CN115294772A - Underground mine bidirectional single-lane vehicle scheduling optimization method and device - Google Patents

Underground mine bidirectional single-lane vehicle scheduling optimization method and device Download PDF

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CN115294772A
CN115294772A CN202211196681.4A CN202211196681A CN115294772A CN 115294772 A CN115294772 A CN 115294772A CN 202211196681 A CN202211196681 A CN 202211196681A CN 115294772 A CN115294772 A CN 115294772A
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vehicle
avoidance
chamber
scheduling
vehicles
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CN115294772B (en
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陈鑫
彭朝辉
张金钟
袁晓慧
徐谦
李金玲
毕林
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Changsha Dimai Technology Co ltd
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Changsha Digital Mine Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
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    • G06Q10/083—Shipping
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • 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
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The application discloses a bidirectional single-lane vehicle scheduling optimization method and device for an underground mine, wherein the method comprises the following steps: taking the road and vehicle parameters as model parameters of a vehicle scheduling optimization mathematical model and inputting the model parameters into the vehicle scheduling optimization mathematical model; obtaining constraint conditions of a vehicle scheduling optimization mathematical model; calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model; obtaining the values of all decision variables under the condition that the values of the objective function are optimal, and taking the values of all decision variables as the resolving result of a vehicle scheduling optimization mathematical model; and scheduling the vehicles on the underground mine bidirectional single lane according to the resolving result. Through the method and the device, the problems existing in the existing vehicle scheduling scheme of the underground mine are solved, so that orderly avoidance is realized before the vehicle enters an avoidance area, the condition of backing a car to the coming vehicle for a long distance is avoided, and the driving efficiency is obviously improved.

Description

Underground mine bidirectional single-lane vehicle scheduling optimization method and device
Technical Field
The application relates to the field of underground mine vehicle scheduling, in particular to a bidirectional single-lane vehicle scheduling optimization method and device for an underground mine.
Background
With the continuous development of mining technology, many small-scale underground mines or underground mines with complex conditions are exploited. In order to ensure the economy of mining of the mine, a single-way lane is always arranged underground. Due to the narrow road, vehicles can only pass in one direction on the lane. However, a great deal of bidirectional passing conditions exist in the production and transportation work of underground mines, particularly the work of loading and unloading ores. Vehicles pass in two directions on a one-way road, the simultaneity cannot be guaranteed, and the vehicles need to back for avoidance on one side. This severely affects underground mine vehicle traffic efficiency. In addition, underground mines have poor underground light and more curves, so that drivers cannot completely master road conditions and have blind vision areas. When vehicles meet, the vehicles are avoided, so that safety accidents such as vehicle scraping, personal casualties and the like are easy to happen, and great potential safety hazards exist.
At present, in order to solve the problem that the width of a one-way road is limited, an avoidance chamber is often arranged in the bidirectional driving process of a vehicle to carry out vehicle passing avoidance. Through a vehicle access identification system and a traffic signal indication system with higher precision, vehicles can be scheduled to avoid. However, when the number of vehicles is too large or the signal light indication fails, the situations of vehicle blockage, traffic chaos and the like often occur, and effective avoidance is difficult. In addition, the identification and indication system is complex and does not meet the efficient dispatching requirement of bidirectional single-lane vehicles in underground mines.
Disclosure of Invention
The embodiment of the application provides a bidirectional single-lane vehicle scheduling optimization method and device for an underground mine, and aims to at least solve the problems existing in the conventional vehicle scheduling scheme of the underground mine.
According to one aspect of the application, a bidirectional single-lane vehicle scheduling optimization method for an underground mine is provided, and comprises the following steps: triggering a vehicle avoidance condition of a bidirectional single lane of the underground mine; acquiring road and vehicle parameters on the bidirectional single lane under the condition that the vehicle avoidance condition is triggered, wherein the road and vehicle parameters comprise: total length L of bidirectional single lane T The running speed v of each vehicle and the number N of vehicles capable of being accommodated in the c-th avoidance chamber c Distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c And the distance L between the f-th heavy vehicle and the e-th empty vehicle f,e (ii) a Inputting the indexes and the sets of the vehicles and the avoidance chamber into the vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model; inputting the road and vehicle parameters into a vehicle dispatching optimization mathematical model as model parameters of the vehicle dispatching optimization mathematical model; obtaining constraint conditions of the vehicle scheduling optimization mathematical model; under the condition that the constraint condition is met, calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model; obtaining the values of all decision variables under the optimal condition of the values of the objective function, and taking the values of all decision variables as the resolving result of the vehicle scheduling optimization mathematical model; wherein the objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 45254DEST_PATH_IMAGE001
Wherein x is t,c The waiting time of the t-th vehicle in the c-th avoidance chamber, H is the total time period, L t The driving distance of the T-th vehicle at the current moment, T is the index of the vehicle, C is the index of the avoidance chamber, T is the set of the vehicles in the bidirectional single lane, and C is the bidirectional single vehicleThe system comprises a set of in-road avoidance chambers and virtual avoidance chambers at two ends; and scheduling the vehicles on the underground mine bidirectional single lane according to the calculation result.
Further, the decision variables in the mathematical model for vehicle scheduling optimization include: waiting time x of tth vehicle in c-th avoidance chamber t,c Whether the f-th heavy vehicle and the e-th empty vehicle meet at the c-th avoidance chamber or not is determined by y f,e,c Whether the t-th vehicle needs to avoid the y in the c-th avoidance chamber t,c Total time period H; distance L traveled by the tth vehicle at the present time t 。
Further, the scheduling the vehicles on the underground mine bidirectional single lane according to the calculation result comprises: generating a vehicle scheduling pre-instruction of the underground mine bidirectional single lane according to the resolving result, wherein the vehicle scheduling pre-instruction comprises the following steps: the vehicle does not have a vehicle intersection condition when passing through the c-th avoidance chamber, the vehicle does not need to be avoided but has a vehicle intersection condition when passing through the c-th avoidance chamber, and the waiting time for the vehicle to enter the c-th avoidance chamber for vehicle avoidance and enter the c-th avoidance chamber; and when the vehicle to be scheduled runs to a scheduling control point, sending a vehicle scheduling instruction according to the vehicle scheduling pre-instruction, wherein the vehicle scheduling instruction is used for scheduling the vehicle to be scheduled.
Further, the scheduling control point refers to: when the current vehicle runs to a position point which is close to the safe distance Ls of the c-th avoidance chamber; the vehicle dispatching instructions comprise: the vehicle to be dispatched moves forward, the vehicle to be dispatched can move forward after waiting for the coming vehicle to enter the avoidance chamber, the vehicle to be dispatched enters the avoidance chamber to wait for vehicle avoidance, and the vehicle drives away from the avoidance chamber after the time h.
Further, the constraint conditions include: decision variable logic constraint, heavy vehicle and empty vehicle intersection uniqueness constraint, vehicle travel distance constraint relative to each avoidance chamber, and avoidance chamber vehicle number constraint.
According to another aspect of the application, the bidirectional single-lane vehicle adjustment device for the underground mine is further providedDegree optimizing apparatus, including: the triggering module is used for triggering the vehicle avoidance condition of the underground mine bidirectional single lane; a first obtaining module, configured to obtain road and vehicle parameters on the bidirectional single lane when the vehicle avoidance condition is triggered, where the road and vehicle parameters include: total length L of bidirectional single lane T The running speed v of each vehicle and the number N of vehicles capable of being accommodated in the c-th avoidance chamber c Distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c Distance L between the f-th heavy vehicle and the e-th empty vehicle f,e (ii) a The first input module is used for inputting the indexes and the sets of the vehicles and the avoidance chamber into the vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model; the second input module is used for inputting the road and vehicle parameters into the vehicle scheduling optimization mathematical model as model parameters of the vehicle scheduling optimization mathematical model; the second acquisition module is used for acquiring the constraint conditions of the vehicle scheduling optimization mathematical model; the calculation module is used for calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model under the condition that the constraint conditions are met; the third acquisition module is used for acquiring the values of all decision variables under the condition that the value of the objective function is optimal, and taking the values of all decision variables as the resolving result of the vehicle scheduling optimization mathematical model; wherein the objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 149345DEST_PATH_IMAGE002
Wherein x is t,c The waiting time of the t-th vehicle in the c-th avoidance chamber, H is the total time period, L t The driving distance of the T-th vehicle at the current moment, T is an index of the vehicle, C is an index of an avoidance chamber, T is a set of vehicles in a bidirectional single lane, and C is a set of the avoidance chamber in the bidirectional single lane and virtual avoidance chambers at two ends; a scheduling module for scheduling the underground mine according to the calculation resultAnd carrying out dispatching on the vehicles on the bidirectional single lane.
Further, the decision variables in the mathematical model for vehicle scheduling optimization include: waiting time x of tth vehicle in the c avoidance chamber t,c Whether the ith heavy vehicle and the ith empty vehicle meet at the c avoidance chamber or not is determined by y f,e,c Whether the t-th vehicle needs to avoid the y in the c-th avoidance chamber t,c Total time period H; distance L traveled by the tth vehicle at the present time t 。
Further, the scheduling module is configured to: generating a vehicle scheduling pre-command of the underground mine bidirectional single lane according to the resolving result, wherein the vehicle scheduling pre-command comprises the following steps: the vehicle does not have a vehicle intersection condition when passing through the c-th avoidance chamber, the vehicle does not need to avoid but has a vehicle intersection condition when passing through the c-th avoidance chamber, and the vehicle needs to enter the c-th avoidance chamber for vehicle avoidance and waiting time for entering the c-th avoidance chamber; and when the vehicle to be scheduled runs to a scheduling control point, sending a vehicle scheduling instruction according to the vehicle scheduling pre-instruction, wherein the vehicle scheduling instruction is used for scheduling the vehicle to be scheduled.
Further, the scheduling control point refers to: when the current vehicle runs to a position point which is close to the safe distance Ls of the c-th avoidance chamber; the vehicle dispatching instructions comprise: the vehicle to be dispatched moves forward, the vehicle to be dispatched can move forward after waiting for the coming vehicle to enter the avoidance chamber, the vehicle to be dispatched enters the avoidance chamber to wait for vehicle avoidance, and the vehicle drives away from the avoidance chamber after the time h.
Further, the constraints include: decision variable logic constraint, heavy vehicle and empty vehicle intersection uniqueness constraint, vehicle travel distance constraint relative to each avoidance chamber, and avoidance chamber vehicle number constraint.
In the embodiment of the application, a vehicle avoidance condition for triggering a bidirectional single lane of an underground mine is adopted; acquiring road and vehicle parameters on the bidirectional single lane under the condition that the vehicle avoidance condition is triggered, wherein the road and vehicle parameters comprise: bidirectional bicycleTotal track length L T The running speed v of each vehicle and the number N of vehicles capable of being accommodated in the c-th avoidance chamber c Distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c Distance L between the f-th heavy vehicle and the e-th empty vehicle f,e (ii) a Inputting the indexes and the sets of the vehicles and the avoidance chamber into the vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model; inputting the road and vehicle parameters into a vehicle dispatching optimization mathematical model as model parameters of the vehicle dispatching optimization mathematical model; obtaining constraint conditions of the vehicle scheduling optimization mathematical model; under the condition that the constraint condition is met, calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model; obtaining the values of all decision variables under the optimal condition of the values of the objective function, and taking the values of all decision variables as the resolving result of the vehicle scheduling optimization mathematical model; wherein the objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 866765DEST_PATH_IMAGE003
Wherein x is t,c The waiting time of the tth vehicle in the c avoidance chamber, H is the total time period, L t The driving distance of the T-th vehicle at the current moment is represented by T, the index of the vehicle is represented by C, the index of an avoidance chamber is represented by T, the set of vehicles in a bidirectional single lane is represented by T, and the set of the avoidance chamber in the bidirectional single lane and virtual avoidance chambers at two ends are represented by C; and scheduling the vehicles on the underground mine bidirectional single lane according to the calculation result. By the method and the device, the problems in the existing vehicle scheduling scheme of the underground mine are solved, so that orderly avoidance is realized before the vehicle enters an avoidance area, the condition of backing a car to the coming car for a long distance is avoided, and the running efficiency is obviously improved; meanwhile, the passing order of mine transportation is maintained, and the passing safety of vehicles is improved to the maximum extent.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a flow chart of a method for optimizing bidirectional single lane vehicle dispatch in an underground mine according to an embodiment of the application;
fig. 2 is a vehicle transportation network state diagram according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The embodiment provides a bidirectional single-lane vehicle scheduling optimization method for an underground mine, which comprises the following steps:
step S1, triggering a vehicle avoidance condition of a bidirectional single lane of an underground mine;
s2, acquiring road and vehicle parameters on the bidirectional single lane under the condition that the vehicle avoidance condition is triggered, wherein the road and vehicle parameters comprise: total length L of bidirectional single lane T The running speed v of each vehicle and the number N of vehicles capable of being accommodated in the c-th avoidance chamber c Distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c And the distance L between the f-th heavy vehicle and the e-th empty vehicle f,e ；
S3, inputting the indexes and the sets of the vehicles and the avoidance chamber into the vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model;
s4, inputting the road and vehicle parameters serving as model parameters of a vehicle scheduling optimization mathematical model into the vehicle scheduling optimization mathematical model;
s5, obtaining constraint conditions of the vehicle scheduling optimization mathematical model;
s6, under the condition that the constraint conditions are met, calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model;
s7, obtaining the values of all decision variables under the condition that the values of the objective function are optimal, and taking the values of all decision variables as the resolving results of the vehicle scheduling optimization mathematical model; wherein the objective function is: the total waiting time of all vehicles in the model is minimized and the total distance traveled by all vehicles is maximized, i.e. the current road vehicle is driven out of the road during the time period H
Figure 652188DEST_PATH_IMAGE004
Wherein x is t,c The waiting time of the t-th vehicle in the c-th avoidance chamber, H is the total time period, L t The driving distance of the T-th vehicle at the current moment is represented by T, the index of the vehicle is represented by C, the index of an avoidance chamber is represented by T, the set of vehicles in a bidirectional single lane is represented by T, and the set of the avoidance chamber in the bidirectional single lane and virtual avoidance chambers at two ends are represented by C;
and S8, scheduling the vehicles on the underground mine bidirectional single lane according to the calculation result.
Through the steps, the problems in the existing vehicle scheduling scheme of the underground mine are solved, so that the vehicles are orderly avoided before entering the avoidance area, the condition of backing the vehicle to the coming vehicle for a long distance is avoided, and the driving efficiency is obviously improved; meanwhile, the passing order of mine transportation is maintained, and the passing safety of vehicles is improved to the maximum extent.
In the above steps, the decision variables in the mathematical model for vehicle scheduling optimization include: waiting time x of tth vehicle in c-th avoidance chamber t,c Whether the f-th heavy vehicle and the e-th empty vehicle meet at the c-th avoidance chamber or not is determined by y f,e,c Whether the tth vehicle needs to avoid the y in the c avoidance chamber t,c Total time period H; distance traveled by the tth vehicle at the present momentIs far from L t 。
The vehicle scheduling method includes various manners, for example, a vehicle scheduling pre-command of a bidirectional single lane of an underground mine can be generated according to the calculation result, wherein the vehicle scheduling pre-command includes: the vehicle does not have a vehicle intersection condition when passing through the c-th avoidance chamber, the vehicle does not need to avoid but has a vehicle intersection condition when passing through the c-th avoidance chamber, and the vehicle needs to enter the c-th avoidance chamber for vehicle avoidance and waiting time for entering the c-th avoidance chamber; and when the vehicle to be scheduled runs to a scheduling control point, sending a vehicle scheduling instruction according to the vehicle scheduling pre-instruction, wherein the vehicle scheduling instruction is used for scheduling the vehicle to be scheduled.
In this alternative embodiment, the scheduling control point refers to: when the current vehicle runs to a position point which is close to the safe distance Ls of the c-th avoidance chamber; the vehicle dispatching instructions comprise: the vehicle to be dispatched moves forward, the vehicle to be dispatched can move forward after waiting for the coming vehicle to enter the avoidance chamber, the vehicle to be dispatched enters the avoidance chamber to wait for vehicle avoidance, and the vehicle drives away from the avoidance chamber after the time h.
There are many kinds of constraints, for example, the constraints may include: decision variable logic constraint, heavy vehicle and empty vehicle intersection uniqueness constraint, vehicle travel distance constraint relative to each avoidance chamber, and avoidance chamber vehicle number constraint.
An embodiment of the present application will be described below with reference to the drawings. Fig. 1 is a flowchart of a bidirectional single-lane vehicle scheduling optimization method for an underground mine according to an embodiment of the present application, and as shown in fig. 1, the present embodiment provides a bidirectional single-lane vehicle scheduling optimization method for an underground mine, including the following steps:
step 101, decomposing an underground mine transportation network into a plurality of independent vehicle dispatching subunits.
And 102, triggering a bidirectional single-lane vehicle avoidance condition of the underground mine.
And 103, constructing a bidirectional single-lane vehicle scheduling optimization mathematical model of the underground mine.
And 104, resolving a bidirectional single-lane vehicle scheduling optimization mathematical model of the underground mine to obtain predicted vehicle operation scheduling parameters.
And (3) gathering:
t: a set of vehicles;
T F : a set of heavy cars;
T E : a set of empty vehicles;
c: a set of avoidance chambers;
C f,e : and (4) gathering the avoidance chambers between the heavy vehicles f and the empty vehicles e.
Indexing:
t: an index of the car;
f: indexing of heavy cars;
e: an index of empty cars;
c: and avoiding the index of the chamber.
Parameters are as follows:
L T : total length of the hybrid lane;
v: the speed of the vehicle;
N c : the number of vehicles which can be accommodated in the c-th avoidance chamber;
L t,c : the distance between the vehicle t and the avoidance chamber c;
L f,e : distance between heavy vehicle f and empty vehicle e.
Decision variables:
x t,c : waiting time of the t vehicle in the c avoidance chamber;
y f,e,c : whether the f-th heavy vehicle and the e-th empty vehicle meet at the c-th avoidance chamber or not;
y t,c : whether the tth vehicle needs to avoid in the c avoidance chamber or not;
h: a time period;
L t : distance traveled by the t-th vehicle.
An objective function:
Figure 404243DEST_PATH_IMAGE005
。
and (3) constraint:
1) Decision variable logic constraint:
x t,c ≥0 ∀t,c；
y f,e,c =0,1 ∀f,e,c；
y t,c =0,1 ∀t,c；
L t ≥0 ∀t；
H≥0；
y t,c ≥y f,e,c ∀f,e,c。
2) The uniqueness constraint of intersection of the heavy vehicle and the empty vehicle is as follows:
Figure 679235DEST_PATH_IMAGE006
。
3) And (3) restricting the driving distance of each vehicle:
Figure 883952DEST_PATH_IMAGE007
。
4) And (3) restraining the running distance of each vehicle relative to each avoidance chamber:
Figure 348431DEST_PATH_IMAGE008
；
Figure 461051DEST_PATH_IMAGE009
。
5) And the avoidance chamber contains the vehicle number restraint:
Figure 392098DEST_PATH_IMAGE010
；
Figure 536640DEST_PATH_IMAGE011
。
the method for solving the vehicle avoidance mathematical model through a linear programming solver to obtain the control parameters related in the vehicle avoidance method comprises the following steps: waiting time of each vehicle in each avoidance chamber, whether the vehicles of heavy vehicles and empty vehicles meet at each avoidance chamber, and whether each vehicle needs to avoid at each avoidance chamber.
And 105, generating a bidirectional single-lane vehicle scheduling pre-instruction of the underground mine according to the resolving result.
And 106, sending a bidirectional single-lane vehicle dispatching instruction of the underground mine when the vehicle runs to a dispatching control point.
Preferably, in step 101, the plurality of independent vehicle dispatching subunits include, but are not limited to, a one-way single lane, a two-way single lane, an empty vehicle, a heavy vehicle, an avoidance chamber, and a virtual avoidance chamber. Wherein, the virtual dodging chamber is that: the road intersection between the one-way single lane and the two-way single lane allows the parking waiting road section to be abstracted to obtain the whole space.
Preferably, in step 102, the underground mine bidirectional single-lane vehicle avoidance condition is that: and (4) when any vehicle runs to the intersection of the one-way single lane and the two-way single lane, judging that the vehicle avoidance condition is triggered.
Preferably, in step 103, the bidirectional single-lane vehicle dispatching optimization mathematical model of the underground mine comprises: 1) A set and index of vehicle scheduling mathematical models; 2) A vehicle scheduling mathematical model decision variable; 3) Vehicle scheduling mathematical model parameters; 4) A vehicle scheduling mathematical model objective function; 5) And (5) vehicle scheduling mathematical model constraints.
Preferably, the indexing of the vehicle scheduling optimization mathematical model and the indexing in the set include: t is an index of a vehicle, f is an index of a heavy vehicle, e is an index of an empty vehicle, and c is an index of a avoidance chamber; the set includes: t-set of vehicles in a bidirectional single lane, T F Set of heavy vehicles in vehicle set T, T E A set of empty vehicles in the vehicle set T, a set of C-bidirectional single-lane avoidance chambers and two-end virtual avoidance chambers, and C f,e -a set of avoidance chambers between the fth heavy vehicle and the e empty vehicle.
Preferably, the decision variables in the mathematical model for vehicle scheduling optimization include: x is the number of t,c Waiting time of tth vehicle in c avoidance chamber, y f,e,c Whether the ith heavy vehicle and the ith empty vehicle meet at the c avoidance chamber, y t,c -whether the tth vehicle is in the c avoiding chamberAvoidance is required; h-total time period; l is t -distance traveled by the tth vehicle at the present moment.
Preferably, the model parameters in the vehicle scheduling optimization mathematical model include: l is a radical of an alcohol T Total length of two-way single lane, v vehicle speed, N c Number of vehicles accommodated in the c-th avoidance chamber, L t,c Distance L between the tth vehicle and the c-th avoidance chamber at the current moment f,e -distance between the f-th heavy vehicle and the e-th empty vehicle.
Preferably, the vehicle scheduling optimization mathematical model objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 211335DEST_PATH_IMAGE012
；
Preferably, the vehicle scheduling optimization mathematical model constraints include: 1) Making a decision variable logic constraint; 2) The uniqueness constraint of intersection of the heavy vehicle and the empty vehicle; 3) The driving distance of each vehicle is restrained; 4) The running distance of each vehicle relative to each avoidance chamber is restricted; 5) The avoidance chamber accommodates the vehicle number constraint.
Preferably, the vehicle scheduling optimization mathematical model decision variable logical constraint comprises: x is the number of t,c ≥0∀t,c；y f,e,c =0,1∀f,e,c；y t,c =0,1 ∀t,c；L t ≥0∀t；H≥0；y t,c ≥y f,e,c ∀f,e,c。
Preferably, in step 104, the vehicle scheduling optimization mathematical model heavy vehicle and empty vehicle intersection uniqueness constraint means: in the time period T, for any c-th avoidance chamber, the f-th heavy vehicle must be intersected with one empty vehicle in the empty vehicle set E once, namely
Figure 187250DEST_PATH_IMAGE013
。
Preferably, in step 104, each vehicle distance-traveled constraint of the vehicle scheduling optimization mathematical model means: distance traveled by the tth vehicle in the total time period H =: (Total time period H-total waiting time of the tth vehicle in each avoidance chamber (including virtual avoidance chamber) × vehicle traveling speed v, namely L t =（H-∑cx t,c ）×v。
Preferably, in step 104, the vehicle scheduling optimization mathematical model traveling distance constraint of each vehicle relative to each avoidance chamber comprises: 1) The driving distance of the heavy vehicle relative to each avoidance chamber is restrained; 2) And the empty vehicles are restrained relative to the running distance of each avoidance chamber. The travel distance constraint of the heavy vehicle relative to each avoidance chamber is as follows: in the total time period H, no matter whether the fth heavy vehicle is intersected with an empty vehicle in the c avoidance chamber vehicle or not, the driving distance of the fth heavy vehicle is not less than the distance from the fth heavy vehicle to the c avoidance chamber, namely
Figure 554778DEST_PATH_IMAGE014
(ii) a The travel distance constraint of the empty vehicle relative to each avoidance chamber is similar to that of the heavy vehicle, and the formula is expressed as follows:
Figure 61982DEST_PATH_IMAGE015
。
preferably, in step 104, the constraint of the number of vehicles accommodated in the vehicle scheduling optimization mathematical model avoidance chamber is that: for any c-th avoidance chamber, in the time period T, the number of vehicles in the avoidance chamber at any time should not exceed the maximum number of vehicles capable of being accommodated in the avoidance chamber, namely
Figure 789636DEST_PATH_IMAGE016
。
Preferably, in step 104, the bidirectional single-lane vehicle dispatching optimization mathematical model of the underground mine is subjected to model solution through a linear programming solver; under the trigger of the current avoidance condition, the model calculation result comprises the following steps: 1) Whether the predicted f-th heavy vehicle and the predicted e-th empty vehicle have vehicle intersection at the c-th avoidance chamber or not is judged; 2) Whether the t vehicle needs to avoid in the c avoiding chamber or not; 3) Waiting time of the t vehicle in the c avoidance chamber; 4) Triggering a time period H of the lower model under the current avoidance condition; 5) Distance traveled by the t-th vehicle for time period H.
Preferably, in step 105, for a single vehicle, within the time period H, the following underground mine bidirectional single-lane vehicle scheduling pre-instructions may be generated: 1) The vehicle passes through the c-th avoidance chamber without the vehicle intersection condition; 2) The vehicle does not need to be avoided through the c-th avoidance chamber, but the vehicle intersection condition exists; 3) The vehicle needs to enter the c avoidance chamber for vehicle avoidance and waiting time for entering the c avoidance chamber.
Preferably, in step 106, the scheduling control point refers to: and when the current vehicle runs to a position point which is close to the safe distance Ls of the c-th avoidance chamber.
Preferably, in step 106, the generating of the corresponding vehicle dispatching command according to the pre-command means: corresponding to the pre-command, the scheduling control point of the current vehicle running to the mth avoidance chamber can generate the following command: 1) The current vehicle is moving forward; 2) The current vehicle can move forward after waiting for the oncoming vehicle to enter the avoidance chamber; 3) And (4) waiting when the current vehicle enters the avoidance chamber, avoiding the vehicle, and driving away from the avoidance chamber after the time h.
The following description is made with reference to an example.
A1: any vehicle in a mine in a certain area runs to an intersection of a one-way single lane and a two-way single lane, and a vehicle avoidance condition is triggered, as shown in fig. 2.
The total length of the mixed lane, the vehicle speed, the number of avoidance chambers and the number of vehicles capable of being accommodated in each avoidance chamber are sequentially set, and the table 1 shows.
TABLE 1 list of model parameters
Figure 574052DEST_PATH_IMAGE017
A2: and the distance between each vehicle and each avoidance chamber at the moment of triggering the model, and the distance between each heavy vehicle and each empty vehicle are shown in a table 2.
TABLE 2 model triggering time distance parameter between each vehicle and each chamber
Figure 99099DEST_PATH_IMAGE018
A3: the constructed vehicle avoidance model comprises the following steps:
and (3) gathering:
t: a set of vehicles;
T F : a set of heavy cars;
T E : a set of empty vehicles;
c: a set of avoidance chambers;
C f,e : and (4) gathering the avoidance chambers between the heavy vehicles f and the empty vehicles e.
Indexing:
t: indexing of the cars;
f: an index of heavy cars;
e: an index of empty cars;
c: and avoiding the index of the chamber.
Parameters are as follows:
L T : total length of the hybrid lane;
v: the speed of the vehicle;
N c : the number of vehicles which can be accommodated in the c-th avoidance chamber;
L t,c : the distance between the vehicle t and the avoidance chamber c;
L f,e : distance between heavy vehicle f and empty vehicle e.
Decision variables:
x t,c : waiting time of the t vehicle in the c avoidance chamber;
y f,e,c : whether the f-th heavy vehicle and the e-th empty vehicle meet at the c-th avoidance chamber or not;
y t,c : whether the tth vehicle needs to avoid in the c avoidance chamber or not;
h: a time period;
L t : distance traveled by the tth vehicle.
An objective function:
Figure 31283DEST_PATH_IMAGE019
。
and (3) constraint:
1) Decision variable logic constraint:
x t,c ≥0 ∀t,c；
y f,e,c= 0,1∀f,e,c；
y t,c= 0,1 ∀t,c；
L t ≥0 ∀t；
H≥0；
y t,c ≥y f,e,c ∀f,e,c。
2) The uniqueness constraint of intersection of the heavy vehicle and the empty vehicle is as follows:
Figure 297048DEST_PATH_IMAGE020
。
3) And (3) restricting the running distance of each vehicle:
Figure 60605DEST_PATH_IMAGE021
。
4) And (3) restraining the running distance of each vehicle relative to each avoidance chamber:
Figure 35514DEST_PATH_IMAGE022
；
Figure 376365DEST_PATH_IMAGE023
。
5) And the avoidance chamber contains the vehicle number restraint:
Figure 462133DEST_PATH_IMAGE024
；
Figure 80196DEST_PATH_IMAGE025
。
a4: solving the model through a linear programming solver to obtain control parameters related in the vehicle avoidance method comprises the following steps: whether the heavy vehicles and the empty vehicles are intersected at each avoidance chamber, whether each vehicle needs to be avoided at each avoidance chamber and waiting time are shown in a table 3.
TABLE 3 vehicle avoidance prediction results
Figure 475274DEST_PATH_IMAGE027
A5: and resolving the model through a linear programming resolver to obtain that the total time period H of all vehicles running out of the road on the current road in the vehicle avoidance method is 10.3min.
According to the underground mine bidirectional single-lane vehicle scheduling optimization method provided by the embodiment, an underground mine transportation network is decomposed into a plurality of independent vehicle scheduling subunits, underground mine bidirectional single-lane vehicle avoidance conditions are triggered, an underground mine bidirectional single-lane vehicle scheduling optimization mathematical model is constructed, the underground mine bidirectional single-lane vehicle scheduling optimization mathematical model is solved, predicted vehicle operation scheduling parameters are obtained, underground mine bidirectional single-lane vehicle scheduling pre-instructions are generated according to the solving results, the underground mine bidirectional single-lane vehicle scheduling instructions are sent out when the vehicles run to a scheduling control point, the underground mine bidirectional single-lane vehicle avoidance passing requirements are met, efficient passing of mine vehicles is achieved, and the maximum economic benefits are brought to enterprises; meanwhile, the vehicle passing order is effectively organized, and the vehicle passing safety is guaranteed.
In this embodiment, an electronic device is provided, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the method in the above embodiments.
The programs described above may be run on a processor or stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that enable storage of information by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
Such an apparatus or system is provided in this embodiment. The device is called two-way single lane vehicle scheduling optimizing apparatus in underground mine, includes: the triggering module is used for triggering the vehicle avoidance condition of the underground mine bidirectional single lane; a first obtaining module, configured to obtain road and vehicle parameters on the bidirectional single lane when the vehicle avoidance condition is triggered, where the road and vehicle parameters include: total length L of bidirectional single lane T The running speed v of each vehicle and the number N of containable vehicles in the c-th avoidance chamber c Distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c Distance L between the f-th heavy vehicle and the e-th empty vehicle f,e (ii) a The first input module is used for inputting the indexes and the sets of the vehicles and the avoidance chamber into the vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model; the second input module is used for inputting the road and vehicle parameters into the vehicle scheduling optimization mathematical model as model parameters of the vehicle scheduling optimization mathematical model; the second acquisition module is used for acquiring the constraint conditions of the vehicle scheduling optimization mathematical model; a calculation module for calculating the vehicle if the constraint condition is satisfiedScheduling values of objective functions corresponding to values of different decision variables in the optimized mathematical model; the third acquisition module is used for acquiring the values of all decision variables under the condition that the value of the objective function is optimal, and taking the values of all decision variables as the resolving result of the vehicle scheduling optimization mathematical model; wherein the objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 54154DEST_PATH_IMAGE028
min, wherein, x t,c The waiting time of the tth vehicle in the c avoidance chamber, H is the total time period, L t The driving distance of the T-th vehicle at the current moment is represented by T, the index of the vehicle is represented by C, the index of an avoidance chamber is represented by T, the set of vehicles in a bidirectional single lane is represented by T, and the set of the avoidance chamber in the bidirectional single lane and virtual avoidance chambers at two ends are represented by C; and the scheduling module is used for scheduling the vehicles on the underground mine bidirectional single lane according to the resolving result.
The system or the apparatus is configured to implement the functions of the method in the foregoing embodiments, and each module in the system or the apparatus corresponds to each step in the method, which has been already described in the method, and is not described again here.
For example, the scheduling module is configured to: generating a vehicle scheduling pre-instruction of the underground mine bidirectional single lane according to the resolving result, wherein the vehicle scheduling pre-instruction comprises the following steps: the vehicle does not have a vehicle intersection condition when passing through the c-th avoidance chamber, the vehicle does not need to avoid but has a vehicle intersection condition when passing through the c-th avoidance chamber, and the vehicle needs to enter the c-th avoidance chamber for vehicle avoidance and waiting time for entering the c-th avoidance chamber; and when the vehicle to be scheduled runs to a scheduling control point, sending a vehicle scheduling instruction according to the vehicle scheduling pre-instruction, wherein the vehicle scheduling instruction is used for scheduling the vehicle to be scheduled.
The problems in the existing vehicle scheduling scheme of the underground mine are solved through the embodiment, so that the vehicle is orderly avoided before entering the avoidance area, the condition of backing a car to the coming car for a long distance is avoided, and the running efficiency is obviously improved; meanwhile, the passing order of mine transportation is maintained, and the passing safety of vehicles is improved to the maximum extent.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. The two-way single lane vehicle scheduling optimization method for the underground mine is characterized by comprising the following steps:
triggering a vehicle avoidance condition of a bidirectional single lane of the underground mine;
acquiring road and vehicle parameters on the bidirectional single lane under the condition that the vehicle avoidance condition is triggered, wherein the road and vehicle parameters comprise: total length L of bidirectional single lane T The running speed v of each vehicle and the number N of containable vehicles in the c-th avoidance chamber c The distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c And the distance L between the f-th heavy vehicle and the e-th empty vehicle f,e ；
Inputting the indexes and the sets of the vehicles and the avoidance chamber into a vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model;
inputting the road and vehicle parameters into a vehicle dispatching optimization mathematical model as model parameters of the vehicle dispatching optimization mathematical model;
obtaining constraint conditions of the vehicle scheduling optimization mathematical model;
under the condition that the constraint condition is met, calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model;
obtaining the value of each decision variable under the condition that the value of the objective function is optimal, and making the value of each decision variable intoCalculating results of the mathematical model for the vehicle dispatching optimization; wherein the objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 474000DEST_PATH_IMAGE001
Wherein x is t,c The waiting time of the tth vehicle in the c avoidance chamber, H is the total time period, L t The distance traveled by the t-th vehicle at the current moment, t is the index of the vehicle, c is the index of the avoidance chamber,Tthe system is a set of vehicles in a bidirectional single lane, and C is a set of avoidance chambers in the bidirectional single lane and virtual avoidance chambers at two ends;
and scheduling the vehicles on the underground mine bidirectional single lane according to the calculation result.
2. The method of claim 1, wherein the decision variables in the vehicle dispatch optimization mathematical model comprise: waiting time x of tth vehicle in c-th avoidance chamber t,c Whether the ith heavy vehicle and the ith empty vehicle meet at the c avoidance chamber or not is determined by y f,e,c Whether the t-th vehicle needs to avoid the y in the c-th avoidance chamber t,c Total time period H; distance L traveled by the tth vehicle at the present time t 。
3. The method of claim 2, wherein scheduling vehicles on the underground mine bidirectional single lane according to the solution comprises:
generating a vehicle scheduling pre-command of the underground mine bidirectional single lane according to the resolving result, wherein the vehicle scheduling pre-command comprises the following steps: the vehicle passes throughcThe avoidance chamber does not have the condition of vehicle intersection, and the vehicle passes through the first chambercThe avoidance chamber does not need to avoid but has the condition of vehicle intersection and the vehicle needs to enter the first chambercThe avoidance chamber carries out vehicle avoidance and enters the firstcWaiting time of the avoidance chamber;
and when the vehicle to be dispatched runs to a dispatching control point, sending a vehicle dispatching instruction according to the vehicle dispatching pre-instruction, wherein the vehicle dispatching instruction is used for dispatching the vehicle to be dispatched.
4. The method of claim 3,
the scheduling control point is as follows: when the current vehicle runs to a position point which is close to the safe distance Ls of the c-th avoidance chamber;
the vehicle dispatching instructions comprise: the vehicle to be dispatched moves forward, the vehicle to be dispatched can move forward after waiting for the coming vehicle to enter the avoidance chamber, the vehicle to be dispatched enters the avoidance chamber to wait for the vehicle to avoid, and the vehicle is driven away from the avoidance chamber after h.
5. The method according to any one of claims 1 to 4,
the constraint conditions include: decision variable logic constraint, heavy vehicle and empty vehicle intersection uniqueness constraint, vehicle travel distance constraint relative to each avoidance chamber, and avoidance chamber vehicle number constraint.
6. The utility model provides a two-way single lane vehicle scheduling optimizing apparatus in underground mine which characterized in that includes:
the triggering module is used for triggering the vehicle avoidance condition of the underground mine bidirectional single lane;
a first obtaining module, configured to obtain road and vehicle parameters on the bidirectional single lane when the vehicle avoidance condition is triggered, where the road and vehicle parameters include: total length L of bidirectional single lane T The running speed v of each vehicle and the number N of vehicles capable of being accommodated in the c-th avoidance chamber c The distance L between the tth vehicle and the c-th avoidance chamber at the current moment t,c Distance L between the f-th heavy vehicle and the e-th empty vehicle f,e ；
The first input module is used for inputting the indexes and the sets of the vehicles and the avoidance chamber into the vehicle scheduling optimization mathematical model as the indexes and the sets of the vehicle scheduling optimization mathematical model;
the second input module is used for inputting the road and vehicle parameters into the vehicle scheduling optimization mathematical model as model parameters of the vehicle scheduling optimization mathematical model;
the second acquisition module is used for acquiring the constraint conditions of the vehicle scheduling optimization mathematical model;
the calculation module is used for calculating values of objective functions corresponding to values of different decision variables in the vehicle scheduling optimization mathematical model under the condition that the constraint conditions are met;
the third acquisition module is used for acquiring the values of all decision variables under the condition that the value of the objective function is optimal, and taking the values of all decision variables as the resolving result of the vehicle scheduling optimization mathematical model; wherein the objective function is: in the time period H when the current road vehicle completely exits the road, the total waiting time of all vehicles in the model is minimized and the total travel distance of all vehicles is maximized, i.e.
Figure 832300DEST_PATH_IMAGE002
Wherein x is t,c The waiting time of the tth vehicle in the c avoidance chamber, H is the total time period, L t The distance traveled by the tth vehicle at the current moment, t is the index of the vehicle, c is the index of the avoidance chamber,Tthe system is a set of vehicles in a bidirectional single lane, and C is a set of avoidance chambers in the bidirectional single lane and virtual avoidance chambers at two ends;
and the scheduling module is used for scheduling the vehicles on the underground mine bidirectional single lane according to the resolving result.
7. The apparatus of claim 6, wherein the decision variables in the vehicle dispatch optimization mathematical model comprise: waiting time x of tth vehicle in c-th avoidance chamber t,c Whether the ith heavy vehicle and the ith empty vehicle meet at the c avoidance chamber or not is determined by y f,e,c Whether the t-th vehicle needs to avoid the underground chamber at the c-thTo avoid y t,c Total time period H; distance L traveled by the tth vehicle at the present time t 。
8. The apparatus of claim 7, wherein the scheduling module is configured to:
generating a vehicle scheduling pre-instruction of the underground mine bidirectional single lane according to the resolving result, wherein the vehicle scheduling pre-instruction comprises the following steps: the vehicle passes throughcThe avoidance chamber does not have the condition of vehicle intersection, and the vehicle passes through the first chambercThe avoidance chamber does not need to avoid but has the condition of vehicle intersection and the vehicle needs to enter the first chambercThe avoidance chamber carries out vehicle avoidance and enters the firstcWaiting time of the avoidance chamber;
and when the vehicle to be dispatched runs to a dispatching control point, sending a vehicle dispatching instruction according to the vehicle dispatching pre-instruction, wherein the vehicle dispatching instruction is used for dispatching the vehicle to be dispatched.
9. The apparatus of claim 8,
the scheduling control point is as follows: when the current vehicle runs to a position point which is close to the safe distance Ls of the c-th avoidance chamber;
the vehicle dispatching instructions comprise: the vehicle to be dispatched moves forward, the vehicle to be dispatched can move forward after waiting for the coming vehicle to enter the avoidance chamber, the vehicle to be dispatched enters the avoidance chamber to wait for vehicle avoidance, and the vehicle drives away from the avoidance chamber after the time h.
10. The apparatus according to any one of claims 6 to 9,
the constraint conditions include: the method comprises the following steps of decision variable logic constraint, intersection uniqueness constraint of heavy vehicles and empty vehicles, travel distance constraint of each vehicle relative to each avoidance chamber, and number constraint of vehicles accommodated in the avoidance chamber.
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