CN114742261B - Collaborative optimization method and system for multi-target equipment layout and logistics system design - Google Patents

Abstract

The present invention discloses a multi-objective equipment layout and logistics system design collaborative optimization method and system, which belongs to the field of industrial engineering technology, including: after optimizing a collaborative optimization mathematical model with the objectives of minimizing total completion time, minimizing the operating distance of intelligent transportation equipment, and minimizing the number of intelligent transportation equipment, a collaborative optimization simulation model is constructed based on the optimized processing equipment position, logistics loading and unloading point position, and the number of intelligent transportation equipment, and the above process is repeated. When the operating result of the collaborative optimization simulation model satisfies the optimization termination condition, the processing equipment position, logistics loading and unloading point position, and the number of intelligent transportation equipment are the best collaborative optimization scheme; the collaborative optimization simulation model constructed by the present invention comprehensively considers the influence of the processing equipment position and logistics system design on the logistics distance, and uses the operating result of the collaborative simulation model as an indicator for judging whether the optimization is terminated, so as to obtain a true and reliable equipment layout scheme and logistics system design scheme.

Description

A multi-objective equipment layout and logistics system design collaborative optimization method and system

Technical Field

The present invention belongs to the field of industrial engineering technology, and more specifically, relates to a multi-objective equipment layout and logistics system design collaborative optimization method and system.

Background Art

According to statistics, logistics and transportation account for about 30% of the current production costs of my country's manufacturing industry, but my country's overall logistics efficiency is low and the level of logistics costs is still very high. How to change this situation is of great significance to the development of my country's manufacturing industry. At this stage, there is huge room for reducing logistics costs in workshops.

With the advancement of intelligent manufacturing, enterprises must inevitably develop towards automation, informatization, and intelligence. It is an inevitable trend for machines to replace people and use intelligent transportation equipment, such as intelligent transport vehicles AGV to transport materials. The AGV system will also become an important part of the intelligent manufacturing digital workshop. The traditional layout method mainly takes the logistics volume as the optimization goal and uses a simplified mathematical model to determine the location of the equipment. It can no longer adapt to the existing production system. The reliability of the resulting equipment layout plan and logistics system design plan is low, as follows:

1. Factory design faces many problems, such as reasonable equipment placement, material handling location planning, and logistics route design. These interdependent sub-problems are traditionally solved through sequential processes. It is difficult to design logistics routes in a workshop where equipment has been arranged, and the logistics distance between the equipment actually arranged may be much longer than expected.

2. The oversimplified mathematical model is very different from the actual factory environment, such as interference in the transportation routes of transportation equipment. At the same time, after the layout plan is determined, it is rarely possible to verify the rationality and stability of the layout plan through simulation methods.

Summary of the invention

In view of the above defects or improvement needs of the prior art, the present invention provides a multi-objective equipment layout and logistics system design collaborative optimization method and system to solve the technical problem of low reliability of equipment layout solutions and logistics system design solutions obtained in the prior art.

In order to solve the above-mentioned purpose, in a first aspect, the present invention provides a multi-objective equipment layout and logistics system design collaborative optimization method, comprising the following steps:

A1. Based on the pre-built multi-objective equipment layout and logistics system design collaborative optimization mathematical model, the current values of the processing equipment location, logistics loading and unloading point location and the number of intelligent transportation equipment are obtained; wherein the collaborative optimization mathematical model is a mathematical model constructed based on the workshop size and the processing equipment size with the goal of minimizing the total completion time, minimizing the running distance of the intelligent transportation equipment and minimizing the number of intelligent transportation equipment;

A2. Construct a multi-objective equipment layout and logistics system design collaborative optimization simulation model based on the current values of processing equipment locations, logistics loading and unloading point locations, and the number of intelligent transportation equipment;

A3. Run the collaborative optimization simulation model to obtain the simulation values of the total completion time, the running distance of the intelligent transportation equipment, and the number of intelligent transportation equipment;

A4. Determine whether the simulation values of the total completion time, the running distance of the intelligent transport equipment and the number of intelligent transport equipment are all less than the corresponding preset values, or whether the current number of iterations is greater than the preset number of iterations. If so, the current values of the processing equipment location, the logistics loading and unloading point location and the number of intelligent transport equipment are used as the best collaborative optimization solution, and the operation ends; otherwise, go to step A5;

A5. Based on the current values of the processing equipment location, the logistics loading and unloading point location, and the number of intelligent transportation equipment, the collaborative optimization mathematical model is optimized to obtain the optimized processing equipment location, the logistics loading and unloading point location, and the number of intelligent transportation equipment;

A6. Use the optimized processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment as the current values of the processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment, and go to step A2 for iteration.

Further preferably, when the collaborative optimization simulation model is running, it is determined based on the time window model whether there is a time conflict between the intelligent transport equipment. If not, the intelligent transport equipment is directly planned to transport materials based on the static shortest path between any two intelligent transport equipment sites in the logistics transportation network constructed by the intelligent transport equipment sites. Otherwise, the intelligent transport equipment is re-planned to transport materials. The time conflict includes one or more of opposite conflicts, same-direction speed conflicts and node conflicts.

Further preferably, the method for obtaining the static shortest path between any two intelligent transportation equipment sites in the above-mentioned logistics transportation network includes:

After the location of the intelligent transportation equipment site is determined based on the location of the logistics loading and unloading point, a logistics transportation network constructed by the intelligent transportation equipment sites is generated, and the static shortest path between any two intelligent transportation equipment sites in the logistics transportation network is obtained.

Further preferably, the intelligent transport equipment site includes: a boundary site and a site corresponding to a logistics loading and unloading point; wherein the boundary sites are evenly distributed on the left and right boundaries of the workshop; the boundary sites on the left boundary of the workshop are recorded as B ₁₁ , B ₂₁ , ..., B _k1 , ..., B _N1 from top to bottom; the boundary sites on the right boundary of the workshop are recorded as B ₁₂ , B ₂₂ , ..., B _k2 , ..., B _N2 from top to bottom; B ₁₁ , B _N1 , B ₁₂ and B _N2 are respectively located at the four corners of the workshop;

Within the spatial range enclosed by _Bi1 , _Bi2 , B _(k+1)2 , and B _(k+1)1 , if the logistics loading and unloading point is on the lower edge of the processing equipment, the intelligent transportation equipment station corresponding to the logistics loading and unloading point is located at the foot of the perpendicular from the logistics loading and unloading point to the line segment B _{(k+1) 2} B _(k+1)1 ; if the logistics loading and unloading point is on the upper edge of the processing equipment, the intelligent transportation equipment station corresponding to the logistics loading and unloading point is located at the foot of the perpendicular from the logistics loading and unloading point to the line segment B _k1 B _k2 .

Further preferably, the expression of the above collaborative optimization mathematical model is:

Objective function:

Constraints:

0＜r＜R

Where T _total is the total completion time; Dis _a is the running distance of the ath intelligent transport equipment; r is the number of intelligent transport equipment; represents the center position of the processing equipment _Mi ; _Mi represents the number of the processing equipment at the position of the i-th processing equipment; l _i and d _i are the length and width of the working area of the processing equipment _Mi, respectively; L and W are the length and width of the workshop, respectively; s is the minimum horizontal interval between processing equipment; h is the minimum vertical interval between processing equipment; Indicates the location of the logistics loading and unloading point IO _i ; IO _i represents the logistics loading and unloading point number corresponding to the processing equipment at the i-th processing equipment position; R is the maximum number of intelligent transportation equipment.

Further preferably, the NSGA-II algorithm is used to optimize the collaborative optimization mathematical model.

Further preferably, the method for optimizing the collaborative optimization mathematical model using the NSGA-II algorithm includes:

1) According to the current value of the processing equipment location, a positive integer encoding method is used as the first layer encoding of the NSGA-II algorithm; according to the current value of the logistics loading and unloading point location, a 0-1 encoding method is used as the second layer encoding of the NSGA-II algorithm; according to the current value of the number of intelligent transportation equipment, a 0-1 encoding method is used as the third layer encoding of the NSGA-II algorithm;

2) Based on the elite mechanism, the target values of individuals are sorted non-dominated to generate new parents;

3) Perform crossover and mutation operations on each layer of gene sequences on the new parent generation to obtain new offspring;

4) Decode each layer of gene sequence on the new offspring to obtain the optimized processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment.

In a second aspect, the present invention provides a multi-objective equipment layout and logistics system design collaborative optimization system, comprising: a memory and a processor, the memory storing a computer program, and the processor executing the multi-objective equipment layout and logistics system design collaborative optimization method provided by the first aspect of the present invention when executing the computer program.

In a third aspect, the present invention provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the multi-objective equipment layout and logistics system design collaborative optimization method provided in the first aspect of the present invention.

In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

1. The present invention provides a multi-objective collaborative optimization method for equipment layout and logistics system design. After optimizing the collaborative optimization mathematical model with the goals of minimizing the total completion time, minimizing the operating distance of intelligent transportation equipment, and minimizing the number of intelligent transportation equipment, a collaborative optimization simulation model is constructed based on the optimized processing equipment location, logistics loading and unloading point location, and the number of intelligent transportation equipment. The above process is repeated. When the operating results of the collaborative optimization simulation model meet the optimization termination conditions, the processing equipment location, logistics loading and unloading point location, and the number of intelligent transportation equipment are the optimal collaborative optimization scheme. The collaborative optimization simulation model constructed by the present invention comprehensively considers the influence of the processing equipment location and logistics system design on the logistics distance, and uses the operating results of the collaborative simulation model as an indicator to judge whether the optimization is terminated, so as to obtain a true and reliable equipment layout scheme and logistics system design scheme.

2. The multi-objective equipment layout and logistics system design collaborative optimization method provided by the present invention adds a time window module to the constructed collaborative optimization simulation model. Based on the time window model, it is judged whether there is a time conflict between the intelligent transportation equipment, which effectively solves the problem of collision avoidance between multiple intelligent transportation equipment, makes the logistics distance more accurate, and the obtained layout plan and logistics system design plan are more realistic and reliable, which can provide active guidance for the design of smart workshops.

3. Since the location of the intelligent transportation equipment station can only be in the channel, and the closer the intelligent transportation equipment station is to the corresponding logistics loading and unloading point, the shorter the loading and unloading time is, the multi-objective equipment layout and logistics system design collaborative optimization method provided by the present invention sets the intelligent transportation equipment station corresponding to the logistics loading and unloading point at the vertical foot position, which can minimize the transportation time.

4. The multi-objective equipment layout and logistics system design collaborative optimization method provided by the present invention solves the problem that the interdependent sub-problems of reasonable equipment location placement, material handling location planning, and logistics handling route design in traditional methods can only be solved by sequential processes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a multi-objective equipment layout and logistics system design collaborative optimization method provided by the present invention;

FIG2 is a flow chart of a multi-objective equipment layout and logistics system design collaborative optimization method provided by an embodiment of the present invention;

FIG3 is a schematic diagram of the layout of processing equipment and logistics loading and unloading points provided in an embodiment of the present invention;

FIG4 is a schematic diagram of an intelligent transportation equipment site and a logistics transportation network provided by an embodiment of the present invention;

FIG5 is a diagram of a multi-objective equipment layout and logistics system design collaborative optimization simulation model provided by an embodiment of the present invention;

FIG6 is a schematic diagram of a coding result obtained by using the NSGA-II three-layer coding method provided in an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

In order to solve the above-mentioned purpose, in a first aspect, the present invention provides a multi-objective equipment layout and logistics system design collaborative optimization method, as shown in FIG1 , comprising the following steps:

A1. Based on the pre-built multi-objective equipment layout and logistics system design collaborative optimization mathematical model, the current values of the processing equipment location, logistics loading and unloading point location and the number of intelligent transportation equipment are obtained; wherein the collaborative optimization mathematical model is a mathematical model constructed based on the workshop size and the processing equipment size with the goal of minimizing the total completion time, minimizing the running distance of the intelligent transportation equipment and minimizing the number of intelligent transportation equipment;

A2. Construct a multi-objective equipment layout and logistics system design collaborative optimization simulation model based on the current values of processing equipment locations, logistics loading and unloading point locations, and the number of intelligent transportation equipment;

Specifically, based on the current values of processing equipment locations, logistics loading and unloading point locations, and the number of intelligent transportation equipment, a multi-objective equipment layout and logistics system design collaborative optimization simulation model is built; and a time window module, a conflict judgment model, and a conflict resolution module are added.

First, based on the current values of processing equipment locations, logistics loading and unloading point locations, and the number of intelligent transportation equipment, a multi-objective equipment layout and logistics system design collaborative optimization simulation model is built; the specific process is as follows:

Processing equipment of different sizes are numbered and sorted according to their positions (in this embodiment, they are sorted from left to right and from top to bottom), and are abstractly represented in a matrix form:

M＝[M ₁ ,M ₂ ,M ₃ ,...,M _n ]

Among them, M is a 1×n matrix, _Mi represents the processing equipment number at the i-th processing equipment position, S is a 2×n matrix, l _i and d _i are the length and width of the working area of the processing equipment _Mi , respectively; the workshop size that can be used for equipment layout is L×W.

According to the values in the matrix M and the length and width of the processing equipment given in the matrix S, the corresponding processing equipment is laid out from the lower left corner from left to right, and the lines are wrapped when the width exceeds the limit. The equipment position matrix after placement is represented by P:

Where P is a 2×n matrix, Indicates the center position of device _Mi.

Secondly, a time window module, a conflict judgment module and a conflict resolution module are added to the above-mentioned collaborative optimization simulation model; since intelligent transport equipment constantly enters and exits the road section, and the same road section can only be occupied by intelligent transport equipment in one direction at the same time, if the driving time arrangement of the intelligent transport equipment is unreasonable, path conflicts and other problems will occur. Therefore, a multi-intelligent transport equipment collision avoidance module is set in the collaborative optimization simulation model to avoid collisions between multiple intelligent transport equipment.

Specifically, the time window module includes a time window model, which is expressed as:

Where T( _ek ) is the time window set of all intelligent transportation equipment (such as intelligent transportation vehicle AGV) on the road section _ek . It represents the time window of the a-th intelligent transport equipment occupying the road section e _k when executing the production plan task j. The road section time window is defined as the time from the a-th intelligent transport equipment entering the road section e _k Time to exit section e _k If the ath intelligent transport device has not passed through the road segment e _k , then = (0, 0). In summary, the time window set of all intelligent transportation equipment on all road sections can be expressed as:

Wherein, m is the total number of road sections; r is the number of intelligent transport equipment. The conflict judgment module is used to judge whether there is a conflict between the intelligent transport equipment in the opposite direction, a speed conflict in the same direction, or a node conflict based on the time window model. If any of the above conflicts exists, it is considered that there is a time conflict.

The judgment conditions for opposite conflicts are: If the time when the qth intelligent transport equipment enters the road section e _m When the pth intelligent transport equipment occupies the road section e _m during the time window when executing the production plan task j, there is a conflict in the opposite direction.

The judgment conditions for the same-direction speed conflict are: and If the time when the qth intelligent transport equipment enters the road section e _m Later than the time when the pth intelligent transport equipment enters the road section e _m And the time when the qth intelligent transport equipment leaves the road section e _m Earlier than the time when the pth intelligent transport equipment leaves the road section e _m There is a speed conflict in the same direction.

The judgment conditions for node conflict are: Among them, δ is the minimum time interval; when the time interval between two intelligent transport equipment entering the road section e _m is less than δ, it is determined that there is a node conflict.

The conflict resolution module is used to re-plan the path of the intelligent transportation equipment to transport materials when there is a time conflict. It uses the time conflict (any one or more of the opposite conflict, the same direction speed and the node conflict) as a constraint condition to transport materials. When planning the path, the constraint condition that there should be no time conflict (that is, the non-time conflict judgment condition) is added to perform path re-planning.

In summary, when the collaborative optimization simulation model is running, it is determined whether there is a time conflict between the intelligent transport equipment based on the time window model. If not, the intelligent transport equipment is directly planned to transport materials based on the static shortest path between any two intelligent transport equipment stations in the logistics transportation network constructed by the intelligent transport equipment stations; otherwise, the intelligent transport equipment is re-planned to transport materials. Among them, the time conflict includes one or more of opposite conflicts, same-direction speed conflicts and node conflicts.

Preferably, the method for obtaining the static shortest path between any two intelligent transportation equipment sites in the above-mentioned logistics transportation network includes: after determining the location of the intelligent transportation equipment site based on the location of the logistics loading and unloading point, generating a logistics transportation network constructed by the intelligent transportation equipment site, and obtaining the static shortest path between any two intelligent transportation equipment sites in the logistics transportation network.

Specifically, the intelligent transportation equipment sites include: boundary sites and sites corresponding to logistics loading and unloading points; wherein the boundary sites are evenly distributed on the left and right boundaries of the workshop; the boundary sites on the left boundary of the workshop are recorded as B ₁₁ , B ₂₁ , ..., B _k1 , ..., B _N1 from top to bottom; the boundary sites on the right boundary of the workshop are recorded as B ₁₂ , B ₂₂ , ..., B _k2 , ..., B _N2 from top to bottom; B ₁₁ , B _N1 , B ₁₂ and B _N2 are located at the four corners of the workshop respectively;

In the space enclosed by B _i1 , B _i2 , B _(k+1)2 , and B _(k+1)1 , if the logistics loading and unloading point is on the lower edge of the processing equipment, the intelligent transportation equipment station corresponding to the logistics loading and unloading point is located at the foot of the perpendicular from the logistics loading and unloading point to the line segment B _{(k+1) 2} B _(k+1)1 ; if the logistics loading and unloading point is on the upper edge of the processing equipment, the intelligent transportation equipment station corresponding to the logistics loading and unloading point is located at the foot of the perpendicular from the logistics loading and unloading point to the line segment B _k1 B _k2 . It should be noted that since the location of the intelligent transportation equipment station can only be in the channel, and the closer the intelligent transportation equipment station is to the corresponding logistics loading and unloading point, the shorter the time required for loading and unloading, it is set at the foot of the perpendicular to minimize the handling time.

Among them, the location of logistics loading and unloading points is expressed as:

The location of the intelligent transportation equipment station corresponding to the logistics loading and unloading point is expressed as:

Among them, IO is a 2×n matrix, Indicates the location of the logistics loading and unloading point IO _i ; IO _i represents the logistics loading and unloading point number corresponding to the processing equipment at the i-th processing equipment position; SP is a 2×n matrix, Indicates the location of the intelligent transport equipment station SP _i . equal It is the location of the logistics channel.

Connect adjacent intelligent transportation equipment sites to form a logistics transportation network.

It should be noted that the path planning method may be the Floyd algorithm, the Dijkstra algorithm, the A* algorithm, etc. In this embodiment, the Floyd algorithm is used to obtain the static shortest path between any two intelligent transportation equipment sites in the logistics transportation network:

Path(i,j)=[ _SPi , _SPj ]={ _SPi , _SPm , _SPn ,..., _SPj }

Among them, the static shortest path between SP _i and SP _j is: SP _i , SP _m , SP _n , ..., SP _j .

It should be noted that the path planning method used when path replanning occurs when there is a time conflict is similar to the above method for obtaining the static shortest path, the difference being that a constraint condition that there can be no time conflict is added (ie, the non-time conflict judgment condition).

A3. Run the collaborative optimization simulation model to obtain the simulation values of the total completion time, the running distance of the intelligent transportation equipment, and the number of intelligent transportation equipment;

A4. Determine whether the simulation values of the total completion time, the running distance of the intelligent transport equipment and the number of intelligent transport equipment are all less than the corresponding preset values, or whether the current number of iterations is greater than the preset number of iterations. If so, the current values of the processing equipment location, the logistics loading and unloading point location and the number of intelligent transport equipment are used as the best collaborative optimization solution, and the operation ends; otherwise, go to step A5;

Specifically, in this embodiment, the preset value of the total completion time is 2.04×10 ³ s; the preset value of the running distance of the intelligent transportation equipment is 4.14×10 ³ m; the preset value of the number of intelligent transportation equipment is 2; and the preset number of iterations is 100 times.

A5. Based on the current values of the processing equipment location, the logistics loading and unloading point location, and the number of intelligent transportation equipment, the collaborative optimization mathematical model is optimized to obtain the optimized processing equipment location, the logistics loading and unloading point location, and the number of intelligent transportation equipment;

Specifically, the expression of the above collaborative optimization mathematical model is:

Objective function:

Constraints:

0＜r＜R

Where, T _total is the total completion time; D _isa is the running distance of the ath intelligent transport equipment; r is the number of intelligent transport equipment; represents the center position of the processing equipment _Mi ; _Mi represents the number of the processing equipment at the position of the i-th processing equipment; l _i and d _i are the length and width of the working area of the processing equipment _Mi, respectively; L and W are the length and width of the workshop, respectively; s is the minimum horizontal interval between processing equipment; h is the minimum vertical interval between processing equipment; represents the location of the logistics loading and unloading point IO _i ; IO _i represents the logistics loading and unloading point number corresponding to the processing equipment at the i-th processing equipment position; R is the maximum number of intelligent transportation equipment. It should be noted that T _total and Dis _a are obtained through the operation of the collaborative simulation model and are directly related to the above constraints.

Furthermore, the above collaborative optimization mathematical model can be optimized by using NSGA-II algorithm, XX, etc. Preferably, in this embodiment, the NSGA-II algorithm is used for optimization. Compared with other algorithms, the NSGA-II algorithm has the characteristics of simplicity and high efficiency in solving multi-objective problems. The optimization of the collaborative optimization mathematical model specifically includes the following steps:

1) According to the current value of the processing equipment location, a positive integer encoding method is used as the first layer encoding of the NSGA-II algorithm; according to the current value of the logistics loading and unloading point location, a 0-1 encoding method is used as the second layer encoding of the NSGA-II algorithm; according to the current value of the number of intelligent transportation equipment, a 0-1 encoding method is used as the third layer encoding of the NSGA-II algorithm;

2) Based on the elite mechanism, the target values of individuals are sorted non-dominated to generate new parents;

3) Perform crossover and mutation operations on each layer of gene sequences on the new parent generation to obtain new offspring;

4) Decode each layer of gene sequence on the new offspring to obtain the optimized processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment.

A6. Use the optimized processing equipment location, logistics loading and unloading point location, and number of intelligent transportation equipment as the current values of the processing equipment location, logistics loading and unloading point location, and number of intelligent transportation equipment, and repeat steps A2-A6 for iteration.

In order to further illustrate the multi-objective equipment layout and logistics system design collaborative optimization method provided by the present invention, it is described in detail below in conjunction with an embodiment:

Example

As shown in FIG1 , this embodiment provides a multi-objective equipment layout and logistics system design collaborative optimization method based on simulation, as shown in FIG2 , and the specific steps are as follows:

In the first part, based on the size of the workshop and the size of the processing equipment, a multi-objective equipment layout and logistics system design collaborative optimization mathematical model is constructed with the goals of minimizing the total completion time, the shortest operating distance of the intelligent transportation equipment, and the minimum number of intelligent transportation equipment; and based on the constructed collaborative optimization mathematical model, the current values of the processing equipment location, the logistics loading and unloading point location, and the number of intelligent transportation equipment are obtained.

The second part is to build a multi-objective equipment layout and logistics system design collaborative optimization simulation model based on the current values of processing equipment locations, logistics loading and unloading point locations, and the number of intelligent transportation equipment;

Specifically, a layout plan is generated based on the location of industrial equipment, the location of logistics loading and unloading points, and the number of intelligent transportation equipment, and a multi-objective equipment layout and logistics system design collaborative optimization simulation model is initially built; the location of the intelligent transportation equipment site is determined by the location of the logistics loading and unloading points, and a logistics transportation network is generated. The static shortest path between the intelligent transportation equipment sites is determined, and a time window module, a conflict judgment module, and a conflict resolution module are added to build a multi-objective equipment layout and logistics system design collaborative optimization simulation model. The specific process is as follows:

1) Generate an equipment layout plan based on the equipment location number, and obtain a schematic diagram of the processing equipment layout and logistics loading and unloading point layout as shown in Figure 3. The steps for generating the equipment layout plan are as follows:

Step 1. Get the size of the workshop and equipment and number the corresponding equipment, abstracting it into a matrix form:

M＝[3 9 4 10 1 6 5 2 7 8]

The elements on the S matrix are the length and width of the processing equipment at the corresponding position of the matrix M. For example, the length and width of equipment _M3 are 8.30m and 3.45m respectively. The workshop size that can be used for equipment layout is 46m×34m.

Step 2. According to the values in the matrix M, the corresponding equipment is laid out from the upper left corner (16.0m, 2.0m) from left to right, and the rows are changed when the length of the workshop is exceeded. The processing equipment position matrix after placement is as follows:

The elements on the S matrix are the length and width of the device at the corresponding position of the matrix M. For example, the horizontal and vertical coordinates of the location of device M ₃ are 25.5m and 9.20m respectively.

2) Determine the location of the intelligent transportation equipment site based on the location of the logistics loading and unloading point, generate the logistics transportation network path between the equipment, and determine the static shortest path between the intelligent transportation equipment sites. The specific steps are as follows:

Step 3. Obtain the location of the logistics loading and unloading points corresponding to the processing equipment, determine the location of the intelligent transportation equipment site and generate the logistics transportation network:

Among them, the IO' matrix is the relative coordinates of the logistics loading and unloading point relative to the processing equipment. The absolute coordinates of the logistics loading and unloading point can be calculated by combining the relative coordinates with the corresponding processing equipment size as follows:

The location of the intelligent transportation equipment site is determined by the location of the logistics loading and unloading point. The coordinates are as follows. The adjacent intelligent transportation equipment sites are connected to form a logistics transportation network:

Specifically, the resulting schematic diagram of the intelligent transportation equipment site and logistics transportation network is shown in FIG4 .

Step 4. Use the Floyd algorithm to determine the static shortest path between intelligent transportation equipment sites. As shown in Table 1 below:

Table 1

Specifically, since intelligent transport equipment constantly enters and exits the road section, and the same road section can only be occupied by intelligent transport equipment vehicles in one direction at the same time, if the travel time of the intelligent transport equipment is not arranged reasonably, path conflicts and other problems will occur. A multi-intelligent transport equipment collision avoidance module is set in the simulation model to avoid collisions between multiple intelligent transport equipment.

Step5. Add time window module;

Step 6. Add conflict judgment module and conflict resolution module.

The final multi-objective equipment layout and logistics system design collaborative optimization simulation model is shown in Figure 5.

The third part determines whether the simulation values of the total completion time, the running distance of the intelligent transport equipment and the number of intelligent transport equipment are all less than the corresponding preset values, or whether the current number of iterations is greater than the preset number of iterations. If so, the current values of the processing equipment location, the logistics loading and unloading point location and the number of intelligent transport equipment are used as the best collaborative optimization solution, and the operation ends; otherwise, based on the current values of the processing equipment location, the logistics loading and unloading point location and the number of intelligent transport equipment, the collaborative optimization mathematical model is optimized to obtain the optimized processing equipment location, logistics loading and unloading point location and the number of intelligent transport equipment; the optimized processing equipment location, logistics loading and unloading point location and the number of intelligent transport equipment are used as the current values of the processing equipment location, logistics loading and unloading point location and the number of intelligent transport equipment, and the second and third parts are repeated for iteration.

Specifically, the schematic diagram of the established collaborative optimization mathematical model is shown in Figure 3. The objectives of the collaborative optimization mathematical model include: the shortest total completion time, the shortest operating distance of the intelligent transportation equipment, and the least number of intelligent transportation equipment. The corresponding constraints include: the location of the processing equipment cannot exceed the size of the workshop, the lateral spacing between the processing equipment must not be less than s, and the longitudinal spacing must not be less than h, the location of the logistics loading and unloading point needs to be at the edge of the processing equipment, and the number of intelligent transportation equipment needs to be within a certain range. In this embodiment, the workshop length L of the equipment layout is 46m, the width W is 34m, the minimum lateral spacing s is 3.8m, the minimum longitudinal spacing h is 3.15m, and the maximum number R of intelligent transportation equipment is 5.

The NSGA-II algorithm is used to optimize the collaborative optimization mathematical model, including:

As shown in Figure 6, according to the placement of the equipment in the workshop, a positive integer encoding method is used as the first layer encoding of NSGA-II, with a length of 10; according to the relative position of the logistics loading and unloading point on the processing equipment, a 0-1 encoding method is used as the second layer encoding of NSGA-II, with a length of 60; according to the number of intelligent transportation equipment, a 0-1 encoding method is used as the third layer encoding of NSGA-II, with a length of 5. The total length of the chromosome is 10+60+5=75.

Based on the elite mechanism, the target values of individuals are sorted in a non-dominated manner, and high-quality individuals are selected to generate new parents.

The crossover and mutation operations are performed on each layer of gene sequences on the new parent generation to obtain new offspring; the crossover rate is 0.8 and the mutation rate is 0.2.

The gene sequences of each layer on the new offspring are decoded hierarchically to obtain the optimized processing equipment positions, logistics loading and unloading point positions and the number of intelligent transportation equipment. Specifically, as shown in Figure 5, the placement sequence of the processing equipment is [3, 9, 4, 10, 1, 6, 5, 2, 7, 8], the position of the logistics loading and unloading point IO ₁ relative to the processing equipment M ₁ is (0.23, 1), the position of the logistics loading and unloading point IO ₁₀ relative to the processing equipment M ₁₀ is (0.71, 1), and the number of intelligent transportation equipment is 3.

This embodiment establishes a multi-objective equipment layout and logistics system design collaborative optimization mathematical model, and uses the NSGA-II algorithm for optimization. Specifically, the multi-objective equipment layout and logistics system design collaborative optimization simulation model constructed based on the optimized processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment is run, and the parameter value of the corresponding target is calculated as the fitness value. After selecting the optimized individuals (processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment) using the non-dominated sorting in NSGA-II, the multi-objective equipment layout and logistics system design collaborative optimization simulation model is automatically constructed based on the optimized processing equipment location, logistics loading and unloading point location and number of intelligent transportation equipment; the above process is iterated to find the best equipment layout plan and logistics system design plan.

In a second aspect, the present invention provides a multi-objective equipment layout and logistics system design collaborative optimization system, comprising: a memory and a processor, the memory storing a computer program, and the processor executing the multi-objective equipment layout and logistics system design collaborative optimization method provided by the first aspect of the present invention when executing the computer program.

The related technical scheme is the same as the multi-objective equipment layout and logistics system design collaborative optimization method provided in the first aspect of the present invention, which will not be described in detail here.

In a third aspect, the present invention provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the multi-objective equipment layout and logistics system design collaborative optimization method provided in the first aspect of the present invention.

The related technical scheme is the same as the multi-objective equipment layout and logistics system design collaborative optimization method provided in the first aspect of the present invention, which will not be described in detail here.

It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A multi-target equipment layout and logistics system design collaborative optimization method is characterized by comprising the following steps:

A1, designing a collaborative optimization mathematical model based on a pre-constructed multi-target equipment layout and a logistics system to obtain current values of the positions of processing equipment, the positions of logistics loading and unloading points and the number of intelligent transportation equipment; the collaborative optimization mathematical model is a mathematical model which is constructed based on workshop size and processing equipment size and aims at the shortest total finishing time, the shortest running distance of the intelligent transportation equipment and the least quantity of the intelligent transportation equipment;

a2, constructing a multi-target equipment layout and logistics system design collaborative optimization simulation model based on the current values of the processing equipment positions, the logistics loading and unloading point positions and the number of intelligent transportation equipment;

A3, operating the collaborative optimization simulation model to obtain simulation values of total finishing time, the operation distance of the intelligent transportation equipment and the quantity of the intelligent transportation equipment;

A4, judging whether simulation values of the total finishing time, the running distance of the intelligent transportation equipment and the quantity of the intelligent transportation equipment are smaller than corresponding preset values or whether the current iteration number is larger than the preset iteration number, if so, taking the current values of the processing equipment position, the logistics loading point position and the quantity of the intelligent transportation equipment as an optimal collaborative optimization scheme, and ending the operation; otherwise, turning to step A5;

A5, optimizing the collaborative optimization mathematical model based on the current values of the processing equipment position, the logistics loading and unloading point position and the number of intelligent transportation equipment to obtain the optimized processing equipment position, the logistics loading and unloading point position and the number of intelligent transportation equipment;

A6, taking the optimized processing equipment position, the logistics loading and unloading point position and the intelligent transportation equipment number as current values of the processing equipment position, the logistics loading and unloading point position and the intelligent transportation equipment number, and turning to the step A2 for iteration.

2. The collaborative optimization method for multi-objective device layout and logistics system design according to claim 1, wherein the collaborative optimization simulation model determines whether there is a time conflict between the intelligent transportation devices based on a time window model when running, if not, it directly plans a path for the intelligent transportation devices based on a static shortest path between any two intelligent transportation device sites in a logistics transportation network constructed by the intelligent transportation device sites; otherwise, the intelligent transportation equipment is re-planned with a path to transport the materials; wherein the time conflict includes one or more of a phase conflict, a co-directional speed conflict, and a node conflict.

3. The collaborative optimization method for multi-objective device layout and logistics system design according to claim 2, wherein the method for acquiring a static shortest path between any two intelligent transportation device sites in the logistics transportation network comprises:

after the position of the intelligent transportation equipment station is determined based on the position of the logistics loading and unloading point, a logistics transportation network constructed by the intelligent transportation equipment station is generated, and a static shortest path between any two intelligent transportation equipment stations in the logistics transportation network is obtained.

4. The multi-objective device layout and logistics system design collaborative optimization method of claim 3, wherein the intelligent transportation device site comprises: a boundary station and a station corresponding to the logistics loading and unloading point; the boundary stations are uniformly distributed on the left boundary and the right boundary of the workshop; boundary stations on the left boundary of the workshop are sequentially marked as B ₁₁、B₂₁、......、B_k1、......、B_N1 from top to bottom; boundary stations B ₁₂、B₂₂、......、B_k2、......、B_N2;B₁₁、B_N1、B₁₂ and B _N2 on the right boundary of the workshop are respectively positioned at four corners of the workshop from top to bottom;

In the space range enclosed by the B _i1、B_i2、B_(k+1)2、B_(k+1)1, if the logistics loading and unloading point is positioned on the lower edge of the processing equipment, the intelligent transportation equipment station corresponding to the logistics loading and unloading point is positioned at the foot hanging position from the logistics loading and unloading point to the line segment B _(k+1)2B_(k+1)1; if the logistics loading and unloading point is on the upper edge of the processing equipment, the intelligent transportation equipment station corresponding to the logistics loading and unloading point is positioned at the position from the logistics loading and unloading point to the foot of the line segment B _k1B_k2.

5. The collaborative optimization method for multi-objective device layout and logistics system design according to any one of claims 1-4, wherein the expression of the collaborative optimization mathematical model is:

Objective function:

Constraint conditions:

0<r<R

Wherein T _total is the total finishing time; dis _a is the running distance of the a-th intelligent transportation device; r is the number of intelligent transportation devices; Representing the central position of the processing apparatus M _i; m _i represents the machining equipment number at the i-th machining equipment position; l _i and d _i are the length and width of the working area of the processing apparatus M _i, respectively; l and W are the length and width of the workshop respectively; s is the minimum transverse interval between processing equipment; h is the minimum longitudinal spacing between the processing equipment; The position of the logistics loading and unloading point IO _i is shown; IO _i represents a logistics loading and unloading point number corresponding to the processing equipment at the ith processing equipment position; r is the maximum number of intelligent transportation devices.

6. The collaborative optimization method for multi-objective device layout and logistics system design of any one of claims 1-4, wherein the collaborative optimization mathematical model is optimized using NSGA-II algorithm.

7. The collaborative optimization method for multi-objective device layout and logistics system design of claim 6, wherein the method for optimizing the collaborative optimization mathematical model using NSGA-II algorithm comprises:

1) According to the current value of the position of the processing equipment, adopting a positive integer coding mode as a first layer of coding of an NSGA-II algorithm; according to the current value of the position of the logistics loading and unloading point, adopting a 0-1 coding mode as a second layer of coding of an NSGA-II algorithm; according to the current value of the number of the intelligent transportation devices, adopting a 0-1 coding mode as a third layer of coding of an NSGA-II algorithm;

2) Based on elite mechanism, non-dominant ordering is carried out on the target values of the individuals to generate new father;

3) Performing crossover and mutation operations on each layer of gene sequences on the new parent layer by layer to obtain a new offspring;

4) And decoding each layer of gene sequence layering on the new offspring to obtain the optimized processing equipment position, logistics loading and unloading point position and intelligent transportation equipment quantity.

8. A multi-objective plant layout and logistics system design collaborative optimization system, comprising: a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the multi-objective device layout and logistics system design co-optimization method of any one of claims 1-7.

9. A machine-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to implement the multi-objective device layout and logistics system design co-optimization method of any one of claims 1-7.