CN110264100B - Multi-yard logistics transportation scheduling method, device, equipment and readable storage medium - Google Patents

Multi-yard logistics transportation scheduling method, device, equipment and readable storage medium Download PDF

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CN110264100B
CN110264100B CN201910568291.7A CN201910568291A CN110264100B CN 110264100 B CN110264100 B CN 110264100B CN 201910568291 A CN201910568291 A CN 201910568291A CN 110264100 B CN110264100 B CN 110264100B
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蔡延光
李帅
蔡颢
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Guangdong University of Technology
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Abstract

The application discloses a multi-parking lot logistics transportation scheduling method, a multi-parking lot logistics transportation scheduling device and a readable storage medium.

Description

Multi-yard logistics transportation scheduling method, device, equipment and readable storage medium
Technical Field
The application relates to the field of logistics transportation scheduling, in particular to a multi-yard logistics transportation scheduling method, device, equipment and readable storage medium.
Background
Logistics is a process of organically combining functions such as transportation, storage, loading, unloading, transportation, packaging, distribution, information processing and the like according to actual needs to meet user requirements in the process of physically flowing articles from a supply place to a receiving place. The logistics industry is internationally considered as the basic industry for national economic development, and the development degree of the logistics industry is one of the important marks for measuring the national modernization degree and the comprehensive national strength.
Logistics transportation is part of demand supply chain activity, and on the one hand needs to guarantee to satisfy the customer to the demand of commodity, service and relevant information, and on the other hand needs to improve transportation efficiency, reduce the cost of transportation. The logistics operation not only determines the overall operation cost of the business enterprise, but also directly affects the stability and balance of the operation of the whole business system, so that the transportation process from the supply place to the receiving place needs to be reasonably planned and controlled. It can be seen that logistics transportation scheduling is a very critical step in the logistics transportation process.
However, because the factors to be considered in the logistics transportation scheduling process are complicated and intricate, the traditional logistics transportation scheduling scheme is time-consuming and low in efficiency, and the current requirements are difficult to meet.
Disclosure of Invention
The application aims to provide a multi-yard logistics transportation scheduling method, a multi-yard logistics transportation scheduling device, a multi-yard logistics transportation scheduling equipment and a readable storage medium, and aims to solve the problems that a traditional logistics transportation scheduling scheme is long in time consumption, low in efficiency and difficult to meet the current requirements.
In a first aspect, the present application provides a method for dispatching logistics transportation in multiple parking lots, including:
the method comprises the steps of obtaining a multi-yard logistics transportation scheduling model, and converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models, wherein the multi-yard logistics transportation scheduling model is a model for describing that vehicles of a plurality of yards complete delivery tasks of a plurality of customer points together;
aiming at each single yard logistics transportation scheduling model, executing search operation according to a harmony search algorithm, and determining the optimal harmony sound in the current iteration process according to a target fitness function, wherein the target fitness function is used for measuring the length of a distribution path corresponding to the harmony sound;
when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process;
when the current iteration times reach the maximum iteration times, obtaining the optimal target sum sound of the single-vehicle yard logistics transportation scheduling model;
and respectively determining an optimal vehicle path corresponding to the optimal harmony of the target of each single-yard logistics transportation scheduling model to serve as a logistics transportation scheduling result of the multi-yard logistics transportation scheduling model.
Preferably, the converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models includes:
determining a parking lot and a customer point in the multi-parking lot logistics transportation scheduling model;
for each customer point, determining a distance between the customer point and each yard;
determining the intimacy between the customer point and each of the yards according to the distance and the intimacy objective function;
and distributing the customer points to the yards with the highest intimacy degree until all the customer points are distributed, and obtaining the customer points of each yard to serve as a plurality of single yard logistics transportation scheduling models.
Preferably, the allocating the customer points to the yard with the highest intimacy degree includes:
and under the condition that the vehicle mileage limit and the load limit are met, the customer points are distributed to the yards with the highest intimacy.
Preferably, the generating a new harmony sound according to the artificial fish swarm algorithm includes:
generating new harmony sound according to an artificial fish swarm algorithm;
and performing tone fine adjustment on the new harmony according to an adaptive adjustment strategy.
Preferably, the fine tuning the tone of the new harmony sound according to the adaptive adjustment strategy includes:
generating a random number;
and when the random number is smaller than the probability of the tone fine adjustment, adjusting the probability and the bandwidth of the tone fine adjustment according to a self-adaptive adjusting function, and executing the tone fine adjustment operation on the group of harmony waves.
In a second aspect, the present application provides a multi-yard logistics transportation scheduling device, comprising:
a conversion module: the system comprises a plurality of parking lots, a plurality of client points and a plurality of single parking lots, wherein the single parking lots are used for receiving and dispatching a plurality of parking lots, and the plurality of parking lots are used for receiving and dispatching a plurality of goods to be delivered;
an iteration module: the system comprises a plurality of single-parking-lot logistics transportation scheduling models, a harmony search algorithm, a target fitness function and a distribution path calculation module, wherein the single-parking-lot logistics transportation scheduling models are used for executing search operation according to the harmony search algorithm and determining the optimal harmony sound in the current iteration process according to the target fitness function, and the target fitness function is used for measuring the length of the distribution path corresponding to the harmony sound;
and a harmony update module: when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process;
a target optimal harmony determination module: the system is used for obtaining the optimal target sum sound of the single yard logistics transportation scheduling model when the current iteration times reach the maximum iteration times;
a scheduling result determination module: and the system is used for respectively determining the optimal vehicle paths corresponding to the optimal harmony of the targets of the single yard logistics transportation scheduling models to serve as the logistics transportation scheduling results of the multi-yard logistics transportation scheduling models.
Preferably, the conversion module includes:
a parameter determination unit: the system is used for determining a parking lot and a customer point in the multi-parking lot logistics transportation scheduling model;
a distance determination unit: for each customer point, determining a distance between the customer point and each yard;
an intimacy degree determination unit: the system is used for determining the intimacy between the customer point and each of the yards according to the distance and the intimacy objective function;
a distribution unit: and the system is used for distributing the customer points to the yards with the highest intimacy degree until all the customer points are distributed, and obtaining the customer points of each yard to be used as a plurality of single yard logistics transportation scheduling models.
Preferably, the harmony update module includes:
and a harmony generating unit: the system is used for generating a group of harmony sounds according to an artificial fish swarm algorithm;
harmony fine adjustment unit: and the tone fine adjustment is carried out on the new harmony according to an adaptive adjustment strategy.
In a third aspect, the present application provides a multi-yard logistics transportation scheduling device, comprising:
a memory: for storing a computer program;
a processor: for executing the computer program to implement the steps of a multi-yard logistics transportation scheduling method as described above.
In a fourth aspect, the present application provides a readable storage medium, on which a computer program is stored, the computer program being executed by a processor for implementing the steps of the method for dispatching transportation of logistics in multi-yard.
According to the multi-yard logistics transportation scheduling method, the multi-yard logistics transportation scheduling device, the multi-yard logistics transportation scheduling equipment and the readable storage medium, in the process of achieving multi-yard logistics transportation scheduling, the multi-yard logistics transportation scheduling problem is converted into the multiple single-yard logistics transportation scheduling problem according to a clustering analysis strategy, in addition, the optimal vehicle path is searched through a harmony search algorithm optimized based on an artificial fish swarm algorithm, the characteristics of high running speed, strong convergence capacity and high optimization efficiency are achieved, and the execution efficiency and the reliability of the logistics transportation scheduling process are remarkably improved.
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For a clearer explanation of the embodiments or technical solutions of the prior art of the present application, the drawings needed for the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a first implementation of a multi-yard logistics transportation scheduling method provided in the present application;
fig. 2 is a flowchart illustrating an implementation of a second method for dispatching logistics transportation in a multi-yard provided by the present application;
fig. 3 is a schematic diagram of a simulation experiment result of a second embodiment of a multi-yard logistics transportation scheduling method provided by the present application;
FIG. 4 is a functional block diagram of an embodiment of a multi-yard logistics transportation scheduling device provided by the present application;
fig. 5 is a schematic structural diagram of an embodiment of a multi-yard logistics transportation scheduling apparatus provided in the present application.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The logistics transportation scheduling is a very critical step in the logistics transportation process, however, because the factors to be considered in the logistics transportation scheduling process are complicated and intricate, the traditional logistics transportation scheduling scheme consumes a long time and is low in efficiency, and the current requirements are difficult to meet. In order to solve the problem, the application provides a multi-yard logistics transportation scheduling method, a multi-yard logistics transportation scheduling device, a multi-yard logistics transportation scheduling equipment and a readable storage medium, so that the execution efficiency and reliability of a logistics transportation scheduling process are remarkably improved.
The following describes a first embodiment of a method for dispatching logistics transportation in multiple parking lots, which is provided by the present application, and with reference to fig. 1, the first embodiment includes:
step S101: acquiring a multi-yard logistics transportation scheduling model, and converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models;
in the process of logistics transportation scheduling, the multi-yard logistics transportation scheduling model specifically refers to a model for describing that vehicles in a plurality of yards complete delivery tasks of a plurality of customer points together, and the single-yard logistics transportation scheduling model specifically refers to a model for describing that a single yard completes delivery tasks of a plurality of customer points. In particular, the "multiple customer points" in the multi-yard logistics transportation scheduling model are not identical to the "multiple customer points" in the single-yard logistics transportation scheduling model, and the "multiple customer points" related to each single-yard logistics transportation scheduling model are not necessarily identical.
More specifically, the multi-yard logistics transportation scheduling model can be described as follows: the method comprises the steps that a plurality of parking lots are provided, each parking lot is provided with one or more vehicles, the vehicles of the parking lots finish delivery tasks of a plurality of customer points together, and each vehicle starts from the corresponding parking lot and returns to the original parking lot after passing through all the customer points in charge of the vehicle. The purpose of the model is to select a proper travel route to minimize the total travel, so as to save energy consumption and improve distribution efficiency. It will be appreciated that the capacity of each vehicle is greater than or equal to the total cargo demand of all customer sites for which it is responsible, all customer sites can only be passed by one vehicle, and the number of passes is one.
As a specific implementation manner, the multi-yard logistics transportation scheduling model may be converted into a plurality of single-yard logistics transportation scheduling models according to a clustering analysis strategy.
Step S102: aiming at each single yard logistics transportation scheduling model, executing search operation according to a harmony search algorithm, and determining optimal harmony in the current iteration process according to a target fitness function;
as described above, the objective of both the multi-yard logistics transportation scheduling model and the single-yard logistics transportation scheduling model is to select an appropriate travel route to minimize the total travel, and therefore, the target fitness function is mainly used for measuring the length of the delivery path corresponding to the sound.
The harmony search algorithm is a novel meta-heuristic algorithm, which is similar to the music band in the process of composing music, and each musician continuously adjusts the tone of each instrument according to own memory and experience to seek the most beautiful harmony process.
Step S103: when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process;
after each iteration process is finished, the embodiment generates new harmony sound according to the artificial fish swarm algorithm. As a specific implementation manner, a random number may be generated first, and when the random number is determined to be smaller than the preset threshold, new harmony is generated according to a group of harmony randomly selected from the harmony memory library; and when the random number is judged to be larger than or equal to the preset threshold value, generating new harmony according to the artificial fish school algorithm. On the basis, fine adjustment can be performed on the new harmony, and the fine-adjusted harmony is used as the basis of the next iterative search process.
Step S104: when the current iteration times reach the maximum iteration times, obtaining the optimal target sum sound of the single-vehicle yard logistics transportation scheduling model;
the maximum iteration number is preset, and the specific numerical value can be set correspondingly according to the actual situation.
Step S105: and respectively determining an optimal vehicle path corresponding to the optimal harmony of the target of each single-yard logistics transportation scheduling model to serve as a logistics transportation scheduling result of the multi-yard logistics transportation scheduling model.
In the multi-yard logistics transportation scheduling method provided by the embodiment, in the process of realizing multi-yard logistics transportation scheduling, the multi-yard logistics transportation scheduling problem is converted into a plurality of single-yard logistics transportation scheduling problems according to a clustering analysis strategy, in addition, the optimal vehicle path is searched by using a harmony search algorithm optimized based on an artificial fish swarm algorithm, the method has the characteristics of high running speed, strong convergence capacity and high optimization efficiency, and the execution efficiency and reliability of the logistics transportation scheduling process are obviously improved.
The second embodiment of the multi-yard logistics transportation scheduling method provided by the present application is described in detail below, and the second embodiment is implemented based on the first embodiment and is expanded to a certain extent on the basis of the first embodiment.
Referring to fig. 2, the second embodiment specifically includes:
step S201: acquiring a multi-yard logistics transportation scheduling model and initializing relevant parameters;
specifically, in this embodiment, the multi-yard logistics transportation scheduling model is as follows:
Figure GDA0003490750510000071
wherein D (unit: kilometer) represents the total length of the route traveled by the transport vehicle, M represents the number of yards, Km(unit: vehicle) represents the number of transport vehicles owned by the yard m; n (unit: number) represents the number of customer sites,
Figure GDA0003490750510000072
(M1, 2.. multidot.m; i, j 0, 1.. multidot.n, unit: km) represents the straight-line distance of the transport vehicle from the customer point i to the customer point j, in particular,
Figure GDA0003490750510000073
(or
Figure GDA0003490750510000074
) Indicating the distance from yard m to customer j,
Figure GDA0003490750510000075
(m＝1,2,...,M；i，j＝0,1,...,N；k＝1,2,...,Km) A variable other than 0 or 1 is used,
Figure GDA0003490750510000076
representing a vehicle k at yard m traveling through customer point i to customer point j.
The process of initializing the relevant parameters specifically comprises two parts, namely a parameter for initializing the multi-yard logistics transportation scheduling model and a parameter for initializing the harmony search algorithm, and the following two parts are introduced respectively:
initializing parameters of a multi-yard logistics transportation scheduling model: initializing the number M of the parking lots and the total number K of the transport vehicles in the parking lot MmWherein the k-th transport vehicle of the yard m has a maximum load of
Figure GDA0003490750510000081
(unit: ton), number of customer pointsN, wherein the i (i, j) th customer site demand load is wi(unit: ton);
parameters for initializing harmonic search algorithms: harmony search algorithm harmony memory bank size HMS, memory bank value probability HMCR and fine-tuning probability minimum PARminFine tuning probability maximum PARmaxInitial bandwidth bw of tone trimming0And the creation times Tmax, the visual field range visual of the artificial fish, the single swimming maximum distance range, the try times try _ number and the current iteration times gn (initially 0) of the algorithm.
Step S202: converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models;
the conversion process specifically includes: calculating the distance between each parking lot and each customer point; determining the intimacy between the train yard and the customer point according to the distance and the intimacy function; and distributing the customer points to each parking lot according to the sequence of the intimacy from large to small until all the customer points are distributed to the corresponding parking lots, and finishing the distribution and clustering. It will be appreciated that prior to allocating a customer site to a yard, it is necessary to verify that the mileage and load limits are met, and if so, to allocate, otherwise to reallocate. The above-mentioned affinity function is shown below:
Figure GDA0003490750510000082
wherein the content of the first and second substances,
Figure GDA0003490750510000083
(M1, 2.. M; j 1, 2.. N) represents distance, DOI (M, j) represents intimacy, M belongs to M, j belongs to N, wherein, alpha is mileage weighting coefficient, beta is load weighting coefficient, and M belongs to NmIndicating the number of clients, w, that have been allocated to yard mjRepresenting the material demand (in tons) at customer point j.
Step S203: a random initialization and acoustic memory library;
the harmony memory bank includes a plurality of harmony sounds, each harmony sound corresponding to a distribution route of the vehicle. The harmony memory bank is initialized randomly in many ways, which can be specifically selected according to actual situations, and this embodiment is not limited specifically. As a specific implementation, the acoustic memory bank may be initialized by using a chaotic initialization method, and a specific initialization process will be described in detail below and will not be described herein.
Step S204: determining a fitness value of each harmony in the harmony memory library;
specifically, for each harmony sound in the harmony sound memory base, the customer points and the sequence of the needed service of the corresponding vehicle are determined according to the harmony sound, and the customer points and the sequence are determined according to the customer points and the sequence
Figure GDA0003490750510000091
Wherein, (M ═ 1, 2.. multidot.M, i, j ═ 0, 1.. multidot.N, K ═ 1, 2.. multidot.Km) (ii) a Finally, the fitness value of the harmony is calculated according to a fitness function, which is as follows:
Figure GDA0003490750510000092
step S205: determining global optimal harmony and a fitness value thereof according to the fitness value, and determining worst harmony and the fitness value thereof;
step S206: generating a new harmony, and adding one to the current iteration number;
as a specific implementation, the process of generating a new harmony includes: generating a first random number; if the first random number is smaller than the memory bank value probability HMRC, randomly selecting a group of harmony from the harmony memory bank, and generating new harmony according to the group of harmony; if the first random number is larger than or equal to the memory bank value probability HMRC, generating new harmony according to an artificial fish school algorithm; generating a second random number; if the second random number is smaller than the fine tuning probability PAR, fine tuning the new harmony according to the fine tuning function; if the second random number is less than the fine tuning probability PAR, no processing is performed. Finally outputting a new harmony sound.
Step S207: when the fitness value of the new harmony is larger than the fitness value of the worst harmony, updating the worst harmony sound according to the new harmony;
step S208: judging whether the current iteration number reaches the maximum iteration number, if so, entering a step S209, otherwise, entering a step S205;
step S209: and outputting the target optimal harmony sound and the fitness value thereof, and outputting the corresponding vehicle path. As a result of logistics transportation scheduling.
As described above, in the present embodiment, each harmony sound corresponds to a travel path of a certain vehicle, specifically, the present embodiment analyzes the harmony sound by using a decoding strategy that combines an equal division and sound extraction value range and a maximum position method, where the decoding strategy is defined as follows:
let p-th harmony be Xp＝[xp1,xp2,...,xpq,...,xpN]The present embodiment is directed to harmony XpPerforming an internal grouping to generate ∑ KmA set, namely Cmj. Then according to the maximum position method, the elements in each set are xpqThe sizes of the elements are arranged in a descending order, and the second dimension value of each element in each set after the arrangement is finished is the customer point and the order of the corresponding vehicles needing service. CmjAs follows:
Cmj＝{(xpq,q)|j-1≤xpq＜j} (4)
wherein M1, 2,1, M, p 0,1, q 0,1, N, K1, 2m。
To further illustrate the above decoding strategy, the following is exemplified: assume that the yard is numbered a, has 4 transport vehicles, and serves 8 customer sites. The resulting set of chords in a certain iteration of the algorithm is X ═ 3.6,2.4,0.7,1.8,2.6,3.2,1.4,3.3]. Internally grouping harmony interior X may result in: cA1＝{(0.7,3)}，CA2＝{(1.8,4),(1.4,7)}，CA3＝{(2.4,2),(2.6,5)}，CA4＝{(3.6,1),(3.2,6),(3.3,8)}。
Sorting each set to obtain: cA1＝{(0.7,3)}，CA2＝{(1.8,4),(1.4,7)}，CA3＝{(2.6,5),(2.4,2)}，CA4＝{(3.6,1),(3.3,8),(3.2,6)}。
Finally, a logistics transportation scheduling scheme corresponding to harmony X can be obtained, namely the path of the vehicle 1 is A-3-A; the path of the vehicle 2 is A-4-7-A; the path of the vehicle 3 is A-5-2-A; the vehicle 4 path is a-1-8-6-a.
In step S203, there are many methods for random initialization and acoustic memory library, and as an optional implementation manner, the present embodiment adopts a chaotic initialization method, which includes the following processes:
firstly, randomly generating an initial chaotic vector;
the initial chaotic vector Y0＝[y01,y02,...,y0j,...,y0N]Wherein y is0j∈(0,1)，j＝1,2,...,N。
Secondly, generating HMS chaotic vectors according to the initial chaotic vectors and the target function;
the ith vector in the HMS chaotic vectors is as follows: y isi＝[yi1,yi2,...,yij,...,yiN]The objective function is:
y(i+1)j＝μyij(1-yij) (5)
HMS, where, in addition, to achieve a completely chaotic state, μ in this example is 4.
Thirdly, mapping the generated HMS chaotic vectors to a value range of a logistics transportation scheduling problem to obtain HMS vectors which accord with the decoding strategy of the embodiment;
the ith vector X in the HMS vectors conforming to the decoding strategy of the embodimenti＝[xi1,xi2,...,xij,...,xiN]Wherein i ═ 1, 2., HMS, Xi＝Yi*K。
And fourthly, putting the HMS vectors into a harmony memory library HM to obtain an initial value of the harmony memory library.
Specifically, the result of initializing the harmony memory library HM is as follows:
Figure GDA0003490750510000111
in step S206 of this embodiment, the process of generating a new harmony specifically includes:
in the first step, a random number rand (0,1) between (0,1) is generated, if rand (0,1)<HMCR, then randomly selecting a group of harmony from the harmony memory library, and recording as XnewTurning to the third step as shown in formula (6); otherwise, go to the second step.
Xnew＝rand[X1,X2,...,XHMS] (6)
Secondly, randomly generating new harmony according to an artificial fish school algorithm;
third, generate random number rand (0,1) between (0,1), if rand (0,1)<PAR, according to the formula (7) for XnewFine adjustment is carried out; otherwise XnewAnd is not changed.
Xnew＝Xnew±rand()×bw (7)
The second step specifically includes: taking XbestGenerating the position X of another artificial fish according to the formula (8) as the position of one artificial fish in the artificial fish swarm algorithmjAnd comparing XbestAnd XjIf the position X is the same as the position XjIs better than the position XbestThen the artificial fish is directed to the position XjIn a step length not exceeding the maximum distance of single advance, and the position after the step is Xnew(ii) a Otherwise, position X needs to be regeneratedj. If the forward condition is not satisfied after try _ number is repeatedly tried, the artificial fish swims once randomly, and the position after the swimming is assigned to XnewThe process is shown as formula (9).
Xj＝Xbest+rand()*visual (8)
Figure GDA0003490750510000112
As a specific implementation manner, the third step may specifically fine-tune the new harmony according to the pitch fine-tuning strategy, and during the pitch fine-tuning process, the pitch fine-tuning probability and the pitch fine-tuning bandwidth may be adaptively adjusted, as shown in equations (10) and (11):
Figure GDA0003490750510000121
Figure GDA0003490750510000122
wherein, PARminFor fine-tuning the probability minimum, PARmaxTo fine-tune the probability maximum, bw0The initial bandwidth is fine-tuned for the pitch, Tmax is the number of creations, gn is the current iteration number of the algorithm.
Therefore, according to the multi-yard logistics transportation scheduling method provided by the embodiment, aiming at the multi-yard logistics transportation scheduling problem, the multi-yard logistics transportation scheduling problem is converted into a plurality of single-yard logistics transportation scheduling problems through a clustering analysis strategy, so that the calculation difficulty is reduced; and searching an optimal path by using a harmony search algorithm optimized based on an artificial fish swarm algorithm, and dynamically adjusting the tone fine tuning probability and the tone fine tuning bandwidth of the harmony search algorithm by adopting a self-adaptive strategy. Therefore, the method has the characteristics of high running speed, strong convergence capability and high optimization efficiency, and obviously improves the execution efficiency and reliability of the logistics transportation scheduling scheme.
In order to prove that the embodiment has more advantages, the application performs a plurality of simulation comparison experiments on a conventional harmony search algorithm and the multi-yard logistics transportation scheduling method of the embodiment aiming at the logistics transportation scheduling problem with 3 transportation vehicles and 20 customer points, the shortest path graph obtained by the multi-yard logistics transportation scheduling method of the embodiment is shown in fig. 3, and the simulation result pair is shown in table 1.
TABLE 1
Figure GDA0003490750510000123
As can be seen from table 1, compared with the conventional harmony search algorithm, the vehicle mileage searched by the multi-yard logistics transportation scheduling method of the embodiment is shorter, and the search process takes shorter time. Therefore, the conclusion can be drawn that compared with the scheme of realizing the logistics transportation scheduling based on the conventional harmony search algorithm, the multi-yard logistics transportation scheduling method has the characteristics of high running speed, strong convergence capability and high optimization efficiency.
In the following, a multi-yard logistics transportation scheduling device provided by an embodiment of the present application is introduced, and a multi-yard logistics transportation scheduling device described below and a multi-yard logistics transportation scheduling method described above may be referred to in correspondence.
Referring to fig. 4, the apparatus includes:
the conversion module 401: the system comprises a plurality of parking lots, a plurality of client points and a plurality of single parking lots, wherein the single parking lots are used for receiving and dispatching a plurality of parking lots, and the plurality of parking lots are used for receiving and dispatching a plurality of goods to be delivered;
the iteration module 402: the system comprises a plurality of single-parking-lot logistics transportation scheduling models, a harmony search algorithm, a target fitness function and a distribution path calculation module, wherein the single-parking-lot logistics transportation scheduling models are used for executing search operation according to the harmony search algorithm and determining the optimal harmony sound in the current iteration process according to the target fitness function, and the target fitness function is used for measuring the length of the distribution path corresponding to the harmony sound;
harmony update module 403: when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process;
the goal optimal harmony determination module 404: the system is used for obtaining the optimal target sum sound of the single yard logistics transportation scheduling model when the current iteration times reach the maximum iteration times;
the scheduling result determination module 405: and the system is used for respectively determining the optimal vehicle paths corresponding to the optimal harmony of the targets of the single yard logistics transportation scheduling models to serve as the logistics transportation scheduling results of the multi-yard logistics transportation scheduling models.
In some specific embodiments, the conversion module 401 includes:
a parameter determination unit: the system is used for determining a parking lot and a customer point in the multi-parking lot logistics transportation scheduling model;
a distance determination unit: for each customer point, determining a distance between the customer point and each yard;
an intimacy degree determination unit: the system is used for determining the intimacy between the customer point and each of the yards according to the distance and the intimacy objective function;
a distribution unit: and the system is used for distributing the customer points to the yards with the highest intimacy degree until all the customer points are distributed, and obtaining the customer points of each yard to be used as a plurality of single yard logistics transportation scheduling models.
In some specific embodiments, the harmony update module 403 includes:
and a harmony generating unit: the system is used for generating a group of harmony sounds according to an artificial fish swarm algorithm;
harmony fine adjustment unit: and the tone fine adjustment is carried out on the new harmony according to an adaptive adjustment strategy.
The multi-yard logistics transportation scheduling apparatus of this embodiment is used for implementing the aforementioned multi-yard logistics transportation scheduling method, and therefore a specific implementation manner in the apparatus can be seen in the foregoing embodiment parts of the multi-yard logistics transportation scheduling method, for example, the conversion module 401, the iteration module 402, the harmony update module 403, the target optimization and harmony determination module 404, and the scheduling result determination module 405, which are respectively used for implementing steps S101, S102, S103, S104, and S105 in the aforementioned multi-yard logistics transportation scheduling method. Therefore, specific embodiments thereof may be referred to in the description of the corresponding respective partial embodiments, and will not be described herein.
In addition, since the multi-yard logistics transportation scheduling device of this embodiment is used for implementing the aforementioned multi-yard logistics transportation scheduling method, the role thereof corresponds to that of the aforementioned method, and details are not repeated here.
In addition, this application still provides a many yards logistics transportation scheduling equipment, refer to fig. 5, and this equipment includes:
the memory 501: for storing a computer program;
the processor 502: for executing the computer program to carry out the steps of:
the method comprises the steps of obtaining a multi-yard logistics transportation scheduling model, and converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models, wherein the multi-yard logistics transportation scheduling model is a model for describing that vehicles of a plurality of yards complete delivery tasks of a plurality of customer points together; aiming at each single yard logistics transportation scheduling model, executing search operation according to a harmony search algorithm, and determining the optimal harmony sound in the current iteration process according to a target fitness function, wherein the target fitness function is used for measuring the length of a distribution path corresponding to the harmony sound; when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process; when the current iteration times reach the maximum iteration times, obtaining the optimal target sum sound of the single-vehicle yard logistics transportation scheduling model; and respectively determining an optimal vehicle path corresponding to the optimal harmony of the target of each single-yard logistics transportation scheduling model to serve as a logistics transportation scheduling result of the multi-yard logistics transportation scheduling model.
As a specific implementation, when the processor 502 executes the computer sub-program in the memory 501, the following may be specifically implemented:
determining a parking lot and a customer point in the multi-parking lot logistics transportation scheduling model; for each customer point, determining a distance between the customer point and each yard; determining the intimacy between the customer point and each of the yards according to the distance and the intimacy objective function; and distributing the customer points to the yards with the highest intimacy degree until all the customer points are distributed, and obtaining the customer points of each yard to serve as a plurality of single yard logistics transportation scheduling models.
As a specific implementation, when the processor 502 executes the computer sub-program in the memory 501, the following may be specifically implemented:
and under the condition that the vehicle mileage limit and the load limit are met, the customer points are distributed to the yards with the highest intimacy.
As a specific implementation, when the processor 502 executes the computer sub-program in the memory 501, the following may be specifically implemented:
generating new harmony sound according to an artificial fish swarm algorithm; and performing tone fine adjustment on the new harmony according to an adaptive adjustment strategy.
As a specific implementation, when the processor 502 executes the computer sub-program in the memory 501, the following may be specifically implemented:
generating a random number; and when the random number is smaller than the probability of the tone fine adjustment, adjusting the probability and the bandwidth of the tone fine adjustment according to a self-adaptive adjusting function, and executing the tone fine adjustment operation on the group of harmony waves.
Finally, the present application also provides a readable storage medium, on which a computer program is stored, which, when being executed by a processor, is configured to implement the steps of the aforementioned multi-yard logistics transportation scheduling method.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
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 Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above detailed descriptions of the solutions provided in the present application, and the specific examples applied herein are set forth to explain the principles and implementations of the present application, and the above descriptions of the examples are only used to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (5)

1. A multi-yard logistics transportation scheduling method is characterized by comprising the following steps:
acquiring a multi-yard logistics transportation scheduling model and initializing relevant parameters; converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models, wherein the multi-yard logistics transportation scheduling models are models describing that vehicles of a plurality of yards complete delivery tasks of a plurality of customer points together; wherein, the logistics transportation scheduling model of the multi-parking lot is
Figure FDA0003497330620000011
Wherein D represents the total length of the path traveled by the transport vehicle in kilometers, M represents the number of yards, KmThe number of the transport vehicles owned by the parking lot m is represented, and the unit is a vehicle; n represents the number of customer sites, in units of units,
Figure FDA0003497330620000012
the unit of the straight-line distance from the client point i to the client point j of the transportation vehicle of the parking lot M is kilometer, and M is 1, 2. i, j ═ 0,1,. ·, N;
Figure FDA0003497330620000013
or
Figure FDA0003497330620000014
Representing the straight-line distance traveled by the transport vehicles of yard m to customer point j,
Figure FDA0003497330620000015
a variable other than 0, i.e., 1, M ═ 1, 2.., M; i, j ═ 0,1,. ·, N; k1, 2m，
Figure FDA0003497330620000016
A vehicle k representing a yard m is driven to a client point j through a client point i; the initialization of the related parameters comprises the initialization of parameters of a multi-yard logistics transportation scheduling model and the initialization of harmony search algorithm parameters; the initializing parameters of the multi-yard logistics transportation scheduling model specifically comprise: number of initialized yards M, number of transport vehicles K owned by yard MmWherein the k-th transport vehicle of the yard m has a maximum load of
Figure FDA0003497330620000017
The number of customer sites N, wherein the ith customer site demand load is wiI, j ═ 1, 2.., N; the initialization harmony search algorithm parameters specifically include: initialization harmony search algorithm harmony memory bank size HMS, memory bank value probability HMCR and fine-tuning probability minimum PARminFine tuning probability maximum PARmaxInitial bandwidth bw of tone trimming0And the creation times Tmax, the visual field range visual of the artificial fish, the single swimming maximum distance range, the try times try _ number and the current iteration times gn of the algorithm;
aiming at each single yard logistics transportation scheduling model, executing search operation according to a harmony search algorithm, and determining the optimal harmony sound in the current iteration process according to a target fitness function, wherein the target fitness function is used for measuring the length of a distribution path corresponding to the harmony sound; the determining the optimal harmony sound in the current iteration process according to the target fitness function comprises the following steps: a random initialization and acoustic memory library; determining a fitness value of each harmony in the harmony memory library; determining global optimal harmony and a fitness value thereof according to the fitness value, and determining worst harmony and the fitness value thereof; the fitness function is:
Figure FDA0003497330620000021
the random initialization and acoustic memory library comprising: randomly generating an initial chaotic vector; the initial chaotic vector Y0＝[y01,y02,...,y0j,…,y0N]Wherein y is0jE (0,1), j corresponding to a customer point, j ═ 1, 2. Generating HMS chaotic vectors according to the initial chaotic vectors and a target function, wherein the ith vector in the HMS chaotic vectors is as follows: y isi＝[yi1,yi2,…,yij,...,yiN]The objective function is: y is(i+1)j＝μyij(1-yij) (ii) a Wherein i corresponds to a customer point, i 1, 2., HMS, μ is 4; mapping the generated HMS chaotic vectors to a value range of a logistics transportation scheduling problem to obtain HMS vectors conforming to a decoding strategy; putting HMS vectors into a harmony memory bank HM to obtain an initial value of the harmony memory bank:
Figure FDA0003497330620000022
when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process; the generating of the new harmony sound according to the artificial fish swarm algorithm comprises the following steps: generating a first random number, which is a random number rand (0,1) between (0, 1); if the first random number is smaller than the memory bank value probability HMRC, a group of harmony is randomly selected from the harmony memory bank and is marked as XnewWherein X isnew＝rand[X1,X2,...,XHMS](ii) a Generating a new harmony from the set of harmony, specifically comprising: taking XbestAs the position of an artificial fish in the artificial fish swarm algorithm, according to the formula Xj＝Xbest+ rand (), visual, rand () representing a random number between (0,1) generates the position X of another artificial fishjAnd comparing XbestAnd XjIf the position X is the same as the position XjIs better than the position XbestThen the artificial fish is directed to the position XjIn a direction not exceedingWalk once through the step length of the maximum distance of single advance, and the position after the walk is Xnew(ii) a Otherwise, position X needs to be regeneratedj(ii) a If the forward condition is not satisfied after try _ number is repeatedly tried, the artificial fish swims once randomly, and the position after the swimming is assigned to XnewThe concrete formula is as follows:
Figure FDA0003497330620000031
if the first random number is larger than or equal to the memory bank value probability HMRC, generating new harmony according to an artificial fish school algorithm; generating a second random number, which is a random number rand (0,1) between (0, 1); if the second random number is less than the fine tuning probability PAR, then according to the fine tuning function Xnew＝Xnew+ -rand (). xbw for new harmony XnewFine adjustment is carried out, and new harmony is output, wherein bw is the bandwidth; otherwise XnewDoes not change; wherein the function is according to a fine tuning function Xnew＝Xnew+ -rand (). xbw for new harmony XnewThe fine adjustment comprises the following steps: adjusting the tone fine-tuning probability and bandwidth according to an adaptive tuning function, and performing tone fine-tuning operations on a set of harmonics
Figure FDA0003497330620000032
Figure FDA0003497330620000033
Where PAR is the pitch trimming probability, bwnewFor adjusted bandwidth, PARminFor fine-tuning the probability minimum, PARmaxTo fine-tune the probability maximum, bw0Fine-tuning the initial bandwidth for the tone, wherein Tmax is the creation times, and gn is the current iteration times of the algorithm;
when the current iteration times reach the maximum iteration times, obtaining the optimal target sum sound of the single-yard logistics transportation scheduling model;
respectively determining optimal vehicle paths corresponding to the optimal harmony of the targets of the single yard logistics transportation scheduling models to serve as logistics transportation scheduling results of the multi-yard logistics transportation scheduling models;
the optimal vehicle path corresponding to the optimal harmony of the target is obtained by analyzing the harmony by adopting a decoding strategy combining an equally divided harmony value domain and a maximum position method, and the method comprises the following steps:
let p-th harmony be Xp＝[xp1,xp2,...,xpq,...,xpN]；
To harmony XpPerforming internal grouping to obtain Cmj(ii) a Said C ismjThe method specifically comprises the following steps:
Cmj＝{(xpq,q)|j-1≤xpq＜j}，
wherein, CmjTo harmony XpSet obtained after internal grouping, j being a client point, xpqIs the qth element of the pth harmony, M is an integer greater than or equal to 1 and less than or equal to M, M is the number of initialized yards, p is an integer greater than or equal to 0 and less than or equal to HMS, HMS is the harmony search algorithm and the harmony memory base size, q is an integer greater than or equal to 0 and less than or equal to N, N is the number of customer points, K is greater than or equal to 1 and less than or equal to KmInteger of (a), KmThe number of transport vehicles owned by the yard m;
according to the maximum position method, the elements in each set are xpqThe second dimension value of each element in each set is the customer point and the sequence of the corresponding vehicle needing service after the arrangement is finished;
the converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models comprises: determining a parking lot and a customer point in the multi-parking lot logistics transportation scheduling model; for each customer point, determining a distance between the customer point and each yard; determining the intimacy between the customer point and each of the yards according to the distance and the intimacy objective function; the intimacy objective function is:
Figure FDA0003497330620000041
wherein the content of the first and second substances,
Figure FDA0003497330620000042
representing the straight-line distance of the transportation vehicle of the parking lot M to the client point j, wherein M is 1, 2.. M; j 1,2, N, DOI (M, j) represents intimacy, M belongs to M, j belongs to N, where α is a mileage weighting coefficient, β is a load weighting coefficient, and M belongs to MmIndicating the number of clients, w, that have been allocated to yard mjRepresenting the material demand of the customer point j; and distributing the customer points to the yards with the highest intimacy degree until all the customer points are distributed, and obtaining the customer points of each yard to serve as a plurality of single yard logistics transportation scheduling models.
2. The multi-yard logistics transportation scheduling method of claim 1 wherein said assigning said customer site to said most intimate yard comprises:
and under the condition that the vehicle mileage limit and the load limit are met, the customer points are distributed to the yards with the highest intimacy.
3. The utility model provides a many yards commodity circulation transportation scheduling device which characterized in that includes:
a conversion module: the system is used for acquiring a multi-yard logistics transportation scheduling model and initializing relevant parameters; converting the multi-yard logistics transportation scheduling model into a plurality of single-yard logistics transportation scheduling models, wherein the multi-yard logistics transportation scheduling models are models describing that vehicles of a plurality of yards complete delivery tasks of a plurality of customer points together; wherein, the logistics transportation scheduling model of the multi-parking lot is
Figure FDA0003497330620000051
Wherein D represents the total length of the path traveled by the transport vehicle in kilometers, M represents the number of yards, KmThe number of the transport vehicles owned by the parking lot m is represented, and the unit is a vehicle; n represents the number of customer sites, in units of units,
Figure FDA0003497330620000052
the unit of the straight-line distance from the client point i to the client point j of the transportation vehicle of the parking lot M is kilometer, and M is 1, 2. i, j ═ 0,1,. ·, N;
Figure FDA0003497330620000053
or
Figure FDA0003497330620000054
Representing the straight-line distance traveled by the transport vehicles of yard m to customer point j,
Figure FDA0003497330620000055
a variable other than 0, i.e., 1, M ═ 1, 2.., M; i, j ═ 0,1,. ·, N; k1, 2m，
Figure FDA0003497330620000056
A vehicle k representing a yard m is driven to a client point j through a client point i; the initialization of the related parameters comprises the initialization of parameters of a multi-yard logistics transportation scheduling model and the initialization of harmony search algorithm parameters; the initializing parameters of the multi-yard logistics transportation scheduling model specifically comprise: number of initialized yards M, number of transport vehicles K owned by yard MmWherein the k-th transport vehicle of the yard m has a maximum load of
Figure FDA0003497330620000057
The number of customer sites N, wherein the ith customer site demand load is wiI, j ═ 1, 2.., N; the initialization harmony search algorithm parameters specifically include: initialization harmony search algorithm harmony memory bank size HMS, memory bank value probability HMCR and fine-tuning probability minimum PARminFine tuning probability maximum PARmaxInitial bandwidth bw of tone trimming0And the creation times Tmax, the visual field range visual of the artificial fish, the single swimming maximum distance range, the try times try _ number and the current iteration times gn of the algorithm;
an iteration module: the system comprises a plurality of single-parking-lot logistics transportation scheduling models, a harmony search algorithm, a target fitness function and a distribution path calculation module, wherein the single-parking-lot logistics transportation scheduling models are used for executing search operation according to the harmony search algorithm and determining the optimal harmony sound in the current iteration process according to the target fitness function, and the target fitness function is used for measuring the length of the distribution path corresponding to the harmony sound; the determining the optimal harmony sound in the current iteration process according to the target fitness function comprises the following steps: a random initialization and acoustic memory library; determining a fitness value of each harmony in the harmony memory library; determining global optimal harmony and a fitness value thereof according to the fitness value, and determining worst harmony and the fitness value thereof; the fitness function is:
Figure FDA0003497330620000061
；
the random initialization and acoustic memory library comprising: randomly generating an initial chaotic vector; the initial chaotic vector Y0＝[y01,y02,...,y0j,...,y0N]Wherein y is0jE (0,1), j corresponding to a customer point, j ═ 1, 2. Generating HMS chaotic vectors according to the initial chaotic vectors and a target function, wherein the ith vector in the HMS chaotic vectors is as follows: y isi＝[yi1,yi2,...,yij,...,yiN]The objective function is: y is(i+1)j＝μyij(1-yij) (ii) a Wherein i corresponds to a customer point, i 1, 2., HMS, μ is 4; mapping the generated HMS chaotic vectors to a value range of a logistics transportation scheduling problem to obtain HMS vectors conforming to a decoding strategy; putting HMS vectors into a harmony memory bank HM to obtain an initial value of the harmony memory bank:
Figure FDA0003497330620000062
and a harmony update module: when the current iteration times do not reach the maximum iteration times, generating new harmony according to an artificial fish school algorithm, and entering the next iteration process; the generating of the new harmony sound according to the artificial fish swarm algorithm comprises the following steps: generating a first random number, the secondA random number rand (0,1) between (0, 1); if the first random number is smaller than the memory bank value probability HMRC, a group of harmony is randomly selected from the harmony memory bank and is marked as XnewWherein X isnew＝rand[X1,X2,...,XHMS](ii) a Randomly generating new harmony according to an artificial fish swarm algorithm, and specifically comprising the following steps: taking XbestAs the position of an artificial fish in the artificial fish swarm algorithm, according to the formula Xj＝Xbest+ rand (), visual, rand () representing a random number between (0,1) generates the position X of another artificial fishjAnd comparing XbestAnd XjIf the position X is the same as the position XjIs better than the position XbestThen the artificial fish is directed to the position XjIn a step length not exceeding the maximum distance of single advance, and the position after the step is Xnew(ii) a Otherwise, position X needs to be regeneratedj(ii) a If the forward condition is not satisfied after try _ number is repeatedly tried, the artificial fish swims once randomly, and the position after the swimming is assigned to XnewThe concrete formula is as follows:
Figure FDA0003497330620000071
if the first random number is larger than or equal to the memory bank value probability HMRC, generating new harmony according to an artificial fish school algorithm; generating a second random number, which is a random number rand (0,1) between (0, 1); if the second random number is less than the fine tuning probability PAR, then according to the fine tuning function Xnew＝Xnew+ -rand (). xbw for new harmony XnewFine adjustment is carried out, and new harmony is output, wherein bw is the bandwidth; otherwise XnewDoes not change;
a target optimal harmony determination module: the system is used for obtaining the optimal target sum sound of the single yard logistics transportation scheduling model when the current iteration times reach the maximum iteration times;
a scheduling result determination module: the system comprises a plurality of single-yard logistics transportation scheduling models, a plurality of single-yard logistics transportation scheduling models and a plurality of multi-yard logistics transportation scheduling models, wherein the single-yard logistics transportation scheduling models are used for generating a plurality of target optimal harmony signals;
the scheduling result determining module is specifically configured to analyze harmony to obtain a vehicle driving path corresponding to harmony by using a decoding strategy combining an equal division harmony value domain and a maximum position method, and includes: let p-th harmony be Xp＝[xp1,xp2,...,xpq,…,xpN](ii) a To harmony XpPerforming internal grouping to obtain Cmj(ii) a Said C ismjThe method specifically comprises the following steps:
Cmj＝{(xpq,q)|j-1≤xpq＜j}，
wherein, CmjTo harmony XpSet obtained after internal grouping, j being a client point, xpqIs the qth element of the pth harmony, M is an integer greater than or equal to 1 and less than or equal to M, M is the number of initialized yards, p is an integer greater than or equal to 0 and less than or equal to HMS, HMS is the harmony search algorithm and the harmony memory base size, q is an integer greater than or equal to 0 and less than or equal to N, N is the number of customer points, K is greater than or equal to 1 and less than or equal to KmInteger of (a), KmThe number of transport vehicles owned by the yard m; according to the maximum position method, the elements in each set are xpqThe second dimension value of each element in each set is the customer point and the sequence of the corresponding vehicle needing service after the arrangement is finished;
the conversion module includes:
a parameter determination unit: the system is used for determining a parking lot and a customer point in the multi-parking lot logistics transportation scheduling model;
a distance determination unit: for each customer point, determining a distance between the customer point and each yard;
an intimacy degree determination unit: the system is used for determining the intimacy between the customer point and each of the yards according to the distance and the intimacy objective function; the intimacy objective function is:
Figure FDA0003497330620000081
wherein the content of the first and second substances,
Figure FDA0003497330620000082
representing the straight-line distance of the transportation vehicle of the parking lot M to the client point j, wherein M is 1, 2.. M; j 1,2, N, DOI (M, j) represents intimacy, M belongs to M, j belongs to N, where α is a mileage weighting coefficient, β is a load weighting coefficient, and M belongs to MmIndicating the number of clients, w, that have been allocated to yard mjRepresenting the material demand of the customer point j;
a distribution unit: the system is used for distributing the customer points to the yards with the highest intimacy degree until all the customer points are distributed, and obtaining the customer points of each yard to be used as a plurality of single yard logistics transportation scheduling models;
the harmony update module includes:
and a harmony generating unit: the system is used for generating a group of harmony sounds according to an artificial fish swarm algorithm;
harmony fine adjustment unit: the tone fine adjustment is carried out on the new harmony according to the self-adaptive adjustment strategy;
the harmony generation unit: specifically for generating a first random number; if the first random number is smaller than the memory bank value probability HMRC, randomly selecting a group of harmony from the harmony memory bank, and generating new harmony according to the group of harmony; if the first random number is larger than or equal to the memory bank value probability HMRC, generating new harmony according to an artificial fish school algorithm;
the harmony fine-tuning unit: specifically, the method is used for generating a second random number, where the second random number is a random number rand (0,1) between (0, 1); if the second random number is less than the fine tuning probability PAR, then according to the fine tuning function Xnew＝Xnew+ -rand (). xbw for new harmony XnewFine adjustment is carried out, wherein bw is a bandwidth; wherein the function is according to a fine tuning function Xnew＝Xnew+ -rand (). xbw for new harmony XnewThe fine adjustment comprises the following steps: adjusting the tone fine-tuning probability and bandwidth according to an adaptive tuning function, and performing tone fine-tuning operations on a set of harmonics
Figure FDA0003497330620000091
Figure FDA0003497330620000092
Where PAR is the pitch trimming probability, bwnewFor adjusted bandwidth, PARminFor fine-tuning the probability minimum, PARmaxTo fine-tune the probability maximum, bw0The initial bandwidth is fine-tuned for the pitch, Tmax is the number of creations, gn is the current iteration number of the algorithm.
4. The utility model provides a many yards commodity circulation transportation scheduling equipment which characterized in that includes:
a memory: for storing a computer program;
a processor: for executing said computer program for implementing the steps of a method for scheduling multi-yard logistics transportation according to any of claims 1-2.
5. A readable storage medium, characterized in that the readable storage medium has stored thereon a computer program, which when executed by a processor is used for implementing the steps of the multi-yard logistics transportation scheduling method according to any one of claims 1-2.
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