CN114418497A - Logistics path optimization method based on mixed sparrow algorithm - Google Patents
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Abstract
The invention discloses a logistics path optimization method based on a mixed sparrow algorithm. The technical scheme is as follows: obtaining order information, and planning an initial logistics path according to the order information; determining an initial distribution scheme according to the cost function model; optimizing an initial logistics path based on a mixed sparrow algorithm, and determining an optimal logistics path; optimizing an initial distribution scheme according to the cost function model to obtain an optimal distribution scheme; and carrying out logistics distribution based on the optimal logistics path and the optimal distribution scheme.
Description
Technical Field
The invention relates to the field of new energy electric automobiles, in particular to a logistics path optimization method based on a hybrid sparrow algorithm.
Background
The path planning is an important problem in the distribution and dispatching of the urban electric logistics vehicles, and the distribution of the electric logistics vehicles is one of the existing distribution modes, is slightly influenced by traffic, and is limited by mileage, cargo capacity and vehicle quantity. However, the existing electric logistics vehicles still face the problems of insufficient driving range and incomplete supporting facilities, and the enthusiasm of enterprises for popularizing and arranging the electric logistics vehicles is seriously influenced. In the existing electric vehicle planning and research make internal disorder or usurp, most of the problems are only the optimization of the distribution process, the problems of site selection and distribution are rarely integrated and researched make internal disorder or usurp, how to scientifically select supporting facilities to efficiently utilize the resources of the electric logistics vehicles and help enterprises to achieve the maximum income is a valuable research make internal disorder or usurp problem. In order to find an optimal urban distribution route which simultaneously meets the electric vehicle limitation and the customer time window limitation, a hybrid planning model is established to carry out deep research on the path problem.
And combining the charging facility layout planning problem with the distribution path optimization problem to provide the BEVRLAP problem. And taking the endurance mileage, the load capacity and the soft time window service satisfaction as constraint conditions, wherein the objective function is a logistics enterprise cost function, and the objective function comprises transportation cost, operation cost, soft time window service satisfaction cost, labor cost and social benefit cost. And (5) solving the model by using a correlation algorithm to finally obtain the vehicle distribution path.
The Sparrow Search Algorithm (SSA) is a new group intelligent optimization algorithm recently proposed. Compared with other intelligent algorithms, the algorithm has better effect, but still has the problems of low convergence speed, insufficient solving precision and easy falling into local optimum. The improvement of the sparrow search algorithm at present mainly lies in the following aspects: one method is to enhance the diversity of the population by adding a cubic mapping and reverse learning strategy and mixing a sine and cosine algorithm and a Gaussian variation strategy, thereby enhancing the robustness of the algorithm; and the other method is to increase the convergence rate and improve the accuracy by adding an adaptive learning factor or introducing a polynomial variation factor.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to solve the problems and provides a material flow path optimization method based on a hybrid sparrow algorithm, which is used for optimizing the traditional SSA algorithm. The chaotic mapping method is adopted to initialize the population of the sparrow search algorithm, so that the global search capability of the algorithm is improved, and the situation that a local optimal solution occurs is avoided. Meanwhile, the concepts of long-term memory and backtracking of the past are introduced into a sparrow search algorithm, the diversity of the population is enhanced by referring to the position information of past individuals, and the population or how much past experience the population can remember at one time is determined by introducing a parameter memory length H. The population may decide on the next step of action based on past experiences, providing a wider view of a number of promising sites, thus reducing the chance of premature convergence or stagnation.
The technical scheme of the invention is as follows:
the invention provides a logistics path optimization method based on a hybrid sparrow algorithm, which comprises the following steps of:
obtaining order information, and planning an initial logistics path according to the order information;
determining an initial distribution scheme according to the cost function model;
optimizing an initial logistics path based on a mixed sparrow algorithm, and determining an optimal logistics path;
optimizing an initial distribution scheme according to the cost function model to obtain an optimal distribution scheme;
and carrying out logistics distribution based on the optimal logistics path and the optimal distribution scheme.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, the cost function model calculates the minimum cost of the distribution scheme through a minimum cost function; wherein the minimum cost includes a vehicle electricity cost C1Vehicle running cost C2Time window penalty cost C3And a charging cost C4The minimum cost function is formulated as follows:
MinCcost of＝C1+C2+C3+C4。
According to an embodiment of the method for optimizing the logistics path based on the hybrid sparrow algorithm, the electricity cost C for the vehicle1Calculating through a vehicle electricity cost function; wherein the vehicle electricity cost C1Including uphill power usage costs, downhill power usage costs, and flat land power usage costs.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the calculation formula of the vehicle electricity cost function is as follows:
(ii) a Wherein i, j respectively represent path node i and path node j, V represents a set of path nodes,
k denotes a vehicle set, K denotes each vehicle,
λ1the power utilization coefficient of the uphill slope is shown,
λ2the electricity usage coefficient for a downhill slope is represented,
λ3the electric coefficient for the flat ground is shown,
dijindicating the distance of path node i to path node j,
Xijkindicating the electricity cost of the kth vehicle from path node i to path node j.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, the vehicle running cost C2 is calculated through a vehicle running cost function; wherein the vehicle driving cost C2 includes a fixed travel cost, a transportation process cost, and a battery loss cost.
According to an embodiment of the method for optimizing the logistics path based on the hybrid sparrow algorithm, the time is
The inter-window penalty cost function formula is as follows:
(ii) a Wherein n is1The number of the distribution nodes is shown,
PⅠa soft time window penalty cost is represented,
PⅡa hard-time penalty cost is represented,
PⅢrepresenting a charge time penalty cost.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the charging cost C is4Calculating through a charging cost function; wherein the charging cost C4Including daytime charging costs and nighttime charging costs.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, the charging cost function formula is as follows:
(ii) a Wherein K represents a set of vehicles, K represents each vehicle,
s1indicating the cost of parking an hour during the day,
s2indicating the parking cost per hour at night,
tsindicates the parking time of each vehicle k,
r1represents the cost of charging per hour during the day,
r2represents the charging cost per hour at night,
trindicating the charging time of each vehicle k.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the soft time penalty cost pi is calculated by a soft time window penalty cost function, and a formula is as follows:
(ii) a Where i represents a path node i, V represents a set of path nodes,
tirepresenting the actual arrival time at the path node i,
wiand oiRespectively representing time nodes, p, defining time periods of arrival at path node i1A penalty index is indicated.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the hard time penalty cost PⅡCalculating by a hard time window penalty cost function, wherein the formula is as follows:
(ii) a Where i represents a path node i, V represents a set of path nodes,
tirepresenting the actual arrival time at the path node i,
wiand oiRespectively representing time nodes defining time periods of arrival at path node i,
p2a penalty index is indicated.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the charging time penalty cost P isⅢCalculating by a charging time penalty cost function, wherein the formula is as follows:
(ii) a Wherein i, j respectively represent path node i and path node j, V represents a set of path nodes,
g represents the distance traveled per unit of electrical quantity by the vehicle,
q represents the current amount of electricity of the vehicle,
dijrepresents path node i to path node jDistance.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the charging time penalty cost function can also control the vehicle charging condition through a charging control function L, and the formula is as follows:
(ii) a Wherein i and j respectively represent a path node i and a path node j, V represents a path node set,
g represents the distance traveled per unit of electrical quantity by the vehicle,
q represents the current amount of electricity of the vehicle,
dijindicating the distance between path node i and path node j,
dminindicating the distance between the path node i and the nearest charging station.
According to an embodiment of the invention, the logistics path optimization method based on the hybrid sparrow algorithm optimizes an initial logistics path by optimizing each vehicle delivery path on the logistics path, and comprises the following steps:
initializing an initial sparrow population; wherein, the sparrows are all vehicles on the logistics path;
dividing the vehicle into a finder and a follower according to a minimum cost model;
updating the position of the finder;
updating the position of the follower by utilizing a probability selection function;
judging whether the position of the finder reaches the maximum updating iteration times or not; if yes, outputting the position of the finder as an optimal logistics path; if not, the finder position is continuously iterated.
According to an embodiment of the logistics path optimization method based on the hybrid sparrow algorithm, the sparrow initial population is initialized through chaotic mapping and comprises position information of initial positions of vehicles and initial position information of optimal positions corresponding to the vehicles.
According to an embodiment of the method for optimizing a logistics path based on a hybrid sparrow algorithm, the calculation formula of the position information of the initial position of the vehicle is as follows:
Zn+1＝βsin((1+2n)πZn)
(ii) a Where n denotes the nth vehicle and β is a control parameter.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, an initial position information calculation formula of each optimal position corresponding to the vehicle is as follows:
Xij＝lb+(ub-lb)×Cn
(ii) a Where lb denotes the lower search space limit, ub denotes the upper search space limit, k denotes each vehicle,
Cnthe parameters of the mapping are represented by,
Xiinitial position information indicating the ith optimum position.
According to an embodiment of the method for optimizing the logistics path based on the hybrid sparrow algorithm, the optimal position is selected through an optimal position probability selection function, and a calculation formula is as follows:
(ii) a Wherein f isiIndicating the probability that the ith optimum position was selected,
j denotes a position passed before the ith optimum position,
h denotes a recall control parameter.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, the finder position is updated based on a GT distribution strategy, and a calculation formula is as follows:
(ii) a Wherein,
indicating the location information of the ith finder at the jth optimal location,
C0the speed-adjustment parameter is represented by,
n (G, T) represents the standard GT distribution,
R2the early-warning value is represented and,
ST represents a safety value of the security value,
q denotes a random number following a normal distribution.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, when the discoverer location fails to be updated, the discoverer location is searched by adopting a reverse search strategy, and the method comprises the following steps:
storing the current optimal position information of the position of the finder into a preset reverse search table, and giving up the current optimal position information;
searching the position of the finder again to obtain the latest position information;
inquiring a reverse search table, and judging whether the latest position information exists in the reverse search table; if so,
re-searching the finder position again; if not, the position of the finder is the latest position information.
According to an embodiment of the hybrid sparrow algorithm-based logistics path optimization method, the probability selection function is used for updating the positions of followers in a sparrow search algorithm, and a calculation formula is as follows:
(ii) a Wherein f isiAn adaptation value representing the location of the ith follower,
gamma represents a random number within a preset interval,
Piindicating the follow probability of the ith follower.
According to an embodiment of the logistics path optimization method based on the hybrid sparrow algorithm, the position of the follower is updated based on the following probability of the follower, and a calculation formula is as follows:
(ii) a Wherein,
indicating the position information of the ith follower at the jth optimal position,
q denotes a random number following a normal distribution,
t represents the number of iterations,
n represents the number of the distribution nodes,
According to one embodiment of the logistics path optimization method based on the hybrid sparrow algorithm, a follower is randomly selected as an alerter, and the alerter is used for warning that the electric quantity of the vehicle is insufficient.
Compared with the prior art, the invention has the following beneficial effects: the present invention (describes the whole technical means of the invention, and then combines the shortcomings of the prior art to describe the whole technical effect of the invention).
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The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
Fig. 1 is a flowchart illustrating an embodiment of a hybrid sparrow algorithm-based logistics optimization method of the present invention.
FIG. 2 is a graph illustrating an embodiment of a soft-time window penalty cost function of the present invention.
FIG. 3 is a flow diagram illustrating an embodiment of the hybrid sparrow algorithm of the present invention.
FIG. 4 is a flow diagram illustrating one embodiment of a reverse search strategy of the present invention to search for finder locations.
FIG. 5 is a block diagram illustrating one embodiment of a reverse search table of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.
An embodiment of a hybrid sparrow algorithm-based logistics path optimization method is disclosed herein, and fig. 1 shows a flowchart of an embodiment of a hybrid sparrow algorithm-based logistics optimization method. Referring to fig. 1, the following is a detailed description of the various steps in the flow.
Step S1: obtaining order information, and planning an initial logistics path according to the order information.
Specifically, in this embodiment, a unified model new energy electric logistics vehicle is used as the transport vehicle. Under the initial condition, all the electric logistics vehicles are full of electric quantity. Each customer point can only be served by an electronic commodity circulation car, and the delivery volume and the volume of getting goods of each customer point can not be greater than the maximum loading volume of electronic commodity circulation car, on each distribution return circuit, the summation of the demand volume of each customer point does not exceed the bearing capacity of vehicle. After a logistics distribution order is obtained, an initial logistics path is preliminarily planned according to information such as the cargo capacity and the number of customer points in the order information.
Step S2: an initial delivery schedule is determined based on the cost function model.
Logistics distribution companies want to reduce the total cost required to be paid through charging pile site selection and reasonable planning of logistics paths, and accordingly profit of the companies is improved. In this embodiment, a cost function model is established according to characteristics of the electric logistics vehicle, and the cost function model calculates the minimum cost of the distribution scheme through a minimum cost function. Wherein the minimum cost includes a vehicle electricity cost C1Vehicle running cost C2Time window penalty cost C3And a charging cost C4The calculation formula is as follows:
MinCcost of＝C1+C2+C3+C4。
Further, in the present embodiment, the vehicle electricity cost C is calculated from the vehicle electricity cost function1The vehicle running process is divided into uphill running, downhill running and flat ground running, and the electricity utilization conditions in the three conditions are different, namely the vehicle electricity utilization cost C1The method comprises the steps of uphill power utilization cost, downhill power utilization cost and flat land power utilization cost, and the calculation formula is as follows:
(ii) a Wherein i and j respectively represent a path node i and a path node j, V represents a path node set, K represents a vehicle set, K represents each vehicle, and lambda1Represents the power utilization coefficient, lambda, of the uphill slope2Representing the electricity consumption coefficient, λ, for downhill slopes3Represents the electrical coefficient for flat ground, dijDenotes the distance, X, from path node i to path node jijkIndicating the electricity cost of the kth vehicle from path node i to path node j.
Further, in the present embodiment, the vehicle running cost C is set2Is divided intoFixing travel cost, transportation process cost and storage battery loss cost, and calculating vehicle running cost C through a vehicle running cost function2The formula is as follows:
(ii) a Wherein i and j respectively represent a path node i and a path node j, V represents a path node set, K represents a vehicle set, K represents each vehicle, e represents a fixed trip cost of each vehicle, m represents a total weight of a transport vehicle, T represents a vehicle service time, B represents a battery capacity, and r represents a storage battery cost.
Further, in the present embodiment, the time window penalty cost C3Including a soft time window penalty cost, a hard time window penalty cost and a charging time penalty cost, calculating a time window penalty cost C through a time window cost function3The calculation formula is as follows:
(ii) a Wherein, PⅠRepresents the soft time window penalty cost, PⅡRepresenting a hard time penalty cost, PⅢRepresenting a charge time penalty cost. In addition, the path node set in this embodiment further includes a distribution node set and a charging station set. The distribution node set, i.e., the customer point set, is represented as {1,2, 3.. and n1}, and the charging station set is represented as {1,2, 3.. and n2}, C3N in the formula1Indicating the number of distribution nodes.
In particular, in terms of time window penalty cost, a new function of a hybrid time window is introduced, which is determined based on the tolerance of the customer to the product delivery time, and is specifically divided by time and the nature of the work. When the delivery day is not a weekday, namely the delivery is performed on weekends, the soft time window is used; when the distribution day is a working day, concrete division is carried out, namely when distribution is carried out at night, a hard time window is adopted in consideration of special time and urgent needs of users generally; and when the system is delivered in the daytime in workdays, users on duty adopt hard time windows according to different division of user occupation, and individual users and non-industrial users adopt soft time windows.
In one embodiment, the soft-time penalty cost PⅠCalculating by a soft time window penalty cost function, wherein the formula is as follows:
(ii) a Where i represents a path node i, V represents a set of path nodes, tiRepresenting the actual arrival at path node i time, wiAnd oiRespectively representing time nodes, p, defining time periods of arrival at path node i1A penalty index is indicated. FIG. 2 is a graph illustrating an embodiment of a soft-time window penalty cost function of the present invention. As shown in FIG. 2, in the case of a soft time window, when the product reaches time tiAt path node i for a prescribed time period [ w ]i,oi]And if the requirement of the path node, namely the delivery time of the customer point is met, the penalty cost is 0. If the product reaches time t>oiOr t < wiThen customer satisfaction decreases with increasing late or early arrival time, i.e. penalty index PⅠWill continue to grow. W is because customer satisfaction decreases over timei≤ti≤oiThe penalty cost is less; t is t>oiOr t < wiThe penalty cost is large.
In another embodiment, the hard time penalty cost PⅡCalculating by a hard time window penalty cost function, wherein the formula is as follows:
(ii) a Where i represents a path node i, V represents a set of path nodes, tiRepresenting actual reach path nodesi time, wiAnd oiRespectively representing time nodes, p, defining time periods of arrival at path node i2A penalty index is indicated. Specifically, in the case of a hard time window, if the product cannot be delivered at node i for a specified period of time [ w [ ]i,oi]And if the internal delivery is reached, the client of the delivery node does not receive the internal delivery.
In one embodiment, the charge-time penalty cost P may be calculated using a charge-time penalty cost functionⅢThe calculation formula is as follows:
(ii) a Wherein i and j respectively represent a path node i and a path node j, V represents a path node set, g represents the driving distance of the vehicle per unit electric quantity, q represents the current electric quantity of the vehicle, and d represents the current electric quantity of the vehicleijIndicating the distance between path node i and path node j.
Specifically, in the case of charging time, when gq > dijIn time, the electric logistics vehicle has sufficient electric quantity at the moment, and can complete the order requirement without charging, so that P is carried out at the momentⅢ0; on the contrary, when gq < dijMeanwhile, the electric logistics vehicle is not enough in electric quantity to complete the task of the current order, so temporary charging is needed, and the charging loss is defined as p3。
In addition, in the present embodiment, for the endurance problem of the new energy distribution vehicle, the charging time penalty cost may be calculated by the charging control function L to control the vehicle charging condition, and the formula is as follows:
specifically, the charging control function L is used to control whether the electric logistics vehicle can be charged. Wherein i and j respectively represent a path node i and a path node j, and V represents a path node set. Suppose g represents the distance traveled by the vehicle per unit of electrical quantity, q represents the current electrical quantity of the vehicle, and dijThen represents the distance of the vehicle between path node i and path node j, dminIndicating the distance of the vehicle between path node i to the nearest charging station.
Further, in the present embodiment, the charging cost C4Charging cost C is calculated through a charging cost function according to daytime charging cost and nighttime charging cost4The formula is as follows:
(ii) a Where K denotes a vehicle set, and K denotes each vehicle. Specifically, charging cost C4Including the cost of parking in line when the vehicle is out of charge to a charging station where different parking costs are established over two time periods of day and night. Wherein s is1Representing the daily hourly parking cost, s2Indicating night hourly parking fee, tsIndicates the parking time of each vehicle k, r1Represents the daytime hourly charging cost, r2Represents the charging cost per hour at night, trIndicating the charging time of each vehicle k.
In summary, the specific calculation formula of the cost function model of this embodiment is as follows:
step S3: and optimizing the initial logistics path based on a hybrid sparrow algorithm, and determining the optimal logistics path.
Specifically, the hybrid sparrow algorithm optimizes an initial logistics path by optimizing each vehicle distribution path on the logistics path, and generates a global optimal path through a sparrow search algorithm according to coordinates of a starting point and a target point. Setting basic parameters of a sparrow search algorithm as an initial population scale, the current iteration times, the maximum iteration times, position information, an early warning value and a safety value. And generating a global optimal path as the global path of the electric logistics vehicle in the current urban logistics distribution by combining multiple iterations of the global optimal path and the cost function model. FIG. 3 is a flow diagram illustrating an embodiment of the hybrid sparrow algorithm of the present invention. Referring to fig. 3, the following is a detailed description of the steps of the hybrid sparrow algorithm:
s31: initializing an initial sparrow population; wherein, sparrows are all vehicles on the logistics path.
Specifically, the initial population directly influences the convergence speed and the optimization precision of the population intelligent algorithm. Traditional population initializations of SSA are mainly generated in a random manner. Randomizing the initialization population does not ensure a uniform distribution of the population in the search space. The chaotic sequence has the characteristics of regularity, randomness, ergodicity and the like. Compared with complete randomization, the SSA initial population fused with the chaotic sequence has better diversity. The main idea of the chaotic sequence is to generate the chaotic sequence through the mapping relation between the intervals [0,1] and convert the chaotic sequence into the search space of the population. The chaotic sequence generation method has various generation modes, namely, the chaotic sequence generation method modifies the sinusoidal mapping to define sz chaotic mapping, and initializes the population by adopting a chaotic mapping method. Each electric logistics vehicle is regarded as a sparrow, and the process of searching food by the sparrow is realized by adopting a sparrow searching algorithm to carry out path planning. And initializing the population of the sparrow search algorithm by adopting a chaotic mapping method. Due to the fact that the sequence generated by chaotic mapping is good in uniformity, the chaotic mapping method is simple to select compared with other chaotic systems, high-level safety is achieved, the global search capability of the algorithm can be improved better, and the situation that a local optimal solution occurs is avoided.
Further, in the present embodiment, the sparrow initial population initialization includes position information of the initial position of the vehicle and initial position information of each optimal position corresponding to the vehicle. The position information calculation formula of the initial position of the vehicle is as follows:
Zn+1＝βsin((1+2n)πZn)。
specifically, Z is the initial population of the sparrow search algorithm, namely the position of each individual, n represents the nth vehicle, and Znβ is a control parameter for the nth vehicle position. The control parameter beta must be greater than zero (beta)>0) And Z isn∈[0，1]And Z0Is an initial condition and can be selected from the range (0, 1). As β approaches 1, the sz mapping becomes chaotic. The chaotic map is chosen because of its simplicity and affirms a high level of security compared to other chaotic systems.
In order to ensure the uniformity and randomness of the sparrow population distribution, the constructed chaotic mapping is introduced into a sparrow algorithm, and initial position information of each optimal position corresponding to each vehicle is calculated. The calculation formula is as follows:
Xij＝lb+(ub-lb)×Cn。
where lb denotes the lower search space limit, ub denotes the upper search space limit, k denotes each vehicle, CnDenotes the mapping parameter, XiInitial position information indicating the ith optimum position. The position of a sparrow can be obtained by taking the sequence of length D (D is the dimension of the target problem).
In this embodiment, a concept of backtracking is introduced into the sparrow search algorithm, and diversity of the population is enhanced by referring to the position information of past individuals, so that a parameter recall length H is introduced, where H is a control parameter defined by a user and determines how many past experiences the population or the population can remember at one time. XbestThe single global optimal position found by the sparrow search algorithm is calculated through an optimal position probability selection function, and the formula is as follows:
wherein f isiRepresents the probability that the ith optimal position is selected, j represents the position that was passed before the ith optimal position,
it represents the ith optimum position of the optical disk,
the ith optimum position is shown, and H represents a recall control parameter.
S32: the vehicle is classified into a finder and a follower according to a minimum cost model.
In this embodiment, the minimum cost model may be used to plan an initial path and calculate the cost of each vehicle at the initial position, so as to divide the vehicle into a finder and a follower.
S33: the finder location is updated.
The present embodiment updates the finder location based on a GT distribution policy. Specifically, since the sparrow search algorithm cannot guarantee the global property of the population, is prone to fall into the local optimum, and needs to enhance the convergence rate, the embodiment proposes a new GT distribution strategy, and uses GT distribution in the discoverer location update stage. GT distribution is based on the combination of Gaussian distribution and T distribution, in the iterative optimization process of the population, the search process in the initial stage is faster, the large step can be adopted to expand the range of the population search, and the calculation formula is as follows:
wherein,
indicating the position information of the ith sparrow, namely the finder, at the jth optimal position, C0Representing the speed adjustment parameters, N (G, T) representing the standard GT distribution, R2Represents an early warning value, ST represents a safety value, and Q represents a random number that follows a normal distribution.
In addition, in this embodiment, when the location update of the finder fails, the location of the finder may also be searched by using a reverse search strategy, and fig. 4 is a flowchart illustrating an embodiment of searching the location of the finder by using the reverse search strategy of the present invention, which describes the embodiment in detail.
S301, storing the current optimal position information of the position of the finder into a preset reverse search table, and abandoning the current optimal position information.
S302: and searching the position of the finder again to obtain the latest position information.
S303: inquiring a reverse search table, and judging whether the latest position information exists in the reverse search table; if yes, searching the position of the finder again; if not, the position of the finder is the latest position information.
In particular, in order to overcome the defect that the algorithm is easy to fall into a local optimal solution, a reverse search strategy is added in the discoverer stage of the sparrow search algorithm. When the position of the finder is not updated, namely the finder sinks into the local optimal solution, the optimal solution, namely the current optimal position information is stored in a preset search table, meanwhile, the optimal solution is abandoned, the finder continues to search for a new solution, and the new solution generated by the finder is compared with elements in the search table. FIG. 5 is a block diagram illustrating one embodiment of a reverse search table of the present invention. As shown in FIG. 5, L denotes, [ L ] Lmin,Lmax]And (4) showing. If the new solution exists in the reverse search table, local search is needed again to obtain the new solution; if not, the algorithm is continued.
S34: the follower position is updated with a probability selection function.
Specifically, in this embodiment, in order to quickly gather the sparrow population in a short time, a new probability selection method is proposed according to the random selection of the following peak in the artificial bee colony algorithm, and the position of the following person in the sparrow search algorithm is updated by the method, and the calculation formula is as follows:
specifically, the probability selection method function can be used in an equation of updating all positions, and is used for searching the global optimal position in long-term memory and updating the position of a follower in a sparrow search algorithm. Wherein f isiIndicating the fitness value of the ith food source in long-term memory, i.e., the food for sparrows in the sparrow algorithm. Gamma denotes the interval [0,5 ]]The random number of (a) is set,
adapted value, P, representing the ith follower position in the t-th iterationiIndicating the follow probability of the ith follower.
And after the following probability of each follower is calculated, updating the position of the follower according to the following probability of each follower, wherein the calculation formula is as follows:
wherein,
represents the position information of the ith follower at the jth optimal position, Q represents a random number obeying normal distribution, t represents the iteration number,
representing the current global worst position. n represents the number of distribution nodes, when i>At n/2, this indicates that the i-th follower with the lower fitness value is not getting food, and is in a state of being very hungry, and needs to fly to other places to forage for more energy.
S35: judging whether the position of the finder reaches the maximum updating iteration times or not; if yes, outputting the position of the finder as the optimal logistics path; if not, the finder position is continuously iterated.
In addition, in the embodiment, the follower can be randomly selected from the tracker population to serve as the alerter, and when the electric quantity of the electric logistics vehicle is not enough to support the next dispatch, the alerter can give out a warning and simultaneously immediately searches for the charging station for charging.
Step S4: optimizing the initial distribution scheme according to the cost function model to obtain the optimal distribution scheme
In order to improve the customer satisfaction degree in the distribution process of the new-energy electric logistics vehicle, the most main reason influencing the customer satisfaction degree is the distribution time and the product quality level, and the cost function model in the embodiment not only considers the hard time window and the soft time window, but also combines the soft time window and the hard time window according to the timeliness of the distributed articles and the customer demand degree, and combines the distribution time and the distribution total cost to describe the customer satisfaction degree on the distribution timeliness. Therefore, the cost function metric line in this embodiment may be used to plan the initial distribution scheme, and may also be used to optimize the initial distribution scheme, that is, to optimize the number of annual electric logistics vehicles and the number of corresponding path nodes in the distribution process, so as to obtain the optimal distribution scheme, and save the distribution time and the distribution cost.
Step S5: and carrying out logistics distribution based on the optimal logistics path and the optimal distribution scheme.
After the logistics path and the distribution scheme are optimized, logistics distribution is carried out according to the obtained optimal logistics path and the obtained optimal distribution scheme, and distribution efficiency and distribution time are improved.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (24)
1. A logistics path optimization method based on a hybrid sparrow algorithm is characterized by comprising the following steps:
obtaining order information, and planning an initial logistics path according to the order information;
determining an initial distribution scheme according to the cost function model;
optimizing an initial logistics path based on a mixed sparrow algorithm, and determining an optimal logistics path;
optimizing an initial distribution scheme according to the cost function model to obtain an optimal distribution scheme;
and carrying out logistics distribution based on the optimal logistics path and the optimal distribution scheme.
2. The hybrid sparrow algorithm-based logistics path optimization method of claim 1, wherein the cost function model calculates a minimum cost of the delivery scheme by a minimum cost function; wherein the minimum cost includes a vehicle electricity cost C1Vehicle running cost C2Time window penalty cost C3And a charging cost C4The minimum cost function is formulated as follows:
MinCcost of＝C1+C2+C3+C4。
3. The hybrid sparrow algorithm-based logistics path optimization method of claim 2, wherein the vehicle electricity cost C1Calculating through a vehicle electricity cost function; wherein the vehicle electricity cost C1Including uphill power usage costs, downhill power usage costs, and flat land power usage costs.
4. The hybrid sparrow algorithm-based logistics path optimization method of claim 3, wherein the vehicle electricity cost function calculation formula is as follows:
wherein i, j respectively represent path node i and path node j, V represents a set of path nodes,
k denotes a vehicle set, K denotes each vehicle,
λ1the power utilization coefficient of the uphill slope is shown,
λ2the electricity usage coefficient for a downhill slope is represented,
λ3the electric coefficient for the flat ground is shown,
dijindicating the distance of path node i to path node j,
Xijkindicating the electricity cost of the kth vehicle from path node i to path node j.
5. The hybrid sparrow algorithm-based logistics path optimization method of claim 2, wherein the vehicle driving cost C2Calculating through a vehicle running cost function; wherein the vehicle running cost C2Including fixed trip costs, transportation costs, and battery loss costs.
6. The hybrid sparrow algorithm-based logistics path optimization method of claim 5, wherein the vehicle travel cost function formula is as follows:
wherein i, j respectively represent path node i and path node j, V represents a set of path nodes,
k denotes a vehicle set, K denotes each vehicle,
e represents the fixed travel cost of each vehicle,
m represents the total weight of the transportation vehicle,
t represents the time of use of the vehicle,
b represents the capacity of the battery,
r represents the battery cost.
7. The hybrid sparrow algorithm-based logistics path optimization method of claim 2, wherein the time window penalty cost C3Calculating through a time window punishment cost function; wherein the time window penalty cost C3Including a soft time window penalty cost, a hard time window penalty cost, and a charge time penalty cost.
8. The hybrid sparrow algorithm-based logistics path optimization method of claim 7, wherein the time window penalty cost function is formulated as follows:
wherein n is1The number of the distribution nodes is shown,
PⅠa soft time window penalty cost is represented,
PⅡa hard-time penalty cost is represented,
PⅢrepresenting a charge time penalty cost.
9. The hybrid sparrow algorithm-based logistics path optimization method of claim 2, wherein the charging cost C4Calculating through a charging cost function; wherein the charging cost C4Including daytime charging costs and nighttime charging costs.
10. The hybrid sparrow algorithm-based logistics path optimization method of claim 9, wherein the charging cost function formula is as follows:
wherein K represents a set of vehicles, K represents each vehicle,
s1indicating the cost of parking an hour during the day,
s2indicating the parking cost per hour at night,
tsindicates the parking time of each vehicle k,
r1represents the cost of charging per hour during the day,
r2represents the charging cost per hour at night,
trto representThe charging time of each vehicle k.
11. The hybrid sparrow algorithm-based logistics path optimization method of claim 7, wherein the soft-time penalty cost PⅠCalculating by a soft time window penalty cost function, wherein the formula is as follows:
where i represents a path node i, V represents a set of path nodes,
tirepresenting the actual arrival time at the path node i,
wiand oiRespectively representing time nodes defining time periods of arrival at path node i,
p1a penalty index is indicated.
12. The hybrid sparrow algorithm-based logistics path optimization method of claim 7, wherein the hard time penalty cost PⅡCalculating by a hard time window penalty cost function, wherein the formula is as follows:
where i represents a path node i, V represents a set of path nodes,
tirepresenting the actual arrival time at the path node i,
wiand oiRespectively representing time nodes defining time periods of arrival at path node i,
p2a penalty index is indicated.
13. The hybrid sparrow algorithm-based logistics path optimization method of claim 7, wherein the charging time penalty cost PⅢPunishment by charging timeThe function is calculated according to the following formula:
wherein i, j respectively represent path node i and path node j, V represents a set of path nodes,
g represents the distance traveled per unit of electrical quantity by the vehicle,
q represents the current amount of electricity of the vehicle,
dijindicating the distance between path node i and path node j.
14. The hybrid sparrow algorithm-based logistics path optimization method of claim 13, wherein the charge time penalty cost function further controls vehicle charging conditions through a charging control function L, the formula is as follows:
wherein i and j respectively represent a path node i and a path node j, V represents a path node set,
g represents the distance traveled per unit of electrical quantity by the vehicle,
q represents the current amount of electricity of the vehicle,
dijindicating the distance between path node i and path node j,
dminindicating the distance between the path node i and the nearest charging station.
15. The hybrid sparrow algorithm-based logistics path optimization method of claim 1, wherein the hybrid sparrow algorithm optimizes an initial logistics path by optimizing each vehicle delivery path on the logistics path, comprising the steps of:
initializing an initial sparrow population; wherein, the sparrows are all vehicles on the logistics path;
dividing the vehicle into a finder and a follower according to a minimum cost model;
updating the position of the finder;
updating the position of the follower by utilizing a probability selection function;
judging whether the position of the finder reaches the maximum updating iteration times or not; if yes, outputting the position of the finder as an optimal logistics path; if not, the finder position is continuously iterated.
16. The hybrid sparrow algorithm-based logistics path optimization method of claim 15, wherein the initial sparrow population is initialized through chaotic mapping, and comprises position information of initial positions of vehicles and initial position information of optimal positions corresponding to the vehicles.
17. The hybrid sparrow algorithm-based logistics path optimization method of claim 16, wherein the position information calculation formula of the initial position of the vehicle is as follows:
Zn+l＝βsin((1+2n)πZn)
where n denotes the nth vehicle and β is a control parameter.
18. The hybrid sparrow algorithm-based logistics path optimization method of claim 16, wherein the initial position information calculation formula of each optimal position corresponding to the vehicle is as follows:
Xi＝lb+(ub-lb)×Cn
where lb denotes the lower search space limit, ub denotes the upper search space limit, k denotes each vehicle,
Cnthe parameters of the mapping are represented by,
Xiinitial position information indicating the ith optimum position.
19. The hybrid sparrow algorithm-based logistics path optimization method of claim 16, wherein the optimal location is selected by an optimal location probability selection function, and the calculation formula is as follows:
wherein f isiIndicating the probability that the ith optimum position was selected,
j denotes a position passed before the ith optimum position,
h denotes a recall control parameter.
20. The hybrid sparrow algorithm-based logistics path optimization method of claim 15, wherein the finder position is updated based on a GT distribution strategy, and the calculation formula is as follows:
wherein,
indicating the location information of the ith finder at the jth optimal location,
C0the speed-adjustment parameter is represented by,
n (G, T) represents the standard GT distribution,
R2the early-warning value is represented and,
ST represents a safety value of the security value,
q denotes a random number following a normal distribution.
21. The hybrid sparrow algorithm-based logistics path optimization method of claim 20, wherein when the discoverer location update fails, a reverse search strategy is adopted to search the discoverer location, comprising the following steps:
storing the current optimal position information of the position of the finder into a preset reverse search table, and giving up the current optimal position information;
searching the position of the finder again to obtain the latest position information;
inquiring a reverse search table, and judging whether the latest position information exists in the reverse search table; if so,
re-searching the finder position again; if not, the position of the finder is the latest position information.
22. The hybrid sparrow algorithm-based logistics path optimization method of claim 15, wherein the probability selection function is used for updating the position of a follower in the sparrow search algorithm, and the calculation formula is as follows:
wherein f isiAn adaptation value representing the location of the ith follower,
gamma represents a random number within a preset interval,
ft ian adaptation value representing the ith follower position, t represents the number of iterations,
Piindicating the follow probability of the ith follower.
23. The method of claim 21, wherein the follower positions are updated based on the follower following probabilities, and the calculation formula is as follows:
wherein,
indicating the position information of the ith follower at the jth optimal position,
q denotes a random number following a normal distribution,
t represents the number of iterations,
n represents the number of the distribution nodes,
24. A hybrid sparrow algorithm-based logistics path optimization method according to claim 15, wherein a follower is randomly selected as an alert, the alert being used to alert the vehicle of low battery.
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