CN116976228B - Method for planning task of double-side dismantling line of retired electromechanical product - Google Patents
Method for planning task of double-side dismantling line of retired electromechanical product Download PDFInfo
- Publication number
- CN116976228B CN116976228B CN202311230347.0A CN202311230347A CN116976228B CN 116976228 B CN116976228 B CN 116976228B CN 202311230347 A CN202311230347 A CN 202311230347A CN 116976228 B CN116976228 B CN 116976228B
- Authority
- CN
- China
- Prior art keywords
- dismantling
- task
- disassembly
- solution
- double
- 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 44
- 238000013439 planning Methods 0.000 title claims abstract description 38
- 238000005265 energy consumption Methods 0.000 claims abstract description 42
- 238000002922 simulated annealing Methods 0.000 claims abstract description 29
- 230000002146 bilateral effect Effects 0.000 claims abstract description 21
- 230000002068 genetic effect Effects 0.000 claims abstract description 20
- 230000035772 mutation Effects 0.000 claims description 27
- 238000003780 insertion Methods 0.000 claims description 13
- 230000037431 insertion Effects 0.000 claims description 13
- 238000001816 cooling Methods 0.000 claims description 12
- 238000005457 optimization Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 11
- 230000008901 benefit Effects 0.000 claims description 9
- 230000003044 adaptive effect Effects 0.000 claims description 8
- 238000000137 annealing Methods 0.000 claims description 8
- 238000009499 grossing Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 5
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 2
- 238000010353 genetic engineering Methods 0.000 claims description 2
- 210000000349 chromosome Anatomy 0.000 description 9
- 238000010586 diagram Methods 0.000 description 9
- 239000002699 waste material Substances 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000012804 iterative process Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000001010 compromised effect Effects 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 231100001261 hazardous Toxicity 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 108091026890 Coding region Proteins 0.000 description 1
- 229920002430 Fibre-reinforced plastic Polymers 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013440 design planning Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000011151 fibre-reinforced plastic Substances 0.000 description 1
- 230000007614 genetic variation Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
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
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Hardware Design (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Geometry (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for planning tasks of a double-side dismantling line of retired electromechanical products, which comprises the steps of determining dismantling information of electromechanical products to be dismantled, wherein the dismantling information comprises a dismantling priority relationship, a dismantling azimuth, a dismantling tool, a dismantling time, a hazard attribute and a dismantling value; based on the workflow of the bilateral dismantling line and dismantling information, setting constraint conditions; based on constraint conditions, a multi-target double-side local dismantling line task planning model which takes the number of work stations, a smooth index, dismantling energy consumption and profits as guidance is established, and is converted into a standard mixed integer linear planning model; and constructing a multi-target genetic simulated annealing algorithm through a coding and decoding strategy based on bilateral disassembly constraint and genetic operation and simulated annealing operation conforming to the disassembly priority constraint, and solving a mixed integer linear programming model to obtain a task planning scheme.
Description
Technical Field
The invention relates to the technical field of bilateral dismantling line design and planning, in particular to a task planning method for a bilateral dismantling line of a retired electromechanical product.
Background
The rapid development of technology speeds up the update of products and shortens the service period of the products, thereby producing a large number of retired electromechanical products. Retired electromechanical products not only occupy a large amount of renewable resources, but also contain environmentally harmful components or materials. If the products cannot be treated timely and normally, not only can the resource waste be caused, but also the environment can be polluted.
Many disassemble enterprises all adopt the disassembly line to disassemble and recycle retired electromechanical products, especially for large retired electromechanical products such as scrapped automobiles, waste refrigerators, scrapped engineering machinery and the like, the disassembly efficiency and economic benefit can be obviously improved by adopting a partial disassembly mode and a bilateral station layout, and meanwhile, the disassembly energy consumption is reduced.
However, the problem of design planning of the bilateral dismantling line is the problem of difficult combination and optimization of NP, the dismantling mode of a dismantling enterprise is simple and rough, the task of the dismantling line is planned according to production experience or simple heuristic rules, large-scale dismantling tasks and dismantling directions are difficult to accurately and efficiently plan, and the problems of low efficiency, production blockage, high cost, high energy consumption and the like of the dismantling line are easily caused. In addition, the influence of the disassembling tool on the disassembling time is ignored in the prior art, the harmfulness of the parts and the influence of the parts on the environment are not considered, the final disassembling scheme is poor in comprehensive performance, and the requirements of enterprises are difficult to meet.
Disclosure of Invention
The invention provides a task planning method for a double-side dismantling line of a retired electromechanical product, which aims to solve the technical problem that the existing dismantling scheme is poor in comprehensive performance.
In order to solve the technical problems, the invention provides a method for planning a double-side dismantling line task of a retired electromechanical product, which comprises the following steps:
Step S1: determining dismantling information of an electromechanical product to be dismantled, wherein the dismantling information comprises a dismantling priority relationship, a dismantling direction, a dismantling tool, a dismantling time, a hazard attribute and a dismantling value;
step S2: based on the workflow of the bilateral dismantling line and the dismantling information, setting constraint conditions;
step S3: based on the constraint conditions, a multi-target double-side local dismantling line task planning model which takes the number of work stations, a smooth index, dismantling energy consumption and profit as guidance is established, and is converted into a standard mixed integer linear planning model;
step S4: and constructing a multi-target genetic simulated annealing algorithm through a coding and decoding strategy based on bilateral disassembly constraint and genetic operation and simulated annealing operation conforming to the disassembly priority constraint, and solving the mixed integer linear programming model to obtain a task planning scheme.
Preferably, the expression of the mixed integer linear programming model in step S3 is:
；
wherein f 1 Representing the number of work orders, f 2 Representing a smoothing index target, f 3 Representing a dismantling profit target, f 4 Indicating the disassembly energy consumption goal.
Preferably, the number of stations is expressed as:
；
wherein W represents a station set; n when station w is opened w =1, otherwise 0; s= {1,2} represents a set of left and right sides of the station, s=1 represents the left side, and s=2 represents the right side; y when s side of paired station w is opened ws =1, otherwise 0;
the expression of the smoothing index target is:
；
；
wherein T is C Representing the beat of the disassembled line; t (T) ws The operation time on the s side of the paired stations w; i represents a disassembly task set; task i is assigned to s-side of paired station w x iws 1, otherwise 0; t is t i The disassembly time of the disassembly task i is represented; t is t b Disassembling tool change time, and disassembling tool of head-tail task of s side of paired station wAt the same time b ws 1, otherwise 0; k represents a position set; the k-th position on the s-side of the paired stations w is different from the dismantling tool at the k+1-th position by d wsk 1, otherwise 0;
the expression of the dismantling profit target is:
；
wherein r is i The disassembly benefits of the disassembly task i are represented; c i The unit time disassembly cost of the disassembly task i is represented; c f Representing the fixed unit time cost of the station; c s A unit time cost representing an invalid operation time in the station; c h Representing additional unit time costs when the workstation processes the jeopardized task; h when the dismantling task i is harmful i 1, otherwise 0;
the expression of the dismantling energy consumption target is as follows:
；
In the formula e i The unit time dismantling energy consumption of the dismantling task i is represented; e, e f The energy consumption of a station in a fixed unit time is represented; e, e s A unit time energy consumption representing an invalid operation time in the station; e, e h Representing the additional unit time energy consumption in processing hazardous tasks at the workstation.
Preferably, in the coding strategy, heuristic rules based on problem features are used to construct a candidate task set to improve the quality of the initial solution.
Preferably, the heuristic rule comprises:
1) Preferentially choose the harmful task and the task immediately before:
；
2) The task that minimizes the remaining time of the accompanying stations (w, s) is preferentially chosen:
；
3) Tasks with high profit are preferentially allocated:
；
4) Preferably, tasks with low energy consumption are allocated:
；
wherein S is k Representing a set of candidate tasks at position k, p ji Representing a priority relationship between the tasks,indicating the completion time of task i in the workstation.
Preferably, the solving of the mixed integer linear programming model in step S4 comprises the steps of:
step S41: completing population initialization according to the disassembly priority relationship and the encoding and decoding strategy;
step S42: calculating an objective function value, screening a population non-inferior solution, updating an external file Q, and setting the current annealing temperature;
Step S43: performing genetic operations on the non-inferior solutions in the external archive Q;
step S44: performing simulated annealing operation, generating a new individual, calculating an objective function value, and judging whether the new individual is accepted or not according to a Metropolis criterion until the variation times are reached;
step S45: comparing the result obtained in the step S45 with each non-inferior solution in the external file Q to finish updating the external file Q;
step S46: updating the population, calculating the number of non-inferior solutions in the external file, and if the number of non-inferior solutions is greater than a set threshold N p Then select the first N p Non-inferior solutions of individual crowding distances as a population; otherwise, supplementing population individuals according to the double-point exchange mutation operation;
step S47: to non-inferior solutionsScreening, when the number of non-inferior solutions is N Q ＜N 0 When the method is used, the next step is directly carried out; otherwise, keep N before 0 The crowded distance is not bad, and is remained in the external file Q, so that the update of the external file is realized;
step S48: calculating index evaluation Hypervolume value of the solution set;
step S49: if the current annealing temperature T>T end Then, a cooling operation is performed, let t=γt, γ represent cooling parameters, and step S44 is repeated; otherwise, outputting the non-inferior solution in the external file to obtain the task planning scheme.
Preferably, the genetic manipulation in step S43 includes a selection manipulation, a crossover manipulation and a mutation manipulation;
The crossing operation adopts a two-point mapping crossing mode, and the two-point mapping crossing mode adopts self-adaptive crossing probability P C The adaptive crossover probability P C The expression of (2) is:
；
wherein P is Cmax Represents the maximum value of the crossover probability, P Cmin Represents the minimum value of the crossover probability, g represents the current iteration number, g max Representing the maximum number of iterations.
Preferably, the mutation operation includes single-point insertion mutation and double-point exchange mutation; the method comprises the steps of carrying out mutation by adopting adaptive mutation probability, wherein the expression of the adaptive mutation probability is as follows:
；
wherein P is Mmax Represents the maximum value of variation probability, P Mmin Representing the minimum probability of variation.
Preferably, the simulated annealing operation in step S44 includes the steps of:
step S441: performing simulated annealing operation on the mutated individual, and searching a neighborhood solution by adopting an insertion operator;
step S442: setting the acceptance probability of the new solution according to the Metropolis criterion; if the new solution dominates the current solution, replacing the current solution with the new solution; if the current solution dominates the new solution, accepting the new solution according to the acceptance probability; if the new solution and the current solution are not mutually dominant, reserving both solutions, and randomly selecting one solution from the solutions as the current solution when entering the next round of circulation;
step S443: and after the neighborhood search is executed, cooling is started, and the simulated annealing algorithm completes primary optimization.
Preferably, the expression of the acceptance probability is:
；
in the method, in the process of the invention,and->The i-th sub-target value of the new solution and the current solution are respectively represented, and T represents the current annealing temperature.
The beneficial effects of the invention at least comprise: the invention provides a method for planning a task of a bilateral dismantling line of a large-scale retired electromechanical product, which comprises the steps of constructing a mixed integer linear programming model of a task planning problem of the bilateral local dismantling line, which takes a work bit, a smooth index, dismantling energy consumption and dismantling profit as guide, and designing a multi-objective genetic simulated annealing algorithm, so that theoretical and technical support is provided for the design and planning of the dismantling line of the large-scale retired electromechanical product, and the aims of remarkably improving the dismantling efficiency and economic benefit of the large-scale retired electromechanical product and reducing the dismantling energy consumption are fulfilled; the classical multi-target genetic simulated annealing algorithm is combined with the problem of double-side local dismantling line task planning, so that the feasibility and the high efficiency of the algorithm are ensured; the bilateral dismantling line task planning method is reasonable and effective, and a high-quality bilateral dismantling line task planning scheme for large-scale retired electromechanical products can be obtained in a short time, so that the large-scale retired electromechanical products are more accurate in dismantling process, the dismantling efficiency and economic benefit of the large-scale retired electromechanical products can be remarkably improved, and meanwhile, the dismantling energy consumption is reduced.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a task priority relationship for bilateral disassembly according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a decoding flow of a multi-objective genetic simulated annealing algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a cross operation of a disassembly task sequence according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a single point insertion mutation operation of a disassembly task sequence according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a two-point exchange variation operation of a disassembly task sequence according to an embodiment of the present invention;
FIG. 7 is a diagram showing a priority relationship between refrigerator disassembly tasks according to an embodiment of the present invention;
FIG. 8 is a box diagram of a multi-objective genetic simulated annealing algorithm and a comparison algorithm according to an embodiment of the present invention;
FIG. 9 is an iteration diagram of a multi-objective genetic simulated annealing algorithm and a comparison algorithm according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of the scattering points of the result of the double-sided disassembling line case of the refrigerator according to the embodiment of the invention;
FIG. 11 is a parallel graph showing the results of a double-sided disassembly line case of a refrigerator according to an embodiment of the present invention;
fig. 12 is a scatter plot matrix of the results of a double-sided disassembly line case for a refrigerator according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
As shown in FIG. 1, the embodiment of the invention provides a method for planning a double-side dismantling line task of a retired electromechanical product.
Before describing the embodiments, description is made of the double-sided disassembly line: when the retired electromechanical products are large in size, scrapped automobiles, waste refrigerators, scrapped engineering machinery and the like are disassembled, the disassembly operation is usually required to be carried out on two sides of the disassembly line, namely, the double-side station disassembly line is adopted. The bilateral disassembling line is provided with a plurality of stations respectively at the left side and the right side of the disassembling line, and the stations are called as two opposite station paired stations. Unlike conventional single-sided dismantling lines, part of tasks in the double-sided dismantling lines have strict constraint on allocation orientations, and tasks in left and right orientations need to be allocated to stations on corresponding sides, so that the complexity of the problems is higher than that of the single-sided dismantling lines. The local dismantling mode is adopted in the bilateral dismantling line, and invalid operation time generated by tool replacement between adjacent tasks is considered. The focus of the double-sided disassembly line is the double-sided allocation of the disassembly tasks and the sequence planning of the disassembly tasks.
The object facing the double-side dismantling line is large-sized equipment, the dismantling task is distributed to two sides of the station, and the stations on the left side and the right side of the paired station are mutually called as accompanying stations. In the bilateral dismantling line, the dismantling task is divided into three tasks according to the dismantling direction: left (L), right (R) and arbitrary (E). The L-side or R-side tasks can only be disassembled at the left or right side stations, while the E-side tasks can be assigned to any one of the left and right sides for disassembly. In the 16-item disassembly task case shown in fig. 2, the disassembly sequence exists between two tasks connected by an arrow, and numerals in brackets represent disassembly time and disassembly direction. Task 1 is a task immediately before task 3, task 3 can be disassembled only after task 1 is disassembled, task 3 can only be allocated to the left side station, task 5 can only be allocated to the right side station, and task 1 can be allocated to any side station.
The method comprises the following steps:
step S1: determining disassembly information of the electromechanical product to be disassembled, wherein the disassembly information comprises a disassembly priority relationship, a disassembly direction, a disassembly tool, a disassembly time, hazard attributes and a disassembly value.
Specifically, all the part information of the large retired electromechanical product is determined according to the three-dimensional information of the product, and the disassembly tasks are divided according to the relevance among the parts and the unremovable attribute of the parts.
Determining a dismantling priority relation between dismantling tasks according to the three-dimensional space structure and the dismantling process sequence of the large-scale retired electromechanical product, and constructing a priority relation matrix and a priority relation graph according to the priority relation; according to the distance between the parts and the two sides of the disassembly line station, the disassembly direction of the disassembly task is determined and is divided into a left side, a right side and any side.
And determining the hazard attribute, the dismantling tool, the dismantling time and the market economic value of each dismantling task according to the attribute of the parts of the large-scale retired electromechanical product.
Step S2: and setting constraint conditions based on the workflow of the bilateral dismantling line and the dismantling information.
Because the embodiment of the invention aims at the double-side dismantling line working condition and the dismantling work of the dismantling information, a series of preset conditions and constraint conditions are required to be set.
First, for the description of a better embodiment, the symbols and decision variables in the model are defined:
i, j: the number of the disassembly tasks is equal to I, I epsilon I, and the maximum number of the disassembly tasks is equal to I;
w: the work stations are numbered in pairs, the work station set is W, W is W, and the maximum work station number is |W| and is not more than |I|;
k: the position of the task in the robot, the position set is K, and the maximum position is |K|; let set K' = {1, …, |k| -1};
t i : disassembling time of the disassembling task i;
t b : disassembling tool replacement time;
r i : disassembling benefits of the disassembling task i;
o i : disassembling the tool type of the task i;
h i : disassembles the hazard attributes of task i: if the task is harmful, h i =1; otherwise h i =0；
c i : the unit time dismantling cost of the dismantling task i;
c s : the unit time cost of invalid operation time in the station;
c f : the fixed unit time cost of the station;
c h : additional unit time cost when the station processes the jeopardized task;
e i : disassembling energy consumption per unit time of the disassembling task i;
e s : the energy consumption of the unit time of invalid operation time in the station;
e f : the fixed unit time energy consumption of the station;
e h : additional unit time energy consumption when the station processes the jeopardized task;
p ij : the attribute of the priority relation among the tasks, if the task i is the task immediately before the task j, p ij =1, otherwise p ij =0；
T C : the beat of the disassembled line is a non-negative variable;
ψ: a maximum number;
s: the left and right sides of the station are denoted by s= {1,2}, s=1 represents the left side, and s=2 represents the right side;
S L : disassembling a task set with the left side as the azimuth;
S R : disassembling a task set with the right side;
x iws : decision variables: 1, task i is assigned to s side of paired station w; otherwise, 0;
y ws : decision variables: 1, the s sides of the paired stations w are opened; otherwise, 0;
z iwsk : decision variables: 1, task i is assigned to the kth position on the s-side of paired station w; otherwise, 0;
n w : decision variables: 1, starting a pair of stations w; otherwise, 0;
: decision variables: the finishing time of task i in the workstation (non-negative variable）；
T ws : decision variables: the working time (non-negative variable) on the s-side of the paired stations w;
a iws : decision variables: 1, task i is the last disassembly task on the s side of the paired stations w; otherwise, 0;
b ws : decision variables: 1, the disassembling tools of the head-tail tasks on the s side of the paired stations w are different; otherwise, 0;
d wsk : decision variables: 1, the k-th position of the s side of the paired stations w is different from the disassembling tool at the k+1-th position; otherwise, 0.
The preset conditions are set as follows:
1) The product to be disassembled is unique in type, sufficient in quantity, complete in parts, and capable of neglecting the conditions of unexpected interruption of a production line and the like;
2) The related information of the dismantling line is determined, wherein the related information comprises beats, unit time cost of stations and energy consumption;
3) The information of the parts and the disassembly task is determined, wherein the information comprises a priority relation, harmfulness, disassembly time and disassembly direction;
4) And determining disassembly tool information, including the type of the disassembly tool, the unit time cost and the energy consumption.
The constraint conditions include:
constraint 1: the partial disassembly mode is adopted, and the expression is as follows:
；
constraint 2: the hazardous task must be disassembled, expressed as:
；
constraint 3: the station time constraint can be expressed as:
；
constraint 4: beat constraint, namely that the operation termination time in the accompanying station does not exceed the beat, and the expression is as follows:
；
；
；
；
；
；
constraint 5: there is a preferential constraint in the assignment of tasks to paired stations, expressed as:
；
constraint 6: the disassembly time of the task needs to meet the priority constraint, and the expression is as follows:
；
；
；
；
constraint 7: the disassembly time of the task in the station needs to meet the sequence constraint, and the expression is as follows:
；
constraint 8: the position constraint, the disassembly task is allocated to a certain position of the accompanying station, and the expression is:
；
Constraint 9: and (3) position constraint, namely assigning a task at most to any position in the accompanying station, wherein the expression is as follows:
；
constraint 10: position constraint, along with the task distribution in the station according to the position sequence, the expression is:
；
constraint 11: along with the position priority constraint in the station, the expression is:
；
constraint 12: the paired station opening constraint has the expression:
；
；
constraint 13: accompanying the station opening constraint, the expression is:
；
constraint 14: the paired stations are opened in sequence, and the following stations are opened in sequence, and the expression is as follows:
；
；
constraint 15: constraint is carried out on the task distributed to the left side and the right side of the station, and the expression is as follows:
；
；
；
；
constraint 16: the time constraint of completion of the task is expressed as follows:
；
constraint 17: accompanying a constraint on a change in tool type at an adjacent location in a workstation, the constraint is expressed as
；
；
；
；
；
；
；
Constraint 18: the last position task constraint in the accompanying station exists when the accompanying station is opened, and the expression is as follows:
；
constraint 19: the task in the accompanying station is not necessarily at the final position, and the expression is:
；
constraint 20: constraints that need to be met when the task in the companion station is in the final position can be expressed as:
；
constraint 21: along with the constraint of changing the tool type at the head-tail position in the station, the expression is as follows:
；
；
；
；
；
；
。
Because different dismantling targets have different dismantling conditions, preset conditions and constraint conditions set in the embodiment of the invention are only used as methods for illustration, and are not used as limitations of the invention.
Step S3: based on constraint conditions, a multi-target double-side local dismantling line task planning model which takes the number of work stations, a smooth index, dismantling energy consumption and profits as guidance is established, and is converted into a standard mixed integer linear planning model.
Specifically, minimizing the number of chemical sites, smoothing the index, maximizing the profit of dismantling, minimizing the energy consumption of dismantling, the expression is:
；
sub-target 1: the pairs of bits and accompanying bits opened in the double-sided disassembly line can be expressed as:
。
sub-target 2: the smoothness index of the double-sided disassembled line can be expressed as
；
Wherein the expression accompanying the working time in the station is
。
Sub-target 3: the break-up profit of the double-sided break-up line can be expressed as
；
Sub-target 4: the dismantling energy consumption of the double-sided dismantling line can be expressed as
。
Step S4: and constructing a multi-target genetic simulated annealing algorithm through a coding and decoding strategy based on bilateral disassembly constraint and genetic operation and simulated annealing operation conforming to the disassembly priority constraint, and solving a mixed integer linear programming model to obtain a task planning scheme.
Specifically, the method comprises the following steps:
step S3.1: initializing algorithm parameters and setting population scale N p Initial temperature T 0 Chain length L, cooling coefficient gamma, termination temperature T end External archive size N 0 Hypervolume reference point R 0 Etc.;
step S3.2: completing population initialization according to the disassembly priority relation matrix P and the coding and decoding strategy, so that an external file Q=R;
step S3.3: calculating a target function value F, screening a population non-inferior solution, updating an external file Q, and setting a current annealing temperature T=T 0 ；
Step S3.4: performing genetic operations such as selection operation, crossover operation, mutation operation and the like on the non-inferior solution in the external file Q, and performing double-point crossover mutation if rand is more than 0.5 in the mutation operation, otherwise performing single-point insertion mutation;
step S3.5: let the loop counter l=1;
step S3.6: performing simulated annealing operation to generate a new individual, calculating an objective function value F, and judging whether the new individual is accepted or not according to a Metropolis criterion;
step S3.7: if L < L, let l=l+1, go to step S3.6; otherwise, directly entering the next step;
step S3.8: comparing the result obtained in the step S3.7 with each non-inferior solution in the external file Q to finish updating the external file Q;
Step S3.9: updating the population, and calculating the number N of non-inferior solutions in Q Q If N Q >N p Evaluating each non-inferior solution and selecting the first N p Distance of crowding L g Larger non-inferior solutions are used as populations; otherwise, supplementing population individuals according to the double-point exchange mutation operation;
step S3.10: screening non-bad solutions, when N Q >N 0 When the N is reserved 0 Distance of crowding L g The larger non-inferior solution is remained in the external file Q, so that the update of the external file is realized; otherwise, directly entering the next step;
step S3.11: calculating the Hypervolume value of the solution set;
step S3.12: if T>T end Then, performing cooling operation to make T=γT, and turning to step S3.4; otherwise, directly entering the next step;
step S3.13: and (5) terminating the algorithm, and outputting a non-inferior solution in the external file to obtain an optimal disassembly sequence solution set.
In step S3.2, the codec strategy comprises:
the coding sequence includes a deconstructing decision sequence X and a deconstructing task sequence S, which may be denoted as [ X; S ]. The un-disassemble decision sequence X consists of 0-1 variables, where 0 indicates that the task is not un-disassembled and 1 indicates that the task needs to be disassembled. The conditions that the disassembly decision sequence X needs to meet are: (1) the decision variables for disassembly of the compromised task and all immediately preceding tasks are 1; (2) the dismantling decision variables need to consider the priority constraint among the tasks, namely the dismantling decision variables of all the immediately preceding tasks of the task with the dismantling decision variable of 1 are 1; (3) the break down decision variables for the other tasks are randomly selected from 0 and 1. The disassembled task sequence S also employs a priority-based constraint to order the tasks during encoding to ensure the feasibility of the initial solution.
In the embodiment of the invention, in the coding strategy, 4 heuristic rules based on the problem characteristics are designed to construct a candidate task set in order to improve the quality of an initial solution. These 4 heuristic rules are respectively:
rule 1: harmful tasks and tasks immediately before the harmful tasks are preferentially selected, so that the number of the disassembling tasks is reduced as much as possible, and the number of work bits is reduced. By S k Representing a candidate task Set meeting priority constraint at a position k, wherein a Set consisting of candidate tasks i at the position k is Set 1 (i) The expression is:
；
rule 2: tasks that minimize the remaining time associated with the workstation (w, s) are preferentially selected to reduce idle time in the workstation. The Set of candidate tasks i at position k is Set 2 (i) The expression is:
；
rule 3: tasks with high profit are preferentially allocated, thereby maximizing the profit of the dismantling. The Set of candidate tasks i at position k is Set 3 (i) The expression is:
；
rule 4: preferably, tasks with low energy consumption are allocated to reduce the energy consumption of the partial disassembly task sequence, and the Set consisting of candidate tasks i at the position k is Set 4 (i) The expression is:
；
candidate tasks obtained by the 4 heuristic rules correspond to 4 sub-targets respectively. In general, it is difficult to have candidate tasks satisfying 4 heuristic rules simultaneously. In the optimization process, for 4 sub-targets, no optimization weights or optimization orders are Set, candidate tasks obtained by the 4 heuristic rules are all better selectable tasks, and the candidate task Set at the position k can be expressed as Set 1 (i)∪Set 2 (i)∪Set 3 (i)∪Set 4 (i) A. The invention relates to a method for producing a fibre-reinforced plastic composite After selecting the task i at the position k, releasing the priority constraint between the task i and other tasks, continuously updating the priority relation between the tasks, and continuously selecting the task at the next position until all the tasks are selected to be completed.
In the decoding strategy, beat constraint and operation azimuth constraint need to be met during decoding. The decoding mode based on the operation direction is adopted, namely, the left task is distributed to the left station, the right task is distributed to the right station, and the tasks on any side are distributed according to the principle of enabling the disassembly line to be shortest. Not all tasks in the partial disassembly line need to be allocated, and whether the tasks are allocated or not needs to be judged first during decoding. In addition to meeting orientation constraints, the selection of stations needs to be considered: (1) whether the adjacent task generates tool change time; (2) whether the head-to-tail task generates tool replacement time or not; (3) whether the attendant station time satisfies the beat constraint.
The specific decoding flow is shown in fig. 3. The final station adjustment is to adjust the tasks belonging to the left and right stations to the left station or the right station meeting the requirements on the premise of meeting the operation azimuth constraint and the beat constraint and not increasing the paired station numbers, thereby reducing the number of the opened accompanying stations.
The step of initializing the population comprises the following steps: after setting the population scale, generating population individuals meeting the requirements in a circulating way according to the coding and decoding strategies. The method has the advantages that when the priority constraint and the disassembly constraint are met, the disassembly task, the disassembly decision variable and the disassembly mode variable at any position are randomly selected, so that population individuals of the optimization algorithm when solving the local destructive disassembly line balance and sequence planning problem have a larger decision space than population individuals of the general disassembly line balance problem. The random coding mode under the constraint condition can enhance the diversity of the individuals in the initialized population.
The selecting operation includes: in the selection operation, the target value of the problem may be taken as the fitness value, but the probability of selecting an individual cannot be calculated directly from the fitness value. The bigger the fitness value is, the bigger the probability that the chromosome is selected is, the non-dominant solutions in the external file are not mutually dominant, and the chromosome is better, and can be used as an individual for selection operation. To speed up the convergence rate of the algorithm, two non-dominant solutions are randomly selected from the external profile to enter the crossover operation.
The interleaving operation includes: the disassembly decision sequence and the disassembly mode decision sequence adopt a gene crossing mode at the same position as the disassembly task sequence. Random crossover, while increasing the diversity of chromosomes, is extremely prone to producing infeasible chromosomes, i.e., the crossed chromosomes fail to meet the priority constraints. After random crossing, the feasibility of the chromosome is often checked, and for infeasible chromosomes, correction is needed, and the algorithm operation efficiency is reduced in the process. In view of this, a two-point mapping crossover approach is designed to ensure that individuals after crossover meet the priority constraints.
The two-point mapping crossover is shown in fig. 4. In the two-point crossing operation, a group of numbers are randomly generated first, the dimension is the same as the task number, the maximum value and the minimum value in the array are selected as two crossing points, for example, the maximum value 0.9 and the minimum value 0.1 of the random number in fig. 4 correspond to the tasks 7 and 1 in the S1 respectively, and the tasks 7 and 1 are selected as two crossing points in the sequence. The sequences outside the two crossing points in S1, i.e., the sequences {6,3} and {2,4,9} are unchanged; the sequence between the two crossing points, i.e. the sequence composed of tasks 7 and 1 and the middle task {7,8,5,1} is mapped by S2 to be {7,1,5,8}, then new offspring individuals Snew1= {6,3,7,1,5,8,2,4,9} can be obtained. The same thing generates child individuals snew2= {7,2,3,5,1,4,8,9,6}. The sequences before the 1 st intersection and the sequences after the 2 nd intersection meet the priority constraint, and the sequences between the intersections also meet the priority constraint in another chromosome, so that the new offspring chromosomes also meet the priority constraint, and the infeasible solution caused by blind intersection is effectively avoided.
In the early stage of algorithm iteration, a larger cross probability is generally selected to enhance the neighborhood searching range of the algorithm; along with the iterative process of the algorithm, the algorithm is continuously approaching to a better solution, and the crossover probability is reduced at the moment, so that the large-range variation of a high-quality sequence is avoided, and therefore, the adaptive crossover probability is adopted, and the expression is as follows:
；
Wherein P is Cmax Represents the maximum value of the crossover probability, P Cmin Represents the minimum value of the crossover probability, g represents the current iteration number, g max Representing the maximum number of iterations.
The mutation operation comprises the following steps: the disassembly decision sequence and the disassembly mode decision sequence adopt a genetic variation mode at the same position as the disassembly task sequence. Random variation can produce infeasible solutions requiring the design of variation operations based on preferential constraints. Two viable variation strategies were designed: single point insertion variation and double point exchange variation.
Single point insertion variation: taking the individual X after the cross operation as a father, randomly selecting a point as a variation point, and taking the immediately preceding task and the immediately following task of the point into consideration, wherein the position where the point can be subjected to variation insertion is between the immediately preceding task and the immediately following task closest to the task. If the selected mutation position has no optional position to insert, i.e. is constrained by the priority relation and cannot execute mutation operation, the mutation point needs to be reselected. As shown in FIG. 5, a group of random numbers is generated, the dimension is the same as the number of the serial tasks, the disassembly task 5 corresponding to the maximum value 0.9 in the array is selected as a variation point, the immediately preceding tasks 6 and 3 of the task 5 are found, the immediately following tasks 4 and 9 are respectively the tasks 3 and 4, the immediately preceding task and the immediately following task closest to the task 5 are respectively the tasks 3 and 4, and the task 5 can select the insertion position between 3 and 4, namely before the tasks 7 and 8 or after the tasks 1 and 2. Selecting a task corresponding to the minimum value in the random number as an insertion point, and forming a new child X after the task 1 new ={6，3，7，8，1，5，2，4，9}。
Double point crossover variation: and taking the individual X after the cross operation as a parent, randomly selecting two points as exchange variation points, respectively searching positions where the two points can be inserted, and if the two points are respectively insertable points of each other, the two points can be exchanged, and a new sequence is formed after the exchange to meet the priority relation. If the two points are not pluggable points to each other, the precedence constraint cannot be satisfied after the swap, and the change point needs to be reselected. As shown in fig. 6, a random group number with the same dimension as the task number is generated, the disassembly tasks 7 and 1 corresponding to the maximum value and the minimum value in the group are selected as variation points, and all insertable positions of the tasks 7 are found in a similar manner to the single-point insertion variation operation: before task 3; tasks 8,5, 1, 2 are followed. Similarly, find all pluggable positions for task 1: tasks 7, 8, 5. By contrast, if task 7 and task 1 are points that can be inserted into each other, then task 7 and task 1 are exchanged to form a new child X new = {6,3,1,8,5,7,2,4,9} satisfies the priority relation.
Similar to the crossover probability, an adaptive mutation probability is set in the algorithm, and the expression is as follows:
；
wherein P is Mmax Represents the maximum value of variation probability, P Mmin Representing the minimum probability of variation.
The simulated annealing operation includes: and performing simulated annealing operation on the mutated individual, and searching a neighborhood solution by adopting an insertion operator. The Metropolis criterion is improved and the acceptance probability of the new solution is designed, the expression is as follows:
；
wherein: prob is the probability of acceptance of the new solution;and->The ith sub-target values of the new solution and the current solution respectively; t is the current annealing temperature.
If the new solution dominates the current solution, replacing the current solution with the new solution; if the current solution dominates the new solution, accepting the new solution according to the acceptance probability; if the new solution and the current solution are not mutually dominant, both solutions are reserved, and one solution is randomly selected as the current solution when entering the next round of circulation. And after the neighborhood search is executed, cooling is started, and the simulated annealing algorithm completes primary optimization.
After the neighborhood search is executed, cooling is started, the simulated annealing algorithm completes primary optimization, and the temperature decay function is as follows:
；
wherein, gamma represents the cooling coefficient, k represents the cooling times, T 0 Indicating the initial temperature.
The embodiment of the invention is explained by taking a waste refrigerator dismantling line as an example, and the application performance of the method of the invention in practical engineering cases is analyzed.
The disassembling line adopts a bilateral station layout mode, one side station mainly disassembles a compressor on the back of the refrigerator, and the other side station mainly disassembles a door body on the front of the refrigerator. The dismantling line of the waste refrigerator mainly adopts manual dismantling, and is combined with the structure and main parts of the refrigerator, the dismantling tasks are divided again, the specific dismantling task information is shown in table 1, 66 dismantling tasks are altogether, and the priority relationship among the tasks is shown in fig. 7. In table 1, the numbers 1 to 5 are used to represent the disassembling tools, which respectively represent the hand, the electric screwdriver 1, the wire cutting pliers, the cutting machine and the electric screwdriver 2, the cost per unit time of the disassembling tools is 0,0.20,0.10,0.30,0.20 yuan, and the energy consumption per unit time of the disassembling tools is 0,1.5,0.5,2.0 and 1.5kw. The value of the component or material obtained for each disassembly task is evaluated with reference to the market value of the component. Compromised tasks include tasks 7, 14, 33, 41, 52. The azimuth information of the dismantling task is as follows: tasks 1-43 are on the front (left side station); any one The transactions 44-60 are on the back (right station); tasks 61-66 are at the bottom (arbitrary side stations). The beat of the dismantling line is set to be 30s, and other auxiliary parameters are respectively c s =0.04 yuan/s, c f =0.005 yuan/s, c h =0.006 yuan/s, e s =1.50kW，e f =0.20kW，e h =0.25kW，t b =2s. The MATLAB 2020 is adopted to write a code of a genetic simulated annealing algorithm, and the algorithm running environment is an Intel Core i5-8400 CPU,2.80GHz,16GB RAM,Windows 10 64 bit operating system.
TABLE 1
5 classical multi-objective algorithms are introduced to compare with the designed multi-objective genetic simulated annealing (MOGSA) algorithm, and the 5 algorithms are respectively non-dominant ordered genetic algorithm III (NSGA-II), intensity Pareto evolution algorithm 2 (SPEA2+SDE) based on density estimation offset, multi-preference vector guided co-evolution algorithm (PICEA-g), grid-based evolution algorithm (GrEA) and super-volume estimation algorithm (HypE). The population and the number of iterations of all algorithms were set to 50 and 200, respectively. Each algorithm runs independently 10 times, taking the Pareto non-dominant solution of all results as the real Pareto front of the problem, and then calculating the HV value, GD value and Spread value of each result. Fig. 8 is a box plot of 10 results of 6 algorithms over 3 indicators. The comparison shows that: the average level of MOGSA on HV, GD and Spread indexes is better than that of 5 comparison algorithms, and the MOGSA has better convergence. Outlier aspect: only HypE has no outlier on HV indicator, other algorithms appear 1 or 2 outliers; PICEA-g and HypE have 1 abnormal value on GD index, and other algorithms have no abnormal value; all algorithms have no outliers on the Spread index. Stability aspects: hypE has slightly poorer stability, and the stability of the other 5 algorithms is not greatly different. Comprehensive comparison shows that the performance of MOGSA is superior to that of 5 comparison algorithms.
Taking HV index as an example, analyzing convergence conditions in different algorithm iterative processes. The reference point for HV is set to (100, 0, 100, 30), and the HV index value for each iteration of the algorithm is calculated. The iteration map is drawn by taking the result data of each algorithm when the maximum HV indicator is obtained, as shown in fig. 9. The comparison shows that: the HV value of MOGSA in the iterative process is always larger than the HV value of 5 comparison algorithms; in particular, the initial solution of MOGSA is obviously better than that of 5 comparison algorithms; with the increase of iteration times, the convergence rate of the algorithm is slow, and particularly after the iteration is performed for 150 times, the performance of SPEA2+SDE is improved to a certain extent at the end of the iteration; the final HV values of MOGSA and the comparison algorithm were 10.69× 106,9.940 × 106,9.987 ×106, 10.09×106, and 10.01× 106,9.874 ×106, respectively, indicating that MOGSA performed optimally, and the order of merit of the 5 comparison algorithms was PICEA-g, grEA, SPEA2+SDE, NSGA-III, hypE in that order. Therefore, the optimizing capability and the convergence of the designed multi-objective genetic simulated annealing (MOGSA) algorithm are both stronger than those of 5 comparison algorithms.
The MOGSA obtains a total of 32 non-dominant solutions when it takes the maximum HV value, the distribution of all solutions in the target space is shown in fig. 10, and the distribution of all solutions over 4 sub-targets is shown in fig. 11. The optimal values for the 4 sub-targets are 18,0, 43.831,0.2743, respectively. The scheme S for obtaining the maximum profit is marked in FIG. 10 1 And minimum energy consumption scheme S 2 。
Each of the schemes shown in fig. 10 has advantages and disadvantages on 4 sub-targets, each of the schemes cannot optimize all the sub-targets, and fig. 11 clearly shows the advantages and disadvantages of each of the schemes on the sub-targets. When the number of the disassembling tasks is small in the scheme, the corresponding number of stations to be opened is small, the disassembling energy consumption is low, and meanwhile, the profit of the disassembling scheme is also reduced; as the number of dismantling tasks increases, the number of work stations, energy consumption and profits will all increase. The analysis of fig. 11 results in a conclusion that matches the actual case of the disassembled wire.
Further, it is understood from the observation of fig. 11 that there is no obvious linear relationship between the smoothing index f2 and the other 3 sub-targets. This is caused by the partial disassembly mode. In the partial disassembly line, if the improvement of the smoothness index is considered from the viewpoint of multi-objective optimization, the number of work stations, the energy consumption index and the profit index cannot be improved, so that the partial disassembly line is taken as a secondary optimization target, and the number of work stations, the disassembly profit and the disassembly energy consumption are taken as primary optimization targets.
The relationship between sub-targets is analyzed using a scatter plot matrix as shown in fig. 12, which shows more remarkable results. The dismantling energy consumption and the number of stations are almost in linear relation, namely the energy consumption is continuously increased along with the increase of the number of stations, which shows that the energy consumption mainly comes from the stations. In the actual disassembling process, the power consumption of equipment such as the conveying operation of the station, ventilation illumination and the like is larger than that of the disassembling tool. Likewise, as the number of stations increases, there is a trend in the profit of the dismantling, but there is no significant linear relationship between the two. The reason for this is: the dismantling values of different parts in the product are different, and the dismantling profits are not increased along with the increase of the number of dismantling tasks; that is, as the number of tasks increases, the cost increase of the workstation is relatively steady, while the profit of the dismantling does not increase linearly. As in the conclusion of fig. 11, the relationship between the smoothing index and other indices appears to be cluttered with no significant relationship.
The foregoing embodiments may be combined in any way, and all possible combinations of the features of the foregoing embodiments are not described for brevity, but only the preferred embodiments of the invention are described in detail, which should not be construed as limiting the scope of the invention. The scope of the present specification should be considered as long as there is no contradiction between the combinations of these technical features.
It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. A method for planning a double-side dismantling line task of a retired electromechanical product is characterized by comprising the following steps of: the method comprises the following steps:
step S1: determining dismantling information of an electromechanical product to be dismantled, wherein the dismantling information comprises a dismantling priority relationship, a dismantling direction, a dismantling tool, a dismantling time, a hazard attribute and a dismantling value;
step S2: based on the workflow of the bilateral dismantling line and the dismantling information, setting constraint conditions;
Step S3: based on the constraint conditions, a multi-target double-side local dismantling line task planning model which takes the number of work stations, a smooth index, dismantling energy consumption and profit as guidance is established, and is converted into a standard mixed integer linear planning model;
the expression of the work site object is as follows:
wherein W represents a station set; n when the paired stations w are opened w =1, otherwise 0; s= {1,2} represents a set of left and right sides of the station, s=1 represents the left side, and s=2 represents the right side; y when s side of paired station w is opened ws =1, otherwise 0;
the expression of the smoothing index target is:
wherein T is C Representing the beat of the disassembled line; t (T) ws The operation time on the s side of the paired stations w; i represents a disassembly task set; task i is assigned to s-side of paired station w x iws 1, otherwise 0; t is t i The disassembly time of the disassembly task i is represented; t is t b Disassembling tool replacement time; the disassembly tools of the head-tail tasks on the s side of the paired stations w are different b ws 1, otherwise 0; k represents a set of positions, the maximum position being |k|, the set K' = {1, …, |k| -1}; the k-th position on the s-side of the paired stations w is different from the dismantling tool at the k+1-th position by d wsk 1, otherwise 0;
the expression of the dismantling profit target is:
Wherein r is i The disassembly benefits of the disassembly task i are represented; c i The unit time disassembly cost of the disassembly task i is represented; c f Representing the fixed unit time cost of the station; c s A unit time cost representing an invalid operation time in the station; c h Representing additional unit time costs when the workstation processes the jeopardized task; h when the dismantling task i is harmful i 1, otherwise 0;
the expression of the dismantling energy consumption target is as follows:
in the formula e i The unit time dismantling energy consumption of the dismantling task i is represented; e, e f The energy consumption of a station in a fixed unit time is represented; e, e s A unit time energy consumption representing an invalid operation time in the station; e, e h The extra unit time energy consumption when the station processes the hazard task is represented;
the expression of the mixed integer linear programming model is as follows:
min F＝(f 1 ,f 2 ,-f 3 ,f 4 )；
wherein f 1 Representing the number of work orders, f 2 Representing a smoothing index target, f 3 Representing a dismantling profit target, f 4 Representing a dismantling energy consumption target;
step S4: and constructing a multi-target genetic simulated annealing algorithm through a coding and decoding strategy based on bilateral disassembly constraint and genetic operation and simulated annealing operation conforming to the disassembly priority constraint, and solving the mixed integer linear programming model to obtain a task planning scheme.
2. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 1, which is characterized in that: in the coding strategy, a candidate task set is constructed by adopting heuristic rules based on problem characteristics so as to improve the quality of an initial solution.
3. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 2, which is characterized in that: the heuristic rules include:
1) Preferentially choose the harmful task and the task immediately before:
Set 1 (i)＝{i|h i ＝1∧i∈S k }∪{j|p ji ＝1∧h i ＝1∧j∈S k }；
2) The task that minimizes the remaining time of the accompanying stations (w, s) is preferentially chosen:
3) Tasks with high profit are preferentially allocated:
4) Preferably, tasks with low energy consumption are allocated:
wherein S is k Representing a set of candidate tasks at position k, p ji Representing a priority relationship between the tasks,indicating the completion time of task i in the workstation.
4. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 1, which is characterized in that: the step S4 of solving the mixed integer linear programming model comprises the following steps:
step S41: completing population initialization according to the disassembly priority relationship and the encoding and decoding strategy;
step S42: calculating an objective function value, screening a population non-inferior solution, updating an external file Q, and setting the current annealing temperature;
step S43: performing genetic operations on the non-inferior solutions in the external archive Q;
step S44: performing simulated annealing operation, generating a new individual, calculating an objective function value, and judging whether the new individual is accepted or not according to a Metropolis criterion until the variation times are reached;
Step S45: comparing the result obtained in the step S45 with each non-inferior solution in the external file Q to finish updating the external file Q;
step S46: updating the population, calculating the number of non-inferior solutions in the external file, and if the number of non-inferior solutions is greater than a set threshold N p Then select the first N p Non-inferior solutions of individual crowding distances as a population; otherwise, supplementing population individuals according to the double-point exchange mutation operation;
step S47: screening the non-inferior solutions, and determining the number N of the non-inferior solutions Q ＜N 0 When the method is used, the next step is directly carried out; otherwise, keep N before 0 The crowded distance is not bad, and is remained in the external file Q, so that the update of the external file is realized;
step S48: calculating index evaluation Hypervolume value of the solution set;
step S49: if the current annealing temperature T>Termination temperature T end Then, a cooling operation is performed, let t=γt, γ represent cooling parameters, and step S44 is repeated; otherwise, outputting the non-inferior solution in the external file to obtain the task planning scheme.
5. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 4, wherein the method comprises the following steps: the genetic manipulation in step S43 includes a selection manipulation, a crossover manipulation and a mutation manipulation;
the crossing operation adopts a two-point mapping crossing mode, and the two-point mapping crossing mode adopts self-adaptive crossing probability P C The sum ofThe adaptive crossover probability P C The expression of (2) is:
wherein P is Cmax Represents the maximum value of the crossover probability, P Cmin Represents the minimum value of the crossover probability, g represents the current iteration number, g max Representing the maximum number of iterations.
6. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 5, wherein the method comprises the following steps: the mutation operation comprises single-point insertion mutation and double-point exchange mutation; the method comprises the steps of carrying out mutation by adopting adaptive mutation probability, wherein the expression of the adaptive mutation probability is as follows:
wherein P is Mmax Represents the maximum value of variation probability, P Mmin Representing the minimum probability of variation.
7. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 4, wherein the method comprises the following steps: the simulated annealing operation in step S44 includes the steps of:
step S441: performing simulated annealing operation on the mutated individual, and searching a neighborhood solution by adopting an insertion operator;
step S442: setting the acceptance probability of the new solution according to the Metropolis criterion; if the new solution dominates the current solution, replacing the current solution with the new solution; if the current solution dominates the new solution, accepting the new solution according to the acceptance probability; if the new solution and the current solution are not mutually dominant, reserving both solutions, and randomly selecting one solution from the solutions as the current solution when entering the next round of circulation;
Step S443: and after the neighborhood search is executed, cooling is started, and the simulated annealing algorithm completes primary optimization.
8. The method for planning the task of double-sided dismantling line of retired electromechanical products according to claim 7, wherein the method comprises the following steps: the expression of the acceptance probability is:
wherein f i new And f i cur The i-th sub-target value of the new solution and the current solution are respectively represented, and T represents the current annealing temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311230347.0A CN116976228B (en) | 2023-09-22 | 2023-09-22 | Method for planning task of double-side dismantling line of retired electromechanical product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311230347.0A CN116976228B (en) | 2023-09-22 | 2023-09-22 | Method for planning task of double-side dismantling line of retired electromechanical product |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116976228A CN116976228A (en) | 2023-10-31 |
| CN116976228B true CN116976228B (en) | 2024-02-02 |
Family
ID=88475291
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311230347.0A Active CN116976228B (en) | 2023-09-22 | 2023-09-22 | Method for planning task of double-side dismantling line of retired electromechanical product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116976228B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110580530A (en) * | 2019-08-30 | 2019-12-17 | 西南交通大学 | Bilateral disassembly line setting method considering station constraint and energy consumption |
| CN114800510A (en) * | 2022-05-06 | 2022-07-29 | 武汉理工大学 | Industrial robot disassembly sequence planning method considering product part failure characteristics |
| CN115329552A (en) * | 2022-07-27 | 2022-11-11 | 武汉理工大学 | Disassembly line balance optimization method, device and equipment considering man-machine cooperation |
| CN116690589A (en) * | 2023-08-07 | 2023-09-05 | 武汉理工大学 | Robot U-shaped dismantling line dynamic balance method based on deep reinforcement learning |
| CN116756918A (en) * | 2023-05-04 | 2023-09-15 | 三峡大学 | Fuzzy time-based lithium battery echelon utilization disassembly line multi-objective optimization mathematical model establishment and solving method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109635510B (en) * | 2019-01-15 | 2022-08-26 | 西南交通大学 | Method for setting parallel overhaul lines of railway freight cars |
-
2023
- 2023-09-22 CN CN202311230347.0A patent/CN116976228B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110580530A (en) * | 2019-08-30 | 2019-12-17 | 西南交通大学 | Bilateral disassembly line setting method considering station constraint and energy consumption |
| CN114800510A (en) * | 2022-05-06 | 2022-07-29 | 武汉理工大学 | Industrial robot disassembly sequence planning method considering product part failure characteristics |
| CN115329552A (en) * | 2022-07-27 | 2022-11-11 | 武汉理工大学 | Disassembly line balance optimization method, device and equipment considering man-machine cooperation |
| CN116756918A (en) * | 2023-05-04 | 2023-09-15 | 三峡大学 | Fuzzy time-based lithium battery echelon utilization disassembly line multi-objective optimization mathematical model establishment and solving method |
| CN116690589A (en) * | 2023-08-07 | 2023-09-05 | 武汉理工大学 | Robot U-shaped dismantling line dynamic balance method based on deep reinforcement learning |
Non-Patent Citations (2)
| Title |
|---|
| 多目标拆卸线平衡问题的Pareto遗传模拟退火算法;汪开普;张则强;朱立夏;邹宾森;;计算机集成制造系统(第06期);全文 * |
| 退役汽车拆解生产线平衡问题仿真研究;袁振宇;郭巍;陆朝晖;胡志力;王骞;魏擎霄;;武汉理工大学学报(第08期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116976228A (en) | 2023-10-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives | |
| Zhang et al. | A multiobjective evolutionary algorithm based on decomposition for hybrid flowshop green scheduling problem | |
| Sun et al. | Hybrid genetic algorithm with variable neighborhood search for flexible job shop scheduling problem in a machining system | |
| Xing et al. | An efficient search method for multi-objective flexible job shop scheduling problems | |
| CN108460463B (en) | High-end equipment assembly line production scheduling method based on improved genetic algorithm | |
| CN101901425A (en) | Flexible job shop scheduling method based on multi-species coevolution | |
| CN112381343B (en) | Flexible job shop scheduling method based on genetic-backbone particle swarm hybrid algorithm | |
| Yu et al. | A knowledge-guided bi-population evolutionary algorithm for energy-efficient scheduling of distributed flexible job shop problem | |
| Zhang et al. | A genetic algorithm and tabu search for multi objective flexible job shop scheduling problems | |
| CN115145235B (en) | Multi-target intelligent scheduling method for casting whole process | |
| CN113569483A (en) | Method for solving multi-target flexible job shop scheduling based on artificial bee colony algorithm | |
| Subbaiah et al. | Scheduling of AGVs and machines in FMS with makespan criteria using sheep flock heredity algorithm | |
| CN116976649B (en) | Method for balancing local destructive dismantling line of retired household appliance | |
| Liu et al. | Multi-objective flexible job shop scheduling problem considering machine switching off-on operation | |
| CN115933568A (en) | Multi-target distributed hybrid flow shop scheduling method | |
| CN116985146B (en) | Robot parallel disassembly planning method for retired electronic products | |
| He et al. | Energy-efficient open-shop scheduling with multiple automated guided vehicles and deteriorating jobs | |
| CN117148796A (en) | Optimization method for solving scheduling problem of multi-target flexible job shop | |
| CN115249113A (en) | Distributed zero-waiting flow shop scheduling method and system with preparation time | |
| CN117555305B (en) | NSGAII-based multi-target variable sub-batch flexible workshop job scheduling method | |
| CN116976228B (en) | Method for planning task of double-side dismantling line of retired electromechanical product | |
| Chu et al. | Competitive Multitasking for Computational Resource Allocation in Evolutionary Constrained Multi-Objective Optimization | |
| CN117170318A (en) | Method for solving fuzzy production scheduling problem based on improved heap optimization algorithm | |
| Zhou et al. | Imperialist competitive algorithm based on VNSOBL optimization for distributed parallel machine scheduling problem | |
| Abeysooriya et al. | Hybrid approach to optimize cut order plan solutions in apparel manufacturing |
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 |