CN115034143A - Multi-resource cooperative intelligent workshop equipment configuration optimization method - Google Patents

Multi-resource cooperative intelligent workshop equipment configuration optimization method Download PDF

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CN115034143A
CN115034143A CN202210781190.XA CN202210781190A CN115034143A CN 115034143 A CN115034143 A CN 115034143A CN 202210781190 A CN202210781190 A CN 202210781190A CN 115034143 A CN115034143 A CN 115034143A
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张惠煜
梁展鹏
陈庆新
毛宁
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Guangdong University of Technology
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Abstract

The invention discloses a multi-resource cooperative intelligent workshop equipment configuration optimization method and a system, and the method specifically comprises the following steps: s1, constructing a simulation system for simulating an actual production environment; s2, establishing a mathematical model for optimizing equipment configuration and setting performance index constraint parameter conditions; s3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring a performance index by using a simulation system in the gray wolf optimization algorithm, updating the population and the position of the gray wolf optimization algorithm according to the performance index, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times; and S4, obtaining the optimized equipment configuration according to the solution of the iterative wolf optimization algorithm. Compared with the prior art, the method solves the mathematical model by means of the gray wolf optimization algorithm, solves the fitness value by using the simulation system and updates the iterative gray wolf optimization algorithm, and achieves the purpose of minimizing the investment cost of workshop equipment under the constraint of the order delivery date and the minimum system output rate on the premise of meeting the maximum production period.

Description

Multi-resource cooperative intelligent workshop equipment configuration optimization method
Technical Field
The invention relates to the field of planning and designing of manufacturing systems, in particular to a multi-resource cooperative intelligent workshop equipment configuration optimization method.
Background
The development of intelligent manufacturing in the traditional manufacturing industry is an important support for promoting the quality improvement and efficiency improvement of the traditional manufacturing industry and promoting the large and strong change of the Chinese manufacturing industry. Personalized customization is an important feature of intelligent manufacturing, and with the continuous advance of intelligent manufacturing strategies, the intelligent modification of factories becomes a main way for promoting intelligent manufacturing transformation in the current manufacturing industry. The intelligent workshop is an important link for realizing intelligent manufacturing by an intelligent factory, and for the planning design of the intelligent workshop, because highly automated and intelligent production equipment is expensive, how to configure the equipment resources in the intelligent workshop can ensure the expected capacity and the on-time and quick delivery of orders with the lowest cost, so that the problem to be solved by planning and designing the customized production workshop is solved.
In the process of customized production, because the arrival time of workpieces, the process path and the processing time thereof, the material transportation time and the operation time of an industrial robot are uncertain, the problem of resource allocation optimization of the production workshops can not be solved through a traditional deterministic mathematical programming model, and great challenges are brought to the planning and design problems of the production workshops. The multi-resource cooperative intelligent workshop normally needs cooperative operation of multiple types of equipment resources to enable the system to normally operate, for example, a workpiece carrying process, a workpiece loading and unloading process and a workpiece processing process need to be carried out under the condition that multiple resources of an AGV, a robot and a processing machine tool are simultaneously in an available state, namely resource cooperative constraint. Therefore, the planning design optimization for the multi-resource collaborative intelligent workshop needs to consider the collaborative relationship among the devices, and the minimum device configuration cost is achieved. The Grey Wolf optimization algorithm (GWOlf Optimizer, GWOO) is a group intelligent optimization algorithm proposed by Mirjallii et al in 2014, and is an optimization search algorithm developed according to the inspiration of Grey Wolf groups for capturing prey, and has the characteristics of strong optimization capability, few parameters and easiness in implementation.
The scheme utilizes a particle swarm algorithm to initialize a population, determines a fitness function, calculates the fitness value of particles, updates the speed and the position of the particles by combining the simulated annealing algorithm, determines the fitness value of new particles and finally obtains an optimization result. The defects of the scheme are that operation is not carried out based on the actual production environment, the equipment configuration system cannot be really characterized, the problem of unstable efficiency of the production system is not solved, and the cost cannot be minimized.
Therefore, by combining the characteristics and the defects of the prior art, the application provides a multi-resource cooperative intelligent workshop equipment configuration optimization method and system.
Disclosure of Invention
The invention provides a multi-resource cooperative intelligent workshop equipment configuration optimization method and system, which can minimize investment costs of an AGV and a manipulator under the constraint of an order delivery date and a minimum system output rate on the premise of meeting a maximum production period.
The primary objective of the present invention is to solve the above technical problems, and the technical solution of the present invention is as follows:
the invention provides a multi-resource cooperative intelligent workshop equipment configuration optimization method, which comprises the following steps:
and S1, constructing a simulation system for simulating the actual production environment.
And S2, establishing a mathematical model for optimizing the quantity and the types of the equipment in the simulation system and setting performance index constraint parameter conditions.
S3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring performance indexes in the gray wolf optimization algorithm by using a simulation system, updating the population and the position of the gray wolf optimization algorithm according to the optimal performance indexes, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times.
And S4, obtaining the optimized equipment configuration according to the solution of the iterative wolf optimization algorithm to the mathematical model.
Further, the step S1 includes:
s11, constructing a simulation system according to an actual production environment, wherein the simulation system comprises a task pool, a raw material warehouse, a processing unit, a finished product warehouse and a track, the processing unit comprises a front buffer area, a processing center and a rear buffer area which are sequentially connected, and the track is a running path of the AGV and the manipulator; and the task pool issues tasks to the AGV and the manipulator.
S12, the working steps of the simulation system are as follows: AGV carries the raw materials to the preceding buffer of each processing unit from raw materials warehouse, and the manipulator is with raw materials from the preceding buffer material loading to machining center, machining center processing raw materials, and the finished product unloading to the back buffer after the manipulator will machining center processing, and AGV carries the finished product that the buffer was deposited after to the finished product warehouse.
S13, the simulation system follows the following rules: the arrival of the workpieces in the system obeys a Poisson distribution process, the arrival of each workpiece is an independent event, and the mean arrival rate is lambda; the system judges whether the raw material warehouse is full before the workpieces arrive, and if the raw material warehouse is full, the system refuses the workpieces to enter; each processing unit only has one processing device, only can allow a single workpiece to be processed, and the processing compliance parameter is R t A negative exponential distribution of (c); the processing equipment needs mechanical arm feeding or discharging before processing, the time of feeding and discharging each time is independent, and the time obeying parameter of feeding and discharging is R m A negative exponential distribution of (c); the system processing units follow the first-come-first-serve rule, the blocking of each processing unit obeys a second-blocking mechanism, and the front buffer area and the rear buffer area of each processing unit have limited capacity and the capacity is the same; when the AGV loads a workpiece in a raw material warehouse, determining which machining center the AGV goes to according to a load balancing principle; in order to ensure the output capacity of the system, the time for transporting the workpiece by the equipment is not considered, and the minimum procedure determines the output capacity of the whole system.
The parameters of the simulation system are as follows: manipulator type i, i ═ 1, 2, 3.,; class i manipulator unit price v i (ten thousand yuan); number x of arranged i-th manipulators i (ii) a The quantity of each manipulator is configured with vector X, X ═ X i }; AGV type j, j ═ 1, 2, 3.,; AGV class i Unit price μ j (ten thousand yuan); configured number y of AGV types i j (ii) a Various AGV quantity configuration vectors Y, Y ═ Y j }; the total cost Q (ten thousand yuan) of equipment investment; is a systemThe average output rate theta (piece/min) is unified; the average production period gamma (min) of the system; the mathematical expectation of the random function E { · }; the AGV runs for a circle of average speed V without waiting; AGV capacity C; the system output rate l; raw material arrival rate λ (pieces/min); buffer capacity B before processing unit 1 (ii) a Back buffer capacity of process unit B 2 (ii) a And (5) a random element xi.
Further, the track is a running path of the AGV and the manipulator, wherein the running path of the AGV is a bidirectional single circular path, and the running path of the manipulator is a bidirectional multiple circular path.
Wherein the front and rear buffer zones in the processing unit are limited buffer zones due to limitations of buffer zone site capacity and delivery terms. Workpieces need to be loaded and unloaded by a manipulator due to the characteristics of large volume, heavy weight and the like.
Further, the AGV loading quantity is determined by AGV capacity, remaining capacity of existing workpieces and destinations in the raw material warehouse, and is expressed mathematically as:
Figure BDA0003729531410000031
wherein, V c The number of workpieces loaded for the AGV, n the number of processing units,
Figure BDA0003729531410000032
the existing W workpieces in the raw material warehouse,
Figure BDA0003729531410000033
the capacity of W remains for the front buffer of the k-th processing unit.
The mathematical expression form of the overall system output capacity is as follows:
Figure BDA0003729531410000034
where l is the system output rate, x i Number of arranged i-th type manipulators, R t For the machining rate of the machining center, R m Is a mechanical armAnd the working speed of feeding and discharging.
Furthermore, the workpieces are generated according to Poisson distribution and are of the same type and enter a raw material warehouse, and the AGV receives a carrying task and then carries the workpieces to the raw material warehouse to carry the workpieces V c And the AGV carries the workpieces to a front buffer area with the least current workpieces according to a load balancing principle.
Wherein, the AGV can handle after the transport is accomplished the task respectively has two kinds, is respectively:
(1) when in use
Figure BDA0003729531410000041
Namely the urgency degree of finished product delivery is higher than that of raw material handling, the AGV goes to a rear buffer zone with the most finished products of the processing unit to deliver the finished products, wherein r is the adaptability value of the AGV reverse driving transfer probability,
Figure BDA0003729531410000042
w workpieces are present for the back buffer of the kth processing unit.
(2) When in use
Figure BDA0003729531410000043
Namely, the emergency degree of finished products discharged from the warehouse is lower than the emergency degree of raw material conveying, and the AGV reversely runs to return to the raw material warehouse to convey the workpieces to the front buffer area.
The purpose of adding the fitness value r is to prevent the AGV from frequently reversing to cause the next AGV to wait.
Further, when the machining center needs to carry out loading or unloading, an application is sent to the task pool; and the manipulator selects the annular track with the shortest distance to process the loading or unloading task according to the rule that the task in the task pool is served first.
Further, the mathematical model for optimizing the number and types of the devices in the simulation system in step S2 is expressed in the form of:
Figure BDA0003729531410000044
wherein (X) * ，Y * ) An optimal vector set for the manipulator and AGV to minimize the total cost; the set performance index constraint parameter conditions are as follows:
E{Γ(X，Y：ξ)}≤Γ max
E{θ(X，Y：ξ)}≥θ min
X，Y∈N +
wherein E { Gamma (X, Y: xi) } is the average production period, and the conditions are set so that the simulation production period Gamma is simulated max Less than the actual average production cycle of the system for ensuring delivery deadlines are met; e { theta (X, Y: xi) } sets a condition for the average yield rate such that the simulation average yield rate theta is min The average output capacity of the system is larger than the average output capacity of the system, so that the system has enough capacity; x, Y ∈ N + Indicating that the X and Y vectors are positive integers.
Because E { Γ (X, Y: ξ) } and E { theta (X, Y: ξ) } cannot be expressed in a mathematical closed form of a manipulator X and the AGV quantity Y, a typical nonlinear integer programming method cannot solve the problem, so that a simulation system is adopted to obtain two performance indexes of an average production period and a system output rate, and then a wolf algorithm is embedded to calculate and optimize equipment configuration, so that the equipment configuration cost is lowest.
Further, the step S3 is specifically:
s31, initializing wolf cluster, adopting integer coding to randomly generate initial solution, dividing feasible solution sequence into i-type manipulator and j-type AGV quantity, defining wolf individual as Z (i), each manipulator quantity configuration vector as X, each AGV quantity configuration vector as Y, wolf cluster as Y
Figure BDA0003729531410000051
S32, importing the encoded wolf pack individuals into a simulation model to solve an adaptive value, operating and simulating parameters of each wolf pack individual in a simulation system for a plurality of times, then averaging to obtain two performance indexes of system output rate theta and average production period T, and solving to obtain an adaptive value H; the grey wolf grade system is formulated, the four grades alpha, beta, delta and omega are divided from high to low, the fitness values H of wolf group individuals are sorted according to the small to large, the first three fitness values are alpha, beta and delta wolf in sequence, and the rest are omega wolfs.
Wherein, the smaller the fitness value is, the better the performance index is.
S33, updating parameters of the grey wolf optimization algorithm according to the iteration times, and updating the population of the grey wolf; updating a convergence factor a and coefficient vectors A and C which are linearly reduced along with the iteration times according to the current iteration times; and (4) eliminating the population updating mechanism of the last N individuals after each iteration.
And S34, updating the gray wolf position, adjusting the position of the gray wolf individual according to the positions of alpha, beta and delta, introducing a dynamic proportion weight method, reducing the probability of the algorithm falling into local optimum, and completing one iteration.
The principle that the gray wolf individual adjusts the position of the gray wolf individual according to the positions of alpha, beta and delta is that when the gray wolf finds a target, the alpha wolf can direct the beta wolf and the delta wolf to surround the target.
And S35, repeating the steps S32 and S33, and saving the current gray wolf optimization algorithm after the set maximum iteration number is reached.
With the increase of the iteration times, the positions of the wolf clusters are dynamically adjusted by the wolf alpha, the wolf beta and the wolf delta, and the probability of finding a global optimal solution is effectively increased.
Further, the mathematical expression of the step S3 is as follows:
Z(i)＝[X|Y]＝[x 1 ，x 2 ，…，x n |y 1 ，y 2 ，…，y n ]
X＝{x i }
Y＝{y j }
Figure BDA0003729531410000063
wherein lnum represents the number of gray wolves in the wolves.
Further, the mathematical expression of the adaptation value H of the ith wolf in S32 is as follows:
H i ＝T i -θ i
further, the parameters of the updated gray wolf optimization algorithm and the mathematical expression form of the gray wolf population in S33 are as follows:
A＝2a·r 1 -a
C＝2·r 2
Figure BDA0003729531410000061
N＝0.3*lnum
where t denotes the number of present iterations, r 1 、r 2 Is a value range of [0,1 ]]A is reduced from 2 to 0.
The N value is too large, and the algorithm is difficult to converge; the N value is too small, the number of generated new solutions is small, the optimization capability of the algorithm is reduced, and by adopting N-0.3 x lnum, the diversity of the population is ensured, and the convergence and optimization of the algorithm are easy to realize.
Further, the mathematical expression that the individual wolf in S34 adjusts its position according to the positions of α, β, δ is as follows:
D α ＝C 1 ·Z α -Z
D β ＝C 2 ·Z β -Z
D δ ＝C 3 ·Z δ -Z
Z 1 ＝Z α -A 1 ·(D α )
Z 2 ＝Zx-A 2 ·(D β )
Z 3 ＝Z δ -A 3 ·(D δ )
wherein D is α 、D β 、D δ Represents the distance vector of the individual alpha, beta, delta and Grey wolf omega, Z α 、Z β 、Z δ Represents the current positions of alpha, beta and delta wolf; c 1 、C 2 、C 3 Represents a random value in [0,1 ]]Vector of range value, Z is the current location of the wolf, Z 1 、Z 2 、Z 3 Is the location vector of the gray wolf.
Further, the mathematical expression of the dynamic proportional weight in S34 is as follows:
Figure BDA0003729531410000062
Figure BDA0003729531410000071
Figure BDA0003729531410000072
Figure BDA0003729531410000073
Figure BDA0003729531410000074
Figure BDA0003729531410000075
Z(t+1)＝W 1 ×σ 1 +W 2 ×σ 2 +W 3 ×σ 3
wherein σ 1 、σ 2 、σ 3 Is the proportional weight of the position vector; w 1 、W 2 、W 3 The learning ratios of the alpha, beta, and delta wolf positions of the wolf are respectively, and Z (t +1) is the updated wolf position.
The method for introducing dynamic proportional weight aims to solve the problem that in the traditional GWO, the gray wolf position is updated by adopting the position average value of three wolfs, namely alpha, beta and delta, and the proportional weight is always equal and unchanged, so that when the alpha wolf in the algorithm is locally optimal, the rest gray wolfs are continuously close to the alpha wolf to fall into the locally optimal state, and the probability of the algorithm falling into the locally optimal state can be obviously reduced.
Further, the step S4 is specifically: and (3) using the iterative gray wolf algorithm to solve a mathematical model for optimizing the quantity and the types of the equipment in the simulation system, wherein the obtained gray wolf position is the optimal solution of the quantity and the types of the equipment.
The invention provides a multi-resource cooperative intelligent workshop equipment configuration optimization system, which comprises a memory and a processor, wherein the memory comprises a multi-resource cooperative intelligent workshop equipment configuration optimization program, and the multi-resource cooperative intelligent workshop equipment configuration optimization program realizes the following steps when being executed by the processor:
and S1, constructing a simulation system for simulating the actual production environment.
And S2, establishing a mathematical model for optimizing the number and the types of the equipment in the simulation system and setting performance index constraint parameter conditions.
S3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring performance indexes in the gray wolf optimization algorithm by using a simulation system, updating the population and the position of the gray wolf optimization algorithm according to the optimal performance indexes, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times.
And S4, obtaining the optimized equipment configuration according to the solution of the iterative wolf optimization algorithm to the mathematical model.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides a multi-resource cooperative intelligent workshop equipment configuration optimization method, which solves a mathematical model for equipment configuration optimization by means of a gray wolf optimization algorithm, solves a fitness value by using a simulation system and updates an iterative gray wolf optimization algorithm, and realizes the minimization of the investment cost of workshop equipment under the constraint of order delivery date and the minimum system output rate on the premise of meeting the maximum production period.
Drawings
Fig. 1 is a flowchart of a multi-resource cooperative intelligent workshop appliance configuration optimization method of the present invention.
FIG. 2 is a schematic diagram of a simulation system according to the present invention.
FIG. 3 is a schematic diagram of a set of processing units of the present invention.
FIG. 4 is a schematic flow chart of the gray wolf optimization algorithm of the embedded simulation system of the present invention.
Fig. 5 is a diagram illustrating an initialization wolf pack according to an embodiment of the invention.
FIG. 6 is a schematic diagram of a multi-resource cooperative intelligent workshop appliance configuration optimization system according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Example 1
As shown in fig. 1 to 4, the present invention provides a multi-resource collaborative intelligent workshop equipment configuration optimization method, which includes the following steps:
and S1, constructing a simulation system for simulating the actual production environment.
And S2, establishing a mathematical model for optimizing the number and the types of the equipment in the simulation system and setting performance index constraint parameter conditions.
S3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring performance indexes in the gray wolf optimization algorithm by using a simulation system, updating the population and the position of the gray wolf optimization algorithm according to the optimal performance indexes, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times.
And S4, obtaining the optimized equipment configuration according to the solution of the iterative wolf optimization algorithm to the mathematical model.
Further, the step S1 includes:
s11, constructing a simulation system according to an actual production environment, wherein the simulation system comprises a task pool, a raw material warehouse, a processing unit, a finished product warehouse and a track, the processing unit comprises a front buffer area, a processing center and a rear buffer area which are sequentially connected, and the track is a running path of the AGV and the manipulator; and the task pool issues tasks to the AGV and the manipulator.
S12, as shown in fig. 2, the simulation system includes the following steps: AGV carries the raw materials to the preceding buffers of each processing unit from raw materials warehouse, and the manipulator is with raw materials from the preceding buffers material loading to machining center, machining center processing raw materials, and the finished product unloading to the back buffers after the manipulator will machining center processing, and AGV carries the finished product that the back buffers were deposited to the finished product warehouse.
S13, the simulation system follows the following rules: the arrival of the workpieces in the system obeys a Poisson distribution process, the arrival of each workpiece is an independent event, and the mean value of the arrival rate is lambda; the system judges whether a raw material warehouse is full before the workpiece arrives, and if the raw material warehouse is full, the system refuses the workpiece to enter; each processing unit only has one processing device, only can allow a single workpiece to be processed, and the processing compliance parameter is R t A negative exponential distribution of; the processing equipment needs mechanical arm feeding or discharging before processing, the time of feeding and discharging each time is independent, and the time obeying parameter of feeding and discharging is R m A negative exponential distribution of; the system processing units follow the first-come-first-serve rule, the blocking of each processing unit obeys a second-blocking mechanism, and the front buffer area and the rear buffer area of each processing unit have limited capacity and the capacity is the same; when the AGV loads a workpiece in a raw material warehouse, determining which machining center to go to according to a load balancing principle; in order to ensure the output capacity of the system, the time for transporting the workpiece by the equipment is not considered, and the minimum procedure determines the output capacity of the whole system.
The parameters of the simulation system are as follows: manipulator type i, i ═ 1, 2, 3.,; class i manipulator unit price v i (ten thousand yuan); number x of arranged i-th manipulators i (ii) a The quantity of each manipulator is configured with vector X, X ═ X i }; AGV type j, j ═ 1, 2, 3.,; AGV class i Unit price μ j (ten thousand yuan); ith (i)Configured number y of AGV classes j (ii) a Various AGV quantity configuration vectors Y, Y ═ Y j }; the total cost Q (ten thousand yuan) of equipment investment; the average output rate theta (piece/min) of the system; the system average production period gamma (min); the mathematical expectation of the random function E { · }; the AGV runs for a circle of average speed V without waiting; AGV capacity C; the system output rate l; raw material arrival rate λ (pieces/min); buffer capacity B before processing unit 1 (ii) a Buffer capacity B behind processing unit 2 (ii) a And (5) a random element xi.
Further, the track is a running path of the AGV and the manipulator, wherein the running path of the AGV is a bidirectional single circular path, and the running path of the manipulator is a bidirectional multiple circular path.
Wherein the front and rear buffer zones in the processing unit are limited buffer zones due to limitations of buffer zone site capacity and delivery terms. Workpieces need to be loaded and unloaded by a manipulator due to the characteristics of large volume, heavy weight and the like.
Further, the AGV loading quantity is determined by AGV capacity, remaining capacity of existing workpieces and destinations in the raw material warehouse, and is expressed mathematically as:
Figure BDA0003729531410000101
wherein, V c The number of workpieces loaded for the AGV, n the number of processing units,
Figure BDA0003729531410000102
the existing W workpieces in the raw material warehouse,
Figure BDA0003729531410000103
the capacity of W remains for the front buffer of the k-th processing unit.
The mathematical expression form of the overall system output capacity is as follows:
Figure BDA0003729531410000104
wherein l is the system output rate，x i The number of arranged i-th type manipulators, R t For the machining rate of the machining center, R m The working speed of the feeding and discharging of the manipulator is achieved.
Furthermore, the workpieces are generated according to Poisson distribution and are of the same type and enter a raw material warehouse, and the AGV receives a carrying task and then carries the workpieces to the raw material warehouse to carry the workpieces V c And the AGV carries the workpieces to a front buffer area with the least current workpieces according to a load balancing principle.
Wherein, the AGV can handle after the transport is accomplished the task respectively has two kinds, is respectively:
(1) when the temperature is higher than the set temperature
Figure BDA0003729531410000105
Namely the emergency degree of the finished products out of the warehouse is higher than the emergency degree of the raw material transportation, the AGV goes to a rear buffer area with the most finished products of the processing unit to carry out the finished products out of the warehouse, wherein r is the adaptability value of the reverse driving transfer probability of the AGV,
Figure BDA0003729531410000106
w workpieces are present for the back buffer of the kth processing unit.
(2) When the temperature is higher than the set temperature
Figure BDA0003729531410000107
Namely, the emergency degree of finished products discharged from the warehouse is lower than the emergency degree of raw material conveying, and the AGV reversely runs to return to the raw material warehouse to convey the workpieces to the front buffer area.
The purpose of adding the fitness value r is to prevent the AGV from frequently reversing to cause the next AGV to wait.
Further, when the machining center needs to carry out loading or unloading, an application is sent to the task pool; and the manipulator selects the annular track with the shortest distance to process the loading or unloading task according to the rule that the task in the task pool is served first.
Further, the mathematical model for optimizing the number and types of the devices in the simulation system in step S2 is expressed in the form of:
Figure BDA0003729531410000111
wherein (X) * ，Y * ) An optimal vector set for the manipulator and AGV to minimize the total cost; the set performance index constraint parameter conditions are as follows:
E{Γ(X，Y：ξ)}≤Γ max
E{θ(X，Y：ξ)}≥θ min
X，Y∈N +
wherein E { Gamma (X, Y: xi) } is the average production period, and the conditions are set so that the simulation production period Gamma is simulated max Less than the system's actual average production cycle for ensuring that the delivery terms are met; e { theta (X, Y: xi) } sets a condition for the average yield rate such that the simulation average yield rate theta is min The average output capacity of the system is larger than the average output capacity of the system, so that the system has enough capacity; x, Y ∈ N + Indicating that the X and Y vectors are positive integers.
Because E { Γ (X, Y: ξ) } and E { theta (X, Y: ξ) } cannot be expressed in a mathematical closed form of a manipulator X and the AGV quantity Y, a typical nonlinear integer programming method cannot solve the problem, so that a simulation system is adopted to obtain two performance indexes of an average production period and a system output rate, and then a wolf algorithm is embedded to calculate and optimize equipment configuration, so that the equipment configuration cost is lowest.
Further, as shown in fig. 4, the step S3 specifically includes:
s31, initializing wolf cluster, randomly generating initial solution by integer coding, dividing feasible solution sequence into i-type manipulator and j-type AGV quantity, defining the wolf individual as Z (i), the manipulator quantity configuration vector as X, the AGV quantity configuration vector as Y, wolf cluster as Y
Figure BDA0003729531410000112
S32, introducing the coded wolf pack individuals into a simulation model to solve adaptive values, operating and simulating parameters of each wolf pack individual in a simulation system for a plurality of times, then averaging to obtain two performance indexes of system output rate theta and average production period T, and solving to obtain adaptive values H; the rank system of the gray wolf is formulated, the rank system is divided into four ranks from high to low, namely alpha, beta, delta and omega, the fitness values H of wolf group individuals are sorted from small to large, the first three ranks with the lowest fitness are the alpha, beta and delta wolf in sequence, and the rest are the omega wolf.
Wherein, the smaller the fitness value is, the better the performance index is.
S33, updating parameters of the grey wolf optimization algorithm according to the iteration times, and updating the population of the grey wolf; updating a convergence factor a and coefficient vectors A and C which are linearly reduced along with the iteration times according to the current iteration times; and (4) eliminating the population updating mechanism of the last N individuals after each iteration.
And S34, updating the gray wolf position, adjusting the position of the gray wolf individual according to the positions of alpha, beta and delta, introducing a dynamic proportion weight method, reducing the probability of the algorithm falling into local optimum, and completing one iteration.
The principle that the gray wolf individual adjusts the position of the gray wolf individual according to the positions of alpha, beta and delta is that when the gray wolf finds a target, the alpha wolf can direct the beta wolf and the delta wolf to surround the target.
And S35, repeating the steps S32 and S33, and saving the current wolf optimization algorithm after the set maximum iteration number is reached.
With the increase of the iteration times, the positions of the wolf clusters are dynamically adjusted by the wolf alpha, the wolf beta and the wolf delta, and the probability of finding a global optimal solution is effectively increased.
Further, the mathematical expression of step S3 is:
Z(i)＝[X|Y]＝[x 1 ，x 2 ，…，x n |y 1 ，y 2 ，…，y n ]
X＝{x i }
Y＝{y j }
Figure BDA0003729531410000122
wherein lnum represents the number of gray wolves in the wolves.
Further, the mathematical expression of the adaptation value H of the ith gray wolf in S32 is as follows:
H i ＝T i -θ i
further, the parameters of the updated gray wolf optimization algorithm and the mathematical expression form of the gray wolf population in S33 are as follows:
A＝2a·r 1 -a
C＝2·r 2
Figure BDA0003729531410000121
N＝0.3*lnum
where t denotes the number of present iterations, r 1 、r 2 Is a value range of [0,1 ]]A is reduced from 2 to 0.
The N value is too large, so that the algorithm is difficult to converge; the N value is too small, the number of generated new solutions is small, the optimization searching capability of the algorithm is reduced, and the N is 0.3 x lnum, so that the diversity of the population is ensured, and the convergence and optimization of the algorithm are easy to realize.
Further, the mathematical expression that the individual wolf in S34 adjusts its position according to the positions of α, β, δ is as follows:
D α ＝C 1 ·Z α -Z
D β ＝C 2 ·Z β -Z
D δ ＝C 3 ·Z δ -Z
Z 1 ＝Z α -A 1 ·(D α )
Z 2 ＝Z β -A 2 ·(D β )
Z 3 ＝Z δ -A 3 ·(D δ )
wherein D is α 、D β 、D δ Represents the distance vector of the individual alpha, beta, delta and Grey wolf omega, Z α 、Z β 、Z δ Represents the current positions of alpha, beta and delta wolf; c 1 、C 2 、C 3 Represents a random value in [0,1 ]]Direction of range valueAmount, Z is the current location of the wolf, Z 1 、Z 2 、Z 3 Is the location vector of the gray wolf.
Further, the mathematical expression of the dynamic proportional weight in S34 is as follows:
Figure BDA0003729531410000131
Figure BDA0003729531410000132
Figure BDA0003729531410000133
Figure BDA0003729531410000134
Figure BDA0003729531410000135
Figure BDA0003729531410000136
Z(t+1)＝W 1 ×σ 1 +W 2 ×σ 2 +W 3 ×σ 3
wherein σ 1 、σ 2 、σ 3 Is the proportional weight of the position vector; w 1 、W 2 、W 3 The learning ratios of the alpha, beta, and delta wolf positions of the wolf are respectively, and Z (t +1) is the updated wolf position.
The method for introducing the dynamic proportional weight aims to solve the problem that in the traditional GWO, the grey wolf position is updated by adopting the position average value of three wolfs, namely alpha, beta and delta, and the proportional weight is always equal and unchanged, so that when the alpha wolf in the algorithm is locally optimal, the rest grey wolfs are continuously close to the alpha wolf to fall into the locally optimal state, and the probability of the algorithm falling into the locally optimal state can be obviously reduced.
Further, the step S4 is specifically: and (3) using the iterative gray wolf algorithm to solve a mathematical model for optimizing the quantity and the types of the equipment in the simulation system, wherein the obtained gray wolf position is the optimal solution of the quantity and the types of the equipment.
Example 2
Based on the above embodiment 1, in conjunction with fig. 5, this embodiment describes the process of initializing the wolf pack in detail.
In a specific embodiment, as shown in fig. 5, the initial solution is randomly generated by using integer codes, and 4-bit integers 2153 are encoded as wolf pack individuals, wherein the first two-bit codes 21 are configured as the number of various types of manipulators, and the second two-bit codes 53 are configured as the number of various types of AGVs.
In a specific embodiment, when the encoded wolf pack individuals are introduced into a simulation model to solve the adaptive value, each individual parameter runs 1000 times in the simulation model and is simulated for 20 times to obtain an average value, two performance indexes of system output rate theta and average production period T are obtained, and the adaptive value H is obtained through solving.
Example 3
As shown in fig. 6, the present invention further provides a multi-resource-coordinated intelligent workshop appliance configuration optimization system, including a memory and a processor, where the memory includes a multi-resource-coordinated intelligent workshop appliance configuration optimization program, and when executed by the processor, the multi-resource-coordinated intelligent workshop appliance configuration optimization program implements the following steps:
and S1, constructing a simulation system for simulating the actual production environment.
And S2, establishing a mathematical model for optimizing the number and the types of the equipment in the simulation system and setting performance index constraint parameter conditions.
S3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring performance indexes in the gray wolf optimization algorithm by using a simulation system, updating the population and the position of the gray wolf optimization algorithm according to the optimal performance indexes, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times.
And S4, obtaining the optimized equipment configuration according to the solution of the iterative wolf optimization algorithm to the mathematical model.
Further, the step S1 includes:
s11, constructing a simulation system according to an actual production environment, wherein the simulation system comprises a task pool, a raw material warehouse, a processing unit, a finished product warehouse and a track, the processing unit comprises a front buffer area, a processing center and a rear buffer area which are sequentially connected, and the track is a running path of the AGV and the manipulator; and the task pool issues tasks to the AGV and the manipulator.
S12, the working steps of the simulation system are as follows: AGV carries the raw materials to the preceding buffer of each processing unit from raw materials warehouse, and the manipulator is with raw materials from the preceding buffer material loading to machining center, machining center processing raw materials, and the finished product unloading to the back buffer after the manipulator will machining center processing, and AGV carries the finished product that the buffer was deposited after to the finished product warehouse.
S13, the simulation system follows the following rules: the arrival of the workpieces in the system obeys a Poisson distribution process, the arrival of each workpiece is an independent event, and the mean value of the arrival rate is lambda; the system judges whether the raw material warehouse is full before the workpieces arrive, and if the raw material warehouse is full, the system refuses the workpieces to enter; each processing unit only has one processing device, only can allow a single workpiece to be processed, and the processing compliance parameter is R t A negative exponential distribution of; the processing equipment needs mechanical arm feeding or discharging before processing, the time of feeding and discharging each time is independent, and the time obeying parameter of feeding and discharging is R m A negative exponential distribution of (c); the system processing units follow the first-come-first-serve rule, the blocking of each processing unit obeys a second-blocking mechanism, and the front buffer area and the rear buffer area of each processing unit have limited capacity and the capacity is the same; when the AGV loads a workpiece in a raw material warehouse, determining which machining center to go to according to a load balancing principle; in order to ensure the output capacity of the system, the time for transporting the workpiece by the equipment is not considered, and the minimum procedure determines the output capacity of the whole system.
The parameters of the simulation system are as follows: manipulator typei, i ═ 1, 2, 3, ·; class i manipulator unit price v i (ten thousand yuan); number x of arranged i-th manipulators i (ii) a The quantity of each manipulator is configured with vector X, X ═ X i }; AGV type j, j ═ 1, 2, 3.,; AGV class i Unit price μ j (ten thousand yuan); configured number y of AGV types i j (ii) a Various AGV quantity configuration vectors Y, Y ═ Y j }; the total cost Q (ten thousand yuan) of equipment investment; the average output rate theta (piece/min) of the system; the average production period gamma (min) of the system; the mathematical expectation of the random function E { · }; the AGV runs for a circle of average speed V without waiting; AGV capacity C; the system output rate l; raw material arrival rate λ (pieces/min); buffer capacity B before processing unit 1 (ii) a Buffer capacity B behind processing unit 2 (ii) a And (5) a random element xi.
Further, the track is a running path of the AGV and the manipulator, wherein the running path of the AGV is a bidirectional single circular path, and the running path of the manipulator is a bidirectional multiple circular path.
Wherein the front and rear buffer zones in the processing unit are limited buffer zones due to limitations of buffer zone site capacity and delivery terms. Workpieces need to be loaded and unloaded by a manipulator due to the characteristics of large volume, heavy weight and the like.
Further, the AGV loading quantity is determined by AGV capacity, remaining capacity of existing workpieces and destinations in the raw material warehouse, and is expressed mathematically as:
Figure BDA0003729531410000151
wherein, V c The number of workpieces loaded for the AGV, n the number of processing units,
Figure BDA0003729531410000161
the existing W workpieces in the raw material warehouse,
Figure BDA0003729531410000162
the capacity of W remains for the front buffer of the k-th processing unit.
The mathematical expression form of the overall system output capacity is as follows:
Figure BDA0003729531410000163
where l is the system output rate, x i The number of arranged i-th type manipulators, R t For the machining rate of the machining center, R m The working speed of the feeding and discharging of the manipulator is shown.
Furthermore, the workpieces are generated according to Poisson distribution and are of the same type and enter a raw material warehouse, and the AGV receives a carrying task and then carries the workpieces to the raw material warehouse to carry the workpieces V c And the AGV carries the workpieces to a front buffer area with the least current workpieces according to a load balancing principle.
Wherein, the AGV can handle after the transport is accomplished the task respectively has two kinds, is respectively:
(1) when in use
Figure BDA0003729531410000164
Namely the emergency degree of the finished products out of the warehouse is higher than the emergency degree of the raw material transportation, the AGV goes to a rear buffer area with the most finished products of the processing unit to carry out the finished products out of the warehouse, wherein r is the adaptability value of the reverse driving transfer probability of the AGV,
Figure BDA0003729531410000165
w workpieces are present for the back buffer of the kth processing unit.
(2) When the temperature is higher than the set temperature
Figure BDA0003729531410000166
Namely, the emergency degree of finished products discharged from the warehouse is lower than the emergency degree of raw material conveying, and the AGV reversely runs to return to the raw material warehouse to convey the workpieces to the front buffer area.
The purpose of adding the fitness value r is to prevent the AGV from frequently reversing to cause the next AGV to wait.
Further, when the machining center needs to feed or discharge, an application is sent to the task pool; and the manipulator selects the annular track with the shortest distance to process the loading or unloading task according to the rule that the task in the task pool is served first.
Further, the mathematical model for optimizing the number and types of the devices in the simulation system in step S2 is expressed by the following mathematical expression:
Figure BDA0003729531410000167
wherein (X) * ，Y * ) The vector set is the optimal vector set of the manipulator and the AGV, and is used for minimizing the total cost; the set performance index constraint parameter conditions are as follows:
E{Γ(X，Y：ξ)}≤Γ max
E{θ(X，Y：ξ)}≥θ min
X，Y∈N +
wherein E { Gamma (X, Y: xi) } is the average production period, and the conditions are set so that the simulation production period Gamma is simulated max Less than the system's actual average production cycle for ensuring that the delivery terms are met; e { theta (X, Y: xi) } sets a condition for the average yield rate such that the simulation average yield rate theta is min The average output capacity of the system is larger than the average output capacity of the system, so that the system has enough capacity; x, Y ∈ N + Indicating that the X and Y vectors are positive integers.
Because E { Γ (X, Y: ξ) } and E { theta (X, Y: ξ) } cannot be expressed in a mathematical closed form of a manipulator X and the number Y of AGV, the problem cannot be solved by a typical nonlinear integer programming method, so that two performance indexes of an average production period and a system output rate are obtained by a simulation system, and then the gray wolf algorithm is embedded to calculate and optimize equipment configuration, so that the equipment configuration cost is lowest.
Further, the step S3 is specifically:
s31, initializing wolf cluster, adopting integer coding to randomly generate initial solution, dividing feasible solution sequence into i-type manipulator and j-type AGV quantity, defining wolf individual as Z (i), each manipulator quantity configuration vector as X, each AGV quantity configuration vector as Y, wolf cluster as Y
Figure BDA0003729531410000171
S32, introducing the coded wolf pack individuals into a simulation model to solve adaptive values, operating and simulating parameters of each wolf pack individual in a simulation system for a plurality of times, then averaging to obtain two performance indexes of system output rate theta and average production period T, and solving to obtain adaptive values H; the rank system of the gray wolf is formulated, the rank system is divided into four ranks from high to low, namely alpha, beta, delta and omega, the fitness values H of wolf group individuals are sorted from small to large, the first three ranks with the lowest fitness are the alpha, beta and delta wolf in sequence, and the rest are the omega wolf.
Wherein, the smaller the fitness value is, the better the performance index is.
S33, updating parameters of the grey wolf optimization algorithm according to the iteration times, and updating the population of the grey wolf; updating a convergence factor a and coefficient vectors A and C which are linearly reduced along with the iteration times according to the current L iteration times; and (4) eliminating the population updating mechanism of the last N individuals after each iteration.
And S34, updating the gray wolf position, adjusting the position of the gray wolf individual according to the positions of alpha, beta and delta, introducing a dynamic proportion weight method, reducing the probability of the algorithm falling into local optimum, and completing one iteration.
The principle that the gray wolf individual adjusts the position of the gray wolf individual according to the positions of alpha, beta and delta is that when the gray wolf finds a target, the alpha wolf can direct the beta wolf and the delta wolf to surround the target.
And S35, repeating the steps S32 and S33, and saving the current gray wolf optimization algorithm after the set maximum iteration number is reached.
With the increase of the iteration times, the positions of the wolf clusters are dynamically adjusted by the wolf alpha, the wolf beta and the wolf delta, and the probability of finding a global optimal solution is effectively increased.
Further, the mathematical expression of the step S3 is as follows:
Z(i)＝[X|Y]＝[x 1 ，x 2 ，…，x n |y 1 ，y 2 ，…，y n ]
X＝{x i }
Y＝{y j }
Figure BDA0003729531410000182
wherein lnum represents the number of gray wolves in the wolves.
Further, the mathematical expression of the adaptation value H of the ith gray wolf in S32 is as follows:
H i ＝T i -θ i
further, the parameters of the updated gray wolf optimization algorithm and the mathematical expression form of the gray wolf population in S33 are as follows:
A＝2a·r 1 -a
C＝2·r 2
Figure BDA0003729531410000181
N＝0.3*lnum
where t denotes the number of present iterations, r 1 、r 2 Is a value range of [0,1]A is reduced from 2 to 0.
The N value is too large, so that the algorithm is difficult to converge; the N value is too small, the number of generated new solutions is small, the optimization searching capability of the algorithm is reduced, and the N is 0.3 x lnum, so that the diversity of the population is ensured, and the convergence and optimization of the algorithm are easy to realize.
Further, the mathematical expression that the individual wolf in S34 adjusts its position according to the positions of α, β, δ is as follows:
D α ＝C 1 ·Z α -Z
D β ＝C 2 ·Z β -Z
D δ ＝C 3 ·Z δ -Z
Z 1 ＝Z α -A 1 ·(D α )
Z 2 ＝Z β -A 2 ·(D β )
Z 3 ＝Z δ -A 3 ·(D δ )
wherein D is α 、D β 、D δ Represents the distance vector of the individual alpha, beta, delta and Grey wolf omega, Z α 、Z β 、Z δ Represents the current positions of alpha, beta and delta wolf; c 1 、C 2 、C 3 Represents a random value in [0,1 ]]Vector of range value, Z is the current location of the wolf, Z 1 、Z 2 、Z 3 Is the location vector of the gray wolf.
Further, the mathematical expression of the dynamic proportional weight in S34 is as follows:
Figure BDA0003729531410000191
Figure BDA0003729531410000192
Figure BDA0003729531410000193
Figure BDA0003729531410000194
Figure BDA0003729531410000195
Figure BDA0003729531410000196
Z(t+1)＝W 1 ×σ 1 +W 2 ×σ 2 +W 3 ×σ 3
wherein σ 1 、σ 2 、σ 3 Is the proportional weight of the position vector; w is a group of 1 、W 2 、W 3 The learning ratios of the alpha, beta, and delta wolf positions of the wolf are respectively, and Z (t +1) is the updated wolf position.
The method for introducing the dynamic proportional weight aims to solve the problem that in the traditional GWO, the grey wolf position is updated by adopting the position average value of three wolfs, namely alpha, beta and delta, and the proportional weight is always equal and unchanged, so that when the alpha wolf in the algorithm is locally optimal, the rest grey wolfs are continuously close to the alpha wolf to fall into the locally optimal state, and the probability of the algorithm falling into the locally optimal state can be obviously reduced.
Further, the step S4 is specifically: and (3) using the iterative gray wolf algorithm to solve a mathematical model for optimizing the quantity and the types of the equipment in the simulation system, wherein the obtained gray wolf position is the optimal solution of the quantity and the types of the equipment.
The drawings depicting the positional relationship of the structures are for illustrative purposes only and are not to be construed as limiting the present patent.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A multi-resource cooperative intelligent workshop equipment configuration optimization method is characterized by comprising the following steps:
s1, constructing a simulation system for simulating an actual production environment;
s2, establishing a mathematical model for optimizing the number and the types of equipment in the simulation system and setting performance index constraint parameter conditions;
s3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring performance indexes in the gray wolf optimization algorithm by using a simulation system, updating the population and the position of the gray wolf optimization algorithm according to the optimal performance indexes, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times;
and S4, decoding a solution obtained by solving the mathematical model by the iterated wolf optimization algorithm to obtain the optimized equipment configuration.
2. The method for optimizing the configuration of the multi-resource-coordinated intelligent workshop appliance according to claim 1, wherein the simulation system specifically comprises: constructing a simulation system according to an actual production environment, wherein the simulation system comprises a task pool, a raw material warehouse, a processing unit, a finished product warehouse and a track, the processing unit comprises a front buffer area, a processing center and a rear buffer area which are sequentially connected, and the track is a running path of an AGV and a manipulator; the task pool issues tasks to the AGV and the manipulator;
the simulation system comprises the following working steps: the method comprises the following steps that an AGV carries raw materials from a raw material warehouse to a front buffer area of each processing unit, a mechanical arm loads the raw materials from the front buffer area to a processing center, the processing center processes the raw materials, the mechanical arm discharges finished products processed by the processing center to a rear buffer area, and the AGV carries the finished products stored in the rear buffer area to a finished product warehouse;
the simulation system follows the following rules: the arrival of the workpieces in the system obeys a Poisson distribution process, the arrival of each workpiece is an independent event, and the mean arrival rate is lambda; the system judges whether a raw material warehouse is full before the workpiece arrives, and if the raw material warehouse is full, the system refuses the workpiece to enter; each processing unit only has one processing device, only can allow a single workpiece to be processed, and the processing compliance parameter is R t A negative exponential distribution of (c); the processing equipment needs mechanical arm feeding or discharging before processing, the time of feeding and discharging each time is independent, and the time obeying parameter of feeding and discharging is R m A negative exponential distribution of; the system processing units follow the first-come-first-serve rule, the blocking of each processing unit obeys a second-blocking mechanism, and the front buffer area and the rear buffer area of each processing unit have limited capacity and the capacity is the same; when the AGV loads a workpiece in a raw material warehouse, determining a processing center to go to according to a load balancing principle; in order to ensure the output capacity of the system, the time of the equipment for transporting the workpiece is not considered, and the whole system output capacity is determined by the process with the shortest processing time.
3. The method of claim 2, wherein the track is a running path of an AGV and a manipulator, wherein the running path of the AGV is a bidirectional single circular path, and the running path of the manipulator is a bidirectional multiple circular path.
4. The method of claim 2, wherein the AGV loading number is determined by AGV capacity, raw warehouse existing workpiece and destination remaining capacity, and is expressed mathematically as:
Figure FDA0003729531400000021
wherein, V c The number of workpieces loaded for the AGV, n the number of processing units,
Figure FDA0003729531400000022
the existing W workpieces in the raw material warehouse,
Figure FDA0003729531400000023
the capacity of the front buffer area of the kth processing unit for the remaining W;
the mathematical expression form of the overall system output capacity is as follows:
Figure FDA0003729531400000024
where l is the system output rate, x i The number of arranged i-th type manipulators, R t For the machining rate of the machining center, R m The working speed of the feeding and discharging of the manipulator is shown.
5. The method for optimizing multi-resource cooperative intelligent workshop equipment configuration according to claim 4, wherein workpieces are optimized according to Poisson's scoreCloth is produced and is the same type of workpiece and enters a raw material warehouse, and the AGV receives a carrying task and then carries V to the raw material warehouse c The AGV transports the workpieces to a front buffer area with the least current workpieces according to a load balancing principle; after the transport is completed, the AGV can process two tasks, namely:
(1) when in use
Figure FDA0003729531400000025
Namely the emergency degree of the finished products out of the warehouse is higher than the emergency degree of the raw material transportation, the AGV goes to a rear buffer area with the most finished products of the processing unit to carry out the finished products out of the warehouse, wherein r is the adaptability value of the reverse driving transfer probability of the AGV,
Figure FDA0003729531400000026
w workpieces are present for the back buffer of the kth processing unit;
(2) when the temperature is higher than the set temperature
Figure FDA0003729531400000027
The emergency degree of finished products discharged from the warehouse is lower than the emergency degree of raw material conveying, and the AGV reversely runs and returns to the raw material warehouse to convey the workpieces to the front buffer area;
when the machining center needs to feed or discharge, an application is sent to the task pool; and the manipulator selects the annular track with the shortest distance to process the loading or unloading task according to the rule that the task in the task pool is served first.
6. The method as claimed in claim 1, wherein the mathematical model used for optimizing the number and types of devices in the simulation system in step S2 is expressed in the form of:
Figure FDA0003729531400000031
wherein (X) * ,Y * ) Set of optimal vectors for manipulator and AGV for enablingThe total cost is lowest; the set performance index constraint parameter conditions are as follows:
E{Γ(X,Y:ξ)}≤Γ max
E{θ(X,Y:ξ)}≥θ min
X,Y∈N +
wherein E { Gamma (X, Y: xi) } is the average production period, and the conditions are set so that the simulation production period Gamma is simulated max Less than the system's actual average production cycle for ensuring that the delivery terms are met; e { theta (X, Y: xi) } sets a condition for the average yield rate such that the simulation average yield rate theta is min The average output capacity of the system is larger than the average output capacity of the system, so that the system has enough capacity; x, Y ∈ N + Indicating that the X and Y vectors are positive integers.
7. The method for optimizing the configuration of the multi-resource cooperative intelligent workshop appliance according to any one of claims 1 to 6, wherein the step S3 is specifically as follows:
s31, initializing wolf cluster, randomly generating initial solution by integer coding, dividing feasible solution sequence into i-type manipulator and j-type AGV quantity, defining the wolf individual as Z (i), the manipulator quantity configuration vector as X, the AGV quantity configuration vector as Y, wolf cluster as Y
Figure FDA0003729531400000032
S32, importing the encoded wolf pack individuals into a simulation model to solve an adaptive value, operating and simulating parameters of each wolf pack individual in a simulation system for a plurality of times, then averaging to obtain two performance indexes of system output rate theta and average production period T, and solving to obtain an adaptive value H; making a gray wolf grade system, dividing the gray wolf into four grades of alpha, beta, delta and omega from high to low, sequencing fitness values H of wolf group individuals from small to large, wherein the first three lowest fitness values are the alpha, beta and delta wolfs in sequence, and the rest are the omega wolfs;
s33, updating parameters of the grey wolf optimization algorithm according to the iteration times, and updating the population of the grey wolf; updating a convergence factor a and coefficient vectors A and C which are linearly reduced along with the iteration times according to the current iteration times; after each iteration, a population updating mechanism of the last N individuals is eliminated;
s34, updating the gray wolf position, adjusting the position of the gray wolf individual according to the positions of alpha, beta and delta, introducing a dynamic proportion weight method, reducing the probability of the algorithm falling into local optimum, and completing one iteration;
and S35, repeating the steps S32 and S33, and saving the current wolf optimization algorithm after the set maximum iteration number is reached.
8. The method for optimizing multi-resource collaborative intelligent plant equipment configuration according to claim 7, wherein the mathematical expression form of the step S3 is as follows:
Z(i)＝[X|Y]＝[x 1 ,x 2 ,…,x n |y 1 ,y 2 ,…,y n ]
X＝{x i }
Y＝{y j }
Figure FDA0003729531400000041
wherein lnum represents the number of gray wolves in the wolves;
the mathematical expression of the adaptation value H of the ith wolf in S32 is as follows:
H i ＝T i -θ i
the parameters for updating the gray wolf optimization algorithm and the mathematical expression form of the gray wolf population in the step S33 are as follows:
A＝2a·r 1 -a
C＝2·r 2
Figure FDA0003729531400000042
N＝0.3*lnum
where t denotes the number of present iterations, r 1 、r 2 Is a value range of [0,1 ]]A is reduced from 2 to 0;
the mathematical expression form of adjusting the self position of the wolf individual according to the positions of alpha, beta and delta in S34 is as follows:
D α ＝C 1 ·Z α -Z
D β ＝C 2 ·Z β -Z
D δ ＝C 3 ·Z δ -Z
Z 1 ＝Z α -A 1 ·(D α )
Z 2 ＝Z β -A 2 ·(D β )
Z 3 ＝Z δ -A 3 ·(D δ )
wherein D is α 、D β 、D δ Represents the distance vector of the individual alpha, beta, delta and Grey wolf omega, Z α 、Z β 、Z δ Represents the current positions of alpha, beta and delta wolf; c 1 、C 2 、C 3 Represents a random value in [0,1 ]]Vector of range value, Z is the current location of the grey wolf, Z 1 、Z 2 、Z 3 Is the location vector of the gray wolf;
the mathematical expression of the dynamic proportion weight in the S34 is as follows:
Figure FDA0003729531400000051
Figure FDA0003729531400000052
Figure FDA0003729531400000053
Figure FDA0003729531400000054
Figure FDA0003729531400000055
Figure FDA0003729531400000056
Z(t+1)＝W 1 ×σ 1 +W 2 ×σ 2 +W 3 ×σ 3
wherein σ 1 、σ 2 、σ 3 Is the proportional weight of the position vector; w 1 、W 2 、W 3 The learning ratios of the alpha, beta, and delta wolf positions of the wolf are respectively, and Z (t +1) is the updated wolf position.
9. The method for optimizing multi-resource collaborative intelligent workshop equipment configuration according to claim 1, wherein the step S4 specifically includes: and the iterative gray wolf algorithm is used for solving a mathematical model for optimizing the quantity and the types of the equipment in the simulation system, and the obtained gray wolf position is decoded and split to obtain the optimal solution of the quantity and the types of the equipment.
10. A multi-resource cooperative intelligent workshop equipment configuration optimization system comprises a memory and a processor, wherein the memory comprises a multi-resource cooperative intelligent workshop equipment configuration optimization program, and the multi-resource cooperative intelligent workshop equipment configuration optimization program realizes the following steps when being executed by the processor:
s1, constructing a simulation system for simulating an actual production environment;
s2, establishing a mathematical model for optimizing the number and the types of equipment in the simulation system and setting performance index constraint parameter conditions;
s3, calculating a mathematical model by using a gray wolf optimization algorithm, acquiring performance indexes in the gray wolf optimization algorithm by using a simulation system, updating the population and the position of the gray wolf optimization algorithm according to the optimal performance indexes, and obtaining the gray wolf optimization algorithm after iteration is completed for a set number of times;
and S4, decoding a solution obtained by solving the mathematical model by the iterative grayish optimization algorithm to obtain optimized equipment configuration.
CN202210781190.XA 2022-07-04 2022-07-04 Multi-resource cooperative intelligent workshop equipment configuration optimization method Pending CN115034143A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116362407A (en) * 2023-04-06 2023-06-30 湘南学院 Facility layout optimization method considering operation performance of manufacturing system
CN116362407B (en) * 2023-04-06 2024-01-16 湘南学院 Facility layout optimization method considering operation performance of manufacturing system

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