CN111948989A - Flexible manufacturing workshop optimal scheduling method and equipment - Google Patents

Flexible manufacturing workshop optimal scheduling method and equipment Download PDF

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CN111948989A
CN111948989A CN202010676577.XA CN202010676577A CN111948989A CN 111948989 A CN111948989 A CN 111948989A CN 202010676577 A CN202010676577 A CN 202010676577A CN 111948989 A CN111948989 A CN 111948989A
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load
energy consumption
machine
equipment
lifting
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CN111948989B (en
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杨志杰
刘正超
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Wuhan University of Technology WUT
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Wuhan University of Technology WUT
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop

Abstract

The invention discloses a method and equipment for optimizing and scheduling a flexible manufacturing workshop, wherein the method comprises the following steps: establishing an optimized dispatching model with the aims of minimizing completion time and reducing energy consumption of the flexible manufacturing workshop based on a working mode of lifting equipment according to a connection relation between a workpiece procedure of the flexible manufacturing workshop and the lifting equipment; solving the established optimized scheduling model by adopting a mixed genetic firefly algorithm oriented to a green transportation strategy to obtain an optimized scheduling scheme; and scheduling the processing machines, the workpiece procedures and the lifting equipment in the flexible manufacturing workshop according to the optimized scheduling scheme so as to obtain a scheduling result with the minimum completion time and the minimum energy consumption. The invention can effectively reduce the production time of the flexible job shop and reduce the energy consumption.

Description

Flexible manufacturing workshop optimal scheduling method and equipment
Technical Field
The invention relates to the field of green dispatching of flexible manufacturing workshops, in particular to an optimized dispatching method and device for a flexible manufacturing workshop.
Background
Energy waste of manufacturing enterprises aggravates pollution emission and seriously affects ecological environment. Especially for conventional heavy manufacturing enterprises, the energy consumption generated during the transportation of heavy workpieces accounts for a large proportion of the total process of workpiece processing. In the field of scheduling of practical flexible manufacturing workshops, the green scheduling and the processing time reduction still have a lot of improvement spaces. For a dispatching plan of lifting equipment in a flexible manufacturing workshop, the optimized dispatching method of the flexible manufacturing workshop considering the working state of the lifting equipment is provided, so that the aims of minimizing the whole energy consumption and completing the time minimization are fulfilled. In recent years, scholars at home and abroad have achieved certain research results on the problem of optimizing and scheduling flexible manufacturing workshops. For the green scheduling problem of the hoisting equipment, how to consider the connection mode of the transportation process of the hoisting equipment, a workpiece procedure and a processing machine to reduce energy consumption and finish time is the problem to be solved at present.
Thus, the prior art has yet to be improved and enhanced.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide an optimized scheduling method and equipment for a flexible manufacturing workshop, so as to solve the green scheduling problem of how to reduce the energy consumption in the processing and manufacturing process and shorten the manufacturing time when the connection mode of a hoisting equipment transportation process, a workpiece process and a processing machine is considered.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a flexible manufacturing shop optimization scheduling method, which includes the following steps:
s1, establishing an optimized dispatching model aiming at minimizing the completion time and reducing the energy consumption of the flexible manufacturing workshop based on the working mode of the lifting equipment according to the connection relation between the workpiece procedure of the flexible manufacturing workshop and the lifting equipment;
s2, solving the established optimized scheduling model by adopting a mixed genetic firefly algorithm facing to a green transportation strategy to obtain an optimized scheduling scheme;
and S3, scheduling the processing machines, the workpiece procedures and the lifting equipment in the flexible manufacturing workshop according to the optimized scheduling scheme so as to obtain a scheduling result with the minimum completion time and the minimum energy consumption.
In a second aspect, the present invention further provides a flexible manufacturing shop optimization scheduling apparatus, including a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the flexible manufacturing shop optimization scheduling method as described above.
Compared with the prior art, the optimized scheduling method and the optimized scheduling equipment for the flexible manufacturing workshop, provided by the invention, make a decision for minimizing the maximum completion time and reducing the energy consumption, provide a scheduling model considering the working state (load operation, load standby, no-load operation and no-load standby) of the hoisting equipment, and provide a hybrid genetic firefly algorithm facing a green transportation strategy based on the connection relation between the hoisting equipment and the workpiece procedures to schedule the workpiece procedures and the hoisting equipment, so that an optimal solution set is obtained, and the maximum completion time and the energy consumption can be effectively reduced.
Drawings
FIG. 1 is a flowchart illustrating a method for optimizing scheduling of a flexible manufacturing shop according to a preferred embodiment of the present invention;
FIG. 2 is a first work flow diagram of a flexible manufacturing plant;
FIG. 3 is a second work flow diagram of a flexible manufacturing plant;
FIG. 4 is a flow chart of a preferred embodiment of a green transportation strategy oriented hybrid genetic firefly algorithm in the flexible manufacturing shop optimization scheduling method provided by the present invention;
FIG. 5 is a schematic diagram of a first simulation comparison of the flexible manufacturing shop optimization scheduling method according to the present invention;
FIG. 6 is a schematic diagram illustrating comparison of simulation for a second application of the optimized scheduling method for a flexible manufacturing shop according to the present invention;
FIG. 7 is a schematic diagram illustrating a third simulation comparison of the flexible manufacturing shop optimization scheduling method according to the present invention;
FIG. 8 is a schematic diagram of a fourth simulation comparison applied in the flexible manufacturing shop optimization scheduling method according to the present invention.
Detailed Description
The invention provides a method and equipment for optimizing and scheduling a flexible manufacturing workshop, which are further described in detail below by referring to the attached drawings and embodiments in order to make the purposes, technical schemes and effects of the invention clearer and clearer. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a method for optimizing and scheduling a flexible manufacturing shop according to an embodiment of the present invention includes the following steps:
and S1, establishing an optimized dispatching model aiming at minimizing the completion time and reducing the energy consumption of the flexible manufacturing workshop based on the working mode of the lifting equipment according to the connection relation between the workpiece procedure of the flexible manufacturing workshop and the lifting equipment.
In this embodiment, the green scheduling problem is described by using the processing time and the energy consumption, specifically, the working modes of the hoisting equipment include an idle-load standby mode, an idle-load operation mode, a load standby mode and a load operation mode, the energy consumption of the flexible manufacturing workshop includes the energy consumption of the machine processing and the energy consumption of the hoisting equipment, the energy consumption of the hoisting equipment is the sum of the idle-load standby energy consumption of the hoisting equipment, the idle-load operation energy consumption of the hoisting equipment, the load standby energy consumption of the hoisting equipment and the load operation energy consumption of the hoisting equipment, and the optimized scheduling model includes an adaptability value calculation model, a machine processing energy consumption calculation model, an idle-load standby energy consumption calculation model of the hoisting equipment, an idle-load operation energy consumption calculation model of the hoisting equipment, a load standby energy consumption calculation model of the hoisting equipment.
Specifically, the fitness value calculation model is as follows:
Figure 100002_1
wherein f represents a fitness value, and CT represents a unit manufacturing time price;
Figure BDA0002584262230000042
indicating the procedure
Figure BDA0002584262230000043
The manufacturing completion time of (1); CE represents the cost per energy consumption price; empRepresents the energy consumption of the machine processing; ectRepresenting the energy consumption of the hoisting equipment.
The machining energy consumption calculation model is as follows:
Figure BDA0002584262230000044
Figure BDA0002584262230000045
wherein E ismpThe energy consumption of the machine processing is shown,
Figure BDA0002584262230000046
the energy consumption generated by processing a workpiece J in the first procedure is shown, wherein J is the serial number of the workpiece; l isjIs a set of processes;
Figure BDA0002584262230000047
is the first step of the workpiece j; efkIs the processing energy consumption of machine k;
Figure BDA0002584262230000048
is a processing procedure on a machine k
Figure BDA0002584262230000049
The time of (d);
Figure BDA00025842622300000410
is a machining procedure of machine k
Figure BDA00025842622300000411
The decision variables of (1).
The no-load standby energy consumption calculation model of the hoisting equipment is as follows:
Figure BDA00025842622300000412
Figure BDA00025842622300000413
Figure BDA00025842622300000414
wherein E isnsThe no-load standby energy consumption of the lifting equipment is realized; j is the workpiece number; j is the set of workpieces; l is a process number, Lj is a process set;
Figure BDA00025842622300000415
is the transportation of the lifting equipment in the standby state under the no-load condition
Figure BDA00025842622300000416
Energy consumption resulting from processing;
Figure BDA00025842622300000417
is a decision variable of a process, a process
Figure BDA00025842622300000418
Is that
Figure BDA00025842622300000419
Next step (2);
Figure BDA00025842622300000420
is a process of
Figure BDA00025842622300000421
Idle standby time of; ps is the standby power of the lifting equipment;
Figure BDA00025842622300000422
is a process of
Figure BDA0002584262230000051
The manufacturing completion time of (1);
Figure BDA0002584262230000052
is a process of
Figure BDA0002584262230000053
The manufacturing start time of (1);
Figure BDA0002584262230000054
is a transportation process of lifting equipment under the condition of no load
Figure BDA0002584262230000055
A rack generation time;
Figure BDA0002584262230000056
is a transportation process of lifting equipment under the condition of no load
Figure BDA0002584262230000057
Time of crane generation.
The no-load operation energy consumption calculation model of the hoisting equipment is as follows:
Figure BDA0002584262230000058
Figure BDA0002584262230000059
Figure BDA00025842622300000510
Figure BDA00025842622300000511
Figure BDA00025842622300000512
Figure BDA00025842622300000513
wherein E isnoThe no-load running energy consumption of the hoisting equipment is reduced;
Figure BDA00025842622300000514
is transported when the lifting equipment operates under the condition of no load
Figure BDA00025842622300000515
Energy consumption resulting from processing; j is the serial number of the workpiece; j is the set of workpieces; l isjIs a set of processes; l is the serial number of the process;
Figure BDA00025842622300000516
is a decision variable of the process and is used for judging the process
Figure BDA00025842622300000517
Whether or not it is
Figure BDA00025842622300000518
Next step (2);
Figure BDA00025842622300000519
is the transportation of the frame of the lifting equipment under the condition of no load
Figure BDA00025842622300000520
The start-up time of (c); pgs is the power at which the rack is powered;
Figure BDA00025842622300000521
the lifting machine in the lifting equipment is transported under the condition of no load
Figure BDA00025842622300000522
The start-up time of (c); pts is the starting power of the crane; ws is the weight of the hoisting equipment; qn is the lifting weight of the handling equipment;
Figure BDA00025842622300000523
the frame of the lifting equipment is transported under the condition of no load
Figure BDA00025842622300000524
The stationary movement time of; pg is the rated power of the rack;
Figure BDA00025842622300000525
the lifting machine of the lifting equipment is used for transporting under the condition of no load
Figure BDA00025842622300000526
The stationary movement time of; pt is the rated power of a lifting machine in lifting equipment; vg is the running speed of the lifting equipment rack; a isgsThe starting acceleration of the frame of the lifting equipment under the load condition; wg is the weight of the frame; wt is the weight of the trolley; vt is the running speed of the crane; a istsStarting acceleration of a lifting machine of lifting equipment under a load condition; k is a machine number; lpxkIs the abscissa of machine k; lpxk1Is the abscissa of machine k 1; a isgbThe braking acceleration of the rack under the condition of the load of the lifting equipment;
Figure BDA0002584262230000061
is a decision variable of the l1 th process step of processing the workpiece j by the machine k;
Figure BDA0002584262230000062
is the decision variable for machine k1 at stage l1 of machining work piece j 1. LpykIs the abscissa of machine k; lpyk1Is the abscissa of machine k 1; a istbThe braking acceleration of the lifting machine under the condition of the load of the lifting equipment.
The load standby energy consumption calculation model of the hoisting equipment is as follows:
Figure BDA0002584262230000063
Figure BDA0002584262230000064
Figure BDA0002584262230000065
wherein E islsLoad standby energy consumption of hoisting equipment; j is the workpiece number; j is the set of workpieces; l is a process number, Lj is a process set; k is a machine number; k is the set of machines;
Figure BDA0002584262230000066
is a transportation process of the lifting equipment in standby time under the condition of load
Figure BDA0002584262230000067
Energy consumption resulting from processing;
Figure BDA0002584262230000068
is a decision variable for representing the machining process of the machine k1
Figure BDA0002584262230000069
And machine k2 processing procedure
Figure BDA00025842622300000610
Whether or not it exists;
Figure BDA00025842622300000611
is a decision variable of the same machine processing and is used for representing the process on the machine k2
Figure BDA00025842622300000612
Whether or not it is
Figure BDA00025842622300000613
Next step (2);
Figure BDA00025842622300000614
is a process of
Figure BDA00025842622300000615
Load standby time of; ps is the standby power of the hoisting equipment;
Figure BDA00025842622300000616
is a process of
Figure BDA00025842622300000617
The manufacturing completion time of (1);
Figure BDA00025842622300000618
is a process of
Figure BDA00025842622300000619
The manufacturing completion time of (1).
The load operation energy consumption calculation model of the hoisting equipment is as follows:
Figure BDA00025842622300000620
Figure BDA00025842622300000621
Figure BDA00025842622300000622
Figure BDA0002584262230000071
Figure BDA0002584262230000072
Figure BDA0002584262230000073
wherein E isloThe energy consumption of the load operation of the hoisting equipment is reduced; j is the workpiece number; j is the set of workpieces; l is a process number, Lj is a process set;
Figure BDA0002584262230000074
is a transportation process when the lifting equipment runs under the condition of load
Figure BDA0002584262230000075
Energy consumption resulting from processing;
Figure BDA0002584262230000076
is transported under the load condition by a frame in hoisting equipment
Figure BDA0002584262230000077
The start-up time of (c); pgs is the startup power of the rack;
Figure BDA0002584262230000078
the lifting machine in the lifting equipment transports under the load condition
Figure BDA0002584262230000079
The start-up time of (c); pts is the starting power of the crane; ws is the weight of the hoisting equipment; wjIs the weight of workpiece j; qn is the lifting weight of the handling equipment;
Figure BDA00025842622300000710
is that the racks are transported under load
Figure BDA00025842622300000711
The stationary movement time of; pg is the rated power of a rack in the lifting equipment;
Figure BDA00025842622300000712
is transported by a lifting machine under the condition of load
Figure BDA00025842622300000713
The stationary movement time of; pt is the rated power of a lifting machine in lifting equipment; vg is the running speed of the lifting equipment rack; a isgsThe starting acceleration of the frame of the lifting equipment under the load condition; wg is the weight of the frame; wt is the weight of the trolley; vt is the running speed of the crane; a istsStarting acceleration of a lifting machine of lifting equipment under a load condition; k is a machine number; lpxkIs the abscissa of machine k; lpxk1Is the abscissa of machine k 1; a isgbIs a machine under the condition of load of hoisting equipmentBraking acceleration of the frame;
Figure BDA00025842622300000714
is a decision variable of the l1 th process step of processing the workpiece j by the machine k;
Figure BDA00025842622300000715
is a decision variable of the l1 th process step of the machine k1 for processing the workpiece j 1; lpykIs the abscissa of machine k; lpyk1Is the abscissa of machine k 1; a istbThe braking acceleration of the lifting machine under the condition of the load of the lifting equipment.
And S2, solving the established optimized scheduling model by adopting a mixed genetic firefly algorithm facing to a green transportation strategy to obtain an optimized scheduling scheme.
Specifically, referring to fig. 2 and 3, in the flexible manufacturing shop, when the manufacturing requirement is clear, the system arranges the relevant workpieces to be processed according to the requirement. The different processes of different workpieces correspond to different machining, the lifting equipment is required to execute a transportation task according to the workpiece manufacturing requirement, the movement of the lifting equipment is completed by the rack and the lifting machine, and the rack and the lifting machine cannot move simultaneously. The final shipping task is complete or machined, meaning that the manufacturing process is complete.
The method for optimizing and scheduling the flexible manufacturing workshop by considering the working state of the hoisting equipment disclosed by the embodiment of the invention aims at minimizing the processing time and reducing the energy consumption, analyzes four transportation modes of the hoisting equipment and establishes a related scheduling model. A mixed Genetic firefly Algorithm (Genetic Algorithm Global Optimization Green Transport national Strategy, GA-GSO-GTHS) based on a Green transportation Strategy is established for scheduling, so that a scheduling result with the shortest total processing time and the smallest energy consumption is obtained.
Further, the step S2 specifically includes:
s21, generating an initial population through a genetic algorithm;
s22, selecting a plurality of best solutions from the initial population as a firefly population, and obtaining a local optimal solution in the firefly population through a firefly algorithm;
s23, performing crossing and mutation operations on the local optimal solution to obtain a progeny population;
s24, optimizing energy consumption of the lifting equipment during operation under the load condition and time consumption of the lifting equipment during standby under the load condition through a green transportation strategy;
s25, updating the population, judging the iteration times, outputting the optimal solution if the iteration times exceed the set iteration times, and returning to the step S22 if the iteration times exceed the set iteration times;
and S26, taking the final result as the optimized scheduling result of the total flexible manufacturing shop.
Specifically, referring to fig. 4, when generating the optimized scheduling scheme, an initial population is generated through a genetic algorithm, then a part of better solutions are selected from the initial population as a firefly population, and a local optimal solution is obtained by using the firefly population through a firefly algorithm (GSO) in a small range, where the firefly algorithm specifically includes the following steps: and updating the firefly value, calculating the inter-neighbor distance, betting the distance through a firefly wheel disc, selecting, updating the position of the firefly, updating the perception radius, judging whether the maximum population value is reached, if so, performing parent selection, and otherwise, updating the firefly value again. After the local optimal solution is obtained, performing cross variation operation to obtain a child population, wherein the child population passes through a heuristic green transport strategy (GTHS). The strategy consists of two parts: and in view of the fact that the energy consumption and the maximum completion time of the flexible manufacturing workshop are mainly related to the operation of the lifting equipment, the load operation energy consumption and the standby time of the lifting equipment are optimized. Firstly, optimizing the energy consumption during operation of the hoisting equipment under the load condition by using an energy consumption optimization method. And secondly, optimizing the standby time consumption of the hoisting equipment under the load condition by using a time optimization method. And after the optimization is completed, updating the population, judging the iteration times, outputting an optimal solution if the iteration times exceed the set iteration times, and returning to the step S22 if the iteration times do not exceed the set iteration times. And finally, taking the obtained result as an optimized scheduling result of the flexible manufacturing workshop. The invention aims at the mixed genetic firefly algorithm of the green transportation strategy to schedule the work piece procedures and the hoisting equipment, thereby obtaining an optimal solution set and effectively reducing the maximum completion time and energy consumption.
In order to evaluate the proposed algorithm performance, the flexible manufacturing shop optimization scheduling method provided by the invention further comprises the following steps:
verifying and analyzing the effectiveness of the GA-GSO-GTHS algorithm by using simulation software to obtain a simulation result;
and comparing the simulation result with other similar algorithms, thereby verifying the effectiveness of the method.
When the method is specifically implemented, in the mixed genetic firefly algorithm oriented to the green transportation strategy, the maximum evolution generation number is 3000, the population scale of the genetic algorithm is 100, the cross probability is 0.6, and the variation probability is 0.05. The population size of the firefly algorithm is 50, the step size s is 5, the maximum sensing radius rs is 20, the volatilization coefficient ρ is 0.4, the increase coefficient γ is 0.6, the sensing radius scaling factor β is 0.08, nt is 5, and l (0) is 5. The simulation experiments were run 20 times independently and the optimal result values were reported.
And S3, scheduling the processing machines, the workpiece procedures and the lifting equipment in the flexible manufacturing workshop according to the optimized scheduling scheme so as to obtain a scheduling result with the minimum completion time and the minimum energy consumption.
Specifically, when scheduling is performed, a plurality of constraints need to be satisfied to achieve the most suitable scheduling result. The plurality of constraint conditions comprise the constraint of the scheduling model on the manufacturing process, the constraint of the scheduling model on the hoisting equipment and the constraint of the scheduling model on decision variables.
Specifically, the constraint of the scheduling model on the manufacturing process is as follows:
Figure BDA0002584262230000101
Figure BDA0002584262230000102
Figure BDA0002584262230000103
Figure 2
wherein the content of the first and second substances,
Figure BDA0002584262230000105
is a process of
Figure BDA0002584262230000106
J is a workpiece number, l is a process number;
Figure BDA0002584262230000107
is a process of
Figure BDA0002584262230000108
The manufacturing completion time of (1);
Figure BDA0002584262230000109
is a manufacturing process
Figure BDA00025842622300001010
K is the machine number;
Figure BDA00025842622300001011
is a processing procedure
Figure BDA00025842622300001012
Machine k of (1);
Figure BDA00025842622300001013
can be processed by
Figure BDA00025842622300001014
A set of machines of (1);
Figure BDA00025842622300001015
is a process in which the machine k is close to
Figure BDA00025842622300001016
The decision variables of (1).
The constraint of the dispatching model on the lifting equipment is as follows:
Pi＝K1，
Figure BDA00025842622300001017
Figure BDA00025842622300001018
when the process is carried out
Figure BDA00025842622300001019
Is that
Figure BDA00025842622300001020
The last working procedure of the process comprises the following steps,
Figure BDA00025842622300001021
when the same machine is used for processing procedures
Figure BDA00025842622300001022
And process step
Figure BDA00025842622300001023
And process of
Figure BDA00025842622300001024
Is that
Figure BDA00025842622300001025
The last working procedure of the process comprises the following steps,
wherein, PiIs the initial position of the lifting equipment; k1Is a machine for processing a first procedure;
Figure BDA00025842622300001026
is a process of
Figure BDA00025842622300001027
The manufacturing completion time of (1);
Figure BDA00025842622300001028
is a transportation process of lifting equipment
Figure BDA00025842622300001029
Load latency of (d);
Figure BDA00025842622300001030
is a process of
Figure BDA00025842622300001031
The manufacturing completion time of (1);
Figure BDA00025842622300001032
is the no-load transportation process of the lifting equipment
Figure BDA00025842622300001033
The position of (a);
Figure BDA00025842622300001034
is a load transportation process of hoisting equipment
Figure BDA00025842622300001035
The position of (a);
Figure BDA00025842622300001036
is a load transportation process of hoisting equipment
Figure BDA00025842622300001037
The position of (a);
Figure BDA00025842622300001038
is the no-load transportation process of the lifting equipment
Figure BDA00025842622300001039
The position of (a);
Figure BDA00025842622300001040
is a load transportation process of hoisting equipment
Figure BDA0002584262230000111
The position of (a);
Figure BDA0002584262230000112
is a load transportation process of hoisting equipment
Figure BDA0002584262230000113
The position of (a).
The constraint of the scheduling model on the decision variable is as follows:
Figure BDA0002584262230000114
Figure BDA0002584262230000115
Figure BDA0002584262230000116
Figure BDA0002584262230000117
wherein the content of the first and second substances,
Figure BDA0002584262230000118
is the decision variable of the machine, whether the machine k is a processing procedure or not
Figure BDA0002584262230000119
Figure BDA00025842622300001110
Is a decision variable of a process, a process
Figure BDA00025842622300001111
Whether or not it is
Figure BDA00025842622300001112
Next step (2);
Figure BDA00025842622300001113
is a decision variable of machine processing, machine k1 processing procedure
Figure BDA00025842622300001114
And machine k2 processing procedure
Figure BDA00025842622300001115
Whether or not it exists;
Figure BDA00025842622300001116
is a decision variable processed by the same machine, on the machine k, the working procedure
Figure BDA00025842622300001117
Whether or not it is
Figure BDA00025842622300001118
The next step of the process.
In order to illustrate the advantages of the method for optimizing and scheduling a flexible manufacturing shop provided by the present invention, the following embodiments are combined to illustrate the present invention:
item information: the experimental data was from a flexible manufacturing plant. Table 1 is the machine parameters including abscissa and ordinate and operating power. The unit of the coordinates is meters and the unit of the power is watts. Table 2 shows item information, in which there are four items of information, each of which has a different number of workpieces, and the corresponding process.
TABLE 1 machine parameters
Figure BDA0002584262230000121
Table 2 item specific information
Figure BDA0002584262230000122
Figure BDA0002584262230000131
Figure BDA0002584262230000141
Figure BDA0002584262230000151
Figure BDA0002584262230000161
Figure BDA0002584262230000171
Fig. 5 to 8 show that various algorithms are scheduled under the same project data, and the iteration number of each algorithm in four projects is related to the fitness value. Wherein GA-GSO represents a hybrid algorithm without using a green transport strategy (GTHS), ABC is an artificial bee colony algorithm, PSO is a particle swarm algorithm, GSO is a firefly algorithm, and DE is a differential evolution algorithm.
As can be seen from FIGS. 5 to 8, comparing the six algorithms simultaneously in the four project data, the GA-GSO-GTHS algorithm proposed by the present invention can effectively reduce the maximum completion time and the energy consumption from the result fitness value. The effectiveness of the proposed optimized scheduling method is demonstrated.
Compared with the simulation experiment results of the six algorithms, the GA-GSO-GTHS algorithm provided by the invention has good solving performance and can obtain a better optimal solution.
Based on the foregoing method for optimizing and scheduling a flexible manufacturing shop, the present invention further provides a flexible manufacturing shop optimizing and scheduling apparatus, which includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the flexible manufacturing shop optimizing and scheduling apparatus implements the method for optimizing and scheduling a flexible manufacturing shop according to the foregoing embodiment.
The flexible manufacturing shop optimized dispatching device provided by the embodiment is used for realizing the flexible manufacturing shop optimized dispatching method, and therefore, the flexible manufacturing shop optimized dispatching device has the technical effects of the flexible manufacturing shop optimized dispatching method, and is not repeated herein.
In summary, the method and the device for optimizing and scheduling the flexible manufacturing workshop provided by the invention provide a scheduling model considering the working states of the hoisting equipment (load operation, load standby, no-load operation and no-load standby) for decision making for minimizing the maximum completion time and reducing the energy consumption, and provide a hybrid genetic firefly algorithm facing a green transportation strategy based on the connection relation between the hoisting equipment and the workpiece procedures to schedule the workpiece procedures and the hoisting equipment, so that an optimal solution set is obtained, and the maximum completion time and the energy consumption can be effectively reduced.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program instructing relevant hardware (such as a processor, a controller, etc.), and the program may be stored in a computer readable storage medium, and when executed, the program may include the processes of the above method embodiments. The storage medium may be a memory, a magnetic disk, an optical disk, etc.
It should be understood that equivalents and modifications of the technical solution and inventive concept thereof may occur to those skilled in the art, and all such modifications and alterations should fall within the scope of the appended claims.

Claims (10)

1. The method for optimizing and scheduling the flexible manufacturing workshop is characterized by comprising the following steps of:
s1, establishing an optimized dispatching model aiming at minimizing the completion time and reducing the energy consumption of the flexible manufacturing workshop based on the working mode of the lifting equipment according to the connection relation between the workpiece procedure of the flexible manufacturing workshop and the lifting equipment;
s2, solving the established optimized scheduling model by adopting a mixed genetic firefly algorithm facing to a green transportation strategy to obtain an optimized scheduling scheme;
and S3, scheduling the processing machines, the workpiece procedures and the lifting equipment in the flexible manufacturing workshop according to the optimized scheduling scheme so as to obtain a scheduling result with the minimum completion time and the minimum energy consumption.
2. The optimal scheduling method for the flexible manufacturing workshop according to claim 1, wherein the working modes of the hoisting equipment comprise an idle-load standby mode, an idle-load running mode, a load standby mode and a load running mode, the energy consumption of the flexible manufacturing workshop comprises machining energy consumption and hoisting equipment energy consumption, the energy consumption of the hoisting equipment is the sum of the idle-load standby energy consumption of the hoisting equipment, the idle-load running energy consumption of the hoisting equipment, the load standby energy consumption of the hoisting equipment and the load running energy consumption of the hoisting equipment, and the optimal scheduling model comprises an adaptability value calculation model, a machining energy consumption calculation model, an idle-load standby energy consumption calculation model of the hoisting equipment, an idle-load running energy consumption calculation model of the hoisting equipment, a load standby energy consumption calculation model of the hoisting equipment and a load running energy consumption calculation model of the hoisting equipment.
3. The method for optimized scheduling of a flexible manufacturing plant according to claim 2, wherein the fitness value calculation model is:
Figure 1
wherein f represents a fitness value, and CT represents a unit manufacturing time price;
Figure FDA0002584262220000012
indicating the procedure
Figure FDA0002584262220000013
The manufacturing completion time of (1); CE represents the cost per energy consumption price; empRepresents the energy consumption of the machine processing; ectRepresenting the energy consumption of the hoisting equipment.
4. The method according to claim 2, wherein the computational model of machine processing energy consumption is:
Figure FDA0002584262220000021
Figure FDA0002584262220000022
wherein E ismpThe energy consumption of the machine processing is shown,
Figure FDA0002584262220000023
the energy consumption generated by processing a workpiece J in the first procedure is shown, wherein J is the serial number of the workpiece; l isjIs a set of processes;
Figure FDA0002584262220000024
is the first step of the workpiece j; efkIs the processing energy consumption of machine k;
Figure FDA0002584262220000025
is a processing procedure on a machine k
Figure FDA0002584262220000026
The time of (d);
Figure FDA0002584262220000027
is a machining procedure of machine k
Figure FDA0002584262220000028
The decision variables of (1).
5. The optimal scheduling method for the flexible manufacturing workshop according to claim 2, wherein the calculation model of the idle standby energy consumption of the lifting equipment is as follows:
Figure FDA0002584262220000029
Figure FDA00025842622200000210
Figure FDA00025842622200000211
wherein E isnsThe no-load standby energy consumption of the lifting equipment is realized; j is the workpiece number; j is the set of workpieces; l is a process number, Lj is a process set;
Figure FDA00025842622200000212
is the transportation of the lifting equipment in the standby state under the no-load condition
Figure FDA00025842622200000213
Energy consumption resulting from processing;
Figure FDA00025842622200000214
is a decision variable of a process, a process
Figure FDA00025842622200000215
Is that
Figure FDA00025842622200000216
Next step (2);
Figure FDA00025842622200000217
is a process of
Figure FDA00025842622200000218
Idle standby time of; ps is the standby power of the lifting equipment;
Figure FDA00025842622200000219
is a process of
Figure FDA00025842622200000220
The manufacturing completion time of (1);
Figure FDA00025842622200000221
is a process of
Figure FDA00025842622200000222
The manufacturing start time of (1);
Figure FDA00025842622200000223
is a transportation process of lifting equipment under the condition of no load
Figure FDA00025842622200000224
A rack generation time;
Figure FDA00025842622200000225
is a transportation process of lifting equipment under the condition of no load
Figure FDA00025842622200000226
Time of crane generation.
6. The optimal scheduling method for the flexible manufacturing workshop according to claim 2, wherein the no-load operation energy consumption calculation model of the lifting equipment is as follows:
Figure FDA0002584262220000031
Figure FDA0002584262220000032
Figure FDA0002584262220000033
Figure FDA0002584262220000034
Figure FDA0002584262220000035
Figure FDA0002584262220000036
wherein E isnoThe no-load running energy consumption of the hoisting equipment is reduced;
Figure FDA0002584262220000037
is transported when the lifting equipment operates under the condition of no load
Figure FDA0002584262220000038
Energy consumption resulting from processing; j is the serial number of the workpiece; j is the set of workpieces; l isjIs a set of processes; l is the serial number of the process;
Figure FDA0002584262220000039
is a decision variable of the process and is used for judging the process
Figure FDA00025842622200000310
Whether or not it is
Figure FDA00025842622200000311
Next step (2);
Figure FDA00025842622200000312
is the transportation of the frame of the lifting equipment under the condition of no load
Figure FDA00025842622200000313
The start-up time of (c); pgs is the power at which the rack is powered;
Figure FDA00025842622200000314
the lifting machine in the lifting equipment is transported under the condition of no load
Figure FDA00025842622200000315
The start-up time of (c); pts is the starting power of the crane; ws is the weight of the hoisting equipment; qn is the lifting weight of the handling equipment;
Figure FDA00025842622200000316
the frame of the lifting equipment is transported under the condition of no load
Figure FDA00025842622200000317
The stationary movement time of; pg is the rated power of the rack;
Figure FDA00025842622200000318
the lifting machine of the lifting equipment is used for transporting under the condition of no load
Figure FDA00025842622200000319
The stationary movement time of; pt is the rated power of a lifting machine in lifting equipment; vg is the running speed of the lifting equipment rack; a isgsThe starting acceleration of the frame of the lifting equipment under the load condition; wg is the weight of the frame; wt is the weight of the trolley; vt is the running speed of the crane; a istsStarting acceleration of a lifting machine of lifting equipment under a load condition; k is a machine number; lpxkIs the abscissa of machine k; lpxk1Machine for makingThe abscissa of device k 1; a isgbThe braking acceleration of the rack under the condition of the load of the lifting equipment;
Figure FDA00025842622200000320
is a decision variable of the l1 th process step of processing the workpiece j by the machine k;
Figure FDA00025842622200000321
is a decision variable of the l1 th process step of the machine k1 for processing the workpiece j 1; lpykIs the abscissa of machine k; lpyk1Is the abscissa of machine k 1; a istbThe braking acceleration of the lifting machine under the condition of the load of the lifting equipment.
7. The optimal scheduling method for the flexible manufacturing workshop according to claim 2, wherein the load standby energy consumption calculation model of the lifting equipment is as follows:
Figure FDA0002584262220000041
Figure FDA0002584262220000042
Figure FDA0002584262220000043
wherein E islsLoad standby energy consumption of hoisting equipment; j is the workpiece number; j is the set of workpieces; l is a process number, Lj is a process set; k is a machine number; k is the set of machines;
Figure FDA0002584262220000044
is a transportation process of the lifting equipment in standby time under the condition of load
Figure FDA0002584262220000045
Energy consumption resulting from processing;
Figure FDA0002584262220000046
is a decision variable for representing the machining process of the machine k1
Figure FDA0002584262220000047
And machine k2 processing procedure
Figure FDA0002584262220000048
Whether or not it exists;
Figure FDA0002584262220000049
is a decision variable processed by the same machine, on a machine k2, the working procedure
Figure FDA00025842622200000410
Whether or not it is
Figure FDA00025842622200000411
Next step (2);
Figure FDA00025842622200000412
is a process of
Figure FDA00025842622200000413
Load standby time of; ps is the standby power of the hoisting equipment;
Figure FDA00025842622200000414
is a process of
Figure FDA00025842622200000415
The manufacturing completion time of (1);
Figure FDA00025842622200000416
is a process of
Figure FDA00025842622200000417
The manufacturing completion time of (1).
8. The optimal scheduling method for the flexible manufacturing workshop according to claim 2, wherein the load operation energy consumption calculation model of the lifting equipment is as follows:
Figure FDA00025842622200000418
Figure FDA00025842622200000419
Figure FDA00025842622200000420
Figure FDA00025842622200000421
Figure FDA0002584262220000051
Figure FDA0002584262220000052
wherein E isloThe energy consumption of the load operation of the hoisting equipment is reduced; j is the workpiece number; j is the set of workpieces; l is a process number, Lj is a process set;
Figure FDA0002584262220000053
is a transportation process when the lifting equipment runs under the condition of load
Figure FDA0002584262220000054
Energy consumption resulting from processing;
Figure FDA0002584262220000055
is transported under the load condition by a frame in hoisting equipment
Figure FDA0002584262220000056
The start-up time of (c); pgs is the startup power of the rack;
Figure FDA0002584262220000057
the lifting machine in the lifting equipment transports under the load condition
Figure FDA0002584262220000058
The start-up time of (c); pts is the starting power of the crane; ws is the weight of the hoisting equipment; wjIs the weight of workpiece j; qn is the lifting weight of the handling equipment;
Figure FDA0002584262220000059
is that the racks are transported under load
Figure FDA00025842622200000510
The stationary movement time of; pg is the rated power of a rack in the lifting equipment;
Figure FDA00025842622200000511
is transported by a lifting machine under the condition of load
Figure FDA00025842622200000512
The stationary movement time of; pt is the rated power of a lifting machine in lifting equipment; vg is the running speed of the lifting equipment rack; ags is the starting acceleration of the frame of the lifting equipment under the load condition; wg is the weight of the frame; wt is the weight of the trolley; vt is the running speed of the crane; a istsStarting acceleration of a lifting machine of lifting equipment under a load condition; k is a machine number; lpxkIs the abscissa of machine k; lpxk1Is the abscissa of machine k1；agbThe braking acceleration of the rack under the condition of the load of the lifting equipment;
Figure FDA00025842622200000513
is a decision variable of the l1 th process step of processing the workpiece j by the machine k;
Figure FDA00025842622200000514
is a decision variable of the l1 th process step of the machine k1 for processing the workpiece j 1; lpykIs the abscissa of machine k; lpyk1Is the abscissa of machine k 1; a istbThe braking acceleration of the lifting machine under the condition of the load of the lifting equipment.
9. The method for optimized scheduling of a flexible manufacturing plant according to claim 1, wherein the step S2 includes:
s21, generating an initial population through a genetic algorithm;
s22, selecting a plurality of best solutions from the initial population as a firefly population, and obtaining a local optimal solution in the firefly population through a firefly algorithm;
s23, performing crossing and mutation operations on the local optimal solution to obtain a progeny population;
s24, optimizing energy consumption of the lifting equipment during operation under the load condition and time consumption of the lifting equipment during standby under the load condition through a green transportation strategy;
s25, updating the population, judging the iteration times, outputting the optimal solution if the iteration times exceed the set iteration times, and returning to the step S22 if the iteration times exceed the set iteration times;
and S26, taking the final result as the optimized scheduling result of the total flexible manufacturing shop.
10. The flexible manufacturing shop optimized dispatching equipment is characterized by comprising a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the flexible manufacturing shop optimization scheduling method according to any one of claims 1 to 9.
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