CN113313451A - Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm - Google Patents

Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm Download PDF

Info

Publication number
CN113313451A
CN113313451A CN202110658726.4A CN202110658726A CN113313451A CN 113313451 A CN113313451 A CN 113313451A CN 202110658726 A CN202110658726 A CN 202110658726A CN 113313451 A CN113313451 A CN 113313451A
Authority
CN
China
Prior art keywords
algorithm
cuckoo
vehicle
logistics
solution
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110658726.4A
Other languages
Chinese (zh)
Inventor
高扬华
陆海良
楼卫东
郁钢
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Tobacco Zhejiang Industrial Co Ltd
Original Assignee
China Tobacco Zhejiang Industrial Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Tobacco Zhejiang Industrial Co Ltd filed Critical China Tobacco Zhejiang Industrial Co Ltd
Priority to CN202110658726.4A priority Critical patent/CN113313451A/en
Publication of CN113313451A publication Critical patent/CN113313451A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G06Q10/08355—Routing methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a multi-objective optimization logistics scheduling method based on an improved cuckoo algorithm, which determines a logistics scheduling multi-objective function after introducing distribution cost and time window punishment factors; adopting a mixed coding mechanism for the path of the distribution task, and using algorithm for processing; when a new solution is generated, the cuckoo algorithm and the particle swarm algorithm are combined, the searching randomness of the cuckoo algorithm and the quick convergence of the particle swarm algorithm are integrated, and tasks and resources can be optimized more effectively; applying a self-adaptive adjustment strategy to the discovery probability and the step length control factor of the cuckoo algorithm to enable the two key parameters to be self-adaptively adjusted along with the iteration times, and optimizing the performance of the algorithm; for the relatively isolated situation among different individuals in the cuckoo algorithm, a fitness weight factor is introduced to increase the communication among populations; and repairing the solution by utilizing the characteristic of the mixed code so as to remove the invalid solution.

Description

Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm
Technical Field
The invention belongs to the technical field of logistics scheduling management, and particularly relates to a multi-objective optimization logistics scheduling method based on an improved cuckoo algorithm.
Background
With the development of market economy, one of the effective ways to gain competitive advantage is to establish modern logistics. Under the support of high technology and informatization, the modern logistics industry develops rapidly, and a relatively mature logistics management concept and an efficient logistics operation system are developed. With the expansion of logistics scale and the improvement of customer service requirements, the complexity of the problem gradually increases, so that a more efficient and stable solution algorithm and a logistics scheduling and path optimization method for improving the logistics service quality are very necessary.
Since the logistics scheduling and path optimization problem is proposed, the logistics scheduling and path optimization problem attracts the attention and participation of many researchers, in the real situation, due to the complexity and changeability of environment and constraint conditions, the solving of the problem becomes quite complex, but through the research and summary for decades, the logistics scheduling and path optimization problem can be generally divided into an accurate algorithm and a heuristic algorithm. The precise algorithm is to obtain the optimal solution through strict logic thinking and calculation steps, and the calculation amount of the algorithm is generally rapidly increased along with the complexity of the problem, so that the method has great limitation in practical application. Heuristic algorithms generally solve problems through reasoning and experimental analysis, obtain heuristics from internal connections of transactions, and further establish solution models, which are very flexible.
The cuckoo algorithm is an algorithm for effectively solving an optimization problem by simulating parasitic brooding behaviors of some species of cuckoos, and the cuckoo algorithm also adopts a related Levy flight search mechanism, so that an efficient optimization searching mode can be achieved. The cuckoo algorithm has the main advantages of few parameters, simple operation, easy realization, optimal random search path, strong optimization capability and the like. However, the cuckoo algorithm has the disadvantage of slow convergence speed, and is easy to fall into a local optimal solution if not updated in real time during selection of some parameters.
Disclosure of Invention
The invention provides a multi-objective optimization logistics scheduling method based on an improved cuckoo algorithm, which combines the cuckoo algorithm with a particle swarm algorithm, not only utilizes the searching randomness of the cuckoo algorithm to expand the searching range of the algorithm, but also can utilize the advantages of the particle swarm algorithm to improve the convergence of the algorithm, overcomes the defects that the particle swarm algorithm is trapped in local optimization and the convergence speed of the cuckoo algorithm is low, and can more effectively optimize tasks and resources.
The technical scheme adopted by the invention is as follows:
the invention provides a multi-objective optimization logistics scheduling method based on an improved cuckoo algorithm, which comprises the following steps:
acquiring initial information of a logistics distribution path optimization problem;
determining an objective function of a logistics distribution path optimization problem;
encoding the logistics distribution path;
solving the encoded logistics distribution path optimization problem by adopting an algorithm combining cuckoo and particle swarm;
carrying out self-adaptive adjustment on key parameters of the cuckoo algorithm, eliminating poor-quality nests and generating new nests;
disturbing the bird nest and updating a solution;
repairing the solution;
performing elite selection until a termination condition is reached, and outputting an optimal solution;
and decoding the optimal solution and outputting a logistics distribution path.
Further, the obtaining of the initial information of the logistics distribution path optimization problem includes:
acquiring vehicle power data and distribution target data; the vehicle dynamics data includes vehicle number, load, and speed; the delivery target data comprises a customer position, a product demand and a time window;
the distance and orientation between the client locations are calculated from the acquired data.
Further, the objective function of the logistics distribution path optimization problem is as follows:
Figure BDA0003114263690000021
wherein f (x) is a cost function, CijRepresenting the unit cost of transportation of the vehicle from position i to position j, dijDenotes the distance, x, between position i and position jijvIndicating that vehicle v is travelling from position i to position j, Pi(Si) Is a penalty cost function, S, of the deviation from the time windowiThe time of arrival at the position i is shown, N is the number of customers, V is the number of vehicles of the logistics distribution center, and the logistics distribution center is shown as i being 0;
the objective function needs to satisfy the constraint condition:
a. the total demand allocated to each vehicle trip does not exceed the vehicle payload:
Figure BDA0003114263690000022
wherein q isiIndicating the position i demand, yivThe delivery task for position i is performed by vehicle v, QvRepresents a rated load capacity of the vehicle v;
b. all customers complete delivery service by V vehicles:
Figure BDA0003114263690000023
yivindicating y if the delivery task for location i is completed by vehicle vivIs 1, otherwise is 0;
c、
Figure BDA0003114263690000024
d. the distribution center is the start and end of the vehicle trip:
Figure BDA0003114263690000025
x0ivindicating that the vehicle v is driven from the distribution center to the position i;
Figure BDA0003114263690000031
xi0vindicating vehiclesv driving from the position i to the distribution center;
e. stepwise distribution route xijvAre connected to yiv：
Figure BDA0003114263690000032
Figure BDA0003114263690000033
f. The penalty cost function for a deviation from the time window is:
Figure BDA0003114263690000034
terepresenting the earliest time earlier than the time window, eiEarliest service time, l, agreed for location iiThe latest service time agreed for location i, θ (-) is a function;
g、xijvand yivThe value of (A) is as follows:
Figure BDA0003114263690000035
further, the encoding the logistics distribution path includes:
a hybrid coding mechanism based on vehicle numbers is adopted, the coding length is N, each distribution task corresponds to a real number code, the integer part of the code represents vehicles responsible for distribution, and the decimal part of the code is used for determining the distribution sequence.
Further, the solving of the encoded logistics distribution path optimization problem by using an algorithm combining cuckoo and particle swarm includes:
randomly generating NP individuals, wherein one individual represents one code;
new solution generation using cuckoo Levy algorithm
Figure BDA0003114263690000036
Random walk by particle swarm algorithm to generate new solution
Figure BDA0003114263690000037
New solutions generated based on the hybrid mechanism are
Figure BDA0003114263690000038
Figure BDA0003114263690000039
Where the index i denotes the ith individual, the index t +1 denotes the number of iterations, d denotes the weight coefficient, d ∈ [0,1 ].
Further, the adaptively adjusting key parameters of the cuckoo algorithm includes:
Figure BDA00031142636900000310
Figure BDA00031142636900000311
wherein, Pa(t) represents the probability of discovery, αstep(t) is a step size factor, PaMinAnd PaMaxLower and upper bounds of probability of discovery, αmaxAnd alphaminThe upper bound and the lower bound of the step size factor, T is the maximum iteration number, and T is the current iteration number;
in an iterative process, according to the discovery probability Pa(t) eliminating poor nests and generating new nests.
Further, the disturbing the bird nest and updating the solution include:
calculating a weight fitness factor of the cuckoo individuals:
Figure BDA0003114263690000041
wherein, wiA weight fitness factor, f, for the ith cuckoo individualiExpressing the fitness value of the ith cuckoo individual, wherein the fitness value is obtained by solving an objective function, fbestAnd fworstRepresenting an optimal fitness value and a worst fitness value;
and (3) adding weight fitness factors among individuals in the brook Levy algorithm, and updating the brook solution as follows:
Figure BDA0003114263690000042
wherein alpha isstep(t) is a step-size factor in the valley bird Levy algorithm, L (delta) is a Levy flight random walk formula,
Figure BDA0003114263690000043
for the optimal position of the t-th iteration, r3Is [0-1 ]]The random number of (a) is set,
Figure BDA0003114263690000044
the weight fitness factor of the ith bird and the jth bird is represented by a superscript t, and the iteration times are represented by the superscript t;
according to the formula
Figure BDA0003114263690000045
Updating a hybrid solution
Figure BDA0003114263690000046
Further, the repairing the solution includes:
and for the tasks violating the time window constraint, adopting in-task and out-task transfer rules to repair the tasks.
Further, the performing elite selection comprises:
and merging the parent population and the offspring population, sequencing according to the fitness value, and selecting the optimal NP individuals to enter the next generation according to the sequencing result.
The invention has the following beneficial effects:
the method combines the cuckoo algorithm and the particle swarm algorithm to generate a new solution, utilizes the searching randomness of the cuckoo algorithm, enlarges the searching range of the algorithm, and utilizes the advantages of the particle swarm algorithm to improve the convergence of the algorithm, thereby overcoming the defects that the particle swarm algorithm is trapped in local optimization and the convergence speed of the cuckoo algorithm is low, and being capable of more effectively optimizing tasks and resources; the method applies a self-adaptive adjustment strategy to the discovery probability and the step length control factor of the cuckoo algorithm, so that the two key parameters are self-adaptively adjusted along with the iteration times, and the performance of the algorithm is optimized; in the invention, for the relatively isolated situation among different individuals in the cuckoo algorithm, a fitness weight factor is introduced to increase the communication among populations; the invention utilizes the characteristic of mixed coding to repair the solution so as to remove the invalid solution.
Drawings
FIG. 1 is a flow chart of the multi-objective optimized logistics scheduling method based on the improved cuckoo algorithm.
Fig. 2 shows an example of encoding and decoding in the present invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention provides a multi-objective optimization logistics scheduling method based on an improved cuckoo algorithm, which comprises the following steps as shown in figure 1:
(1) initializing a logistics distribution path optimization problem, acquiring vehicle transport capacity data and distribution target data,
the vehicle dynamic data comprises data such as the number, load, speed and the like of the vehicles; the delivery target data includes customer location, product demand, time window. On the basis of which the distances and orientations between the client positions are calculated.
Now suppose that the delivery center is to deliver products to N customers, and there are V vehicles that can participate in the delivery, dijRepresenting the distance between target positions i and j, ior j 1, 2.
(2) And determining an objective function of the logistics distribution path optimization problem.
The position and the demand of the customer are determined, but the customer demand has a time window requirement, so that the problem can be converted into a task scheduling and path optimization problem with the time window, each vehicle only needs to complete one line, starts from the logistics distribution center, sequentially completes the customer point service of the sub-line, and finally returns to the distribution center. Each customer puts a limit on the delivery time period, the upper and lower bounds of the time window define the earliest and latest service time of the customer site, and when the service time deviates from the time window, the delivery center pays a certain penalty cost for the service time. In order to fully reflect the service quality and timeliness of logistics distribution and improve the customer experience of distribution, a mixed time window is adopted to describe the time constraint of a customer: the customer accepts delivery services earlier than the time window but at a certain penalty cost, while all services later than the time window will be rejected. Therefore, the objective function of the logistics distribution task is:
Figure BDA0003114263690000051
f (x) is a cost function, minf (x) indicates that the overall goal of the optimization is cost minimization, including cost consumption of transportation of the travel path and time cost penalty due to deviation from a time window, where CijRepresenting the unit cost, x, of transportation of the vehicle from location i to location jijvIndicating that vehicle v is travelling from i to j, Pi(Si) Is a time cost penalty function, SiIndicates the time of arrival at position i, and i-0 indicates the distribution center.
Wherein, the following constraint conditions need to be satisfied:
a) the total demand allocated to each vehicle trip does not exceed the vehicle load Q:
Figure BDA0003114263690000052
wherein q isiIndicates the customer i demand, yivThe delivery task for position i is performed by vehicle v, QvRepresenting the nominal load capacity of the vehicle v.
b) Ensuring that all customers are finished with delivery service by V vehicles:
Figure BDA0003114263690000061
yiva variable of 0,1 indicating if the delivery job for location i is completed by vehicle v. Therefore, the sum of all combinations of i and v should be N. Once a client is serviced by two vehicles, the cumulative result will exceed N.
c) Ensure that each customer is served by only one vehicle:
Figure BDA0003114263690000062
d) the distribution center is the start and end of the vehicle trip:
Figure BDA0003114263690000063
x0ivindicating that the vehicle v drives from the distribution center to the vehicle i;
Figure BDA0003114263690000064
xi0vindicating that vehicle v is traveling from i to the distribution center.
e)xijvAnd yijvThere is a relationship between them, the path x is distributed segment by segmentijvAre connected to yiv：
Figure BDA0003114263690000065
Figure BDA0003114263690000066
f) The penalty cost function for a deviation from the time window is:
Figure BDA0003114263690000067
the system shows that the system is punished when being delivered in advance, and is not punished when being delivered in time, and is not allowed to be delayed; θ is a penalty cost function, teRepresenting the earliest time earlier than the time window, eiEarliest service time, l, stipulated for client iiAppointing the latest service time for the client i;
g) two events xijvAnd yivThe values of (A) are as follows:
Figure BDA0003114263690000068
Figure BDA0003114263690000069
(3) and (4) random initialization and encoding. In order to develop an optimization algorithm and encode the solution of the algorithm, namely the path traveled by each vehicle, the invention adopts a hybrid encoding mechanism based on vehicle numbers, as shown in fig. 2, the encoding length is N and is consistent with the number of distribution targets. Each delivery task corresponds to a real number code, wherein the integer part of the code represents the vehicles responsible for delivery and the decimal part of the code is used to determine the delivery order. If the integral part exceeds the number V of vehicles in the algorithm process, the V is left. The decimal part in the code is not limited to the number of bits, and the two-digit number is only used for convenience of illustration.
A set of codes represents a distribution scheme. Taking fig. 2 as an example, the code represents that the vehicle 1 takes a route from 0 to 1 to 6 to 4 to 10, the vehicles 2 and 3 also have respective routes, and the 3 vehicles complete the distribution tasks of 10 customers to form a complete distribution scheme. An individual in the algorithm of the present invention represents a group of codes. The method comprises the steps of randomly generating codes (individuals) for the first time, subsequently generating new solutions by utilizing an algorithm, calculating a fitness value for each solution, and gradually guiding to the minimum fitness value.
(4) A new solution is generated. The method comprises the steps of entering an improved cuckoo algorithm for optimization, firstly setting algorithm parameters including the number NP, the maximum iteration number T, termination conditions and the like;
generation of NP individuals X by stochastic method1，X2，...XNP. Second, a new solution is generated.
The two methods are combined for integrating the local random walk capability and the global search random walk capability of the cuckoo algorithm and the expansion space search capability of the particle swarm algorithm.
Firstly, a new solution is generated by using a formula by using a cuckoo Levy method, wherein the formula of the cuckoo Levy flight is as follows:
Figure BDA0003114263690000071
wherein alpha isstepIs a step size factor, αstepShould be related to the size of the problem,
Figure BDA0003114263690000072
indicating the current position, which is the position for deciding the next step
Figure BDA0003114263690000073
The only factor. Therefore, the Levy flight random walk has better searching capability in a solution space and larger large step range, and the Levy flight random walk formula is as follows:
L(δ)～μ＝t-δ(1<δ<3) (3)
where δ is the expected or average value of the occurrence of an event per unit time, and the random walk step size exhibits a poisson distribution. Levy flies to generate some new solutions can improve the speed of local search, but a large number of new solutions are generated by a random algorithm, and the solutions are far away from the current optimal solution to ensure that the system does not fall into local optimal.
The new solution generated by the random walk of the particle swarm algorithm is recorded as
Figure BDA0003114263690000074
The motion of the population group in the particle swarm algorithm is calculated as follows:
Figure BDA0003114263690000075
Figure BDA0003114263690000076
where v and P represent the velocity and position of the particle i at iteration t,
Figure BDA0003114263690000077
is the position, g, of the particle i that is optimal for the iteration of the roundbest(t) is the current global optimum, ω is the inertial weight, c1And c2Representing the influence coefficients of the current optimum and the global optimum on the acceleration, r1And r2Is [0-1 ]]The random number of (2).
The new solution generated by the hybrid mechanism is
Figure BDA0003114263690000078
The calculation method of (2) is as follows:
Figure BDA0003114263690000079
d is a weight coefficient, and d belongs to [0,1 ].
(5) And eliminating the bird nest. To find the probability PaEliminating poor nests and generating new nests.
In the standard cuckoo algorithm, the probability P is foundaAnd step size control factor alphastepIs constant, which is not good for the later convergence speed and convergence accuracy of the algorithm, if PaIs always larger, alphastepThe method is small, the convergence time of the algorithm can be shortened, but the algorithm is easy to converge to local optimum; and if PaSmaller, αstepLarger, the convergence speed becomes slower. Thus can be paired with PaAnd alphastepAdjustments are made to improve the cuckoo algorithm. Adaptive parameters are made hereAdjusting a strategy to enable key parameters of the cuckoo algorithm to be adjusted along with the iteration times in a self-adaptive mode, wherein the calculation method comprises the following steps:
Figure BDA0003114263690000081
Figure BDA0003114263690000082
wherein P isaMinAnd PaMaxControlling the upper and lower bounds of the probability of discovery, where alphamaxAnd alphaminAnd controlling the upper and lower bounds of the step length control factor, wherein T is the maximum iteration number, and T is the current iteration number.
(6) The bird nest is disturbed, and the bird nest is disturbed,
the cuckoo algorithm updates positions mainly through Levy flight and random walk of individuals, information exchange among groups is avoided, group intelligence is lacked to improve searching capacity, and high-quality individuals are required to influence generation of new solution positions with certain weight. The goodness of an individual may be measured by fitness. Therefore, a cuckoo algorithm fitness weight is introduced to strengthen group communication and enable a bird nest to generate disturbance. The fitness weight calculation formula of the ith cuckoo individual is as follows:
Figure BDA0003114263690000083
wherein f isiExpressing the fitness value of the ith cuckoo individual, calculating the fitness value based on an objective function, fbestAnd fworstRepresenting the optimal fitness value and the worst fitness value.
Therefore, the weight fitness factor among the groups is increased in the flight of the cuckoo algorithm Levy to strengthen group communication, and the updating formula is as follows:
Figure BDA0003114263690000084
r3is [0-1 ]]The random number of (a) is set,
Figure BDA0003114263690000085
for the fitness weight of the individual calculated by equation (9), the superscript t represents the number of iterations, αstepIs alpha in formula (8)step(t)。
And based thereon, updating the hybrid solution
Figure BDA0003114263690000086
(7) And repairing the mixed solution.
And for the tasks violating the time window constraint, adopting in-task and out-task transfer rules to repair the tasks. After the solution is analyzed, for the vehicle k, if a certain task k is in the vehicle kdIf the time constraint is violated, the task moves forward to the task adjacent to the time constraint; updating the task time, if the time window constraint is still violated, continuing to move forward until kdMove to the first position. If the solution is not feasible after the operation, the task is moved backwards according to the same rule as before, but the displacement direction is backward until the task is moved to the last position. Repeating the operation on the remaining tasks violating the time window constraint, and transferring the tasks which cannot be transferred by the tasks to another vehicle k'.
(8) And (4) selecting elite. And merging the parent population and the child population, sequencing the parent population and the child population according to the fitness value, and selecting the optimal NP individuals to enter the next generation according to the sequencing result.
(9) And judging whether a termination condition is reached or not, wherein the termination condition comprises the iterative solution times, whether the difference value with the average value of the previous optimal solutions is smaller than a certain threshold value or not, and the like. If the termination condition is reached, outputting an optimal solution; otherwise, returning to the step (4).
(10) And decoding the optimal solution and outputting a distribution task and a distribution path.
When decoding, each real number corresponds to a client according to the encoding rule. Interpreting the integer of real numbers as the vehicle responsible for delivery to the customer; if the integer parts of several real numbers are the same, it is stated that they are distributed by the same vehicle, and the distribution sequence is ordered according to the decimal part of each real number. The positions of all the customers of the routes after the vehicles are sent from the distribution center are obtained from the above steps, and a complete distribution scheme is formed.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (9)

1. A multi-objective optimization logistics scheduling method based on an improved cuckoo algorithm is characterized by comprising the following steps:
acquiring initial information of a logistics distribution path optimization problem;
determining an objective function of a logistics distribution path optimization problem;
encoding the logistics distribution path;
solving the encoded logistics distribution path optimization problem by adopting an algorithm combining cuckoo and particle swarm;
carrying out self-adaptive adjustment on key parameters of the cuckoo algorithm, eliminating poor-quality nests and generating new nests;
disturbing the bird nest and updating a solution;
repairing the solution;
performing elite selection until a termination condition is reached, and outputting an optimal solution;
and decoding the optimal solution and outputting a logistics distribution path.
2. The method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm according to claim 1, wherein the obtaining of the initial information of the logistics distribution path optimization problem comprises:
acquiring vehicle power data and distribution target data; the vehicle dynamics data includes vehicle number, load, and speed; the delivery target data comprises a customer position, a product demand and a time window;
the distance and orientation between the client locations are calculated from the acquired data.
3. The method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm as claimed in claim 1, wherein the objective function of the logistics distribution path optimization problem is as follows:
Figure FDA0003114263680000011
wherein f (x) is a cost function, CijRepresenting the unit cost of transportation of the vehicle from position i to position j, dijDenotes the distance, x, between position i and position jijvIndicating that vehicle v is travelling from position i to position j, Pi(Si) Is a penalty cost function, S, of the deviation from the time windowiThe time of arrival at the position i is shown, N is the number of customers, V is the number of vehicles of the logistics distribution center, and the logistics distribution center is shown as i being 0;
the objective function needs to satisfy the constraint condition:
a. the total demand allocated to each vehicle trip does not exceed the vehicle payload:
Figure FDA0003114263680000012
wherein q isiIndicating the position i demand, yivThe delivery task for position i is performed by vehicle v, QvRepresents a rated load capacity of the vehicle v;
b. all customers complete delivery service by V vehicles:
Figure FDA0003114263680000021
yivindicating delivery if location iTask is completed by vehicle v then yivIs 1, otherwise is 0;
c、
Figure FDA0003114263680000022
d. the distribution center is the start and end of the vehicle trip:
Figure FDA0003114263680000023
x0ivindicating that the vehicle v is driven from the distribution center to the position i;
Figure FDA0003114263680000024
xi0vindicating that the vehicle v is driving from the position i to the distribution center;
e. stepwise distribution route xijvAre connected to yiv：
Figure FDA0003114263680000025
Figure FDA0003114263680000026
f. The penalty cost function for a deviation from the time window is:
Figure FDA0003114263680000027
terepresenting the earliest time earlier than the time window, eiEarliest service time, l, agreed for location iiThe latest service time agreed for location i, θ (-) is a function;
g、xijvand yivThe value of (A) is as follows:
Figure FDA0003114263680000028
4. the method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm as claimed in claim 3, wherein the encoding of the logistics distribution path comprises:
a hybrid coding mechanism based on vehicle numbers is adopted, the coding length is N, each distribution task corresponds to a real number code, the integer part of the code represents vehicles responsible for distribution, and the decimal part of the code is used for determining the distribution sequence.
5. The method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm according to claim 1, wherein the solving of the encoded logistics distribution path optimization problem by using the algorithm of combining cuckoo and particle swarm comprises:
randomly generating NP individuals, wherein one individual represents one code;
new solution generation using cuckoo Levy algorithm
Figure FDA0003114263680000029
Random walk by particle swarm algorithm to generate new solution
Figure FDA00031142636800000210
New solutions generated based on the hybrid mechanism are
Figure FDA00031142636800000211
Figure FDA0003114263680000031
Where the index i denotes the ith individual, the index t +1 denotes the number of iterations, d denotes the weight coefficient, d ∈ [0,1 ].
6. The method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm as claimed in claim 1, wherein the adaptive adjustment of the cuckoo algorithm key parameters comprises:
Figure FDA0003114263680000032
Figure FDA0003114263680000033
wherein, Pa(t) represents the probability of discovery, αstep(t) is a step size factor, PaMinAnd PaMaxLower and upper bounds of probability of discovery, αmaxAnd alphaminThe upper bound and the lower bound of the step size factor, T is the maximum iteration number, and T is the current iteration number;
in an iterative process, according to the discovery probability Pa(t) eliminating poor nests and generating new nests.
7. The method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm as claimed in claim 5, wherein the disturbing of the bird nest and the updating of the solution comprise:
calculating a weight fitness factor of the cuckoo individuals:
Figure FDA0003114263680000034
wherein, wiA weight fitness factor, f, for the ith cuckoo individualiExpressing the fitness value of the ith cuckoo individual, wherein the fitness value is obtained by solving an objective function, fbestAnd fworstRepresenting an optimal fitness value and a worst fitness value;
and (3) adding weight fitness factors among individuals in the brook Levy algorithm, and updating the brook solution as follows:
Figure FDA0003114263680000035
wherein alpha isstep(t) is a step-size factor in the valley bird Levy algorithm, L (delta) is a Levy flight random walk formula,
Figure FDA0003114263680000036
for the optimal position of the t-th iteration, r3Is [0-1 ]]The random number of (a) is set,
Figure FDA0003114263680000037
the weight fitness factor of the ith bird and the jth bird is represented by a superscript t, and the iteration times are represented by the superscript t;
according to the formula
Figure FDA0003114263680000038
Updating a hybrid solution
Figure FDA0003114263680000039
8. The method for multi-objective optimization logistics scheduling based on the improved cuckoo algorithm as claimed in claim 7, wherein the repairing of the solution comprises:
and for the tasks violating the time window constraint, adopting in-task and out-task transfer rules to repair the tasks.
9. The method for multi-objective optimization logistics scheduling based on improved cuckoo algorithm according to claim 1, wherein the performing elite selection comprises:
and merging the parent population and the offspring population, sequencing according to the fitness value, and selecting the optimal NP individuals to enter the next generation according to the sequencing result.
CN202110658726.4A 2021-06-15 2021-06-15 Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm Pending CN113313451A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110658726.4A CN113313451A (en) 2021-06-15 2021-06-15 Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110658726.4A CN113313451A (en) 2021-06-15 2021-06-15 Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm

Publications (1)

Publication Number Publication Date
CN113313451A true CN113313451A (en) 2021-08-27

Family

ID=77378665

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110658726.4A Pending CN113313451A (en) 2021-06-15 2021-06-15 Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm

Country Status (1)

Country Link
CN (1) CN113313451A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114169832A (en) * 2021-12-15 2022-03-11 中国建设银行股份有限公司 Logistics distribution scheduling method and device, storage medium and processor
CN116184812A (en) * 2023-04-24 2023-05-30 荣耀终端有限公司 Signal compensation method, electronic equipment and medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109034468A (en) * 2018-07-19 2018-12-18 南京邮电大学 A kind of logistics distribution paths planning method with time window based on cuckoo algorithm

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109034468A (en) * 2018-07-19 2018-12-18 南京邮电大学 A kind of logistics distribution paths planning method with time window based on cuckoo algorithm

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
周炳海等: "基于改进多目标布谷鸟搜索算法的汽车装配线物料配送调度", 《湖南大学学报》 *
李琳等: "机场地勤保障车辆调度问题研究", 《沈阳航空航天大学学报》 *
雷蕾: "基于群智能优化算法的物流配送路径优化研究与应用", 《中国优秀博硕士学位论文全文数据库 信息科技辑》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114169832A (en) * 2021-12-15 2022-03-11 中国建设银行股份有限公司 Logistics distribution scheduling method and device, storage medium and processor
CN116184812A (en) * 2023-04-24 2023-05-30 荣耀终端有限公司 Signal compensation method, electronic equipment and medium

Similar Documents

Publication Publication Date Title
CN111862579B (en) Taxi scheduling method and system based on deep reinforcement learning
CN109034468A (en) A kind of logistics distribution paths planning method with time window based on cuckoo algorithm
CN113313451A (en) Multi-objective optimization logistics scheduling method based on improved cuckoo algorithm
CN113032904B (en) Model construction method, task allocation method, device, equipment and medium
CN109451012B (en) End cloud collaborative load balancing scheduling method, system and storage medium
CN110110903B (en) Neural evolution-based distribution vehicle path planning method
CN106134136A (en) Calculate the long-term dispatch transmitted for the data on wide area network
CN112995289B (en) Internet of vehicles multi-target computing task unloading scheduling method based on non-dominated sorting genetic strategy
Hildebrandt et al. Supervised learning for arrival time estimations in restaurant meal delivery
CN113848970A (en) Multi-target collaborative path planning method for vehicle and unmanned aerial vehicle
CN113708969A (en) Cooperative embedding method of cloud data center virtual network based on deep reinforcement learning
CN115576343A (en) Multi-target vehicle path optimization method combining unmanned aerial vehicle distribution
CN111754095A (en) Vehicle scheduling method, device and system based on artificial intelligence
Alaia et al. A Comparative Study of the PSO and GA for the m-MDPDPTW
CN114626632A (en) Vehicle scheduling method, system, equipment and medium based on ant lion algorithm
CN112862083B (en) Deep neural network inference method and device in edge environment
CN112016750A (en) Improved method for solving problem of vehicle path with constraint
CN117236541A (en) Distributed logistics distribution path planning method and system based on attention pointer network
CN115423393B (en) Order scheduling method and device of dynamic self-adaptive scheduling period based on LSTM
CN115062868B (en) Pre-polymerization type vehicle distribution path planning method and device
CN114707707A (en) Method and system for scheduling AGV task based on improved genetic algorithm
CN114723091A (en) Network contract freight vehicle scheduling optimization method based on mixed heuristic algorithm
CN113741418A (en) Method and device for generating heterogeneous vehicle formation cooperative path
CN108960567A (en) A kind of equipment support task objective programming method that oriented mission is resource matched
CN115065683B (en) Vehicle edge network task allocation and unloading method based on vehicle clustering

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20210827

RJ01 Rejection of invention patent application after publication