CN109064098B - Vehicle dynamic scheduling method and system - Google Patents
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Abstract
The invention discloses a vehicle dynamic scheduling method and a vehicle dynamic scheduling system, which have good real-time performance and accuracy, are simple and reliable to implement, are suitable for real-time vehicle scheduling in a logistics company park, well solve the problems of accurately positioning and scheduling logistics vehicles in real time when the logistics vehicles in the logistics company park are too dense, effectively improve the congestion condition of the logistics vehicles in the logistics park and improve the vehicle scheduling efficiency in the park. The technical scheme is as follows: the method comprises the steps of firstly obtaining relevant information of each logistics vehicle in a logistics park by an RFID reader, then determining relevant information of vehicles near one logistics vehicle, and then dynamically scheduling the logistics vehicles by adopting a priority multi-service-window waiting M/M/n queuing model according to the obtained accurate positions of the logistics vehicles.
Description
Technical Field
The invention relates to a vehicle positioning and dynamic scheduling technology, in particular to a dynamic scheduling technology for vehicles in a logistics park based on a queuing theory.
Background
With the rapid development of industries such as e-commerce and the like, logistics companies have more delivery and discharge services, so that a large number of logistics vehicles enter and exit a factory area, but the vehicles have no effective information guidance in the factory area and stop at will, which often causes a series of problems such as vehicle congestion, low discharge efficiency, untimely discharge and the like in the factory area. The problems also cause the low efficiency of enterprise transaction, and seriously restrict the development of logistics companies. The traditional vehicle scheduling is difficult to meet the requirements of real-time performance, high efficiency and low cost in the current e-commerce environment. At present, some logistics companies have introduced a GPS logistics vehicle dispatching system in consideration of the demand of cargo transportation, however, when a high-rise building is in a dense road section or is blocked by a viaduct, a tunnel and the like, the GPS signal is severely attenuated, and the signal strength is weak in some cities with mountains, which all can cause the GPS system signal to be unstable or even fail to work normally. Meanwhile, because the GPS dispatching system signal is often misreported by interfered logistics vehicles when reporting stations, the real-time, stable and accurate dispatching of the vehicles in the logistics park becomes a difficult subject.
Therefore, at the present stage, the following two major problems need to be solved for vehicle scheduling in the park of the logistics company:
(1) how accurately logistics vehicles are positioned in the park: only if the vehicles arriving at the park are accurately positioned enough and the specific position of each logistics vehicle is known, the logistics vehicles can be better dispatched.
(2) How to dynamically schedule logistics vehicles in real time in a park: when the position of each logistics vehicle is determined, each vehicle is optimally scheduled according to specific conditions, and the loading and unloading efficiency of the logistics vehicles is improved.
The RFID is a wireless radio frequency identification technology, does not depend on satellite signals, can also schedule vehicles in real time in high-rise building intensive road sections or sheltered areas such as viaducts, tunnels and the like or in multiple mountain areas, has low construction cost, automatically sends a signal to be received by an antenna after a vehicle-mounted radio frequency card enters a receiving and transmitting antenna area, then processes and integrates the related information to be transmitted to a scheduling center, and the scheduling center effectively schedules a plurality of logistics vehicles.
The queuing theory is a mathematical theory and a method for researching the random accumulation and dispersion phenomenon of a system and the working process of a random service system, the number of logistics vehicles entering a logistics park is large, and the situations of congestion, blockage and the like can be certainly caused. The queuing rule is divided into a waiting system, a loss system and a mixing system. In intense market competition, how to efficiently transport goods is the most concerned problem for express companies.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to solve the problems and provides a vehicle dynamic scheduling method and a vehicle dynamic scheduling system, which have good real-time performance and accuracy, are simple and reliable to implement, are suitable for real-time vehicle scheduling in a park of a logistics company, well solve the problems of accurately positioning and scheduling logistics vehicles in real time when the logistics vehicles in the park of the logistics company are too dense, effectively improve the congestion condition of the logistics vehicles in the park, and improve the vehicle scheduling efficiency in the park.
The technical scheme of the invention is as follows: the invention discloses a vehicle dynamic scheduling method, which comprises the following steps:
step 1: acquiring relevant information including vehicle positions and destinations of logistics vehicles arriving in a logistics park;
step 2: determining relevant information including vehicle positions and destinations of other logistics vehicles near a certain logistics vehicle;
and step 3: establishing a priority multi-service window waiting system M/M/n queuing model according to the acquired vehicle position of the logistics vehicle;
and 4, step 4: and obtaining an optimal scheduling scheme based on the priority multi-service window waiting M/M/n queuing model, and dynamically scheduling each logistics vehicle according to the optimal scheduling scheme.
According to an embodiment of the vehicle dynamic scheduling method, in step 1, the related information of the logistics vehicle is acquired through the RFID reader.
According to an embodiment of the vehicle dynamic scheduling method of the present invention, in step 1, a distance estimation method is used to determine the position of the logistics vehicle: and (3) identifying the tags by using surface acoustic waves, measuring the distance between each reader-writer and the tags by using a signal arrival time based method, and positioning the tags by using a trilateration method based on three distance values.
According to an embodiment of the vehicle dynamic scheduling method of the present invention, in step 3, the multiple service windows refer to a plurality of service desks in the system for servicing the logistics vehicles; the M/M/n queuing model is a queuing model established based on three elements that the arrival interval time of the logistics vehicles is in negative exponential distribution, the service time of the loading and unloading stations is in negative exponential distribution, and a plurality of loading and unloading station service desks meet the queuing theory; the waiting system is a queuing model established based on the condition that logistics vehicles need to wait in a queue after the logistics vehicles carry out loading and unloading tasks; the priority is a system that the logistics vehicles sent to a certain regional division are about to finish loading and unloading according to the feedback of the loading and unloading stations, and the logistics vehicles corresponding to the regional division are preferentially dispatched.
According to an embodiment of the vehicle dynamic scheduling method of the present invention, in step 4, the optimal scheduling scheme refers to the number of loading and unloading stations required to be set for minimizing the logistics vehicle queuing time.
The invention also discloses a vehicle dynamic scheduling system, which comprises:
the logistics vehicle information acquisition module is used for acquiring relevant information including vehicle positions and destinations of logistics vehicles arriving in the logistics park;
the logistics vehicle vicinity information acquisition module is used for determining the relevant information including the vehicle position and the destination of other logistics vehicles in the vicinity of a certain logistics vehicle;
the model establishing module is used for establishing a priority multi-service window waiting M/M/n queuing model according to the acquired vehicle position of the logistics vehicle;
and the dynamic scheduling module obtains an optimal scheduling scheme based on the M/M/n queuing model with the priority and the multiple service windows for waiting, and dynamically schedules each logistics vehicle according to the optimal scheduling scheme.
According to an embodiment of the vehicle dynamic scheduling system, the logistics vehicle information acquisition module acquires the related information of the logistics vehicle through the RFID reader.
According to an embodiment of the vehicle dynamic scheduling system of the present invention, the logistics vehicle information obtaining module determines the position of the logistics vehicle by using a distance estimation method: and (3) identifying the tags by using surface acoustic waves, measuring the distance between each reader-writer and the tags by using a signal arrival time based method, and positioning the tags by using a trilateration method based on three distance values.
According to an embodiment of the vehicle dynamic scheduling system, in the model building module, the multiple service windows refer to a plurality of service desks in the system for serving the logistics vehicles; the M/M/n queuing model is a queuing model established based on three elements that the arrival interval time of the logistics vehicles is in negative exponential distribution, the service time of the loading and unloading stations is in negative exponential distribution, and a plurality of loading and unloading station service desks meet the queuing theory; the waiting system is a queuing model established based on the condition that logistics vehicles need to wait in a queue after the logistics vehicles carry out loading and unloading tasks; the priority is a system that the logistics vehicles sent to a certain regional division are about to finish loading and unloading according to the feedback of the loading and unloading stations, and the logistics vehicles corresponding to the regional division are preferentially dispatched.
According to an embodiment of the vehicle dynamic scheduling system, in the dynamic scheduling module, the optimal scheduling scheme refers to the number of the loading and unloading stations required to be set for minimizing the logistics vehicle queuing time.
Compared with the prior art, the invention has the following beneficial effects: according to the method, the RFID reader is used for acquiring the related information of each logistics vehicle in the logistics park, the related information of vehicles near one logistics vehicle is determined, and the logistics vehicles are dynamically dispatched by adopting a priority multi-service-window waiting M/M/n queuing model according to the acquired accurate positions of the logistics vehicles. Compared with the traditional technology, the RFID technology is applied to vehicle scheduling, and intelligent and efficient logistics vehicle scheduling work can be achieved. The invention adopts the priority multi-service window waiting system M/M/n queuing model to reasonably schedule the logistics vehicles in the logistics park, thereby improving the vehicle scheduling efficiency of the park and further improving the market competitiveness of the logistics company.
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The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
FIG. 1 shows a flow chart of an embodiment of a vehicle dynamic scheduling method of the present invention.
Fig. 2 shows a schematic diagram of a trilateration method.
FIG. 3 shows a block diagram of one embodiment of the vehicle dynamic dispatch system of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.
Fig. 1 shows a flow of an embodiment of a vehicle dynamic scheduling method of the present invention. Referring to fig. 1, the following is a detailed description of implementation steps of the vehicle dynamic scheduling method of the embodiment.
Step S1: and acquiring related information of each logistics vehicle arriving in the logistics park by the RFID reader.
The RFID reader, namely the radio frequency identification technology, can sense the RFID label on any logistics vehicle and acquire related information including the position, destination and the like of the logistics vehicle.
In this step, the location of the logistics vehicle is determined by using the RFID technology, and a distance estimation method (i.e., an algorithm for estimating the location of the object to be located by using the characteristics of a triangle) is used. Referring to fig. 2, a distance d between each reader/writer i and a tag is measured based on a signal arrival time method using a surface acoustic wave identification tagiIt can be expressed as the following equation:
di＝Tt-Ts-Tx-Tc
wherein, TxIs the time delay of the system, TcIs the cable transmission delay between the receiving antenna and the demodulator during the pre-calibration pulse, TSIs the time delay, T, of all tagstIndicating the time of arrival of the signal at each reader/writer i. With 3 estimated distances, the system locates the tag using trilateration.
Step S2: information about vehicles in the vicinity of a logistics vehicle is determined.
In this step, the related information of other vehicles near a certain logistics vehicle is the destination of the logistics vehicle before and after the logistics vehicle according to the related information such as the position and destination of each logistics vehicle in the arrival logistics park acquired by the RFID reader.
Step S3: and adopting a priority multi-service window waiting system M/M/n queuing model according to the position of each logistics vehicle acquired in the step S1.
The M/M/n queuing model with the priority multi-service window waiting system in the step is a queuing model with the priority of the waiting system of a multi-service object multi-service platform. The classification is carried out according to three main characteristics of a queuing system, and the classification is carried out by using Kendel marks X/Y/Z: filling out the distribution of successive arrival intervals at X; filling service time distribution at Y; fill out the number of parallel stations at Z. The multi-service window refers to a plurality of service desks in the system for serving the logistics vehicles; the M/M/n queuing model is a queuing model established based on three elements that the arrival interval time of the logistics vehicles is in negative exponential distribution, the service time of the loading and unloading stations is in negative exponential distribution, and a plurality of loading and unloading station service desks meet the queuing theory; the waiting system is a queuing model established based on the condition that logistics vehicles need to wait in a queue after the logistics vehicles carry out loading and unloading tasks; the priority level means that the logistics vehicles sent to a certain regional department are about to finish loading and unloading according to the feedback of the loading and unloading stations, and the logistics vehicles corresponding to the regional department can be preferentially dispatched.
The model is built as follows.
In order to facilitate the research on the priority multi-service window waiting system M/M/n queuing model, a certain assumption is made on an actual system based on the sequence of three elements of the queuing system:
(1) there are two priorities, the order of service is based on priority first, in terms of "first come first serve" within the same priority.
(2) The logistics vehicles are vehicles which arrive at the park, the arrival of the two priority logistics vehicles is random, single and independent, the time intervals among the two priority logistics vehicles obey exponential distribution, and the number of arriving customers in a certain time period obeys poisson distribution.
(3) There are several service desks in the system, each service desk can only serve one logistics vehicle at a time, and the service capacity of each service desk is assumed to be the same. When the logistics vehicles arrive, if the service stations are idle, the logistics vehicles are served, and if the service stations are busy, the shortest queue is selected for waiting.
(4) The service time is independent of the arrival time of the logistics vehicles, and the logistics vehicles of each service desk in the system are arranged in a queue.
The logistics vehicles arrive at a logistics park randomly and obey Poisson distribution, and the probability of arriving n logistics vehicles within time t is as follows:
When the logistics vehicle arrives and all loading and unloading stations are occupied, the logistics vehicle needs to wait in line, so the queuing model is waiting. Loading and unloading the logistics vehicles is a priority service.
The plurality of loading and unloading stations are arranged in parallel, the service time is of a random type, the service time v of the random type follows a negative exponential distribution, the distribution function of which is:
P(v≤t)＝1-e-μt(t≥0)
where mu is the average service rate,
is the average service time.
After the processing, a priority multi-service window waiting system M/M/n queuing model is established.
Step S4: and dynamically scheduling the logistics vehicles.
In the step, an optimal scheduling scheme is calculated based on a priority multi-service window waiting system M/M/n queuing model, the number of loading and unloading stations required to be set for enabling the logistics vehicle to have the shortest queuing time is calculated, and each logistics vehicle is dynamically scheduled according to the optimal scheduling scheme.
The process of deriving the optimal scheduling scheme from the queuing model is, for example, as follows.
Assuming that the first-level logistics vehicle is according to the parameter lambda1(λ1>0) The second-level logistics vehicles arrive according to the parameter lambda2(λ2>0) And by analogy, the first-level vehicle represents a vehicle which is normally in line, and the second-level vehicle represents that if a certain logistics vehicle is waiting in line, but no vehicle needs to be served at the loading and unloading station of the corresponding regional area, the vehicle can be dispatched to the loading and unloading station of the corresponding regional area in advance.
Because the service time required for each logistics vehicle is independent, assuming a negative exponential distribution obeying the same parameter μ (μ > 0). Assuming that n logistics vehicles arrive, there are m loading and unloading stations per regional division.
Order to
ρ is a measure of the capacity of the loading and unloading station to undertake the loading and unloading tasks and meet the demand. The steady state index is then:
let Pn＝limt→∞P{N(t)＝n},n＝0,1,2…,
When P ism＜1,
Regardless of the initial conditions.
And has the following components:
wherein
Assuming that the probability that a high-priority logistics vehicle can preferentially go to the corresponding loading and unloading point is alphak(0＜αk< 1) wherein
And (4) showing.
Denote the average queue length in steady state by NtRepresenting the average wait queue length in steady state, then:
by WkRepresenting the average customer waiting time (including service time) for k priorities at steady state, and m represents the number of sites served for loading and unloading, the model can be expressed as follows:
when in use
B0＝1,
It is assumed here that
Thereby enabling the kth priority to reach steady state.
The average captain (including the customers being served) for the kth priority at steady state can be expressed as:
Lk＝λkWk,k＝1,2,…,N
let us now assume that we compare the number of loading and unloading stations of a regional division, assuming that the rate of arrival of the logistics vehicles λ is 1, with the vehicle arrival rate λ of the first priority1Vehicle arrival rate λ of second priority 0.42The station loading rate mu of each area is 6, and the situation when s is 1 and s is 2 is compared, so that the best loading station needs to be set up for each area is judged.
When s is 1, λ1When the value is 0.4, mu is 6,
when s is 2, lambda1When the value is 0.4, mu is 6,
when s is 1, λ2When the value is 0.6, mu is 6,
when s is 2, lambda2When the value is 0.6, mu is 6,
from the comparison of the above calculations, when s is 1, that is, there is only one loading/unloading point per division, the vehicle of the first priority has to wait for 0.238 hours, and the vehicle of the second priority has to wait for only 0.325 hours. However, when s is 2, i.e., there are two loading and unloading points per area, the first priority vehicle waits for 0.029 hours and the second priority vehicle only waits for 0.066 hours. Therefore, the waiting time of the logistics vehicles can be greatly shortened by adding one regional loading and unloading station.
Considering the cost of building a logistics park, a plurality of loading and unloading are not required to be set up in each regional division, and an optimal method for simultaneously reducing the waiting time of vehicles and the construction cost of the logistics park is required. Based on big data analysis, a plurality of loading and unloading stations can be set up in a regional division with a large quantity of express mails, and only one loading and unloading station can be set up in a regional division with a small quantity of express mails.
As can be seen from the above example, the waiting time of the logistics vehicles can be greatly shortened by setting up one more station for each regional division. Considering the construction cost of the logistics park, 2-3 loading and unloading stations can be arranged in the regions with large express quantity, 1-2 loading and unloading stations are arranged in relatively more regions, and only 1 loading and unloading station is arranged in relatively less regions, so that the queuing rate of logistics vehicles is greatly reduced, and the vehicle dispatching efficiency of the logistics park can be greatly improved.
Fig. 3 shows the structure of an embodiment of the vehicle dynamic scheduling system of the present invention. Referring to fig. 3, the system of the present embodiment includes: the system comprises a logistics vehicle information acquisition module, a logistics vehicle vicinity information acquisition module, a model building module and a dynamic scheduling module.
The logistics vehicle information acquisition module is used for acquiring relevant information including vehicle positions and destinations of logistics vehicles arriving in the logistics park. The logistics vehicle information acquisition module acquires the related information of the logistics vehicle through the RFID reader. The logistics vehicle information acquisition module adopts a distance estimation method to determine the position of the logistics vehicle: and (3) identifying the tags by using surface acoustic waves, measuring the distance between each reader-writer and the tags by using a signal arrival time based method, and positioning the tags by using a trilateration method based on three distance values.
The logistics vehicle vicinity information acquisition module is used for determining relevant information including vehicle positions and destinations of other logistics vehicles in the vicinity of a certain logistics vehicle.
The model establishing module is used for establishing a priority multi-service window waiting system M/M/n queuing model according to the acquired vehicle position of the logistics vehicle. In the model building module, the multiple service windows refer to a plurality of service desks in the system for serving the logistics vehicles; the M/M/n queuing model is a queuing model established based on three elements that the arrival interval time of the logistics vehicles is in negative exponential distribution, the service time of the loading and unloading stations is in negative exponential distribution, and a plurality of loading and unloading station service desks meet the queuing theory; the waiting system is a queuing model established based on the condition that logistics vehicles need to wait in a queue after the logistics vehicles carry out loading and unloading tasks; the priority is a system that the logistics vehicles sent to a certain regional division are about to finish loading and unloading according to the feedback of the loading and unloading stations, and the logistics vehicles corresponding to the regional division are preferentially dispatched.
The dynamic scheduling module obtains an optimal scheduling scheme based on the M/M/n queuing model with the priority and the multiple service windows for waiting, calculates the number of loading and unloading stations required to be set for enabling the logistics vehicle to have the shortest queuing time, and dynamically schedules each logistics vehicle according to the optimal scheduling scheme.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A vehicle dynamic scheduling method, comprising:
step 1: acquiring relevant information including vehicle positions and destinations of logistics vehicles arriving in a logistics park;
step 2: determining relevant information including vehicle positions and destinations of other logistics vehicles near a certain logistics vehicle;
and step 3: establishing a priority multi-service window waiting system M/M/n queuing model according to the acquired vehicle position of the logistics vehicle;
and 4, step 4: obtaining an optimal scheduling scheme based on a priority multi-service window waiting M/M/n queuing model, and dynamically scheduling each logistics vehicle according to the optimal scheduling scheme;
in step 3, the multiple service windows refer to a plurality of service desks in the system for serving the logistics vehicles; the M/M/n queuing model is a queuing model established based on three elements that the arrival interval time of the logistics vehicles is in negative exponential distribution, the service time of the loading and unloading stations is in negative exponential distribution, and a plurality of loading and unloading station service desks meet the queuing theory; the waiting system is a queuing model established based on the condition that logistics vehicles need to wait in a queue after the logistics vehicles carry out loading and unloading tasks; the priority is a system which is sent to logistics vehicles of a certain regional division to be loaded and unloaded according to the feedback of loading and unloading stations and preferentially dispatches the logistics vehicles corresponding to the regional division;
wherein, the queuing model of M/M/n satisfies the following conditions: the service sequence is based on the priority firstly, and the service is carried out according to the first-come first-served basis in the same priority; the logistics vehicles are vehicles which have arrived at the park, the arrival of the two priority logistics vehicles is random, single and independent, the time intervals among the two priority logistics vehicles obey exponential distribution, and the number of arriving customers in a certain time period obeys poisson distribution; the system is provided with a plurality of service desks, each service desk can only serve one logistics vehicle at a time, and the service capacities of the service desks are assumed to be the same; when the logistics vehicles arrive, if idle service desks exist, the logistics vehicles are served, and if the service desks are busy, the shortest queue is selected for waiting; the service time is independent of the arrival time of the logistics vehicles, and the logistics vehicles of each service desk in the system are arranged in a queue;
in step 3, the logistics vehicles arrive at the logistics park randomly, and obey poisson distribution, and the probability of arriving at n logistics vehicles within time t is as follows:
when the logistics vehicles arrive and all loading and unloading stations are occupied, the logistics vehicles need to wait in line, so that the queuing model is a waiting system, and the loading and unloading of the logistics vehicles are services with priority;
the plurality of loading and unloading stations are arranged in parallel, the service time is of a random type, the service time v of the random type follows a negative exponential distribution, the distribution function of which is:
P(v≤t)＝1-e-μt,t≥0
where mu is the average service rate,
is the average service time;
after the processing, a priority multi-service window waiting system M/M/n queuing model is established.
2. The dynamic vehicle scheduling method according to claim 1, wherein in step 1, the information related to the logistics vehicle is obtained through an RFID reader.
3. The dynamic vehicle dispatching method according to claim 2, wherein in step 1, the position of the logistics vehicle is determined by using a distance estimation method: and (3) identifying the tags by using surface acoustic waves, measuring the distance between each reader-writer and the tags by using a signal arrival time based method, and positioning the tags by using a trilateration method based on three distance values.
4. The dynamic vehicle scheduling method according to claim 1, wherein in step 4, the optimal scheduling scheme is the number of loading and unloading stations required to be set for minimizing the logistic vehicle queuing time.
5. A vehicle dynamic scheduling system, the system comprising:
the logistics vehicle information acquisition module is used for acquiring relevant information including vehicle positions and destinations of logistics vehicles arriving in the logistics park;
the logistics vehicle vicinity information acquisition module is used for determining the relevant information including the vehicle position and the destination of other logistics vehicles in the vicinity of a certain logistics vehicle;
the model establishing module is used for establishing a priority multi-service window waiting M/M/n queuing model according to the acquired vehicle position of the logistics vehicle;
the dynamic scheduling module obtains an optimal scheduling scheme based on the M/M/n queuing model with the priority multi-service window waiting system, and dynamically schedules each logistics vehicle according to the optimal scheduling scheme;
in the model building module, the multiple service windows refer to a plurality of service desks in the system for serving the logistics vehicles; the M/M/n queuing model is a queuing model established based on three elements that the arrival interval time of the logistics vehicles is in negative exponential distribution, the service time of the loading and unloading stations is in negative exponential distribution, and a plurality of loading and unloading station service desks meet the queuing theory; the waiting system is a queuing model established based on the condition that logistics vehicles need to wait in a queue after the logistics vehicles carry out loading and unloading tasks; the priority is a system which is sent to logistics vehicles of a certain regional division to be loaded and unloaded according to the feedback of loading and unloading stations and preferentially dispatches the logistics vehicles corresponding to the regional division;
wherein, the queuing model of M/M/n satisfies the following conditions: the service sequence is based on the priority firstly, and the service is carried out according to the first-come first-served basis in the same priority; the logistics vehicles are vehicles which have arrived at the park, the arrival of the two priority logistics vehicles is random, single and independent, the time intervals among the two priority logistics vehicles obey exponential distribution, and the number of arriving customers in a certain time period obeys poisson distribution; the system is provided with a plurality of service desks, each service desk can only serve one logistics vehicle at a time, and the service capacities of the service desks are assumed to be the same; when the logistics vehicles arrive, if idle service desks exist, the logistics vehicles are served, and if the service desks are busy, the shortest queue is selected for waiting; the service time is independent of the arrival time of the logistics vehicles, and the logistics vehicles of each service desk in the system are arranged in a queue;
in the model building module, the logistics vehicles arrive at the logistics park randomly and obey Poisson distribution, and the probability of arriving at n logistics vehicles within time t is as follows:
when the logistics vehicles arrive and all loading and unloading stations are occupied, the logistics vehicles need to wait in line, so that the queuing model is a waiting system, and the loading and unloading of the logistics vehicles are services with priority;
the plurality of loading and unloading stations are arranged in parallel, the service time is of a random type, the service time v of the random type follows a negative exponential distribution, the distribution function of which is:
P(v≤t)＝1-e-μt,t≥0
where mu is the average service rate,
is the average service time;
after the processing, a priority multi-service window waiting system M/M/n queuing model is established.
6. The vehicle dynamic scheduling system of claim 5, wherein the logistics vehicle information obtaining module obtains the information related to the logistics vehicle through an RFID reader.
7. The vehicle dynamic scheduling system of claim 6, wherein the logistic vehicle information acquisition module determines the position of the logistic vehicle by using a distance estimation method: and (3) identifying the tags by using surface acoustic waves, measuring the distance between each reader-writer and the tags by using a signal arrival time based method, and positioning the tags by using a trilateration method based on three distance values.
8. The dynamic vehicle dispatching system of claim 5, wherein in the dynamic dispatching module, the optimal dispatching plan is the number of loading and unloading stations required to be set for minimizing the logistic vehicle queuing time.
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Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102019004753A1 (en) * | 2019-07-08 | 2021-01-14 | Daimler Ag | Process for the coordination of an autonomous vehicle fleet, as well as vehicle fleet coordination system |
| CN111191672B (en) * | 2019-11-20 | 2023-05-12 | 重庆特斯联智慧科技股份有限公司 | Community express receiving and sending method and system based on support vector machine |
| CN111401735A (en) * | 2020-03-13 | 2020-07-10 | 上海东普信息科技有限公司 | Intelligent queuing method, device, equipment and storage medium for logistics vehicles |
| CN111626578A (en) * | 2020-05-18 | 2020-09-04 | 上海东普信息科技有限公司 | Distribution method, device, equipment and storage medium of logistics truck |
| CN112037558B (en) * | 2020-08-31 | 2021-08-17 | 安徽海螺集团有限责任公司 | GPS truck scheduling method applied to intelligent cement factory |
| CN112381255A (en) * | 2020-11-13 | 2021-02-19 | 天津矿山工程有限公司 | Digital mine mining and transporting equipment scheduling method and system, intelligent terminal and storage medium |
| CN113158462A (en) * | 2021-04-21 | 2021-07-23 | 电子科技大学成都学院 | Method for selecting taxi dispatching mode |
| CN113240906B (en) * | 2021-05-27 | 2022-06-14 | 山东产研信息与人工智能融合研究院有限公司 | Vehicle guiding method and system based on real-time monitoring of road congestion in logistics park |
| CN115083198B (en) * | 2022-06-15 | 2023-11-24 | 国汽(北京)智能网联汽车研究院有限公司 | Multi-vehicle power resource scheduling method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604021A (en) * | 2009-07-09 | 2009-12-16 | 复旦大学 | A kind of localization method of radio frequency identification system and device |
| CN103399295A (en) * | 2013-07-30 | 2013-11-20 | 福建师范大学 | Location implementation method based on RFID (Radio Frequency Identification) indoor positioning device |
| CN104508509A (en) * | 2012-07-06 | 2015-04-08 | 奈达科技瑞典有限公司 | Methods, nodes and computer program for positioning of a device |
| CN105184525A (en) * | 2015-05-21 | 2015-12-23 | 北京巨柱智伟能源环保科技有限公司 | Green logistics service system |
| CN107918849A (en) * | 2017-10-23 | 2018-04-17 | 深圳职业技术学院 | A kind of intelligent scheduling device and method of electronic logistics van |
| WO2018073667A2 (en) * | 2016-10-17 | 2018-04-26 | Uber Technologies, Inc. | Determining service provider behavior with ranged transmissions |
| CN108322615A (en) * | 2018-02-01 | 2018-07-24 | 福建工程学院 | A kind of more technical ability calling customer service system method for optimizing configuration |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1665315A (en) * | 2005-04-15 | 2005-09-07 | 北京邮电大学 | Method for controlling overload of intelligent network based on service control point in multi-service environment |
| JP5310802B2 (en) * | 2011-07-20 | 2013-10-09 | 住友電気工業株式会社 | Traffic evaluation apparatus, computer program, and traffic evaluation method |
| JP5741310B2 (en) * | 2011-08-10 | 2015-07-01 | 富士通株式会社 | Train length measuring device, train length measuring method, and train length measuring computer program |
| CN103646295B (en) * | 2013-11-28 | 2016-07-06 | 东南大学 | Electric automobile charging and conversion electric network integration dispatching method based on service station universal model |
| CN106991492B (en) * | 2017-03-13 | 2020-09-08 | 北京交通大学 | Northern climate quick-charging pure electric bus operation scheduling optimization method |
-
2018
- 2018-08-09 CN CN201810900508.5A patent/CN109064098B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604021A (en) * | 2009-07-09 | 2009-12-16 | 复旦大学 | A kind of localization method of radio frequency identification system and device |
| CN104508509A (en) * | 2012-07-06 | 2015-04-08 | 奈达科技瑞典有限公司 | Methods, nodes and computer program for positioning of a device |
| CN103399295A (en) * | 2013-07-30 | 2013-11-20 | 福建师范大学 | Location implementation method based on RFID (Radio Frequency Identification) indoor positioning device |
| CN105184525A (en) * | 2015-05-21 | 2015-12-23 | 北京巨柱智伟能源环保科技有限公司 | Green logistics service system |
| WO2018073667A2 (en) * | 2016-10-17 | 2018-04-26 | Uber Technologies, Inc. | Determining service provider behavior with ranged transmissions |
| CN107918849A (en) * | 2017-10-23 | 2018-04-17 | 深圳职业技术学院 | A kind of intelligent scheduling device and method of electronic logistics van |
| CN108322615A (en) * | 2018-02-01 | 2018-07-24 | 福建工程学院 | A kind of more technical ability calling customer service system method for optimizing configuration |
Non-Patent Citations (2)
| Title |
|---|
| 基于M/M/n排队模型的云资源调度策略研究;游卓浩;《中国优秀硕士学位论文全文数据库 信息科技辑》;20160315(第03期);正文第7-24页 * |
| 游卓浩.基于M/M/n排队模型的云资源调度策略研究.《中国优秀硕士学位论文全文数据库 信息科技辑》.2016,(第03期), * |
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