CN104360660A - Aluminum profile turning and extruding workshop energy consumption optimized scheduling method based on ant colony algorithm - Google Patents
Aluminum profile turning and extruding workshop energy consumption optimized scheduling method based on ant colony algorithm Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000005265 energy consumption Methods 0.000 title claims abstract description 52
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 title claims abstract description 13
- 229910052782 aluminium Inorganic materials 0.000 title claims abstract description 13
- 238000005457 optimization Methods 0.000 claims abstract description 15
- 239000003016 pheromone Substances 0.000 claims description 25
- 229910000838 Al alloy Inorganic materials 0.000 claims description 12
- 238000001125 extrusion Methods 0.000 claims description 12
- 239000004411 aluminium Substances 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 7
- 238000003754 machining Methods 0.000 claims description 6
- 230000003750 conditioning effect Effects 0.000 claims description 3
- 230000036962 time dependent Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 17
- 230000005611 electricity Effects 0.000 abstract description 6
- 238000004458 analytical method Methods 0.000 abstract description 3
- 241000257303 Hymenoptera Species 0.000 abstract 1
- 238000004364 calculation method Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 4
- SAZUGELZHZOXHB-UHFFFAOYSA-N acecarbromal Chemical compound CCC(Br)(CC)C(=O)NC(=O)NC(C)=O SAZUGELZHZOXHB-UHFFFAOYSA-N 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
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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] or 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] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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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] or computer integrated manufacturing [CIM]
- G05B19/41885—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] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- General Engineering & Computer Science (AREA)
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- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an aluminum profile turning and extruding workshop energy consumption optimized scheduling method based on the ant colony algorithm. According to the method, the aluminum profile extruding workshop production scheduling process is abstracted into a non-equal parallel machine scheduling problem with the time difference electricity price and the energy consumption of the scheduling process taken into consideration, the ant colony optimization algorithm based on the iterative calculation is provided on the basis of the analysis on this type of scheduling process, the scheduling of machines and workpieces is achieved through ants in the machine-workpiece-machine iterative mode, and the solve errors caused when the machines and the workpieces are independently scheduled in a traditional ant colony algorithm are reduced.
Description
Technical field
The present invention relates to workshop energy optimization dispatching technique, particularly relate to a kind of aluminium section bar car based on ant group algorithm extruding workshop energy optimization dispatching method.
Background technology
In reality is manufactured, the machine (aniso-parallel machine) of different efficiency often runs simultaneously, and this formulates to the production schedule and brings great difficulty.Therefore, under guarantee enterprise normal existence condition, reducing the energy resource consumption of aniso-parallel machine production run and reduce production cost, is one of key problem of manufacturing industry concern.Particularly with in Extrusion Process of Aluminum Alloy Profile workshop, the rock gas that the consumption of Extrusion Process of Aluminum Alloy Profile need of production is a large amount of and electricity, belong to high energy consumption manufacture process. there is peak period and low peak period in electric power supply, namely so-called " peak, paddy, flat ", it is the power price (time difference electricity price) of different time sections shown in Fig. 1, utilize the mistiming plan of arranging production, the scheduling of production of low ebb phase of increasing electric power can reduce energy loss effectively. during this external extruding is produced, just can close after the complete all aluminium bars of machine pusher, midway can not be shut down, therefore when extruder terminates the extruding of a collection of product, and fail new aluminium bar when entering, very high unloaded cost can be caused.In actual production, usually new second-hand machine uses together, and the production efficiency of machine differs, and the power surges phase high new engine of efficiency of arranging production is produced, and electric power low ebb phase inefficient second-hand machine of then can arranging production is produced to reach energy-conservation object.
But need in actual production consider processing deadline and workpiece drag time phase, minimize deadline or the time phase of dragging often with the loss of machine energy consumption for cost, for the scheduling scheme of 3 workpiece, 1 machine, process time of workpiece, the time of reaching and time of delivery are as shown in table 1, and machine operation energy consumption and unit interval standby energy consumption are respectively 0.5kwh/h and 1kwh/h.The machine energy consumption of different scheduling schemes and deadline are as shown in Figure 2.
Process time of table 1. workpiece, the time of reaching and time of delivery
The scheduling problem of workpiece time of arrival is np hard problem, according to complexity theory,
r m | (
m 1 ,
m 2 ,
m m ),
sT sd | E min problem is also that NP is difficult. for np hard problem, ant colony optimization algorithm is current one of most efficient scheduling algorithm. in order to reduce the complicacy solved, separate structure be generally divided into two benches: the first stage ant select certain machine as processing machine, subordinate phase ant selects certain workpiece to process on this machine, a two benches of ant is sought footpath and is represented machine choice work pieces process, and ant repeatedly two benches seeks footpath until all workpiece are scheduled.The supposed premise of the constructing plan of this two stage solution is: workpiece drags the energy consumption cost of current cost and machine to be independently.But as shown in Figure 2, drag current cost sub-goal and machine energy consumption cost sub-goal and dependent, but connect each other, minimize and drag current cost often to sacrifice machine energy consumption for target (see Fig. 2 scheme 1, scheme 2), simply two sub-goals are carried out separately pheromones search and algorithm performance can be caused to decline.
Summary of the invention
In view of this, fundamental purpose of the present invention is to provide a kind of aluminium section bar car based on ant group algorithm to extrude workshop energy optimization dispatching method, by using the method can draw the optimum solution of Extrusion Process of Aluminum Alloy Profile workshop least energy consumption, thus can the power consumption in arranged rational Extrusion Process of Aluminum Alloy Profile workshop.
To achieve these goals, technical scheme of the present invention is achieved in that
Based on the aluminium section bar car extruding workshop energy optimization dispatching method of ant group algorithm, described Optimization Scheduling comprises the following steps:
Step 1: build Extrusion Process of Aluminum Alloy Profile workshop energy consumption scheduling mathematic model, determine Extrusion Process of Aluminum Alloy Profile workshop least energy consumption objective function E
min, described objective function E
mincomprise two sub-goals: workpiece drags the energy consumption cost of current cost, machine; Objective function E
minas shown in formula (1.1):
（1.1）
This objective function should meet following condition:
（1.2）
（1.3）
（1.4）
Formula (1.2) represents that workpiece can only be processed on a machine;
Formula (1.3) represents when the deadline of workpiece is by start time and machining Time dependent;
Formula (1.4) represents that the start time of workpiece depends on the deadline of a workpiece and the time of arrival of this workpiece;
Below the parametric variable of objective function:
: the quantity of workpiece;
: the quantity of machine;
: be arranged in machine
the piece count of upper processing;
w 1 : workpiece drags current cost coefficient;
w 2 : machine energy consumption cost coefficient;
r i : each workpiece
iindependently time of arrival;
d i : each workpiece
itime of delivery;
t ij : machine is to the process time of workpiece;
p i1: the
ithe unit interval of individual workpiece drags current cost;
p j2: the
jthe unit interval operation energy consumption cost of platform machine;
p j3: the
jthe unit interval standby energy consumption cost of platform machine;
f (t):the power price of different time sections;
Decision-making scalar:
for judging whether certain workpiece is processed in specified machine, if
then represent workpiece
at machine
upper processing, otherwise not at machine
upper processing;
Step 2: pheromones and initialization thereof
seek footpath process according to the two benches of ant, pheromones is divided into τ j and τ ij two parts, and τ j represents machine
pheromones on Mj, initial value is
; τ ij represents machine
mj and workpiece
between pheromones, initial value τ ij=0;
Step 3: the structure that ant group algorithm three stage separates
First the machine that can obtain the earliest is selected
, then select the workpiece that workpiece drags current cost minimum on machine
, finally according to workpiece
select the machine that machine energy consumption cost is minimum
; The process selected again by machine will drag current cost sub-goal and machine energy consumption cost sub-goal to connect, boosting algorithm performance; Specific as follows:
1. select machine
First the machine of Choice and process, the heuristic rule of employing is the machine that can obtain the earliest, and this can make the deadline of workpiece minimum.In order to increase search randomness, given parameters
and random number
if,
, ant selects the machine that can obtain the earliest, otherwise presses the probability distribution of formula (1.6)
select machine
:
(1.5)
(1.6)
2. select workpiece
According to Number of Jobs, use taboo list
tabu k (
k=1,2 ...,
n) recording workpiece selected by current ant, taboo list makes dynamic conditioning along with ant seeks footpath. given parameters
and random number
if,
, ant selects the minimum workpiece dragging current cost, otherwise presses the probability distribution of formula (1.8)
select workpiece
:
(1.7)
(1.8)
(1.9)
heuristic function, reflection machine
upper processing work
drag current cost, the minimum workpiece of prioritizing selection integrated cost is produced on this machine;
αbe information heuristic factor, reflect the impact that ant group motion process accumulating information is selected current ant;
βbe expect heuristic factor, represent the attention degree of heuristic information in ant is selected;
3. select machine
For workpiece
, the machine that can obtain the earliest
might not be process the minimum machine of this workpiece energy consumption, therefore adopt the method for iteration, again according to the minimum selection machine of machining energy consumption
, shown in (2.0):
(2.0)
< workpiece
, machine
> is the result that ant once seeks footpath, namely selects workpiece
at machine
on process.Ant carries out seeking footpath repeatedly, until all work pieces process complete, namely the job sequence of workpiece is the sequence of separating;
Step 4: Pheromone update
After ant has traveled through all workpiece, need to adjust current quantity of information of seeking in the result in footpath
k, adjust according to formal style (2.1) below:
（2.1）
Wherein, 1-
ρbe that pheromones remains the factor, what represent current iteration seeks footpath result seeks footpath influence degree to whole ant group, Δ
τ ij (
t) represent pheromones increment in current iteration.
qrepresent pheromones intensity, affect convergence of algorithm speed to a certain extent,
e(
t) represent ant current iteration seek footpath result.
Aluminium section bar car based on ant group algorithm extruding workshop provided by the invention energy optimization dispatching method, has following technical advantage:
The present invention by abstract for Extrusion Process of Aluminum Alloy Profile Workshop Production scheduling process for a kind of consider time difference electricity price extremely energy consumption aniso-Parallel Machine Scheduling Problems, on basis by the analysis to this type of scheduling problem, a kind of ant colony optimization algorithm based on iterative calculating is proposed, ant realizes the scheduling of machine and workpiece through " machine-workpiece-machine " iterative mode, decrease traditional ant group algorithm machine and workpiece are dispatched separately bring solve error.
Accompanying drawing explanation
Fig. 1 is the power price distribution plan of different time sections;
Fig. 2 be different scheduling scheme machine total energy consumption, drag time phase and deadline comparison diagram;
Fig. 3 is of the present invention based on the aluminium section bar car extruding workshop energy optimization dispatching method process flow diagram based on ant group algorithm;
Fig. 4 is the process flow diagram of the structure that ant group algorithm three stage separates
Embodiment
Below in conjunction with drawings and the specific embodiments, detailed, complete description is carried out to technical scheme of the present invention and application principle; described by obvious embodiment is only the part of technical solution of the present invention and application principle; any amendment that those skilled in the art is non-to make through creative work, equivalently to replace and improvement etc., all should be included within protection scope of the present invention.
See Fig. 3, for the present invention is based on the aluminium section bar car extruding workshop energy optimization dispatching method process flow diagram based on ant group algorithm, specifically comprise the following steps,
Step 1: build Extrusion Process of Aluminum Alloy Profile workshop energy consumption scheduling mathematic model, determine Extrusion Process of Aluminum Alloy Profile workshop least energy consumption objective function E
min, described objective function E
mincomprise two sub-goals: workpiece drags the energy consumption cost of current cost, machine; Objective function E
minas shown in formula (1.1):
（1.1）
This objective function should meet following condition:
（1.2）
（1.3）
（1.4）
Formula (1.2) represents that workpiece can only be processed on a machine;
Formula (1.3) represents when the deadline of workpiece is by start time and machining Time dependent;
Formula (1.4) represents that the start time of workpiece depends on the deadline of a workpiece and the time of arrival of this workpiece;
Below the parametric variable of objective function:
: the quantity of workpiece;
: the quantity of machine;
: be arranged in machine
the piece count of upper processing;
w 1 : workpiece drags current cost coefficient;
w 2 : machine energy consumption cost coefficient;
r i : each workpiece
iindependently time of arrival;
d i : each workpiece
itime of delivery;
t ij : machine is to the process time of workpiece;
p i1: the
ithe unit interval of individual workpiece drags current cost;
p j2: the
jthe unit interval operation energy consumption cost of platform machine;
p j3: the
jthe unit interval standby energy consumption cost of platform machine;
f (t):the power price of different time sections;
Decision-making scalar:
for judging whether certain workpiece is processed in specified machine, if
then represent workpiece
at machine
upper processing, otherwise not at machine
upper processing;
Step 2: pheromones and initialization thereof
seek footpath process according to the two benches of ant, pheromones is divided into τ j and τ ij two parts, and τ j represents machine
pheromones on Mj, initial value is
; τ ij represents machine
mj and workpiece
between pheromones, initial value τ ij=0;
Step 3: the structure that ant group algorithm three stage separates
First the machine that can obtain the earliest is selected
, then select the workpiece that workpiece drags current cost minimum on machine
, finally according to workpiece
select the machine that machine energy consumption cost is minimum
; The process selected again by machine will drag current cost sub-goal and machine energy consumption cost sub-goal to connect, boosting algorithm performance; Idiographic flow is shown in Figure 4:
1. select machine
First the machine of Choice and process, the heuristic rule of employing is the machine that can obtain the earliest, and this can make the deadline of workpiece minimum.In order to increase search randomness, given parameters
and random number
if,
, ant selects the machine that can obtain the earliest, otherwise presses the probability distribution of formula (1.6)
select machine
:
(1.5)
(1.6)
2. select workpiece
According to Number of Jobs, use taboo list
tabu k (
k=1,2 ...,
n) recording workpiece selected by current ant, taboo list makes dynamic conditioning along with ant seeks footpath. given parameters
and random number
if,
, ant selects the minimum workpiece dragging current cost, otherwise presses the probability distribution of formula (1.8)
select workpiece
:
(1.7)
(1.8)
(1.9)
heuristic function, reflection machine
upper processing work
drag current cost, the minimum workpiece of prioritizing selection integrated cost is produced on this machine;
αbe information heuristic factor, reflect the impact that ant group motion process accumulating information is selected current ant;
βbe expect heuristic factor, represent the attention degree of heuristic information in ant is selected;
3. select machine
For workpiece
, the machine that can obtain the earliest
might not be process the minimum machine of this workpiece energy consumption, therefore adopt the method for iteration, again according to the minimum selection machine of machining energy consumption
, shown in (2.0):
(2.0)
< workpiece
, machine
> is the result that ant once seeks footpath, namely selects workpiece
at machine
on process.Ant carries out seeking footpath repeatedly, until all work pieces process complete, namely the job sequence of workpiece is the sequence of separating;
Step 4: Pheromone update
After ant has traveled through all workpiece, need to adjust current quantity of information of seeking in the result in footpath
k, adjust according to formal style (2.1) below:
（2.1）
Wherein, 1-
ρbe that pheromones remains the factor, what represent current iteration seeks footpath result seeks footpath influence degree to whole ant group, Δ
τ ij (
t) represent pheromones increment in current iteration.
qrepresent pheromones intensity, affect convergence of algorithm speed to a certain extent,
e(
t) represent ant current iteration seek footpath result.
In order to verify validity of the present invention, the present invention adopts and carries out emulation experiment by split-run test method (split-plot), and the factor of influence affecting algorithm performance has: machine quantity
m, piece count
n, workpiece process time
t ij , workpiece time of arrival
r i , the workpiece time of delivery
d i with the ratio (ratio of unit interval machine switching on and shutting down energy consumption cost and unit interval machine standby energy consumption) of unit consumption of energy, arranging of each factor is as shown in table 2.
The process time of workpiece
t ij obedience is uniformly distributed, and is designated as
t ij =
u[2,30] and
t ij =
u[2,50] two kinds, the time of arrival of workpiece
r i , the time of delivery
d i can calculate according to process time:
,
, wherein
crepresent the well-to-do coefficient of delivery. adopt the ratio of unit interval production of machinery energy consumption cost and unit interval machine standby energy consumption herein
p j2/ p j3 reflect machine energy consumption ratio. the unit interval drags current cost
p i1 with
unit interval energy consumption cost
p j2 get from the random integers between 1 to 10
randi(10,1), workpiece drags current cost coefficient
w 1 with machine energy consumption cost coefficient
w 2 get from the random number between 0 to 1 and
w 1 +
w 2 =1. form 24 kinds of simulation example altogether according to the factor of influence of table 2. time difference electricity price
f (t)represent with following formula:
（2.2）
Factor of influence in table 2. simulation example
The present invention by abstract for Extrusion Process of Aluminum Alloy Profile Workshop Production scheduling process for a kind of consider time difference electricity price extremely energy consumption aniso-Parallel Machine Scheduling Problems, on basis by the analysis to this type of scheduling problem, a kind of ant colony optimization algorithm based on iterative calculating is proposed, ant realizes the scheduling of machine and workpiece through " machine-workpiece-machine " iterative mode, decrease traditional ant group algorithm machine and workpiece are dispatched separately bring solve error.
Claims (1)
1., based on the aluminium section bar car extruding workshop energy optimization dispatching method of ant group algorithm, it is characterized in that, described Optimization Scheduling comprises the following steps:
Step 1: build Extrusion Process of Aluminum Alloy Profile workshop energy consumption scheduling model, determine Extrusion Process of Aluminum Alloy Profile workshop least energy consumption objective function E
min, described objective function E
mincomprise two sub-goals: workpiece drags the energy consumption cost of current cost, machine; Objective function E
minas shown in formula (1.1):
（1.1）
This objective function should meet following condition:
（1.2）
（1.3）
（1.4）
Formula (1.2) represents that workpiece can only be processed on a machine;
Formula (1.3) represents when the deadline of workpiece is by start time and machining Time dependent;
Formula (1.4) represents that the start time of workpiece depends on the deadline of a workpiece and the time of arrival of this workpiece;
Below the parametric variable of objective function:
: the quantity of workpiece;
: the quantity of machine;
: be arranged in machine
the piece count of upper processing;
w 1 : workpiece drags current cost coefficient;
w 2 : machine energy consumption cost coefficient;
r i : each workpiece
iindependently time of arrival;
d i : each workpiece
itime of delivery;
t ij : machine is to the process time of workpiece;
p i1: the
ithe unit interval of individual workpiece drags current cost;
p j2: the
jthe unit interval operation energy consumption cost of platform machine;
p j3: the
jthe unit interval standby energy consumption cost of platform machine;
f (t):the power price of different time sections;
Decision-making scalar:
for judging whether certain workpiece is processed in specified machine, if
then represent workpiece
at machine
upper processing, otherwise not at machine
upper processing;
Step 2: pheromones and initialization thereof
seek footpath process according to the two benches of ant, pheromones is divided into τ j and τ ij two parts, and τ j represents machine
pheromones on Mj, initial value is
; τ ij represents machine
mj and workpiece
between pheromones, initial value τ ij=0;
Step 3: the structure that ant group algorithm three stage separates
First the machine that can obtain the earliest is selected
, then select the workpiece that workpiece drags current cost minimum on machine
, finally according to workpiece
select the machine that machine energy consumption cost is minimum
; The process selected again by machine will drag current cost sub-goal and machine energy consumption cost sub-goal to connect, boosting algorithm performance; Specific as follows:
Select machine
First the machine of Choice and process, the heuristic rule of employing is the machine that can obtain the earliest, and this can make the deadline of workpiece minimum;
In order to increase search randomness, given parameters
and random number
if,
, ant selects the machine that can obtain the earliest, otherwise presses the probability distribution of formula (1.6)
select machine
:
(1.5)
(1.6)
Select workpiece
According to Number of Jobs, use taboo list
tabu k (
k=1,2 ...,
n) recording workpiece selected by current ant, taboo list makes dynamic conditioning along with ant seeks footpath. given parameters
and random number
if,
, ant selects the minimum workpiece dragging current cost, otherwise presses the probability distribution of formula (1.8)
select workpiece
:
(1.7)
(1.8)
(1.9)
heuristic function, reflection machine
upper processing work
drag current cost, the minimum workpiece of prioritizing selection integrated cost is produced on this machine;
αbe information heuristic factor, reflect the impact that ant group motion process accumulating information is selected current ant;
βbe expect heuristic factor, represent the attention degree of heuristic information in ant is selected;
Select machine
For workpiece
, the machine that can obtain the earliest
might not be process the minimum machine of this workpiece energy consumption, therefore adopt the method for iteration, again according to the minimum selection machine of machining energy consumption
, shown in (2.0):
(2.0)
< workpiece
, machine
> is the result that ant once seeks footpath, namely selects workpiece
at machine
on process;
Ant carries out seeking footpath repeatedly, until all work pieces process complete, namely the job sequence of workpiece is the sequence of separating;
Step 4: Pheromone update
After ant has traveled through all workpiece, need to adjust current quantity of information of seeking in the result in footpath
k, adjust according to formal style (2.1) below:
（2.1）
Wherein, 1-
ρbe that pheromones remains the factor, what represent current iteration seeks footpath result seeks footpath influence degree to whole ant group, Δ
τ ij (
t) represent pheromones increment in current iteration.
qrepresent pheromones intensity, affect convergence of algorithm speed to a certain extent,
e(
t) represent ant current iteration seek footpath result.
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| CN106970604A (en) * | 2017-05-15 | 2017-07-21 | 安徽大学 | A kind of multiple target Job Scheduling algorithm based on ant group algorithm |
| CN109377111A (en) * | 2018-12-13 | 2019-02-22 | 合肥工业大学 | Job scheduling method and device based on modified-immune algorithm |
| CN110244675A (en) * | 2019-06-14 | 2019-09-17 | 海安中科智能制造与信息感知应用研发中心 | Mobile robot team control intelligent dispatching method based on ant group algorithm |
| CN111665799A (en) * | 2020-05-20 | 2020-09-15 | 同济大学 | Time constraint type parallel machine energy-saving scheduling method based on collaborative algorithm |
| CN111931898A (en) * | 2020-07-23 | 2020-11-13 | 兰州理工大学 | Intelligent electrolytic aluminum distribution scheduling method based on deep immune clone algorithm |
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