CN105045223A - Multi-product batch process production scheduling method under uncertain conditions - Google Patents
Multi-product batch process production scheduling method under uncertain conditions Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 23
- 238000010923 batch production Methods 0.000 title abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000009826 distribution Methods 0.000 claims abstract description 13
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract 2
- 238000013461 design Methods 0.000 claims description 3
- 238000012432 intermediate storage Methods 0.000 abstract description 3
- 238000004088 simulation Methods 0.000 abstract 1
- 238000002948 stochastic simulation Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000003780 insertion Methods 0.000 description 3
- 230000037431 insertion Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
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- 230000002123 temporal effect Effects 0.000 description 2
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- 238000002474 experimental method Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
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- G05B19/00—Programme-control systems
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- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a multi-product batch process production scheduling method under uncertain conditions, which comprises the steps of describing the product processing time in the production process by adopting a method of probability distribution; establishing a Monte-Carlo simulation based random expected value model by taking the maximum expected completion time as a target; carrying out effective solving on the random model by using a group searching algorithm of discrete codes, and acquiring a batch process production scheduling scheme under the uncertain conditions. The multi-product batch process production scheduling method under the uncertain conditions provides an effective method for scheduling with the batch process processing time being uncertain, and has the characteristics of wide adaptability, good effectiveness and high reliability; problem solving under various random distributions can be coped by adopting a target value of random simulation; the group searching algorithm of the discrete codes is adopted, thereby ensuring the efficiency in dispatching solution searching; and the method can acquire a good production dispatching scheme within a short time for the batch process of an infinite intermediate storage tank type and an intermediate storage tank free type.
Description
Technical field
The invention belongs to production scheduling technical field, be specifically related to a kind of multi-products ordering production scheduling method under processing time condition of uncertainty.
Background technology
Because batch process produces multiple product in a batch mode, the utilization factor of its equipment and time is lower, and the different batches of different product or identical product may propose processing request to some equipment simultaneously aborning simultaneously, thus clashes.Therefore how effectively to arrange production process, properly according to the market demand or production task distributing equipment and time, play a very important role for the production efficiency and economic benefit that improve batch process, wherein, the target that enterprise is concerned about very much is Maximal Makespan.In actual production process, often there is the processing time uncertain situation of product, make the Optimized Operation scheme performance index obtained by various deterministic models reduce even no longer feasible.Therefore, the uncertain factor in processing time must be taken into full account, with the feasibility of assured plan and optimality.But existing dispatching system generally can only tackle deterministic models, processing time uncertain situation is also processed usually used as determinacy.Along with the increase of problem scale and the raising of scheduling scheme validity and requirement of real-time, there is higher requirement to multi-products ordering scheduling solution technique under condition of uncertainty.
As the novel colony intelligence optimized algorithm of one, group searching algorithm is that He etc. proposes by the behavior of hunting of animal collective inspires.In group searching algorithm, the individuality in population plays the part of different roles according to the difference division of labor of " find-participate in ".In population, find that the individuality of current optimal location is called as discoverer, to participate in and the individuality sharing discoverer's achievement is called as participant.Local optimum is absorbed in order to prevent algorithm, it is tactful that group searching algorithm introduces " going around ", participant is divided into tagger and patrolman, once discoverer has found prey trace, tagger can be close to discoverer fast, and patrolman can enter row stochastic going around near colony simultaneously.Consider that group searching algorithm has higher validity and robustness when solving complicated optimum problem, the present invention is introduced into during multi-products ordering scheduling solves under condition of uncertainty, devise the links of group searching algorithm, to obtain preferably scheduling scheme, improve enterprises production efficiency.
Summary of the invention
The object of the invention is for the multi-products ordering production scheduling problems under product treatment time condition of uncertainty, to minimize the expectation of Maximal Makespan for target, propose a kind of dispatching method based on group searching algorithm.
The present invention takes following technical solution:
A kind of multi-products ordering production scheduling method under condition of uncertainty, comprises the following steps.
Step 1: preparatory stage: be loaded into the processing time information of each each operation of product and the storage class of multi-products ordering in real time.
Step 2: stochastic simulation stage: according to the temporal information of each operation be loaded into, adopt inverse transformation method to produce the processing time of NS the corresponding stochastic distribution of obedience, NS is stochastic simulation frequency in sampling.
Step 3: algorithm initialization stage: to multi-products ordering, processes according to the situation of displacement, and the Product processing order namely on every platform equipment is the same.Be the arrangement of product serial number by each individual UVR exposure of algorithm, to each equipment: the product be arranged in above first processes, arrange posterior product aftertreatment.Then group searching algorithm population is carried out following initialization: a solution is by NEH generate rule, and separate stochastic generation (popsize is Population Size) for other popsize-1, each is separated to utilize stochastic simulation data assessment.
Step 4: the phylogenetic scale of group searching algorithm: the optimization thought continuing to use group searching algorithm, in conjunction with the neighborhood concepts of scheduling problem and the cross and variation operator of intelligent algorithm, carry out the operational design (in this step, if relate to assessment scheduling solution, the stochastic simulation data utilizing step 2 to produce) of discoverer, tagger and patrolman.
Step 4.1: discoverer acts on population preferably in solution, adopts individuality and optimumly inserts disturbance, thus obtains currently separating certain disturbance solution around, then separates based on this, carries out insertions Local Search, until acquisition local optimum.Optimum perturbation process of inserting, on the basis of current solution, is selected several unduplicated product serial numbers, is inserted into by the product of correspondence on the optimal location in other positions; Insert Local Search and successively each product is carried out insertion neighborhood search, till knowing that solution can not improve again.
Step 4.2: the individuality of correspondence and discoverer's individuality intersect by tagger, thus obtain the solution with discoverer's individual comparability.By current solution and population preferably solution intersect, selectable crossover operator has partially matched crossover, order intersection, location-based intersection, intersection, recycling cross, linear precedence intersection etc. based on order.
Step 4.3: patrolman mainly plays the effect of going around at random, adopts and realizes in two ways: random generation meets the scheduling solution of producing constraint; Or larger disturbance is done once on current basis of preferably separating.The individuality that patrolman obtains will replace the poorest person in population, namely adopt " last-one-out system ".
Step 4.4: judge whether end condition meets, if do not meet, returns step 4, otherwise terminates and export current best scheduling scheme.
Beneficial effect of the present invention is:
(1) adopt the desired value of stochastic simulation, can complete be uniformly distributed, problem solving under the various stochastic distribution such as exponential distribution;
(2) adopt the group searching algorithm of discrete codes, reduce the solution space of continuous domain search, ensure that the high efficiency of searching is separated in scheduling;
(3) to unlimited relay tank and the batch process without relay tank type, the Random Expected Value Model that the method is set up is all feasible, and the Algorithm for Solving time is shorter.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the uncertain batch processes scheduling method of the present invention.
Fig. 2 is group searching algorithm coding of the present invention decoding schematic diagram.
Fig. 3 is optimal scheduling scheme Gantt chart of the present invention.
In Fig. 2 and Fig. 3, horizontal ordinate is the time, and ordinate is equipment, the numeral product code numbering in rectangular block, the completion date of the numeral corresponding product in the rectangular block lower right corner, and makespan is the Maximal Makespan of all products.
Embodiment
Below in conjunction with accompanying drawing, the present invention will be further described.
This dispatching method is applied in certain interval chemical plant multi-product process by present embodiment.Consider two kinds of intermediate storage modes: unlimited relay tank and without relay tank.Unlimited relay tank type has enough relay tanks to store when product leaves equipment, and does not have relay tank completely without relay tank type, therefore can only be trapped on equipment, thus cause the increase of production cycle.This batch process has 5 equipment, 10 products.The uncertain processing time, wherein N represented normal distribution as table 1.
The uncertain treatment schedule of table 1
The LPT device scheduling strategy of present embodiment, as Fig. 1 shows, comprises the following steps.
Step 1: preparatory stage: be loaded into the processing time information of each each operation of product and the storage class of multi-products ordering in real time.
Step 2: stochastic simulation stage: according to the temporal information of each operation be loaded into, adopt inverse transformation method to produce the NS individual processing time meeting corresponding stochastic distribution, NS is stochastic simulation frequency in sampling.
If normal distribution is
, producing the process of obeying this distribution random numbers is:
By being uniformly distributed
u(0,1) generates
;
By being uniformly distributed
u(0,1) generates
;
；
；
Return x.
This process NS time is performed to the processing time of each operation, NS sample can be obtained, for obeying NS matrix of corresponding normal distribution.Consider problem scale and Riming time of algorithm, product number is less than to the occasion of 50, gets NS=5000, if product number is greater than 50, then NS is suitably reduced the requirement meeting problem solving real-time.
When needing the effect assessing certain scheduling solution, according to law of great number, ask for Maximal Makespan
expectation time can estimate with following formula:
Wherein,
for product arrangement
maximal Makespan stochastic variable,
for stochastic variable i-th sample value, NS is frequency in sampling.
If the processing time is other stochastic distribution, the estimation of expectation value similarly can be obtained.
Step 3: algorithm initialization stage: to multi-products ordering, processes according to the situation of displacement, and the Product processing order namely on every platform equipment is the same.Be the arrangement of product serial number by each individual UVR exposure of algorithm, to each equipment: the product be arranged in above first processes, arrange posterior product aftertreatment.Then group searching algorithm population is carried out following initialization: a solution is by NEH generate rule, and separate stochastic generation (popsize is Population Size) for other popsize-1, each is separated to utilize stochastic simulation data assessment.Fig. 2 is coding and decoding schematic diagram, and illustrating product coding is operation plan Gantt chart corresponding when getting average (1,2,3,4,5,6,7,8,9, the 10) processing time, and Maximal Makespan that this scheduling is separated is 525.
Step 4: the phylogenetic scale of group searching algorithm: the optimization thought continuing to use group searching algorithm, in conjunction with the neighborhood concepts of scheduling problem and the cross and variation operator of intelligent algorithm, carry out the operational design (in this step, if relate to assessment scheduling solution, the stochastic simulation data utilizing step 2 to produce) of discoverer, tagger and patrolman.
Step 4.1: discoverer acts on current population preferably in solution, adopts individuality and optimumly inserts disturbance, thus obtains currently separating certain disturbance solution around, then separates based on this, carries out insertions Local Search, until acquisition local optimum.
In present embodiment, optimum perturbation process of inserting is on the basis of current solution, and select three unduplicated product serial numbers, be inserted into by the product of correspondence on the optimal location in other positions, implementation procedure is:
Step (1): list is inserted in order
iLfor sky, Stochastic choice 3 unduplicated products, and put into
iL;
Step (2): from
iLmiddle taking-up product, find it
pposition.Will
pin this product be inserted into desired positions in other n-1 position, thus obtain new
p;
Step (3): if
iLbe not empty, then go to step (2); Otherwise, terminate.
Step 4.2: the individuality of correspondence and discoverer's individuality intersect by tagger, thus obtain the solution with discoverer's individual comparability.By current solution and population preferably solution intersect, present embodiment adopts partially matched crossover, and the better individuality obtained after intersecting and current tagger's individuality are compared, if more excellent, then current individual is replaced, otherwise remains unchanged.
Step 4.3: patrolman mainly plays the effect of going around at random, object is that the solution that use one produces at random replaces individuality poor in population.Random generation meets the scheduling solution of producing constraint and obtains by producing arbitrarily a random product arrangement; Also can on current basis of preferably separating, some products of Stochastic choice are inserted into other random sites, to carry out larger disturbance.These two kinds of modes are carried out according to the probability of respective 0.5, and then the individuality of generation is replaced the poorest person in population by patrolman's operation.
Step 4.4: judge whether end condition meets, employing maximum iteration time is end condition.Because algorithm performance is better, present embodiment problem scale is 20, adopts 50 on behalf of maximum iteration time.If cycle index is less than 50, then return step 4.Otherwise end loop also exports optimum results, what now obtain is scheduling to the optimal scheduling scheme that algorithm finds.
Such as, the optimum individual product that after 50 iteration, group searching algorithm finds is arranged as (3,1,8,4,5,6,9,10,7,2), and separating desired value is 500.11.If according to the mean value computation in processing time, then operation plan Gantt chart such as Fig. 3 shows.This scheduling scheme is all shown as more excellent scheduling in many experiments test, and the working time of whole dispatching algorithm is in 0.5 millisecond, can meet the requirement of dispatching system real-time.
Above content is the explanation to preferred embodiment of the present invention, and those skilled in the art can be helped to understand technical scheme of the present invention more fully.But these embodiments only illustrate, can not assert that the specific embodiment of the present invention is only limitted to the explanation of these embodiments.
Claims (4)
1. the multi-products ordering production scheduling method under a processing time condition of uncertainty, it is characterized in that: the mode of the method probability of use distribution is to describe the uncertain processing time, establishing with Maximal Makespan is the Random Expected Value Model of target, sliding-model control has been carried out to the group searching algorithm of Filled function, the probabilistic model adopting the group searching Algorithm for Solving of discrete codes to set up, obtains preferably production scheduling scheme.
2. the multi-products ordering production scheduling method under processing time condition of uncertainty according to claim 1, it is characterized in that: the described uncertain processing time, its probability distribution is known, carries out Monte Carlo simulation according to this probability model, carrys out approximate treatment expectation target value.
3. the multi-products ordering production scheduling method under processing time condition of uncertainty according to claim 1, it is characterized in that: the Three role discoverer of described group searching algorithm, tagger, patrolman have all carried out discretize design, wherein, discoverer carries out the Local Search based on inserting on the basis of preferably dispatching solution; Tagger by current solution and preferably dispatch solution carry out interlace operation, to obtain better individuality; Patrolman to the overall situation preferably solution carry out disturbance, and replace the poorest scheduling scheme in colony.
4. the multi-products ordering production scheduling method under processing time condition of uncertainty according to claim 1, it is characterized in that: described probabilistic model is the multi-products ordering production scheduling problems model under processing time condition of uncertainty, comprise unlimited relay tank and without relay tank two type.
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| US20120254238A1 (en) * | 2009-06-03 | 2012-10-04 | International Business Machines Corporation | Managing uncertain data using monte carlo techniques |
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| EP1484704A1 (en) * | 2003-06-02 | 2004-12-08 | Institut Francais Du Petrole | Method for helping oilfield management decision making in the presence of technically and economically uncertain parameters |
| CN1912861A (en) * | 2005-08-08 | 2007-02-14 | 上海市计量测试技术研究院 | Method of analog computing synthesis indeterminacy using Monte carlo acounting |
| US20120254238A1 (en) * | 2009-06-03 | 2012-10-04 | International Business Machines Corporation | Managing uncertain data using monte carlo techniques |
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