CN100593766C - Right changing type accidental scheduling method based on real time condition - Google Patents
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Abstract
The invention discloses a variable weight type random scheduling method based on real-time working conditions; the scheduling method optimizes the problem of the dynamic random rescheduling in terms of classification of various perturbation, rescheduling driving mechanism, the construction of rescheduling majorized set, the selection rule of the workpieces to be processed and rescheduling optimization algorithm, classifies various perturbation in the production process into two classes of invisible perturbation and dominant perturbation, adopts corresponding rescheduling driving mechanisms according to different types of perturbation, thus quickly and effectively dealing with various uncertain problems which randomly occur in the production process; the scheduling method is suitable for the mode with a plurality of varieties and single-piece small lot production, can rapidly and effectively handle the various uncertain problems which randomly occur in the production scheduling processand has strong coupling capacity with the real-time working conditions, thus being capable of improving the self-adaptability of a workshop production system to the uncertain working conditions and the agility of production scheduling, reducing unnecessary rescheduling, enhancing reaction capacity to the working conditions and lowering the loss of the production system brought by the various uncertain problems.
Description
Technical field:
The invention belongs to the advanced manufacturing technology field, specifically relate to the dynamic random reschedule optimization method of workshop under the condition of uncertainty.
Background technology:
Under the modern production manufacturing mode, uncertain problem in the production scheduling process be mainly reflected in production information obtain non-timely with the randomness of imperfection, workpiece time of arrival and manufacture process in the frequent various disturbances (as equipment failure, activity time error accumulation etc.) that take place, multiple goal requirement such as will realize also meanwhile that life cycle of the product is short, low cost of manufacture and market response are rapid, these problems are all had higher requirement to the real-time of dispatching method, dynamic and with the coupling ability of actual condition.
Existing domestic and international researcher mainly concentrates on the structure to whole dispatching method, as distributed architecture, based on the research of acting on behalf of Agent architecture etc. for the research of Workshop Dynamic scheduling structure aspect more; In the research to the dynamic dispatching derivation algorithm under the condition of uncertainty, many robustness and optimization efficient with the raising dispatching algorithm are target; And aspect the random perturbation in processing production process, the employing expert system that has forms knowledge base with the processing experience of various disturbances, the research trends allocation rule that has realizes dynamic operation ordering, the local optimization methods of the utilization rolling window that has is carried out reschedule optimization, the utilization inspection and the preventative maintenance that also have are means, reduce the incidence of equipment failure.Though these methods have solved the problem of the system stability difference that various disturbance caused in the production run to a certain extent, but for the exhaustive division of the various disturbances of many kinds, single and mini-batch production workshop, the aspect researchs such as workpiece selection rule of equal weight are less in the coupling between work information and the scheduling strategy, reschedule driving mechanism and the task-set to be processed.
Summary of the invention:
What the objective of the invention is provides a kind of right changing type accidental scheduling method based on real-time working condition, be applicable to many kinds, single and mini-batch production production model, the various uncertain problems of can be fast and occurring at random in the process for producing scheduling process effectively, and it is strong with the coupling ability of real-time working condition, can improve the Workshop Production system to the adaptivity of uncertain operating mode and the agility of production scheduling, reduce unnecessary reschedule and raising to the reaction capacity of operating mode, reduce the loss that various uncertain problems bring to production system.
Technical scheme of the present invention is as follows:
Right changing type accidental scheduling method based on real-time working condition is characterized in that: from these several aspects of structure, workpiece to be processed selection rule and reschedule optimized Algorithm of the classification of various disturbances, reschedule driving mechanism, reschedule optimization collection dynamic random reschedule problem is optimized respectively:
(1), by the various disturbances in the production run being divided into dominance disturbance and stealthy disturbance two classes, adopt corresponding reschedule driving mechanism at dissimilar disturbances, fast and effectively the various uncertain problems that occur at random in the processing production process:
1. dominance disturbance: be characterized in once taking place separately obviously to influence manufacturing schedule, and the normal operation of production system is produced damaging influence;
2. recessive disturbance: the normal manufacturing schedule of influence that the generation that is characterized in an often disturbance can be not clearly, but when these small sample perturbations after adding up after a while, equally also can have a strong impact on the normal operation of production system;
(2), aspect the reschedule driving mechanism, adopt corresponding reschedule to drive rule respectively according to the different disturbances in the production run, promptly initiatively trigger-type and passive trigger-type driving rule are adopted in dominance disturbance and recessive disturbance respectively:, can initiatively trigger reschedule when arriving critical value for the dominance disturbance; And, need monitoring in real time for recessive disturbance, according to the definite critical value that triggers the controlled variable of reschedule of actual condition, trigger reschedule when arriving critical value;
(3), aspect the structure of reschedule optimization collection, each time in the Optimization Dispatching, workpiece to be processed is chosen again to form reschedule optimization collection, and these workpieces to be processed comprise workpiece and the newly arrived workpiece that is not selected in still unprocessed workpiece, the last scheduling after the generation disturbance in the last scheduling; Each reschedule only is optimized calculating to the workpiece of optimizing in the collection, realizes the optimization of the overall situation by the local optimum of bringing in constant renewal in; By setting up reschedule optimization collection, in conjunction with rolling Optimization of Time Domain method, large-scale overall scheduling problem is decomposed into a plurality of scheduling sublayer problems of rolling and carrying out on time domain, the overall scheduling problem of complexity is simplified; Workpiece when each reschedule in the reply reschedule optimization collection becomes power to be handled, and promptly the weight of workpiece is reappraised and is adjusted;
(4), aspect the workpiece to be processed selection rule, when choosing workpiece to be processed and enter reschedule optimization collection, can give weight to each workpiece to be processed according to certain or comprehensive productive target, select workpiece to enter reschedule optimization collection according to qualifications according to the size of workpiece weight again; For workpiece to be processed selection rule, be that each operation of workpiece is carried out the length coupling average process time with equal weight; Choose the close workpiece of operation processing technology and enter the optimization collection, reduce the adjustment time of machine; Choose the more workpiece of operation and enter the optimization collection, to reduce the processing technology constraint;
(5), optimization aim:
Be provided with a m platform machine and n workpiece, q
iThe process number that expression workpiece i comprises, O
Ij kThe j procedure of expression workpiece i is processed S on machine k
Ij kThe beginning process time of j procedure on machine k of expression workpiece i, T
Ij kThe process time of j procedure on machine k of expression workpiece i, each workpiece comprises one or multi-channel operation and the per pass operation is processed on the machine of appointment; Regulation goal is processed for the workpiece in the reschedule optimization collection is assigned on the machine of appointment, and processing sequence is optimized arrangement, makes the inter process standby time the shortest, improves the machinery utilization rate; The hypothesis and the constraint condition that wherein need to satisfy are:
1. each workpiece processing operation is determined in advance;
2. can not interrupt each operation midway in the process;
3. all workpiece can be processed constantly zero;
4. synchronization, a machine can only be processed a procedure, and workpiece p and i can not be simultaneously processed, suppose that operation i processes before operation p, that is:
S
h pq-S
k ij-T
k ij≥0 (1)
5. constraint is successively arranged between the operation of same workpiece, could begin subsequent processing j after a last operation i finishes, that is:
S
h ij-S
k i(j-1)-T
k i(j-1)≥0
The optimization aim function is:
(6), optimize (Hybrid PSO) algorithm proposing the hybrid fine particles group aspect the reschedule optimized Algorithm, with respect to basic particle swarm optimization algorithm, mainly carried out the improvement of three aspects:
1. be solution discrete optimization problem, and convenient description to practical problems, coding is taked the natural number coding mode;
The array that each particle in the algorithm is made up of N double figures, comprised the constraint of workpiece-operation-time one machine, N is the summation of the process number of all workpiece, the workpiece that the representative of first figure place will be processed in each double figures, the number of times α that first figure place occurs represents the α procedure of this workpiece, represents this operation to process on which platform machine for second in each double figures;
2. evolutionary rate and variation mode are used for reference the interlace operation and the mutation operation of genetic algorithm, have simplified the operation of evolving and have improved efficiency of evolution;
The mutation operation of hybrid fine particles colony optimization algorithm is taked the exchange variation, determines at random in pbest that promptly N to numeral, carries out place-exchange to every pair of numeral; And by changing the diversity that N can control the pbest variation; Variation take the to make a variation method of pbest, promptly with certain probability with all digital switches among the pbest, rearrangement; This probability is the aberration rate of Hybrid Particle Swarm; Particle through variation is necessary for feasible solution; Therefore adopt and generate the method that a new primary replaces pbest at random, as variation;
3. the Metropolis criterion for sampling of simulated annealing is dissolved in the global optimum's position calculation in the particle swarm optimization algorithm, is prevented that effectively algorithm is absorbed in the local optimum point during evolution.
Described right changing type accidental scheduling method based on real-time working condition is characterized in that: described dominance disturbance comprises: the catastrophic failure of process equipment, urgent part add, material supply is untimely; Recessive disturbance comprises: actual manufacturing procedure time, operation are adjusted the error accumulation of time and material haulage time.
The present invention is applicable to many kinds, single and mini-batch production production model, the various uncertain problems of can be fast and occurring at random in the process for producing scheduling process effectively, and it is strong with the coupling ability of real-time working condition, can improve the Workshop Production system to the adaptivity of uncertain operating mode and the agility of production scheduling, reduce unnecessary reschedule and raising to the reaction capacity of operating mode, reduce the loss that various uncertain problems bring to production system.
Description of drawings
The optimization result that Fig. 1 selects for the first time.
The optimization result that Fig. 2 selects for the second time.
Fig. 3 mixing PSO crossing operation figure.
Fig. 4 mixing PSO finds the solution process flow diagram
Fig. 5 is process equipment perturbation analysis figure.
Embodiment:
(1), by the various disturbances in the production run being divided into dominance disturbance and stealthy disturbance two classes, adopt corresponding reschedule driving mechanism at dissimilar disturbances, fast and effectively the various uncertain problems that occur at random in the processing production process.
1. dominance disturbance: be characterized in once taking place separately obviously to influence manufacturing schedule, and the normal operation of production system is produced damaging influence.Disturbances such as for example, the catastrophic failure of process equipment, urgent part add, material supply is untimely;
2. recessive disturbance: the normal manufacturing schedule of influence that the generation that is characterized in an often disturbance can be not clearly, but when these small sample perturbations after adding up after a while, equally also can have a strong impact on the normal operation of production system.For example, actual manufacturing procedure time, operation are adjusted the disturbances such as error accumulation of time and material haulage time.
(2), aspect the reschedule driving mechanism, adopt corresponding reschedule to drive rule respectively according to the different disturbances in the production run, promptly adopting initiatively respectively to dominance disturbance and recessive disturbance, trigger-type and passive trigger-type drive rule.For the dominance disturbance, can initiatively trigger reschedule, for example, when device fails, start reschedule, and in current available devices, remove this faulty equipment, more current workpiece to be processed is carried out reschedule; And for recessive disturbance, to determine to trigger the critical value of reschedule according to actual condition, by critical value decision trigger point, be not to trigger reschedule by once recessive disturbance separately, this critical value can determine according to the indexs such as max cap. of actual delivery phase, buffer zone, for example, with plan error takes place when actual process time of per pass operation process time, and build up when reaching critical value, restart reschedule.
With the process equipment fault is example, as shown in Figure 5.Define a reschedule time threshold θ, equipment failure be divided into catastrophic failure and minor failure, this threshold value with the average process time of different workpieces, different workshop to the requirement of scheduling time and difference.Be generally less than θ the unusual man-hour of minor failure, therefore do not need to carry out reschedule.Surpass θ the unusual man-hour of catastrophic failure, need carry out reschedule, if fault can be got rid of at short notice during reschedule, during reschedule with the workpiece on this equipment according to unusual man-hour, process time of operation is postponed to handle; If equipment can not be repaired at short notice, then select for use stand-by equipment to process, the workpiece that will optimize then in the collection carries out reschedule according to optimized Algorithm, discharges new scheduling scheme.After the long-time fault restoration of equipment, but the equipment after repairing is joined in the set of controlling equipment, and carry out reschedule.
It is strong with the coupling ability of real-time working condition that this reschedule drives rule, and avoided periodic scheduling and common event to drive the shortcoming of scheduling.
(3) aspect structure reschedule optimization collection, use for reference the ultimate principle of PREDICTIVE CONTROL, workpiece selected in the Optimization Dispatching is each time constituted a reschedule optimization collection, each reschedule only is optimized calculating to the workpiece of optimizing in the collection, promptly realizes the optimization of the overall situation by the local optimum of bringing in constant renewal in.By setting up reschedule optimization collection, in conjunction with rolling Optimization of Time Domain method, large-scale overall scheduling problem is decomposed into a plurality of scheduling sublayer problems of rolling and carrying out on time domain, overall scheduling problem to complexity is simplified, avoided rule-based scheduling only to see the near-sighted defective in a step, constantly can predict work information on the bigger time domain in decision-making, and system be carried out part adjustment according to actual conditions.Also tackle when each reschedule and optimize the collection workpiece and become power and handle, reschedule becomes the power method and is meant before each reschedule is carried out the weight of workpiece is dynamically being adjusted.Before carrying out reschedule each time, all to choose again to form new optimization collection all unprocessed workpiece, these unprocessed workpiece comprise still unprocessed workpiece after the disturbance, the workpiece and the newly arrived workpiece that are not selected in the last scheduling take place in the last scheduling.What these workpiece had once is endowed weight in the scheduling last, if but when reschedule, still be optimized calculating according to original weight, the situation that will inevitably cause existing workpiece weight and actual weight to depart from, especially after reschedule repeatedly, some is selected to enter optimizes collection, but not processed always workpiece because weight departs from greatlyyer, just might have influence on the realization of productive targets such as delivery date.
(4) aspect the workpiece to be processed selection rule,, therefore enter the workpiece quantity of optimization collection and the operation quantity of each workpiece and be inversely proportional to because the scale of reschedule optimization collection is limited.The quantity of choosing workpiece when reschedule is many more, and the constraint of inter process is just few more.And be selected the computation complexity that factors such as operation process time of workpiece and the close degree of technology also can influence optimized Algorithm.Enter when optimizing collection choosing workpiece to be processed, can give weight to each workpiece to be processed, select workpiece to enter according to qualifications to optimize collection to be optimized according to the size of workpiece weight again according to certain or comprehensive productive target.The present invention proposes workpiece to be processed selection rule with equal weight, each operation that is about to workpiece is carried out the length coupling average process time, and choose the close workpiece of operation processing technology and enter and optimize collection, reduce the adjustment time of machine, under the identical prerequisite of optimized Algorithm efficient, obtain the better optimize effect, and the frequency of reschedule and the agility of reschedule response are all had very big influence.
For example, for the identical workpiece of a collection of weight, select workpiece J1, J2, J4, J7 and J9 to enter and optimize collection, have 5 workpiece in the optimization collection like this, totally 40 procedures calculate and can get according to the uniqueness of each workpiece processing order, totally 157 groups of constraints draw Gantt chart as shown in Figure 1.Be minimised as optimization aim with the inter process standby time, it is 91 for the minimum clearance time that the computation optimization of process PSO obtains optimum solution, and at this moment, the minimum average B configuration flowing time is 50, and maximum completion date is 77.
Select for the second time process number less workpiece J4, J7, J8, J9, J10, J11, J13, J14 and J15 to enter and optimize collection, 77 groups of constraints of totally 40 procedures, be minimised as optimization aim with the inter process standby time equally, obtaining optimum solution through the computation optimization of mixing PSO is 7 for the minimum clearance time, at this moment, the minimum average B configuration flowing time is 36, and maximum completion date is 50, and Gantt chart as shown in Figure 2.This shows the importance of the workpiece to be processed selection rule of equal priority for scheduling.
(5) proposing hybrid fine particles group optimization (Hybrid PSO) algorithm aspect the reschedule optimized Algorithm, mixing PSO and with respect to basic PSO, mainly carried out the improvement of three aspects:
1. be solution discrete optimization problem, and convenient description to practical problems, coding is taked the natural number coding mode.
The array that each particle in the algorithm is made up of N double figures has comprised the constraint of workpiece-operation-time-machine.N is the summation of the process number of all workpiece.The workpiece that the representative of first figure place will be processed in each double figures, the number of times α that first figure place occurs represents the α procedure of this workpiece, represents this operation to process on which platform machine for second in each double figures.
For example: pbest [21 23 12 31 32 12 23 13 33] wherein first procedure of second workpiece of first double figures " 21 " expression processes on first machine, the second operation work of second workpiece of second double figures " 23 " expression is processed on the 3rd machine, first procedure of the 3rd double figures " 12 " expression unit one is processed on second machine, by that analogy.
2. evolutionary rate and variation mode are used for reference the interlace operation and the mutation operation of genetic algorithm, have simplified the operation of evolving and have improved efficiency of evolution.
Give the particle p that is about to renewal with the digital copy between pbest or two point of crossing of gbest, upgrading the back is p ', as shown in Figure 3.
Mix the mutation operation of PSO algorithm and take the exchange variation, determine at random in pbest that promptly N to numeral, carries out place-exchange to every pair of numeral.And by changing the diversity that N can control the pbest variation.Variation take the to make a variation method of pbest, promptly with certain probability with all digital switches among the pbest, rearrangement.This probability is the aberration rate of Hybrid Particle Swarm.Particle through variation is necessary for feasible solution.Therefore adopt and generate the method that a new primary replaces pbest at random, as variation.
3. the Metropolis criterion for sampling of simulated annealing is dissolved in the global optimum's position calculation in the PSO algorithm, is prevented that effectively algorithm is absorbed in the local optimum point during evolution.
The fitness function of this algorithm is-F that according to above-mentioned 3 improvement, that mixes PSO finds the solution process flow diagram as shown in Figure 4.
Claims (2)
1, based on the right changing type accidental scheduling method of real-time working condition, it is characterized in that: from these several aspects of structure, workpiece to be processed selection rule and reschedule optimized Algorithm of the classification of various disturbances, reschedule driving mechanism, reschedule optimization collection dynamic random reschedule problem is optimized respectively:
(1), by the various disturbances in the production run being divided into dominance disturbance and stealthy disturbance two classes, adopt corresponding reschedule driving mechanism at dissimilar disturbances, fast and effectively the various uncertain problems that occur at random in the processing production process:
1. dominance disturbance: be characterized in once taking place separately obviously to influence manufacturing schedule, and the normal operation of production system is produced damaging influence;
2. recessive disturbance: the normal manufacturing schedule of influence that the generation that is characterized in an often disturbance can be not clearly, but when these small sample perturbations after adding up after a while, equally also can have a strong impact on the normal operation of production system;
(2), aspect the reschedule driving mechanism, adopt corresponding reschedule to drive rule respectively according to the different disturbances in the production run, promptly initiatively trigger-type and passive trigger-type driving rule are adopted in dominance disturbance and recessive disturbance respectively:, can initiatively trigger reschedule when arriving critical value for the dominance disturbance; And, need monitoring in real time for recessive disturbance, according to the definite critical value that triggers the controlled variable of reschedule of actual condition, trigger reschedule when arriving critical value;
(3), aspect the structure of reschedule optimization collection, each time in the Optimization Dispatching, workpiece to be processed is chosen again to form reschedule optimization collection, and these workpieces to be processed comprise workpiece and the newly arrived workpiece that is not selected in still unprocessed workpiece, the last scheduling after the generation disturbance in the last scheduling; Each reschedule only is optimized calculating to the workpiece of optimizing in the collection, realizes the optimization of the overall situation by the local optimum of bringing in constant renewal in; By setting up reschedule optimization collection, in conjunction with rolling Optimization of Time Domain method, large-scale overall scheduling problem is decomposed into a plurality of scheduling sublayer problems of rolling and carrying out on time domain, the overall scheduling problem of complexity is simplified; Workpiece when each reschedule in the reply reschedule optimization collection becomes power to be handled, and promptly the weight of workpiece is reappraised and is adjusted;
(4), aspect the workpiece to be processed selection rule, when choosing workpiece to be processed and enter reschedule optimization collection, can give weight to each workpiece to be processed according to certain or comprehensive productive target, select workpiece to enter reschedule optimization collection according to qualifications according to the size of workpiece weight again; For workpiece to be processed selection rule, be that each operation of workpiece is carried out the length coupling average process time with equal weight; Choose the close workpiece of operation processing technology and enter the optimization collection, reduce the adjustment time of machine; Choose the more workpiece of operation and enter the optimization collection, to reduce the processing technology constraint;
(5), optimization aim:
Be provided with a m platform machine and n workpiece, q
iThe process number that expression workpiece i comprises, Q
Ij kThe j procedure of expression workpiece i is processed S on machine k
Ij kThe beginning process time of j procedure on machine k of expression workpiece i, T
Ij kThe process time of j procedure on machine k of expression workpiece i, each workpiece comprises one or multi-channel operation and the per pass operation is processed on the machine of appointment; Regulation goal is processed for the workpiece in the reschedule optimization collection is assigned on the machine of appointment, and processing sequence is optimized arrangement, and valve is the shortest when making the inter process machine idle, improves the machinery utilization rate; The hypothesis and the constraint condition that wherein need to satisfy are:
1. each workpiece processing operation is determined in advance;
2. can not interrupt each operation midway in the process;
3. all workpiece can be processed constantly zero;
4. synchronization, a machine can only be processed a procedure, and workpiece p and i can not be simultaneously processed, suppose that operation i processes before operation p, that is:
5. constraint is successively arranged between the operation of same workpiece, could begin subsequent processing j after a last operation i finishes, that is:
The optimization aim function is:
(6), optimize Hybrid PSO algorithm having proposed the hybrid fine particles group aspect the reschedule optimized Algorithm, with respect to basic particle swarm optimization algorithm, mainly carried out the improvement of three aspects:
1. be solution discrete optimization problem, and convenient description to practical problems, coding is taked the natural number coding mode;
The array that each particle in the algorithm is made up of N double figures, comprised the constraint of workpiece-operation-time-machine, N is the summation of the process number of all workpiece, the workpiece that the representative of first figure place will be processed in each double figures, the number of times α that first figure place occurs represents the α procedure of this workpiece, represents this operation to process on which platform machine for second in each double figures;
2. evolutionary rate and variation mode are used for reference the interlace operation and the mutation operation of genetic algorithm, have simplified the operation of evolving and have improved efficiency of evolution;
The mutation operation of hybrid fine particles colony optimization algorithm is taked the exchange variation, determines at random in pbest that promptly N to numeral, carries out place-exchange to every pair of numeral; And by changing the diversity that N can control the pbest variation; Variation take the to make a variation method of pbest, promptly with certain probability with all digital switches among the pbest, rearrangement; This probability is the aberration rate of Hybrid Particle Swarm; Particle through variation is necessary for feasible solution; Therefore adopt and generate the method that a new primary replaces pbest at random, as variation;
3. the Metropolis criterion for sampling of simulated annealing is dissolved in the global optimum's position calculation in the particle swarm optimization algorithm, is prevented that effectively algorithm is absorbed in the local optimum point during evolution.
2, the right changing type accidental scheduling method based on real-time working condition according to claim 1 is characterized in that: described dominance disturbance comprises: the catastrophic failure of process equipment, urgent part add, material supply is untimely; Recessive disturbance comprises: actual manufacturing procedure time, operation are adjusted the error accumulation of time and material haulage time.
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| 一种求解车间作业调度问题的混合微粒群算法. 葛茂根，扈静，蒋增强，张铭鑫，刘明周.中国制造业信息化，第36卷第15期. 2007 |
| 一种求解车间作业调度问题的混合微粒群算法. 葛茂根，扈静，蒋增强，张铭鑫，刘明周.中国制造业信息化，第36卷第15期. 2007 * |
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