CN112131761A - Factory dispatching method and system based on group intelligent algorithm - Google Patents
Factory dispatching method and system based on group intelligent algorithm Download PDFInfo
- Publication number
- CN112131761A CN112131761A CN202011333543.7A CN202011333543A CN112131761A CN 112131761 A CN112131761 A CN 112131761A CN 202011333543 A CN202011333543 A CN 202011333543A CN 112131761 A CN112131761 A CN 112131761A
- Authority
- CN
- China
- Prior art keywords
- goods
- dispatchable
- time
- machine
- batch
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/18—Manufacturability analysis or optimisation for manufacturability
Abstract
The invention provides a factory dispatching method and a system based on a group intelligent algorithm, wherein the factory dispatching method comprises the following steps: collecting data including the total quantity of the batches of goods, the arrival sequence of each batch of goods, the dispatchable machine of each batch of goods, the preprocessing time of each batch of goods and the use state of the dispatchable machine; data are sorted; performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises dispatchable machine stations corresponding to each batch of goods during iterative convergence, the arrangement sequence of the goods of each batch at each station, the total processing time of each dispatchable machine station and iterative convergence time; and outputting the operation result. The invention realizes the homogenization of the goods arrangement of each machine through the ant colony algorithm, thereby improving the utilization rate of the machine, avoiding the waste of machine resources and ensuring that the machine which can be dispatched in the production flow has definite feeding quantity.
Description
Technical Field
The invention relates to the field of semiconductor manufacturing, in particular to a factory dispatching method and a factory dispatching system based on a group intelligent algorithm.
Background
In the wafer manufacturing process, a single machine is usually used to perform manufacturing corresponding to multiple batches of goods, and the goods are usually sorted according to a certain rule during the material feeding, wherein the goods with the top-ranked batches are first entered into the manufacturing process, and the regular sorting method does not consider the effective allocation among the resources (e.g., machines), which may result in uneven discharging of the machines and a certain capacity loss.
If a station with a Qtime (pause time) exists in a production flow, a rule of human intervention is usually adopted, so that machines in the production flow have no clear feeding quantity and feeding time, partial machines are not fully utilized, and resources are wasted.
Disclosure of Invention
The invention provides a factory dispatching method and a factory dispatching system based on a group intelligent algorithm, which can solve the problems of uneven goods arrangement of all machines and insufficient utilization of part of machines.
In order to solve the above problems, the present invention provides a factory dispatching method based on a group intelligence algorithm, comprising the following steps: step S1: collecting data including a total number of lots of the good put into production, an arrival sequence of the good at each lot, dispatchable machines of the good at each lot, a number of sites of the good at each lot, a preparation time of the good at each lot, and a use status of all dispatchable machines; step S2: sorting the data; step S3: performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises the dispatchable machine stations corresponding to each batch of the goods during iterative convergence, the arrangement sequence of each batch of the goods at each station, the total processing time of each dispatchable machine station and iterative convergence time; step S4: and outputting the operation result.
Optionally, step S2 includes: and arranging the dispatchable machine platform of each batch of the goods and the preprocessing time of each batch of the goods according to the collected data.
Optionally, step S3 includes: step S31: substituting the data obtained by sorting into an ant colony algorithm, and carrying out parameter initialization setting; step S32: setting iteration times, and starting circulation; step S33: each ant traverses all the dispatchable machines; step S34: recording the end value of preprocessing time of the last batch of goods at the last station, updating the optimal solution and updating the pheromone concentration; step S35: judging whether the ant colony algorithm of the iteration meets the end condition, if so, executing the step S36; if not, returning to the step S32, and adding 1 to the iteration number; step S36: and recording the preprocessing time of each dispatchable machine station and the time required by the processing production of all batches of goods in the optimal solution, finishing the operation and obtaining an operation result.
Further, in step S33, each batch of goods traversed by each ant needs to satisfy the following conditions at all the dispatchable machines at each station:
Si，j+1≥Eij ---------------------（1）
Eij= Sij+ Cijk ---------------------（2）
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m, k is more than or equal to 1 and less than or equal to p,
indicating the point in time when the ith lot of goods began to be produced at site j,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
indicating the production end time point of the ith lot at site j,
indicating the time required for the station j of the kth station to produce the i-th batch of goods to be preprocessed,
is constant, and the station j of the ith lot is in the k machine processing production
The value is 1, and the k-th machine is not used for processing production
The value is 0.
Optionally, the data further includes a Qtime constraint time for the good with the Qtime limit.
Further, step S2 includes: sorting out the dispatchable machine stations and preprocessing time of each batch of the goods according to the collected data; and sorting out Qtime constraint time of each batch of the goods between adjacent stations.
Further, step S3 includes: step S31: substituting the data obtained by sorting into an ant colony algorithm, and carrying out parameter initialization setting; step S32: setting iteration times, and starting circulation; step S33: each ant traverses all the dispatchable machines; step S34: recording the end value of preprocessing time of the last batch of the goods at the last station, updating the optimal solution and updating the pheromone concentration; step S35: judging whether the ant colony algorithm of the iteration meets the end condition, if so, executing the step S36; if not, returning to the step S32, and adding 1 to the iteration number; step S36: recording the use time period and the idle time period of each dispatchable machine station in the optimal solution, selecting the goods which are not constrained by Qtime and have preprocessing time less than or equal to the idle time of the dispatchable machine stations in the idle time period to process and produce, finishing the operation, and obtaining the operation result.
Further, step S33 includes: each batch of the goods traversed by each ant needs to satisfy the following conditions at all the dispatchable machines of each station:
Si，j+1≥Eij ---------------------（1）
Eij=Sij+Cijk ---------------------（2）
Ti，j，j+1=Si，j+1-Eij ---------------------（4）
Ti，j，j+1≤Qi，j，j+1 ---------------------（5）
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m, k is more than or equal to 1 and less than or equal to p,
indicating the point in time when the ith lot of goods began to be produced at site j,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
indicating the production end time point of the ith lot at site j,
indicating the time required for the station j of the kth station to produce the i-th batch of goods to be preprocessed,
is constant, and the station j of the ith lot is in the k machine processing production
The value is 1, and the k-th machine is not used for processing production
The value is 0;
representing the time difference between the end of production at site j and the start of production at site j +1 for the ith lot,
represents the Qtime constraint time between the end of production at site j and the beginning of production at site j +1 for the ith lot.
Further, the operation result further includes: and each dispatchable machine station is put into production at each time point.
Further, all ants have traversed all dispatchable machines for each lot at each of the sites, all sites having been sorted, subject to a set maximum number of iterations being met. Further, the parameters include a total quantity of the lot of goods put into production, a number of sites of the goods per lot, a number of ants, an initial value of pheromones, and a maximum number of iterations.
The invention also provides a factory dispatching system based on the group intelligent algorithm, which comprises the following steps: the data collection module is used for collecting data, wherein the data comprises the total quantity of batches of goods put into production, the arrival sequence of the goods in each batch, the dispatchable machine stations of the goods in each batch, the preprocessing time of the goods in each batch and the use states of all the dispatchable machine stations; the data collecting module is used for collecting the data collected by the data collecting module and sending the collected data to the operation module; the operation module is used for performing ant colony algorithm according to the data to obtain operation results, the operation results comprise the dispatchable machine stations corresponding to each batch of the goods during iterative convergence, the arrangement sequence of each batch of the goods at each station, the total processing time of each dispatchable machine station and the iterative convergence time, and the operation module sends the operation results obtained through operation to the output module; and the output module is used for outputting the operation result.
The invention also provides a factory dispatching system based on the group intelligent algorithm, which comprises the following steps: a data collection module for collecting data including a total quantity of lots of the good put into production, an arrival sequence of the good for each lot, a number of sites of the good for each lot, a dispatchable machine for each lot of the good, a preparation time of the good for each lot, a use status of all the dispatchable machines, a Qtime constraint time of the good with a Qtime limit; the data collecting module is used for collecting the data collected by the data collecting module and sending the collected data to the operation module; the operation module is used for performing an ant colony algorithm according to the data to obtain an operation result, recording the use time period and the idle time period of each dispatchable machine station in the optimal solution, selecting goods which are free from Qtime constraint and have preprocessing time less than or equal to the idle time of the dispatchable machine stations in the idle time period for processing production, wherein the operation result comprises the dispatchable machine stations corresponding to each batch of goods in iterative convergence, the arrangement sequence of each batch of goods at each station, the total processing time of each dispatchable machine station and the iterative convergence time, and the operation module sends the operation result obtained by operation to the output module; and the output module is used for outputting the operation result.
Compared with the prior art, the method has the following beneficial effects:
the invention provides a factory dispatching method and a system based on a swarm intelligent algorithm, wherein the factory dispatching method based on the swarm intelligent algorithm comprises the following steps: step S1: collecting data including a total quantity of lots of goods put into production, an arrival sequence of the goods for each lot, dispatchable machines of the goods for each lot, a preparation time of the goods for each lot, and usage statuses of all dispatchable machines; step S2: sorting the data; step S3: performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises a dispatchable machine table corresponding to each batch of goods during iterative convergence, the arrangement sequence of each batch of goods at each station, the total processing time of each dispatchable machine table and iterative convergence time; step S4: and outputting the operation result. According to the invention, the homogenization of the arrangement of each machine is realized through the ant colony algorithm, so that the utilization rate of the machine is improved, the waste of machine resources is avoided, the dispatchable machines in the production flow have definite feeding quantity, part of machines are fully utilized, and the waste of resources is avoided.
Drawings
FIG. 1 is a flow chart of a factory dispatching method based on a swarm intelligence algorithm according to the present invention.
Fig. 2 is a schematic flowchart of the ant colony algorithm according to the first embodiment of the present invention.
Fig. 3 is a flowchart illustrating an ant colony algorithm according to a second embodiment of the present invention.
Detailed Description
As described in the background, the cargo is generally sorted according to a certain rule when the cargo is produced, for example, two existing machines a and B, the cargo LOTs to be arranged include a first LOT1, a second LOT2 and a third LOT3, and the machine processing time of the first LOT1, the second LOT2 and the third LOT3 is 2 hours, 1 hour and 1 hour respectively, the first LOT1 can be produced in machine a or machine B, and both the second LOT2 and the third LOT3 can be produced only on machine a. If a machine B is in production operation and a machine a is idle, it is now necessary to arrange a first LOT of LOT1, a second LOT of LOT2, and a third LOT of LOT3 to enter production, according to the existing rules, the first LOT of LOT1 will enter machine a for production, and the second LOT of LOT2 and the third LOT of LOT3 will also enter machine a for production, which leads to the following situations: 1. after the production operation of the machine B is finished, the machine B is idle, so that the machine resources are wasted; 2. the first LOT of LOT1, the second LOT of LOT2, and the third LOT of LOT3 took a 4 hour period in the production process, resulting in a total elapsed time.
For some goods that need to be produced in the following production flow, the production flow of these goods can be completed only if the production flow includes a plurality of stations, for example, four stations including station PA, station PB, station PC and station PD in turn. The Qtime (pause time) between the site PA and the site PB (namely after the production of the site PA is finished and before the production of the site PB is started) is 2 hours, the Qtime between the site PB and the site PC is 2 hours, and the Qtime between the site PC and the site PD is 4 hours, at this time, the engineer estimates that the site PA can input 20 batches of goods at most, the site PB can receive 16 batches of goods at most from the site PA, and the site PC can receive 8 batches of goods at most from the site PB. In general, in order to avoid the occurrence of the timeout of the Qtime, only less than 8 batches of goods can be put into the station PA, which easily causes the insufficient utilization rate of the machine and wastes the machine resources. In addition, the number of shipments is usually based on manual experience, with great uncertainty and subjectivity, and it is therefore crucial to give a clear put recommendation.
The invention has the core idea that a factory dispatching method and a system based on a group intelligent algorithm are provided, and the factory dispatching method based on the group intelligent algorithm comprises the following steps: step S1: collecting data including a total quantity of lots of goods put into production, an arrival sequence of the goods for each lot, dispatchable machines of the goods for each lot, a preparation time of the goods for each lot, and usage statuses of all dispatchable machines; step S2: sorting the data; step S3: performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises a dispatchable machine table corresponding to each batch of goods during iterative convergence, the arrangement sequence of each batch of goods at each station, the total processing time of each dispatchable machine table and iterative convergence time; step S4: and outputting the operation result. According to the invention, the homogenization of the arrangement of each machine is realized through the ant colony algorithm, so that the utilization rate of the machine is improved, the waste of machine resources is avoided, the dispatchable machines in the production flow have definite feeding quantity, part of machines are fully utilized, and the waste of resources is avoided.
The present invention will be described in further detail below with reference to a group intelligent algorithm-based factory dispatch method and system. The present invention will now be described in more detail with reference to the accompanying drawings, in which preferred embodiments of the invention are shown, it being understood that one skilled in the art may modify the invention herein described while still achieving the advantageous effects of the invention. Accordingly, the following description should be construed as broadly as possible to those skilled in the art and not as limiting the invention.
In the interest of clarity, not all features of an actual implementation are described. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific details must be set forth in order to achieve the developer's specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art.
In order to make the objects and features of the present invention more comprehensible, embodiments of the present invention are described in detail below with reference to the accompanying drawings. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise ratio for the purpose of facilitating and distinctly aiding in the description of the embodiments of the invention.
The first embodiment.
FIG. 1 is a flow chart of a factory dispatching method based on a swarm intelligence algorithm. As shown in fig. 1, the embodiment provides a factory dispatching method based on a group intelligence algorithm, which includes the following steps:
step S1: collecting data including a total quantity of lots of goods put into production, an arrival sequence of the goods for each lot, dispatchable machines of the goods for each lot, a preparation time of the goods for each lot, and usage statuses of all dispatchable machines;
step S2: sorting the data;
step S3: performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises a dispatchable machine table corresponding to each batch of goods during iterative convergence, the arrangement sequence of each batch of goods at each station, the total processing time of each dispatchable machine table and iterative convergence time;
step S4: and outputting the operation result.
The following describes in detail a factory dispatch method based on a swarm intelligence algorithm according to an embodiment of the present invention with reference to fig. 1 and 2.
Step S1 is executed to collect data including the total quantity of the lots of goods put into production, the arrival sequence of the goods in each lot, the number of sites of the goods in each lot, the dispatchable machines of the goods in each lot, the preprocessing time of the goods in each lot, and the usage status of all dispatchable machines.
In this embodiment, n lots of goods need to be processed, their processing routes are the same, and need to be processed in p dispatchable machines, and the pre-processing time of each lot of goods on the dispatchable machines is known, and once the lot of goods starts to be processed, the lot of goods cannot be stopped until the processing is finished; meanwhile, the same batch of goods needs to be produced and processed on the same dispatching machine, each dispatching machine carries out continuous and uninterrupted processing on the same batch of goods (that is, a batch of goods enters one dispatching machine to start production, and other batches of goods can be produced after the batch of goods is completely produced), and each batch of goods comprises m stations, wherein n, m and p are all positive integers and are more than or equal to 1.
Step S2 is then executed to collate the data. And arranging the dispatching machine stations of each batch of the goods and the processing time information of each batch of the goods according to the collected data.
In this embodiment, the pre-processing time on the dispatchable machine platform is required to sort out each batch of the goods at each of the stations.
Fig. 2 is a flowchart illustrating the ant colony algorithm according to the present embodiment. As shown in fig. 2, next, step S3 is executed to perform an ant colony algorithm based on the data to obtain an operation result.
The Ant Colony Optimization (ACO) is used as a colony intelligent algorithm, and in the existing implementation, the ant colony algorithm principle is as follows:
1. the ants release pheromone on the path;
2. randomly selecting a path to walk when the intersection which has not been walked is touched, and releasing pheromones related to the path length;
3. pheromone concentration is inversely proportional to the path length. When the subsequent ants touch the intersection again, the path with higher pheromone concentration is selected.
4. The pheromone concentration on the optimal path is increasing.
5. And finally finding the optimal food searching path by the ant colony.
The ant colony algorithm is used for changing the rule that one machine corresponds to a plurality of batches of goods, the mode that the plurality of machines correspond to the plurality of batches of goods can be adopted, the pre-arrangement calculation can be carried out, the homogenization of the arrangement of the machines is realized, the utilization rate of the machines is improved, and the waste of machine resources is avoided.
The ant colony algorithm in this embodiment includes the following steps:
and step S31, substituting the sorted data into an ant colony algorithm, and performing parameter initialization setting. According to the actual situation, the total quantity of the batches of the goods put into production, the number of sites of each batch of the goods, the number of ants, the initial value of pheromone, the maximum iteration number and the like are set,
the number of ants is generally set to be twice of the number of dispatchable machines.
And step S32, setting the iteration times, starting the loop, setting the iteration times from 1, adding 1 to the iteration times when the loop is needed after the operation is completed once, and ending the loop until the iteration times are increased to N.
In step S33, each ant traverses all the dispatchable machines.
Each good that each ant needs to traverse needs to satisfy the following conditions when all dispatchable machines of each site:
Si，j+1≥Eij ---------------------（1）
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
indicating the production end time point of the ith lot at site j.
This formula indicates that the same batch of the goods needs to be produced at the previous site after the production at the previous site is finished before the production at the subsequent site can be started.
Eij= Sij+ Cijk ---------------------（2）
Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m, k is more than or equal to 1 and less than or equal to p,
indicating the point in time when the ith lot of goods began to be produced at site j,
indicating the production end time point of the ith lot at site j,
indicating the time required for the station j of the kth station to produce the i-th batch of goods to be preprocessed.
The formula indicates that the same batch of the goods is continuously produced on the dispatchable machine.
Wherein the content of the first and second substances,
is constant, and the station j of the ith lot is in the k machine processing production
The value is 1, and the k-th machine is not used for processing production
The value is 0.
The formula shows that each batch of goods can only be processed by the same dispatching machine at the same station, and each dispatching machine can only process the same batch of goods at the same time period.
And step S34, recording the end value of the preprocessing time of the goods in the last batch at the last station, updating the optimal solution and updating the pheromone concentration.
Step S35, judging whether the ant colony algorithm of the iteration meets the end condition, if yes, executing step S36; if not, return to step S32 and increment the number of iterations by 1.
The termination conditions include: whether all the sites are sequenced or not and whether the set maximum iteration times are met or not are judged.
Step S36, recording the preprocessing time of each dispatchable machine and the time required for producing all batches of goods when the optimal solution is obtained, ending the operation, and obtaining the operation result, where the operation result includes the dispatchable machine corresponding to each batch of goods when the iteration converges, the arrangement order of each batch of goods at each station, and the total processing time of each dispatchable machine.
Step S4 is executed to output the operation result.
As an example, the total quantity of the LOTs of the goods put into production is 7 LOTs, and the LOTs are sequentially a first LOT1 to a seventh LOT7 according to the arrival sequence, and the processing routes of the goods of the 7 LOTs are the same; the number of stations is 1; the dispatchable machines comprise four dispatchable machines M1, M2, M3 and M4, and the four dispatchable machines are all in an idle state; the number of ants is 8; the maximum iteration number is 200, and the initial value of pheromone of each batch of the goods at each station is 100; the first LOT of LOT1 to the third LOT of LOT3 can be produced in three dispatchable machines M1, M3 and M4, the fourth LOT of LOT4 and the fifth LOT of LOT5 can be produced in M1 and M2, and the sixth LOT of LOT6 and the seventh LOT of LOT of LOT7 can be produced in M2 and M4; the preprocessing time of the first LOT of LOT1 to the third LOT of LOT3 is 1 hour, the preprocessing time of the fourth LOT of LOT4 and the fifth LOT of LOT5 is 2 hours, and the preprocessing time of the sixth LOT of LOT LOT6 and the seventh LOT of LOT7 is 3 hours.
The data are collated as in table 1 below:
the blank of the table shows numbers indicating that the lot of goods on the row of the blank can be produced in the dispatchable machine on which the blank is located, and the specific numerical value shown indicates the preprocessing time of the lot of goods in the dispatchable machine. The data can be easily substituted into the ant colony algorithm through the table.
Through the ant colony algorithm, the operation result obtained after the ant colony algorithm is as follows:
(1) the status of each lot of goods assigned to a dispatchable machine is as follows in table 2:
wherein, P11 indicates that the LOT1 is at site 1, P21 indicates that the LOT2 is at site 1, P31 indicates that the LOT3 is at site 1, P41 indicates that the LOT4 is at site 1, P51 indicates that the LOT5 is at site 1, P61 indicates that the LOT6 is at site 1, P71 indicates that the LOT7 is at site 1, and v indicates that the dispatchable tool is selected, indicating that the dispatchable tool is not selected.
(2) Seven batches of the order of the goods to the dispatchable machine: P21P 11P 41P 71P 61P 31P 51.
(3) Processing time of each dispatching machine:
the time required for processing by machine M1 is 4 hours, the time required for processing by machine M2 is 3 hours, the time required for processing by machine M3 is 3 hours, and the time required for processing by machine M4 is 3 hours.
(4) The convergence time was 4 hours. Therefore, the factory dispatching method based on the group intelligent algorithm can improve the utilization rate of the machine, make full use of part of machines and avoid waste of machine resources.
The embodiment also provides a factory dispatching system based on the swarm intelligence algorithm, which comprises a data collection module, a data sorting module, an operation module and an output module.
The data collection module is used for collecting data, wherein the data comprises the total quantity of the batches of goods put into production, the arrival sequence of the goods in each batch, the dispatchable machine stations of the goods in each batch, the preprocessing time of the goods in each batch and the use states of all the dispatchable machine stations. The data sorting module is used for sorting the data collected by the data collection module so as to be easily substituted into the ant colony algorithm. And the data collection module sends the collected data to the operation module.
The operation module is used for performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises dispatchable machine stations corresponding to each batch of goods during iterative convergence, the arrangement sequence of the goods in each batch at each station, the total processing time of each dispatchable machine station and iterative convergence time. And the operation module sends an operation result obtained by operation to the output module. And the output module outputs the operation result.
Example two.
Fig. 3 is a flowchart illustrating the ant colony algorithm according to the present embodiment. As shown in fig. 3, the present embodiment provides a factory dispatching method based on a swarm intelligence algorithm, which specifically includes the following steps:
step S1 is executed first, and data is collected, where the data includes the total quantity of the lots of the goods put into production, the arrival sequence of the goods in each lot, the number of sites of the goods in each lot, the dispatchable machines of the goods in each lot, the preprocessing time of the goods in each lot, the usage statuses of all the dispatchable machines, the Qtime constraint time of the goods with Qtime (pause time) limit, and the like.
Step S2 is then executed to collate the data. Arranging the dispatching machine tables and the processing time information of each batch of goods according to the collected data; the Qtime constraint time of each batch of the goods between each adjacent site is also needed to be cleared up.
Step S3 is then executed: and performing an ant colony algorithm according to the data to obtain an operation result.
And step S31, substituting the sorted data into an ant colony algorithm, and performing parameter initialization setting. According to actual conditions, the total quantity of the batches of goods put into production, the number of sites of the goods in each batch, the number of ants, an initial pheromone value, the maximum iteration number and the like are set.
The number of ants is generally set to be twice of the number of dispatchable machines.
And step S32, setting the iteration times, starting the loop, setting the iteration times from 1, adding 1 to the iteration times when the loop is needed after the operation is completed once, and ending the loop until the iteration times are increased to N.
Step S33, each ant traverses all the dispatchable machines, judges whether the Qtime constraint condition is satisfied or not by calculating the time difference between the sites, and selects the dispatchable machines satisfying the condition.
Each good that each ant needs to traverse needs to satisfy the following conditions when all dispatchable machines of each site:
Si，j+1≥Eij ---------------------（1）
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
indicating the production end time point of the ith lot at site j.
This formula indicates that the same batch of the goods needs to be produced at the previous site before it can begin production at the subsequent site.
Eij=Sij+Cijk ---------------------（2）
Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m, k is more than or equal to 1 and less than or equal to p,
indicating the point in time when the ith lot of goods began to be produced at site j,
indicating the production end time point of the ith lot at site j,
indicating the time required for the station j of the kth station to produce the i-th batch of goods to be preprocessed.
The formula indicates that the same batch of the goods is continuously produced on the dispatchable machine.
Wherein the content of the first and second substances,
is constant, and the station j of the ith lot is in the k machine processing production
Value of 1, and not processing on the k-th machineIn production of
The value is 0.
The formula shows that each batch of goods can only be processed by the same dispatching machine at the same station, and each dispatching machine can only process the same batch of goods at the same time period.
Ti，j，j+1=Si，j+1-Eij ---------------------（4）
Wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
indicating the production end time point of the ith lot at site j,
indicating the time difference between the end of production at site j and the start of production at site j +1 for the ith lot.
Ti，j，j+1≤Qi，j，j+1 ---------------------（5）
Wherein the content of the first and second substances,
representing the time difference between the end of production at site j and the start of production at site j +1 for the ith lot,
represents the Qtime constraint time between the end of production of lot i at site j and the beginning of production at site j +1 (i.e., between lot i at site j and site j + 1).
This equation indicates that the time difference between adjacent sites (the time difference between the end of production at site j and the start of production at site j + 1) should meet the Qtime constraint time.
And step S34, recording the end value of the preprocessing time of the goods in the last batch at the last station, updating the optimal solution and updating the pheromone concentration.
Step S35, judging whether the ant colony algorithm of the iteration meets the end condition, if yes, executing step S36; if not, return to step S32 and increment the number of iterations by 1.
Wherein the end condition includes: and under the condition of meeting the set maximum iteration number, all ants traverse all dispatchable machine tables of each batch of goods at each site, and all sites are sorted.
Step S36, recording the usage time period and the idle time period of each dispatchable machine during the optimal solution, and selecting a cargo with no Qtime constraint and with a processing time less than or equal to the idle time of the dispatchable machine for production in the idle time period, ending the operation, and obtaining an operation result, where the operation result includes the dispatchable machine corresponding to each batch of cargo during iterative convergence, the arrangement order of each batch of cargo at each station, the total processing time of each dispatchable machine, the iterative convergence time, and the time point of each dispatchable machine for putting into production.
Step S4 is executed to output the operation result.
As an example, the total quantity of the LOTs of goods put into production is 5, and the LOTs of goods are first LOT1 to fifth LOT5 in sequence from arrival, and the processing routes of the 5 LOTs of goods are the same; the number of the stations is 2, and the stations are respectively a station 1 and a station 2; the dispatching machines comprise 6 machines M1, M2, M3, M4, M5 and M6, and the 6 machines are all in an idle state; the number of ants is 12; the maximum iteration number is 200, and the initial value of pheromone of each batch of the goods at each station is 100; first LOT1 may be produced at site 1 in station M1, station M2, and station M4, at site 2 in station M5, second LOT2 may be produced at site 1 in station M1 and station M3, at site 2 in station M5 and station M6, third LOT3 may be produced at station 1 in station M2 and station M3, at site 2 in station M5, fourth LOT4 at site 1 in station M1 and station M4, at site 2 in station M5 and station M6, fifth LOT5 at site 1 in station M2 and station M4, and at site 2 in station M5 and station M6; the preprocessing time of the first LOT of LOT1 at site 1 is 2 hours, the preprocessing time at site 2 is 2 hours, the preprocessing time of the second LOT of LOT2 at site 1 is 1 hour, the preprocessing time at site 2 is 3 hours, the preprocessing time of the third LOT of LOT3 at site 1 is 2 hours, the preprocessing time at site 2 is 2 hours, the preprocessing time of the fourth LOT of LOT4 at site 1 is 3 hours, the preprocessing time at site 2 is 4 hours, the preprocessing time of the fifth LOT of LOT5 at site 1 is 3 hours, and the preprocessing time at site 2 is 2 hours; first LOT LOT1 has no Qtime constraint after site 1 production ends and before site 2 production begins, second LOT LOT2 has no Qtime constraint after site 1 production ends and before site 2 production begins; the Qtime constraint time for the third LOT of LOT3 after the end of site 1 production and before the start of site 2 production was 1 hour, the fourth LOT of LOT4 after the end of site 1 production and before the start of site 2 production had no Qtime constraint, and the Qtime constraint time for the fifth LOT of LOT5 after the end of site 1 production and before the start of site 2 production was 3 hours.
The data is collated as table 3 below:
wherein, the goods of each batch in the table correspond to two stations, wherein the upper station is a station 1, and the lower station is a station 2; the goods of the batch indicated by the number displayed in the blank space as the line of the blank space can be produced in the dispatching machine where the blank space is located, and the specific numerical value displayed indicates the preprocessing time of the goods of the batch in the dispatching machine where the station is located. The data can be easily substituted into the ant colony algorithm through the table.
The Qtime constraint times for the first LOT LOT1 through the fifth LOT LOT5 after the end of site 1 production and before the start of site 2 production, respectively, are also collated as:
[999999，999999，1，999999，3]
where 99999999 indicates no Qtime constraint, the numeral 1 indicates that the Qtime constraint time for the third LOT of LOT3 after the end of production at site 1 and before the start of production at site 2 is 1 hour, and the numeral 3 indicates that the Qtime constraint time for the fifth LOT of LOT5 after the end of production at site 1 and before the start of production at site 2 is 3 hours.
Through the ant colony algorithm, the operation result obtained after the ant colony algorithm is as follows:
(1) the status of each lot of goods assigned to a dispatchable machine is as follows in table 4:
wherein, P11 indicates that the first LOT1 is at site 1, P12 indicates that the first LOT1 is at site 2, P21 indicates that the second LOT2 is at site 1, P22 indicates that the second LOT2 is at site 2, P31 indicates that the third LOT3 is at site 1, P32 indicates that the third LOT3 is at site 2, P41 indicates that the fourth LOT4 is at site 1, P42 indicates that the fourth LOT4 is at site 2, P51 indicates that the fifth LOT5 is at site 1, and P52 indicates that the fifth LOT5 is at site 2.
(2) The sequence of distributing five batches of goods to the dispatchable machines: P31P 32P 11P 21P 12P 22P 51P 52P 41P 42.
(3) The processing time of each dispatching machine is as follows:
the processing time of the machine station M1 is 3 hours, the processing time of the machine station M2 is 3 hours, the processing time of the machine station M3 is 2 hours, the processing time of the machine station M4 is 3 hours, the processing time of the machine station M5 is 7 hours, and the processing time of the machine station M6 is 6 hours.
(4) The convergence time was 9 hours.
(5) Time difference between different sites: the time difference between site 1 and site 2 for the first LOT of LOT1 was 2 hours, the time difference between site 1 and site 2 for the second LOT of LOT2 was 3 hours, the time difference between site 1 and site 2 for the third LOT of LOT3 was 0, the time difference between site 1 and site 2 for the fourth LOT of LOT LOT4 was 2 hours, and the time difference between site 1 and site 2 for the fifth LOT of LOT LOT5 was 0. From the above data, it can be seen that the transportation result satisfies the Qtime constraint.
The embodiment also provides a factory dispatching system based on the swarm intelligence algorithm, which comprises a data collection module, a data sorting module, an operation module and an output module.
The data collection module is used for collecting data, wherein the data comprises the batch total amount of the goods in production, the arrival sequence of the goods in each batch, the station number of the goods in each batch, the dispatching machine of the goods in each batch, the preprocessing time of the goods in each batch, the use states of all the dispatching machines, and the Qtime constraint time of the goods with the Qtime limit. The data sorting module is used for sorting the data collected by the data collection module so as to be easily substituted into the ant colony algorithm. And the data collection module sends the collected data to the operation module.
The operation module is used for performing an ant colony algorithm according to the data, recording the use time period and the idle time period of each dispatchable machine station in the optimal solution, and selecting goods without Qtime constraint and with the processing time less than or equal to the idle time of the dispatchable machine station to produce so as to obtain an operation result. The operation result comprises the dispatchable machine stations corresponding to each batch of goods during iterative convergence, the arrangement sequence of each batch of goods at each station, the total processing time of each dispatchable machine station and the iterative convergence time, so as to improve the utilization rate of the dispatchable machine stations. And the operation module sends an operation result obtained by operation to the output module. And the output module outputs the operation result.
In summary, the factory dispatching method and system based on the swarm intelligent algorithm provided by the invention comprises the following steps: step S1: collecting data including a total quantity of lots of goods put into production, an arrival sequence of the goods for each lot, dispatchable machines of the goods for each lot, a preparation time of the goods for each lot, and usage statuses of all dispatchable machines; step S2: sorting the data; step S3: performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises the dispatchable machine stations corresponding to each batch of the goods during iterative convergence, the arrangement sequence of each batch of the goods at each station, the total processing time of each dispatchable machine station and iterative convergence time; step S4: and outputting the operation result. According to the invention, the homogenization of the arrangement of each machine is realized through the ant colony algorithm, so that the utilization rate of the machine is improved, the waste of machine resources is avoided, the dispatchable machines in the production flow have definite feeding quantity, part of machines are fully utilized, and the waste of resources is avoided.
In addition, it should be noted that the description of the terms "first", "second", and the like in the specification is only used for distinguishing each component, element, step, and the like in the specification, and is not used for representing a logical relationship or a sequential relationship between each component, element, step, and the like, unless otherwise specified or indicated.
It is to be understood that while the present invention has been described in conjunction with the preferred embodiments thereof, it is not intended to limit the invention to those embodiments. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (13)
1. A factory dispatching method based on a group intelligent algorithm is characterized by comprising the following steps:
step S1: collecting data including a total number of lots of the good put into production, an arrival sequence of the good at each lot, dispatchable machines of the good at each lot, a number of sites of the good at each lot, a preparation time of the good at each lot, and a use status of all dispatchable machines;
step S2: sorting the data;
step S3: performing an ant colony algorithm according to the data to obtain an operation result, wherein the operation result comprises the dispatchable machine stations corresponding to each batch of the goods during iterative convergence, the arrangement sequence of each batch of the goods at each station, the total processing time of each dispatchable machine station and iterative convergence time; and
step S4: and outputting the operation result.
2. The factory dispatch method of claim 1, wherein the step S2 comprises:
and arranging the dispatchable machine platform of each batch of the goods and the preprocessing time of each batch of the goods according to the collected data.
3. The factory dispatch method of claim 1, wherein the step S3 comprises:
step S31: substituting the data obtained by sorting into an ant colony algorithm, and carrying out parameter initialization setting;
step S32: setting iteration times, and starting circulation;
step S33: each ant traverses all the dispatchable machines;
step S34: recording the end value of preprocessing time of the last batch of goods at the last station, updating the optimal solution and updating the pheromone concentration;
step S35: judging whether the ant colony algorithm of the iteration meets the end condition, if so, executing the step S36; if not, returning to the step S32, and adding 1 to the iteration number; and
step S36: and recording the preprocessing time of each dispatchable machine station and the time required by the processing production of all batches of goods in the optimal solution, finishing the operation and obtaining an operation result.
4. The factory dispatching method as claimed in claim 3, wherein in step S33, each batch of the goods traversed by each ant is required to satisfy the following conditions at all the dispatchable machines at each site:
Si，j+1≥Eij ---------------------（1）
Eij= Sij+ Cijk ---------------------（2）
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m, k is more than or equal to 1 and less than or equal to p, and SijIndicating the point in time when the ith lot of goods began to be produced at site j,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
represents the production end time point, C, of the ith lot of goods at site jijkIndicating the time required for the station j of the kth station to produce the i-th batch of goods to be preprocessed,
is constant, and the station j of the ith lot is in the k machine processing production
The value is 1, and the k-th machine is not used for processing production
The value is 0.
5. A plant dispatch method as claimed in claim 1, wherein the data further includes a Qtime constraint time for goods having a Qtime limit.
6. The factory dispatch method of claim 5, wherein the step S2 comprises:
sorting out the dispatchable machine stations and preprocessing time of each batch of the goods according to the collected data; and sorting out Qtime constraint time of each batch of the goods between adjacent stations.
7. The factory dispatch method of claim 6, wherein the step S3 comprises:
step S31: substituting the data obtained by sorting into an ant colony algorithm, and carrying out parameter initialization setting;
step S32: setting iteration times, and starting circulation;
step S33: each ant traverses all the dispatchable machines;
step S34: recording the end value of preprocessing time of the last batch of goods at the last station, updating the optimal solution and updating the pheromone concentration;
step S35: judging whether the ant colony algorithm of the iteration meets the end condition, if so, executing the step S36; if not, returning to the step S32, and adding 1 to the iteration number; and
step S36: recording the use time period and the idle time period of each dispatchable machine station in the optimal solution, selecting the goods which are not constrained by Qtime and have preprocessing time less than or equal to the idle time of the dispatchable machine stations in the idle time period to process and produce, finishing the operation, and obtaining the operation result.
8. The factory dispatch method of claim 7, wherein the step S33 comprises:
each batch of the goods traversed by each ant needs to satisfy the following conditions at all the dispatchable machines of each station:
Si，j+1≥Eij ---------------------（1）
Eij=Sij+Cijk ---------------------（2）
Ti，j，j+1=Si，j+1-Eij ---------------------（4）
Ti，j，j+1≤Qi，j，j+1 ---------------------（5）
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to m, k is more than or equal to 1 and less than or equal to p, and SijIndicating the point in time when the ith lot of goods began to be produced at site j,
indicating the point in time when the ith lot of goods began to be produced at site j +1,
indicating the production end time point of the ith lot at site j,
indicating the time required for the station j of the kth station to produce the i-th batch of goods to be preprocessed,
is constant, and the station j of the ith lot is in the k machine processing production
The value is 1, and the k-th machine is not used for processing production
The value is 0; t isi，j，j+1Representing the time difference between the end of production at site j and the start of production at site j +1 for the ith lot,
represents the Qtime constraint time between the end of production at site j and the beginning of production at site j +1 for the ith lot.
9. The method of factory dispatch as claimed in claim 8, wherein the operation results further comprise: and each dispatchable machine station is put into production at each time point.
10. The factory dispatch method of claim 3 or 7, wherein the end condition comprises:
and under the condition of meeting the set maximum iteration number, all ants traverse all dispatchable machine tables of each batch of goods at each site, and all sites are sorted.
11. A method as claimed in claim 3 or 7, wherein said parameters comprise the total number of lots of goods put into production, the number of sites per lot of said goods, the number of ants, initial values of pheromones, maximum number of iterations.
12. A factory dispatching system based on a group intelligent algorithm is characterized by comprising:
the data collection module is used for collecting data, wherein the data comprises the total quantity of batches of goods put into production, the arrival sequence of the goods in each batch, the number of stations of the goods in each batch, the dispatchable machine stations of the goods in each batch, the preprocessing time of the goods in each batch and the use states of all the dispatchable machine stations;
the data collecting module is used for collecting the data collected by the data collecting module and sending the collected data to the operation module;
the operation module is used for performing ant colony algorithm according to the data to obtain operation results, the operation results comprise the dispatchable machine stations corresponding to each batch of the goods during iterative convergence, the arrangement sequence of each batch of the goods at each station, the total processing time of each dispatchable machine station and the iterative convergence time, and the operation module sends the operation results obtained through operation to the output module; and
and the output module is used for outputting the operation result.
13. A factory dispatching system based on a group intelligent algorithm is characterized by comprising:
a data collection module for collecting data including a total quantity of lots of goods put into production, an arrival order of the goods for each lot, a number of sites for each lot of the goods, a dispatchable machine for each lot of the goods, a preparation time for each lot of the goods, a use status of all the dispatchable machines, a Qtime constraint time for the goods with a Qtime limit;
the data collecting module is used for collecting the data collected by the data collecting module and sending the collected data to the operation module;
the operation module is used for performing an ant colony algorithm according to the data to obtain an operation result, recording the use time period and the idle time period of each dispatchable machine station in the optimal solution, selecting goods which are free from Qtime constraint and have preprocessing time less than or equal to the idle time of the dispatchable machine stations in the idle time period for processing production, wherein the operation result comprises the dispatchable machine stations corresponding to each batch of goods in iterative convergence, the arrangement sequence of each batch of goods at each station, the total processing time of each dispatchable machine station and the iterative convergence time, and the operation module sends the operation result obtained by operation to the output module; and
and the output module is used for outputting the operation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011333543.7A CN112131761A (en) | 2020-11-25 | 2020-11-25 | Factory dispatching method and system based on group intelligent algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011333543.7A CN112131761A (en) | 2020-11-25 | 2020-11-25 | Factory dispatching method and system based on group intelligent algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112131761A true CN112131761A (en) | 2020-12-25 |
Family
ID=73852106
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011333543.7A Pending CN112131761A (en) | 2020-11-25 | 2020-11-25 | Factory dispatching method and system based on group intelligent algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112131761A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926757A (en) * | 2021-03-30 | 2021-06-08 | 上海华力微电子有限公司 | Method and system for realizing scheduling of key batch products |
| CN112947340A (en) * | 2021-02-22 | 2021-06-11 | 同济大学 | Dynamic dispatching method for simulating pheromone mechanism |
| CN113723763A (en) * | 2021-08-06 | 2021-11-30 | 广东伟达智能装备股份有限公司 | Order production efficiency management method and device, terminal equipment and storage medium |
| WO2023216561A1 (en) * | 2022-05-11 | 2023-11-16 | 弥费科技(上海)股份有限公司 | Completion time-based method and system for dispatching vehicle in advance and computer device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060241986A1 (en) * | 2005-04-22 | 2006-10-26 | Harper Charles N | Production optimizer for supply chain management |
| CN101901426A (en) * | 2010-07-15 | 2010-12-01 | 华中科技大学 | Dynamic rolling scheduling method based on ant colony algorithm |
| CN102253662A (en) * | 2011-04-11 | 2011-11-23 | 同济大学 | Scheduling method for semiconductor production line based on multi-ant-colony optimization |
| CN104914835A (en) * | 2015-05-22 | 2015-09-16 | 齐鲁工业大学 | Flexible job-shop scheduling multi-objective method |
| CN109615194A (en) * | 2018-11-29 | 2019-04-12 | 上海集成电路研发中心有限公司 | A kind of producing line data pretreatment and method |
| CN111160711A (en) * | 2019-12-06 | 2020-05-15 | 安徽大学 | Parallel machine batch scheduling method based on ant colony algorithm |
-
2020
- 2020-11-25 CN CN202011333543.7A patent/CN112131761A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060241986A1 (en) * | 2005-04-22 | 2006-10-26 | Harper Charles N | Production optimizer for supply chain management |
| CN101901426A (en) * | 2010-07-15 | 2010-12-01 | 华中科技大学 | Dynamic rolling scheduling method based on ant colony algorithm |
| CN102253662A (en) * | 2011-04-11 | 2011-11-23 | 同济大学 | Scheduling method for semiconductor production line based on multi-ant-colony optimization |
| CN104914835A (en) * | 2015-05-22 | 2015-09-16 | 齐鲁工业大学 | Flexible job-shop scheduling multi-objective method |
| CN109615194A (en) * | 2018-11-29 | 2019-04-12 | 上海集成电路研发中心有限公司 | A kind of producing line data pretreatment and method |
| CN111160711A (en) * | 2019-12-06 | 2020-05-15 | 安徽大学 | Parallel machine batch scheduling method based on ant colony algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 曹岩等: "蚁群算法在离散型车间派工中的应用", 《西安工业大学学报》 * |
| 郭乘涛: "基于问题分解与蚁群算法的半导体晶圆制造系统调度方法的研究", 《中国博士学位论文全文数据库（电子期刊）》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112947340A (en) * | 2021-02-22 | 2021-06-11 | 同济大学 | Dynamic dispatching method for simulating pheromone mechanism |
| CN112926757A (en) * | 2021-03-30 | 2021-06-08 | 上海华力微电子有限公司 | Method and system for realizing scheduling of key batch products |
| CN113723763A (en) * | 2021-08-06 | 2021-11-30 | 广东伟达智能装备股份有限公司 | Order production efficiency management method and device, terminal equipment and storage medium |
| WO2023216561A1 (en) * | 2022-05-11 | 2023-11-16 | 弥费科技(上海)股份有限公司 | Completion time-based method and system for dispatching vehicle in advance and computer device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112131761A (en) | Factory dispatching method and system based on group intelligent algorithm | |
| CN107506956B (en) | Based on improvement particle cluster algorithm supply chain production and transport coordinated dispatching method and system | |
| Hyun et al. | A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines | |
| Belvaux et al. | Modelling practical lot-sizing problems as mixed-integer programs | |
| Gupta et al. | Flexibility of manufacturing systems: concepts and measurements | |
| Gharehgozli et al. | Polynomial time algorithms to minimize total travel time in a two-depot automated storage/retrieval system | |
| CN107679583A (en) | The method that fault solution is obtained based on big data | |
| Guo et al. | A bi-level evolutionary optimization approach for integrated production and transportation scheduling | |
| Hosseinian et al. | Modeling of the time-dependent multi-skilled RCPSP considering learning effect: An evolutionary solution approach | |
| CN105843189B (en) | A kind of efficient scheduling rule selection method for semiconductor production line based on simplified simulation model | |
| CN111476466B (en) | Digital workshop electric energy management research method based on context awareness | |
| CN109409763A (en) | A kind of dynamic test assignment dispatching method and dispatching platform based on Greedy grouping strategy | |
| CN104572297A (en) | Hadoop job scheduling method based on genetic algorithm | |
| CN107357270A (en) | A kind of method for scheduling task in the foamed manufactures of EPS | |
| CN109872091A (en) | A kind of Job Scheduling method and device based on ant group algorithm | |
| Zhang et al. | A decomposition-based multi-objective evolutionary algorithm for hybrid flowshop rescheduling problem with consistent sublots | |
| WO2022214468A1 (en) | Computer-implemented method and computer program for assembly component quantity planning for assembly parts for production optimization of a production system, assembly component quantity planning system and production planning and control system | |
| CN115471133A (en) | Workshop comprehensive scheduling system based on order management and rolling optimization scheduling | |
| CN114626718A (en) | Agricultural machinery scheduling method based on order resource sharing and agricultural machinery resource sharing | |
| PRESTON WHITE JR et al. | Job-shop scheduling: limits of the binary disjunctive formulation | |
| CN106094751B (en) | A kind of dispatching method and device of raw material | |
| CN111260144A (en) | Method for solving single-machine batch scheduling problem under condition of random arrival of different workpieces | |
| Vasant | Hybrid mesh adaptive direct search genetic algorithms and line search approaches for fuzzy optimization problems in production planning | |
| Mirzapour Al-e-hashem et al. | A new approach to solve a mixed-model assembly line with a bypass subline sequencing problem | |
| Yu et al. | Comparison of two typical scheduling rules of line-seru conversion problem |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201225 |
|
| RJ01 | Rejection of invention patent application after publication |