CN115358488B - Buffer-based vehicle scheduling method, electronic device and storage medium - Google Patents
Buffer-based vehicle scheduling method, electronic device and storage medium Download PDFInfo
- Publication number
- CN115358488B CN115358488B CN202211263868.1A CN202211263868A CN115358488B CN 115358488 B CN115358488 B CN 115358488B CN 202211263868 A CN202211263868 A CN 202211263868A CN 115358488 B CN115358488 B CN 115358488B
- Authority
- CN
- China
- Prior art keywords
- sequence
- workshop
- production sequence
- production
- vehicles
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000003860 storage Methods 0.000 title claims abstract description 23
- 238000004519 manufacturing process Methods 0.000 claims abstract description 206
- 238000011156 evaluation Methods 0.000 claims abstract description 41
- 230000009466 transformation Effects 0.000 claims abstract description 8
- 238000011144 upstream manufacturing Methods 0.000 claims description 60
- 230000006870 function Effects 0.000 claims description 51
- 238000005070 sampling Methods 0.000 claims description 16
- 238000003466 welding Methods 0.000 claims description 12
- 238000010422 painting Methods 0.000 claims description 10
- 239000011248 coating agent Substances 0.000 claims description 6
- 238000000576 coating method Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 3
- 239000003086 colorant Substances 0.000 claims description 2
- 238000003780 insertion Methods 0.000 claims description 2
- 230000037431 insertion Effects 0.000 claims description 2
- 238000003756 stirring Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 13
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000012163 sequencing technique Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 238000010845 search algorithm Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000003973 paint Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000011158 quantitative evaluation Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003094 perturbing effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- General Factory Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a buffer-based vehicle scheduling method, electronic equipment and a storage medium. The method comprises the following steps: acquiring at least one ideal production sequence of a group of vehicles to be scheduled in each workshop of a production line; arranging and combining all ideal production sequences to obtain a plurality of sequence combinations; carrying out random transformation on ideal production sequences in each sequence combination to obtain a target solution set; selecting a target combination from the unselected sequence combinations of the target solution set; sequentially determining the arrangement mode of the group of vehicles to be scheduled in each buffer area of the production line and the actual production sequence in each workshop by using a mathematical programming method; and if the difference of the evaluation function values corresponding to the actual production sequences and the target combination does not meet the set range, returning to the selection operation of the target combination until the set circulation termination condition is met. This embodiment allows the production line schedule to be as close as possible to the ideal production sequence for each plant.
Description
Technical Field
The embodiment of the invention relates to the field of optimization of vehicle production sequences, in particular to a buffer-based vehicle scheduling method, electronic equipment and a storage medium.
Background
The whole automobile production in the automobile industry belongs to a mixed flow production mode, namely automobiles of different models and different quantities can be produced on the same assembly line. The vehicle needs to go through four process steps in the production process, namely 'stamping-welding assembly (vehicle body workshop) -coating (paint workshop) -final assembly (final assembly workshop)', the whole process is carried out one by one along the production line, the process is not repeatable, and the assembly sequence is not changeable. Each workshop has an own optimized sequencing target, the scheduling sequence is adjusted by means of buffer areas among the workshops, and the reasonable commissioning sequencing relation relates to material flow, staff efficiency, order timeliness, customer satisfaction and the like. Therefore, a scientific, efficient and stable automobile production advanced planning and scheduling mode is urgently needed by enterprises.
At present, most of production plan scheduling businesses of car enterprises are not scientific and efficient enough, and some small-sized car enterprises are still in the manual scheduling stage, are not supported by a scheduling algorithm, have large workload, depend on experience, and cannot be replaced by personnel and grow slowly. Some existing scheduling systems are incomplete, a scheduling plan lacks quantitative evaluation, and further scientific optimization cannot be achieved, so that automobile production faces various pain points such as slow cooperative response, difficulty in quantitative evaluation, low management precision and the like.
Disclosure of Invention
The embodiment of the invention provides a buffer-based vehicle scheduling method, electronic equipment and a storage medium, which make full use of the scheduling capability of a buffer and enable the production line scheduling to be as close as possible to an ideal production sequence of each workshop.
In a first aspect, an embodiment of the present invention provides a buffer-based vehicle scheduling method, including:
acquiring at least one ideal production sequence of a group of vehicles to be scheduled in each workshop of a production line;
arranging and combining all the ideal production sequences to obtain a plurality of sequence combinations, wherein each sequence combination comprises one ideal production sequence of each workshop;
carrying out random transformation on ideal production sequences in each sequence combination, and forming a target solution set by each sequence combination and the transformed sequence combination;
selecting a target combination from the unselected sequence combinations of the target solution set;
sequentially determining the arrangement modes of the group of vehicles to be scheduled in each buffer area of the production line and the actual production sequences in each workshop by using a mathematical programming method to minimize the difference between each actual production sequence and the target combination, wherein the arrangement modes and the actual production sequences jointly form a vehicle scheduling mode;
if the difference of the evaluation function values corresponding to the actual production sequences and the target combination does not meet the set range, returning to the selection operation of the target combination until the set cycle termination condition is met;
wherein the evaluation function is used for evaluating the advantages and disadvantages of the production sequence.
In a second aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, the one or more programs cause the one or more processors to implement the method for buffer-based vehicle scheduling of any of the embodiments.
In a third aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the buffer-based vehicle scheduling method according to any embodiment.
The embodiment of the invention generates the target solution set of the production sequence of each workshop through the permutation and combination and the limited transformation of the ideal production sequence of each workshop, thereby ensuring the diversity of the sequence regulation target of the buffer area; by sequence reordering, the difference of target production sequences at the upstream and downstream of the buffer area is smaller on the premise of not changing the target value, and the solving speed of a subsequent scheduling mode is effectively improved; through a mixed integer programming model with specific constraint conditions, the difference between an actual production sequence and a target production sequence of each workshop is ensured to be minimum, and the sequence regulation target and the sequence regulation rule of a buffer area are met; and finally, the final scheduling mode is ensured to still keep good production performance through the inspection of the evaluation function value. The whole method gives consideration to the calculation efficiency, the result stability and the excellence, and can obtain the high-quality vehicle production sequence in a short time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of an automobile production line according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of buffer reordering according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of the arrangement of a group of vehicles to be scheduled in each buffer area and the actual production sequence in each workshop according to the embodiment of the present invention.
FIG. 4 is a flowchart of a method for buffer-based vehicle scheduling according to an embodiment of the present invention.
Fig. 5 is another flow chart provided in the embodiment of the present invention, which sequentially determines the arrangement of the group of vehicles to be scheduled in each buffer area and the actual production sequence in each workshop by using a mathematical programming method.
FIG. 6 is a flow chart for obtaining at least one desired production sequence for a group of vehicles to be scheduled in each workshop of the production line according to an embodiment of the present invention.
FIG. 7 is a flow chart of another method for obtaining at least one desired production sequence for a group of vehicles to be scheduled in workshops of a production line according to an embodiment of the present invention.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the actual overall vehicle manufacturing process, the vehicle body passes through a plurality of workshops and a plurality of buffer zones. Fig. 1 is a schematic flow chart of an automobile production line according to an embodiment of the present invention, and as shown in the drawing, a stamped automobile body sequentially passes through a welding shop, a welding buffer area (WBS), a painting shop, a painting buffer area (PBS), and a final assembly shop. The production sequence of the vehicles to be scheduled in the workshop consists of a set of vehicle IDs, for example {1, 2, \8230;, N }, wherein 1,2, \8230;, N denotes the corresponding vehicle ID.
The buffer area refers to an area used for vehicle sequence adjustment between upstream and downstream workshops, and one buffer area exists between every two workshops. As shown in fig. 2, assuming that the buffer capacity is B × L, where B denotes the number of channels, L denotes the channel capacity, and B and L are both natural numbers, fig. 2 shows a buffer of a 4 × 3 structure. When the buffer area is inbound, given a production sequence of the upstream workshop, a proper channel needs to be selected to enter the buffer area according to the rule of entering the buffer area. When the vehicle leaves the buffer zone, the vehicle at the forefront of one channel is selected each time, namely the first vehicle leaves the station, and the first-in first-out rule is met in each channel. And obtaining the production sequence of the downstream workshop after all the vehicles are out of the station. Fig. 3 shows the arrangement of a group of vehicles to be scheduled in each buffer zone and the actual production sequence in each workshop, taking the production line of a welding workshop- > WBS- > painting workshop- > PBS- > general assembly workshop as an example. Specifically, 20 vehicles (numbered 0-19) to be scheduled enter a welding workshop according to the sequence of 0,1,2 \8230and19, and welding is completed in sequence. The vehicles 0-3 sequentially enter the rightmost channel of the WBS buffer area after being welded, the vehicles 4 enter the middle channel of the WBS buffer area after being welded, the vehicles 5 enter the rightmost channel of the WBS buffer area after being welded, 8230, and the like, wherein the arrangement mode of the vehicles in the WBS buffer area is shown in figure 3. Then, vehicles 13 in the WBS buffer area firstly enter a painting workshop, vehicles 0-2 sequentially enter a painting workshop 8230, and the like, so that an actual production sequence 13,0,1,2,14,3 8230, 17 and 18 of the painting workshop is obtained, and painting is sequentially completed according to the sequence. The vehicle number 13 enters the rightmost channel of the PBS buffer area after finishing coating, the vehicle number 0 enters the second channel on the left side of the PBS buffer area after finishing coating, the vehicle number 1 enters the leftmost channel of the PBS buffer area \8230afterfinishing coating, and the like, and the arrangement mode of the vehicles in the PBS buffer area is shown in FIG. 3. Finally, vehicles 13,7 and 8 in the PBS buffer area sequentially enter the assembly workshop, then the vehicle 1 enters the assembly workshop 8230, and the like, so that the actual production sequence 13,7,8,1,10 8230, 6,15 of the assembly workshop is obtained.
Based on the above, the invention provides a vehicle scheduling method based on a buffer area, which makes full use of the scheduling capability of the buffer area to optimize the production sequence of vehicles in each workshop and the arrangement mode of the vehicles in each buffer area, so that the overall scheduling of a production line is as close as possible to the ideal production sequence of each workshop. In actual production, vehicles enter a workshop and a buffer area according to the optimized production sequence and arrangement mode, so that consumables, processes (which are characterized by evaluation functions) and the like on a production line are optimized. The method is executed by an electronic device, and specifically includes the following steps, as shown in fig. 4:
and S110, acquiring at least one ideal production sequence of a group of vehicles to be scheduled in each workshop of the production line.
In this embodiment, the number N of vehicles to be scheduled in each group does not exceed the capacity of each buffer area on the production line, that is, does not exceed the sequence adjustment capability of each buffer area. After a certain group of vehicles to be scheduled is determined, the ideal production sequence of the group of vehicles in each workshop is regarded as known and used as the input of the subsequent operation.
The ideal production sequence for a particular plant refers to the most desirable production sequence for that plant, as determined by the plant's production sequence evaluation function. Different workshops correspond to different evaluation functions, optionally, the evaluation functions of the welding workshops are the number of violations of vehicle type ratio constraints, and the lower the function value is, the better the function value is; the evaluation function of the coating workshop is the switching number of the colors of the vehicles in the production sequence, and the lower the function value is, the better the function value is; the evaluation function of the assembly workshop is set according to the leveling degree of the production sequence, and the higher the leveling degree is, the lower the target function value is, and the better the production sequence is.
Usually, there are several ideal production sequences for each plant, which all optimize the evaluation function value of the plant. The manner in which the ideal production sequence is obtained will be described in detail in the examples which follow.
And S120, arranging and combining all the ideal production sequences to obtain a plurality of sequence combinations, wherein each sequence combination comprises one ideal production sequence of each workshop.
Specifically, assuming that the welding shop, the painting shop and the final assembly shop each include 3 ideal production sequences, 3 × 3 × 3 ideal production sequences are arranged and combined to obtain 3 × 3 × 3 sequence combinations, where each sequence combination includes: 1 ideal production sequence for the welding shop, 1 ideal production sequence for the paint shop, and 1 ideal production sequence for the final assembly shop.
S130, carrying out random transformation on the ideal production sequences in each sequence combination, and forming a target solution set by each sequence combination and the transformed sequence combination.
Each sequence combination in the target solution set is used as a target for buffer reordering. Optionally, several (for example, 2 to 4) vehicles in the sequence are randomly selected for position interchange, and the sequence combinations before transformation and the sequence combinations after transformation together form a target solution set. Here, the random transform means finite transform, and is fine-tuning of the original sequence without causing excessive variation. Therefore, the evaluation function values of all sequence combinations in the target solution set are ideal and can be used as the target of buffer region order adjustment; and the number of the target production sequences is far greater than that of the ideal production sequences, so that the diversity of the solution can be better ensured, the convergence of the solving process is facilitated, and the practicability is stronger.
S140, selecting a target combination from the unselected sequence combinations of the target solution set.
Optionally, first, selecting an initial target combination from unselected sequence combinations of the target solution set; then, under the condition that evaluation function values of all workshops are not changed, all ideal production sequences in the initial target combination are reordered so as to reduce the difference of the ideal production sequences of the workshops between the upstream and the downstream; and forming a final target combination by each reordered sequence, and using the final target combination as an object of subsequent operation. The embodiment reorders the sequence of a single workshop on the premise of not changing the target value, so that the sequence difference between the upstream and downstream of the buffer area is smaller, and the solving speed of the subsequent buffer area arrangement mode can be effectively improved.
Further, the reordering of each ideal production sequence in the initial target combination without changing the evaluation function value of each plant to reduce the difference between the ideal production sequences of upstream and downstream plants specifically includes the following steps:
step one, taking an ideal production sequence to be rearranged in any workshop as a first sequence, and taking an ideal production sequence determined by an upstream workshop or a downstream workshop of the workshop as a second sequence. This embodiment will reorder the first sequence, keeping the second sequence unchanged.
And step two, calculating the position deviation of each vehicle in the first sequence relative to the second sequence.
And thirdly, selecting a plurality of vehicles with the largest position deviation, and changing the positions of the plurality of vehicles in the first sequence through insertion and/or exchange operation so as to reduce the difference between the new first sequence and the second sequence.
Step four, if the evaluation function value corresponding to the new first sequence is not changed, the new first sequence is reserved; otherwise, the original first sequence is restored.
And step five, repeating the operation of the step three and the operation of the step four on the final first sequence until a set termination condition is met. The conditions include: the set number of repetitions is reached, the set operation time is reached, or the difference between the new first sequence and the second sequence cannot be further reduced, etc.
S150, sequentially determining the arrangement mode of the group of vehicles to be scheduled in each buffer area of the production line and the actual production sequence in each workshop by using a mathematical programming method, so that the difference between each actual production sequence and the target combination is minimized; and the arrangement mode and the actual production sequence jointly form a vehicle scheduling mode.
To illustrate the specific process of solving the scheduling by using the mathematical programming method, the parameters and variables used in the solving process are preferably introduced. Suppose that the actual production sequence from the current upstream workshop includes N vehicles, and each vehicle has a corresponding position in the target production sequence of the current downstream workshop, wherein the target production sequence refers to a target for performing sequencing on the production sequence of the downstream workshop by using the buffer area. N vehicles enter the buffer area for temporary storage and then are sequentially released to enter the current downstream workshop. The buffer zone is composed of B parallel channels, each channel has a capacity of L, vehicles In each channel obey a First-In-First-Out (FIFO) rule, and all vehicles In the sequence can be temporarily stored In the buffer zone at the same time. The following parameters and variables are used in the solution process:
(1) Parameter(s)
B: represents the number of lanes (lanes or channels) in the buffer;
l: indicating the number of vehicles that can be accommodated by each lane;
t: indicating the number of vehicles held in the buffer;
s ik : the value is 0 or 1, if the vehicle at the ith position in the actual production sequence of the upstream workshop (called the upstream vehicle i for short) is distributed to the kth position of the target production sequence of the downstream workshop, s ik Is 1; otherwise s ik Is 0;
(2) Decision variables
e ik : the value is 0 or 1, and if the upstream vehicle i is assigned to the kth position of the actual production sequence of the downstream plant, then e ik =1; otherwise is e ik =0；
z ai : the value is 0 or 1, if the upstream vehicle i is allocated to lane a of the buffer, then z ai Is 1; otherwise z ai Is 0;
u ja : the value is 0 or 1, if the vehicle at the jth position (called downstream vehicle j for short) in the actual production sequence of the downstream workshop comes from the lane a of the buffer area, u ja Is 1; otherwise u ja Is 0;
y ik ：y ik =| s ik - e ik l, the value is 0 or 1, if the position k of the upstream vehicle i in the actual production sequence of the downstream workshop is different from the position k in the target production sequence of the downstream workshop, y ik Is 1; otherwise y ik Is 0.
Based on the above parameters and variables, the mathematical programming solving process of S150 includes the following steps:
step one, constructing a mixed integer programming model of the following constraint conditions:
wherein the constraint (1) is that a ratio of the number of vehicles whose positions are expected to be non-different in the actual production sequence and the target production sequence of the downstream plant to the total number of vehicles is maximized; constraints (2) and constraints (3) limit the one-to-one correspondence relationship between the actual production sequence of the vehicles in the upstream workshop and the actual production sequence of the vehicles in the downstream workshop, wherein the constraints (2) indicate that any upstream vehicle i corresponds to one downstream vehicle k (the k-th position vehicle in the actual production sequence of the downstream workshop), and the constraints (3) indicate that any downstream vehicle k corresponds to one upstream vehicle i; constraint (4) indicates that each upstream vehicle j corresponds to one lane; constraint (5) representing vehicle occupying laneaDoes not exceed L; constraint (6) indicates that the number of vehicles held by the buffer is maintained at t; constraint (7) indicates that each downstream vehicle j corresponds to a lane; the constraint (8) defines the decision variable z ai And u aj The relationship of (1); constraint (9) ensures that vehicles entering the same lane of the buffer zone have to follow the FIFO principle, in particular when vehicles are upstreami1 andi2 are all distributed on the laneaWhen the water-saving agent is used in the water-saving process,
constraint (9) becomes
Only if vehicles are on the wayi1 and in the laneaMiddle row upstream vehiclei2, the constraint (9) can only be established.
And step two, taking the sequence corresponding to each workshop in the target combination as a target production sequence of each workshop, and taking the target production sequence of the most upstream workshop as an actual production sequence of the most upstream workshop.
And step three, taking the most upstream workshop as a current upstream workshop, taking a downstream workshop of the current upstream workshop as a current downstream workshop, and taking a buffer area between the current upstream workshop and the current downstream workshop as a current buffer area.
And step four, substituting the actual production sequence of the current upstream workshop into the mixed integer programming model constructed in the step one, and solving the mixed integer programming model by using an open source solver to obtain the arrangement mode of the group of vehicles to be scheduled in the current buffer area and the actual production sequence of the current downstream workshop. Optionally, the model is solved using Gurobi.
And step five, taking the current downstream workshop as a new current upstream workshop, and returning to the determining operation of the current downstream workshop and the current buffer area in the step three until the actual production sequences of all the workshops are solved. And the arrangement modes of the group of vehicles to be scheduled in all buffer areas and the actual production sequences in all workshops jointly form the scheduling mode of the group of vehicles to be scheduled.
And S160, if the evaluation function difference value corresponding to each actual production sequence and the target combination does not meet the set range, returning to the selection operation of the target combination until the set circulation termination condition is met.
Specifically, the difference between the evaluation function values of each actual production sequence and each target production sequence in the target combination is calculated, if the sum of the differences of all the function values is within the expected set range, the algorithm is terminated, and the scheduling mode obtained in S150 is output; if the sum of the differences of all function values is beyond the expected setting range, the method returns to S140 to select a new target combination, and enters the next round of loop of S140 and S150 until the set loop termination condition is met. Fig. 5 is another flow chart for sequentially determining the arrangement of the group of vehicles to be scheduled in each buffer area and the actual production sequence in each workshop by using a mathematical programming method according to the embodiment of the present invention, and more clearly shows the loop iteration relationship of each step.
The cycle termination condition may be that the evaluation function difference satisfies the set range, or reaches a set cycle number, and this embodiment is not particularly limited. Further, the range may be set according to different examples, for example, for 50 vehicles to be scheduled, the range is set to 0.3%; for 600 vehicles to be scheduled, the range is set to 6%, etc. And when the circulation stopping condition is that the set circulation times are reached, the method further comprises the following steps: and selecting the scheduling mode with the minimum evaluation function difference from the scheduling modes obtained in each cycle as a final scheduling mode.
In the embodiment, the target solution set of the production sequences of each workshop is generated by the permutation and the limited transformation of the ideal production sequences of each workshop, so that the diversity of the sequence adjustment targets of the buffer area is ensured; by sequence reordering, the difference of target production sequences at the upstream and downstream of the buffer area is smaller on the premise of not changing the target value, and the solving speed of a subsequent scheduling mode is effectively improved; through a mixed integer programming model with specific constraint conditions, the minimum difference between an actual production sequence and a target production sequence of each workshop is ensured, and the sequence regulation target and the sequence regulation rule of a buffer area are met; and finally, the final scheduling mode is ensured to still keep good production performance through the inspection of the evaluation function value. The whole method gives consideration to the calculation efficiency, the result stability and the excellence, and can obtain the high-quality vehicle production sequence in a short time.
In one embodiment, the scheduling determination process of S140 and S150 may include the following three cases according to the reordering manner:
in case one, reordering is performed in the order from upstream to downstream (called forward), and the scheduling is solved according to the reordered sequence. That is, reordering satisfies the performance of downstream sequences as much as possible while ensuring that upstream sequences are optimal.
Specifically, first, the ideal production sequence of the most upstream plant is taken as the reordered sequence of the most upstream plant, and the ideal production sequences of the downstream plants are sequentially reordered according to the reordered sequence of the upstream plants in the order from upstream to downstream, and the evaluation function values of the plants are kept unchanged. A first target combination is composed of the reordered sequences. That is, in each reordering, the ideal production sequence of the downstream plant is reordered (i.e., as the first sequence in S140) keeping the reordered sequence of the upstream plant unchanged (i.e., as the second sequence in S140).
Then, sequentially determining a first arrangement mode in each buffer area and a first actual production sequence of each workshop by using a mathematical programming method, minimizing the difference between each first actual production sequence and the first target combination, and forming a first vehicle scheduling mode by the first arrangement mode and the first actual production sequence.
And in the second case, reordering is carried out from the downstream to the upstream (reverse permutation for short), and the scheduling mode is solved according to the reordered sequence. That is, the performance of the upstream sequence is satisfied as much as possible on the premise of ensuring the optimal downstream sequence in the sequence filling and arranging process.
Specifically, first, the ideal production sequence of the most downstream plant is taken as the reordered sequence of the most downstream plant, and the ideal production sequence of each upstream plant is reordered in order from downstream to upstream according to the reordered sequence of each downstream plant, and the evaluation function value of each plant is kept unchanged. And forming a second target combination by each reordered sequence. That is, in each reordering, the ideal production sequence of the upstream plant is reordered (i.e., as the first sequence in S140) keeping the reordered sequence of the downstream plant unchanged (i.e., as the second sequence in S140).
Then, a mathematical programming method is used for sequentially determining a second arrangement mode in each buffer area and a second actual production sequence of each workshop, so that the difference between each second actual production sequence and the first target combination is minimized, and a second vehicle scheduling mode is formed by the second arrangement mode and the second actual production sequence.
And a third situation, namely taking the processes described in the first situation and the second situation as two parallel branches to be simultaneously carried out, respectively obtaining a first vehicle scheduling mode and a second vehicle scheduling mode, and selecting a mode with a smaller corresponding objective function value as a final vehicle scheduling mode.
The embodiment provides two sequencing modes of forward and backward, so that the performance of the target combination is better, and the high quality of the vehicle scheduling mode is further ensured.
On the basis of the above-described embodiment and the following embodiments, this embodiment refines the acquisition process of the ideal production sequence. Fig. 6 is a flowchart for acquiring at least one ideal production sequence of a group of vehicles to be scheduled in each workshop of a production line according to an embodiment of the present invention, as shown in fig. 6, specifically including the following steps:
s210, initializing a current production sequence of a group of vehicles to be scheduled in a target workshop, and initializing the current sampling iteration times.
The target workshop is any single workshop on the vehicle production line, such as a welding workshop, a painting workshop or a final assembly workshop. The current sampling iteration times are used for recording the iteration times of the iteration updating production sequence through the tabu search algorithm.
Specifically, before vehicle scheduling, firstly, information of a group of vehicles to be scheduled, including the number N of vehicles and vehicle IDs, is acquired; then, based on this information, the current production sequence and the current number of sampling iterations of the group of vehicles in the target plant are initialized as input for a subsequent improved tabu search algorithm. Optionally, the current production sequence is initialized to {1, 2, \8230;, N }, where 1,2, \8230;, N represents the corresponding vehicle ID; the current number of sampling iterations is set to 0.
S220, randomly dividing a plurality of set disturbance operations of the vehicle production sequence into a plurality of sets.
And the plurality of set perturbation operations are used for perturbing the current production sequence to generate a plurality of feasible solutions close to the current sequence. The plurality of perturbation operations comprises at least one of: random swapping, random subsequence churning, random subsequence mirroring, random subsequence movement, homochromatic subsequence movement, and enhanced swapping. The random exchange means that vehicle types at a plurality of positions in the sequence are randomly selected for replacement; random subsequence stirring means randomly selecting a subsequence in the sequence and disturbing the sequence of vehicles in the subsequence; randomly selecting a section of subsequence in the sequence and reversing the sequence of the subsequences; random subsequence moving means that a subsequence in the sequence is randomly selected and inserted into another position of the sequence; the homochromatic subsequence moving means that a subsequence with the same color is randomly selected from the sequence and inserted into another position of the sequence; the enhanced switching means that all switching is traversed, that is, the switching operation is executed once.
Random perturbation can guarantee the diversity of the solution. In the step, a large number of disturbance operations are randomly divided, and each obtained set comprises a plurality of disturbance operations with limited number.
And S230, extracting a set from the set which is not extracted according to the current sampling iteration times, and disturbing the current production sequence by using each disturbance operation in the set.
In this step, a set is extracted from the perturbation operation set obtained in step S220, and the perturbation operation in the set is used to perturb the current production sequence, so as to generate a plurality of perturbed sequences, which together form a current feasible solution neighborhood, where each perturbed sequence is a feasible solution.
Optionally, the number of the sets is X, where X is a natural number, and modulo X is obtained by using the current sampling iteration number; and extracting the set corresponding to the modulus remainder from the set which is not extracted. And when all the sets are extracted, randomly dividing the multiple set disturbance operations again to obtain multiple new sets, and performing subsequent extraction operation according to the multiple new sets.
S240, selecting a local optimal solution with the optimal evaluation function value from each disturbed sequence, and updating a global optimal solution according to the local optimal solution and the tabu list.
In the step, a tabu list in a tabu search algorithm is utilized, and a solution with the optimal evaluation function value is selected from the current feasible solution neighborhood to serve as a local optimal solution of the sampling iteration. Then, checking whether the disturbance operation corresponding to the local optimal solution exists in a tabu list or not; if the disturbance operation does not exist, recording the disturbance operation into the tabu list, and taking the local optimal solution as a new global optimal solution; and if the local optimal solution exists, the evaluation function value corresponding to the local optimal solution is superior to the evaluation function value corresponding to the current global optimal solution, and the local optimal solution is used as a new global optimal solution.
The global optimal solution is the optimal solution in all feasible solutions after all sampling iterations are finished, and the global optimal solution is updated in real time in each sampling iteration. The tabu list is used for recording the searched disturbance operation, and particularly for one iteration search, the disturbance operation in the tabu list only corresponds to one feasible solution in the iteration search. The tabu list can avoid repeated searching on a feasible solution, and ensures quick convergence of searching. Optionally, the length of the tabu list is set to T, and the tabu period is set to 200.
In addition, it should be noted that the current feasible solution neighborhood changes continuously with the change of the current production sequence, and after the sampling iteration is finished, the searched current feasible solution neighborhood is emptied, so that excessive storage resources are avoided.
And S250, selecting a suboptimal solution of an evaluation function value from each disturbed sequence as a new current production sequence, adding 1 to the current sampling iteration number, and returning to the extraction operation of the set until a set iteration termination condition is met.
After the global optimal solution is updated in S240, the sampling iteration is ended. And selecting a suboptimal solution of an evaluation function value from each disturbed sequence to serve as a new current production sequence, repeating the operations of S230 and S240, and executing next sampling iteration until a set iteration termination condition is met. Optionally, the iteration termination condition includes: iterations exceeding the set number of times do not form an improvement on the global optimal solution, or reach a specified calculation time, and the like, and the embodiment is not particularly limited.
And S260, taking the final global optimal solution as an ideal production sequence of the target workshop.
And after all iterations are ended, taking the final global optimal solution as an ideal production sequence of the target workshop. Fig. 7 is another flow chart for obtaining at least one ideal production sequence of a group of vehicles to be scheduled in each workshop of the production line according to the embodiment of the present invention, and more clearly shows the loop iteration relationship among the steps.
In the embodiment, a large number of sequence perturbation operations are randomly divided in tabu search, and only one set is selected to construct a feasible solution neighborhood in each search iteration, so that on one hand, the size of the feasible solution neighborhood is limited by limiting the number of the perturbation operations in the set, and the search time and the calculation time are reduced; on the other hand, through the randomness of set division, the diversity of the set and the coverage of feasible solutions are fully ensured, and the global optimal solution can still be searched. In addition, the objects of the set partitioning and the recorded objects of the tabu list are disturbance operations of the sequence, not the disturbed sequence itself, so that the result of one partitioning can be reused in multiple iterations, and compared with a specific sequence, the disturbance operation requires a smaller storage space (for example, the disturbance operation can be identified by a simple ID), which is more beneficial to saving resources.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 8, the electronic device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device may be one or more, and one processor 60 is taken as an example in fig. 8; the processor 60, the memory 61, the input device 62 and the output device 63 in the apparatus may be connected by a bus or other means, and the connection by the bus is exemplified in fig. 8.
The memory 61 is a computer readable storage medium for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the buffer-based vehicle scheduling method in the embodiment of the present invention. The processor 60 implements the above-described buffer-based vehicle scheduling method by executing software programs, instructions, and modules stored in the memory 61 to execute various functional applications and data processing of the apparatus.
The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 61 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 61 may further include memory located remotely from the processor 60, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 62 may be used to receive entered numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 63 may include a display device such as a display screen.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the buffer-based vehicle scheduling method according to any of the embodiments.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, or a conventional procedural programming language such as C or a similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (9)
1. A buffer-based vehicle scheduling method, comprising:
acquiring at least one ideal production sequence of a group of vehicles to be scheduled in each workshop of a production line;
arranging and combining all the ideal production sequences to obtain a plurality of sequence combinations, wherein each sequence combination comprises an ideal production sequence of each workshop;
carrying out random transformation on ideal production sequences in each sequence combination, and forming a target solution set by each sequence combination and the transformed sequence combination;
selecting a target combination from the unselected sequence combinations of the target solution set;
sequentially determining the arrangement mode of the group of vehicles to be scheduled in each buffer area of the production line and the actual production sequence in each workshop by using a mathematical programming method, minimizing the difference between each actual production sequence and the target combination, and forming a vehicle scheduling mode by the arrangement mode and the actual production sequence; specifically, the method comprises the following steps: step one, constructing a mixed integer programming model meeting the following constraint conditions: maximizing the number of vehicles with no position difference in the actual production sequence and the target production sequence of the downstream workshop; the vehicles correspond to each other in the actual production sequence of the upstream workshop and the actual production sequence of the downstream workshop one by one;
each vehicle corresponds to one channel; the number of vehicles in the channel and the buffer zone does not exceed the respective capacity; vehicles entering the same channel of the buffer area need to follow the first-in first-out principle; step two, taking a sequence corresponding to each workshop in the target combination as a target production sequence of each workshop, and taking a target production sequence of the most upstream workshop as an actual production sequence of the most upstream workshop; taking the most upstream workshop as a current upstream workshop, taking a downstream workshop of the current upstream workshop as a current downstream workshop, and taking a buffer area between the current upstream workshop and the current downstream workshop as a current buffer area; step four, substituting the actual production sequence of the current upstream workshop into the mixed integer programming model constructed in the step one, and solving the mixed integer programming model by using an open source solver to obtain the arrangement mode of the group of vehicles to be scheduled in the current buffer area and the actual production sequence of the current downstream workshop; step five, taking the current downstream workshop as a new current upstream workshop, and returning to the determining operation of the current downstream workshop and the current buffer area in the step three until the actual production sequences of all the workshops are solved;
if the difference of the evaluation function values corresponding to the actual production sequences and the target combination does not meet the set range, returning to the selection operation of the target combination until the set cycle termination condition is met;
wherein the evaluation function is used for evaluating the advantages and disadvantages of the production sequence.
2. The method of claim 1, wherein selecting a target combination from the unselected sequence combinations of the target solution set comprises:
selecting an initial target combination from the unselected sequence combinations of the target solution set;
reordering each ideal production sequence in the initial target combination under the condition of not changing each workshop evaluation function value so as to reduce the difference of the ideal production sequences of upstream and downstream workshops;
the final target combination is composed of the reordered sequences.
3. The method of claim 2, wherein reordering each desired production sequence in the initial target combination to reduce variance in upstream and downstream plant desired production sequences without changing each plant evaluation function value comprises:
step one, taking an ideal production sequence to be rearranged in any workshop as a first sequence, and taking an ideal production sequence determined by an upstream workshop or a downstream workshop of the workshop as a second sequence;
step two, calculating the position deviation of each vehicle in the first sequence relative to the second sequence;
selecting a plurality of vehicles with the largest position deviation, and changing the positions of the plurality of vehicles in the first sequence through insertion and/or exchange operation so as to reduce the difference between the new first sequence and the second sequence;
step four, if the evaluation function value corresponding to the new first sequence is not changed, the new first sequence is reserved; otherwise, the original first sequence is recovered;
and step five, repeating the operation of the step three and the operation of the step four on the final first sequence until the set termination condition is met.
4. The method of claim 2, wherein said reordering each desired production sequence in said initial target combination without changing each plant evaluation function value comprises:
taking the ideal production sequence of the most upstream workshop as the reordered sequence of the most upstream workshop, sequentially reordering the ideal production sequence of each downstream workshop according to the reordered sequence of each upstream workshop from the upstream to the downstream, and keeping the evaluation function value of each workshop unchanged; at the same time
Taking the ideal production sequence of the most downstream workshop as the reordered sequence of the most downstream workshop, sequentially reordering the ideal production sequence of each upstream workshop according to the reordered sequence of each downstream workshop from downstream to upstream, and keeping the evaluation function value of each workshop unchanged;
the final target combination is composed of the reordered sequences, and comprises the following steps:
forming a first target combination by each sequence after the sequence is reordered from the upstream to the downstream;
the second target combination is composed of the sequences reordered in the order from downstream to upstream.
5. The method of claim 4, wherein the determining sequentially, using a mathematical programming method, the arrangement of the group of vehicles to be scheduled in each buffer area of the production line and the actual production sequence in each workshop to minimize the difference between each actual production sequence and the target combination, the arrangement and the actual production sequence together forming a vehicle scheduling pattern, comprises:
sequentially determining a first arrangement mode of the group of vehicles to be scheduled in each buffer area and a first actual production sequence in each workshop by using a mathematical programming method, minimizing the difference between each first actual production sequence and the first target combination, and forming a first vehicle scheduling mode by the first arrangement mode and the first actual production sequence;
sequentially determining a second arrangement mode of the group of vehicles to be scheduled in each buffer area and a second actual production sequence in each workshop by using a mathematical programming method, minimizing the difference between each second actual production sequence and the second target combination, and forming a second vehicle scheduling mode by the second arrangement mode and the second actual production sequence;
and selecting a mode with a better corresponding objective function value from the first vehicle scheduling mode and the second vehicle scheduling mode as a final vehicle scheduling mode.
6. The method of claim 1, wherein each plant comprises: a welding workshop, a coating workshop and a final assembly workshop;
the evaluation function of the welding workshop is the number of violations of vehicle type ratio constraints;
the evaluation function of the painting workshop is the switching number of the vehicle colors in the production sequence;
and the evaluation function of the final assembly workshop is constructed according to the leveling degree of the production sequence.
7. The method of claim 1, wherein said obtaining at least one desired production sequence for a group of vehicles to be scheduled in each cell of the production line comprises:
initializing a current production sequence of a group of vehicles to be scheduled in a target workshop, and initializing the current sampling iteration times;
randomly dividing a plurality of set disturbance operations of a vehicle production sequence into a plurality of sets;
extracting a set from the set which is not extracted according to the current sampling iteration times, and disturbing the current production sequence by using each disturbance operation in the set; the plurality of perturbation operations comprises at least one of: random swapping, random subsequence stirring, random subsequence mirroring, random subsequence movement, homochromatic subsequence movement, and enhanced swapping;
selecting a local optimal solution with the optimal evaluation function value from each disturbed sequence, and updating a global optimal solution according to the local optimal solution and a tabu list;
selecting a suboptimal solution of an evaluation function value from each disturbed sequence as a new current production sequence, adding 1 to the current sampling iteration number, and returning to the extraction operation of the set until a set iteration termination condition is met;
and taking the final global optimal solution as an ideal production sequence of the group of vehicles to be scheduled in the target workshop.
8. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the buffer-based vehicle scheduling method of any of claims 1-7.
9. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the method for buffer-based vehicle scheduling of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211263868.1A CN115358488B (en) | 2022-10-17 | 2022-10-17 | Buffer-based vehicle scheduling method, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211263868.1A CN115358488B (en) | 2022-10-17 | 2022-10-17 | Buffer-based vehicle scheduling method, electronic device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115358488A CN115358488A (en) | 2022-11-18 |
| CN115358488B true CN115358488B (en) | 2023-04-07 |
Family
ID=84007824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211263868.1A Active CN115358488B (en) | 2022-10-17 | 2022-10-17 | Buffer-based vehicle scheduling method, electronic device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115358488B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118553115A (en) * | 2024-07-30 | 2024-08-27 | 中汽数据(天津)有限公司 | Vehicle ramp collaborative import method, device and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111291969A (en) * | 2020-01-14 | 2020-06-16 | 东南大学 | Automobile reordering method based on genetic algorithm |
| CN112183939A (en) * | 2020-09-02 | 2021-01-05 | 上汽大通汽车有限公司南京分公司 | Intelligent scheduling method in finished automobile manufacturing field |
| CN112558559A (en) * | 2020-11-11 | 2021-03-26 | 东南大学 | Series linear buffer and sequence recovery method thereof |
| CN112801501A (en) * | 2021-01-27 | 2021-05-14 | 重庆大学 | Vehicle reordering and dispatching system based on two-stage linear buffer area |
| CN115130766A (en) * | 2022-07-07 | 2022-09-30 | 西南交通大学 | Multi-workshop automobile mixed flow and production scheduling collaborative optimization method considering multiple process routes |
-
2022
- 2022-10-17 CN CN202211263868.1A patent/CN115358488B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111291969A (en) * | 2020-01-14 | 2020-06-16 | 东南大学 | Automobile reordering method based on genetic algorithm |
| CN112183939A (en) * | 2020-09-02 | 2021-01-05 | 上汽大通汽车有限公司南京分公司 | Intelligent scheduling method in finished automobile manufacturing field |
| CN112558559A (en) * | 2020-11-11 | 2021-03-26 | 东南大学 | Series linear buffer and sequence recovery method thereof |
| CN112801501A (en) * | 2021-01-27 | 2021-05-14 | 重庆大学 | Vehicle reordering and dispatching system based on two-stage linear buffer area |
| CN115130766A (en) * | 2022-07-07 | 2022-09-30 | 西南交通大学 | Multi-workshop automobile mixed flow and production scheduling collaborative optimization method considering multiple process routes |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115358488A (en) | 2022-11-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109948944B (en) | Satellite task scheduling method and system | |
| CN112766813A (en) | Air-space cooperative observation complex task scheduling method and system | |
| CN104050324B (en) | Mathematical model construction method and solving method for single-star task planning problem | |
| CN115358488B (en) | Buffer-based vehicle scheduling method, electronic device and storage medium | |
| CN109636011A (en) | A kind of multishift operation plan scheduling method based on improved change neighborhood genetic algorithm | |
| CN109544998A (en) | A kind of flight time slot distribution Multipurpose Optimal Method based on Estimation of Distribution Algorithm | |
| CN110456633B (en) | Airborne multi-platform distributed task allocation method | |
| CN115330283B (en) | Workshop-based and production line-based vehicle production sequence optimization method | |
| Chen et al. | Integrated short-haul airline crew scheduling using multiobjective optimization genetic algorithms | |
| CN113077109B (en) | Intelligent scheduling system, method, equipment and medium for machine patrol plan | |
| CN115600774A (en) | Multi-target production scheduling optimization method for assembly type building component production line | |
| CN104217109A (en) | Method for realizing hybrid and active scheduling on quick satellites | |
| CN109934364A (en) | A kind of cutter Panel management allocator based on Global Genetic Simulated Annealing Algorithm | |
| CN110618862A (en) | Method and system for scheduling satellite measurement and control resources based on maximal clique model | |
| CN115271130B (en) | Dynamic scheduling method and system for maintenance order of ship main power equipment | |
| CN111586146A (en) | Wireless internet of things resource allocation method based on probability transfer deep reinforcement learning | |
| CN108106624A (en) | A kind of more people's Dispatch by appointment paths planning methods and relevant apparatus | |
| Andrade et al. | Scheduling software updates for connected cars with limited availability | |
| CN113887782A (en) | Genetic-firework mixing method and system for maintenance resource distribution scheduling | |
| CN115034557A (en) | Agile satellite emergency task planning method | |
| CN117872763A (en) | Multi-unmanned aerial vehicle road network traffic flow monitoring path optimization method | |
| CN111861397A (en) | Intelligent scheduling platform for client visit | |
| CN105139161B (en) | A kind of Modeling of Virtual Enterprise and dispatching method based on Petri network | |
| CN109635328A (en) | Integrated circuit layout method and distributed design approach | |
| CN114781508A (en) | Clustering-based satellite measurement and control scheduling method and system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |