computer i73.4GHz, inside saves as 16GB.

Experiment content: the Performance Ratio of this method and IG method

By the validity that flock of birds optimization method (Migrating Birds Optimization, MBO) and IG method are carried out performance comparatively validate this method of migrating of present embodiment.In order to eliminate the error of calculation of bringing due to randomness as far as possible, make result of calculation have more validity and generality, each reschedule problem-instance is moved 20 times continuously.Wherein, workpiece number and the number of machines of n.vs.m problem of representation.

Table 2 is for the operation result comparison of 450 examples

Table 2 has compared the scheduling performance of MBO and IG.IG is that Ruiz etc. is by a kind of effective local search approach proposing.MBO has obtained reasonable result in all 450 problems as can be seen from Table 2.In addition, the MBO that relatively represents of mean value has good stability.This explanation MBO has superior performance solving in Flow Shop reschedule problem, also has good stability simultaneously.

Experimental analysis:

From above-mentioned experiment relatively, MBO merges and for solving Flow Shop Scheduling, and no matter performance or the stability aspect of scheduling scheme all have greater advantage:

(1) scheduling scheme aspect of performance

In reschedule process, adopt the neighborhood search mechanism of inserting or exchanging, can effectively guarantee the processing in time between two accidents of reschedule scheme.

(2) scheduling scheme stability aspect

MBO migrates bird by continuous transmission search information to follow-up, can effectively improve performance; By the leading bird of continuous transposing, can guarantee the diversity of scheduling scheme, guarantee diversity and the stability of scheduling scheme.

Claims (1)

1. a Flow Shop rescheduling method, is characterized in that: comprise the following steps:

Step 1: the accident of the current Flow Shop of Real-time Obtaining, reschedule workpiece, machine can complete constantly by the moment and positive manufacturing procedure;

Described accident comprises that workpiece arrives at random, workpiece random revocation, workpiece change and equipment random fault process time;

Described reschedule workpiece comprises workpiece to be processed and the new workpiece that inserts;

Described machine can obtain constantly with the idle machine that is constantly the reschedule moment, or the completion of the positive processing work of busy machine/fault machine constantly;

The original plan completion moment that described positive manufacturing procedure completion is constantly non-fault machine or the reschedule of fault machine are constantly;

Step 2: for different accidents, to going into operation, operation and the operation that do not go into operation are divided into groups: if accident is workpiece, arrive at random, the former dispatching sequence of the operation that maintenance has gone into operation on first lathe, to the workpiece execution step 3 that does not go into operation operation and newly add; If accident is workpiece random revocation, the former dispatching sequence of the operation that maintenance has gone into operation on first lathe performs step 3 to the operation that do not go into operation after deleting this to cancel workpiece; If accident is to change workpiece process time, calculate be subject to the positive manufacturing procedure that accident affects completion constantly, execution step 3; If accident is equipment random fault, calculate the available moment be subject to the lathe that accident affects, execution step 3;

Step 3: determine Flow Shop reschedule target and constraint condition: take to minimize maximum completion date and minimize the workpiece quantity changing on-stream time and set up Flow Shop reschedule objective function as target, the constraint condition of this function comprises:

The completion date constraint of workpiece: the completion date that under reschedule, workpiece is processed on lathe is not less than time and the machine failure time sum that under on-stream time that under reschedule, workpiece is processed on lathe, reschedule, workpiece is processed on lathe;

The on-stream time constraint of workpiece: under reschedule condition, be not less than its release time the on-stream time of workpiece;

The process sequence constraint of lathe: under reschedule condition, overlapping processing phenomenon can not appear in two workpiece of next-door neighbour of processing on same lathe;

The process sequence constraint of workpiece: under reschedule condition, the order relation that same workpiece is processed on two streamlines of next-door neighbour, workpiece is not less than it at the completion date of a upper streamline in the on-stream time of next streamline;

Step 4: for Flow Shop reschedule target and constraint condition, adopt the flock of birds optimization method generation Flow Shop reschedule scheme of migrating;

Step 4.1: produce one according to former operation plan and migrate bird, and add and migrate flock of birds, random several of circulation are initially migrated bird and added and migrate flock of birds;

Step 4.2: each is migrated to bird calculating target function value, and the bird that migrates of selecting to migrate target function value optimum in flock of birds migrates bird as leading, and all the other are migrated bird and arrange according to V-arrangement at random;

Step 4.3: arrange and to migrate flock of birds maximum iteration time and every and leadingly migrate bird and lead the maximum iteration time of migrating flock of birds;

Step 4.4: adopt to insert or the neighborhood search mechanism of exchange is migrated bird and carried out Local Search leading, obtain some random neighborhood solutions, these solutions are carried out to ascending order arrangement according to target function value, choose the neighborhood solution of target function value optimum and upgrade the current leading bird that migrates;

Step 4.5: migrate bird and select several untapped random neighborhood solutions leading, first migrates bird to pass to V-arrangement queue the right and left;

Step 4.6: for migrating the non-leading some neighborhood solutions of the random generation of bird of migrating in flock of birds, and these fields are separated and migrated with this upper strata of migrating bird the field solution that bird transmits and arrange according to target function value ascending order, choose that the neighborhood solution renewal of target function value optimum is current migrates bird;

Step 4.7: non-ly in flock of birds leadingly migrate bird and select several untapped random neighborhood solutions migrating, the lower one deck that passes to V-arrangement queue is migrated bird;

Step 4.8: leadingly migrate bird while leading the maximum iteration time of migrating flock of birds when reaching every, change the leading bird that migrates: adjust and leadingly migrate bird to V-arrangement queue rearmost position, in V-arrangement queue, first is migrated bird and is set to the leading bird that migrates;

Step 4.9: judge whether to meet and migrate flock of birds maximum iteration time, if do not meet, carry out next iteration; Otherwise, the current bird that migrates of choosing target function value optimum in migrating flock of birds, as Flow Shop reschedule scheme;

Step 5: Flow Shop reschedule scheme is issued to each operation of Flow Shop and carries out reschedule.

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