CN111191813A - Vehicle distribution path optimization method based on cargo load and soft time window limitation - Google Patents
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Abstract
The invention requests to protect a vehicle distribution path optimization method based on cargo load and soft time window limitation, on the basis of the existing electric vehicle path optimization, the influence of the vehicle load on the energy consumption of a battery is mainly considered, soft time window constraint is also considered, an electric vehicle path optimization model with the minimized distribution cost (departure cost, driver cost, charging cost and punishment time window cost) as a target is established, and then a genetic algorithm is adopted for solving to obtain a distribution path optimization scheme. The invention establishes a charging cost function of the electric vehicle based on energy consumption; establishing a soft time window penalty cost function of the electric vehicle; and can formulate the delivery route according to customer's goods weight, demand of charging etc. in the actual conditions to reach the delivery cost minimum.
Description
Technical Field
The invention belongs to the field of logistics distribution, and also belongs to the field of computer application and the technical field of complex function optimization, and particularly relates to an electric vehicle distribution path optimization method based on cargo load and soft time window limitation.
Background
In recent years, with the rapid development of economy, the consumption level and the purchasing demand of users are increasingly improved, and the logistics activity is increased sharply. However, a series of environmental pollution problems caused by energy consumption, carbon emission and the like of logistics vehicles also appear, and how to apply new technology, new process and new material to reduce the negative effect of logistics activities becomes an important problem in the development of modern logistics industry. The electric automobile has the characteristics of low consumption, low emission and low pollution, and is one of effective ways for solving the problem, so that a series of policy and regulations are successively issued by the country to promote the development of the electric automobile industry and supporting service facilities thereof, and popular leaders in the industries of Jingdong, Alibaba and the like also vigorously implement the electromotization of the automobile. Compared with the traditional fuel vehicle, the electric vehicle has shorter driving distance and needs to go to the charging station for charging irregularly in the distribution process, and because the charging facilities are relatively less at the present stage, the situation that the electric vehicle goes around to the charging station often occurs, so that the normal logistics distribution process is influenced, the time in transit of the distribution vehicle is prolonged, and the logistics service quality is reduced. Therefore, how to reasonably optimize the distribution route of the electric vehicle becomes an important problem to be solved urgently.
The electric vehicle routing problem is developed from the vehicle routing problem. In recent years, most of research on the distribution problem of electric vehicles focuses on the charging technology and the location problem of a charging station, and the research results of the path optimization problem based on electric logistics vehicles are relatively few. The existing electric vehicle path planning research mainly considers constraint conditions such as battery electric quantity and optimal charging path, and constructs a model by considering different influence factors (low carbon, driving range, time window and the like). At present, constraint conditions such as low carbon, driving range, charging strategies and the like are mainly considered in an electric logistics distribution route optimization model, only the influence of a transport distance on electric energy consumption of a vehicle is considered in the aspect of electric energy consumption, although the influence of other factors such as the position of the electric vehicle on the electric energy consumption is rarely considered in the near future, the influence of cargo load on the electric energy consumption of the electric vehicle is not considered, so that the actual electric energy consumption of the vehicle during running deviates from the theory, the condition of electric energy consumption of the vehicle midway occurs, and the scientificity of the electric vehicle distribution route is further influenced. Electric vehicles are also adopted in logistics distribution to provide distribution service for each customer point, and each customer point has a distribution time limit. A certain penalty will be accepted if the delivery fails to reach the client within the time window. The total cost formed by the charging cost, the punishment cost, the departure cost and the driver cost in logistics distribution is minimum, and scientific and reasonable planning needs to be carried out on the vehicle running path in distribution. The invention carries out deep analysis on the researched problems and constructs a mathematical model corresponding to the problems on the premise of certain basic assumption.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The method is capable of making a distribution route according to the weight of the client goods, the charging requirement and the like in actual conditions, and therefore the distribution cost is minimum. The technical scheme of the invention is as follows:
a vehicle distribution path optimization method based on cargo load and soft time window limitation comprises the following steps:
step1, acquiring actual conditions of travel constraint, cargo load and time constraint of an electric vehicle, and constructing an improved electric vehicle path optimization model with minimized distribution cost, wherein the distribution cost comprises vehicle exit cost, driver cost, charging cost and penalty time window cost, an introduced energy consumption formula is introduced into a target function of the improved electric vehicle path optimization model, the influence of a soft time window and vehicle load on battery energy consumption is added into energy consumption, and constraint conditions are set for the target function of the improved electric vehicle path optimization model;
and 2, designing a genetic algorithm coding scheme by adopting a natural number coding mode, fusing the genetic algorithm coding scheme and a target function into a fitness function of the algorithm because the coding scheme of the genetic algorithm cannot completely reflect the constraint condition of the model, and adopting a new improved cross operator to retain excellent parent sub-path information as much as possible in the cross operation and retain and inherit the excellent sub-paths in the parent chromosomes into the child chromosomes in order to improve the optimizing capability of the algorithm in the later evolution stage. And (3) solving an objective function of the improved electric vehicle path optimization model in the step1 to obtain an optimal distribution path.
Further, the objective function of the improved delivery path optimization objective model of the electric vehicle in step1 is
Wherein, V is the set of all nodes, V ═ O ∪ C ∪ F ∪ O ', which is composed of the customer set C, the charging station set F, the distribution center O and the virtual distribution center O' together, K ═ K {1, 2.., K } represents the set of electric vehicles1、C2、C3、C4Respectively representing the use cost, the driver cost, the charging cost and the punishment cost of the vehicle; c0、Cl、CeThe unit vehicle use cost, the unit running cost and the unit electricity price are represented; sikService time, S, of electric vehicle k at customer node iikService time of the electric vehicle k at the charging node i; lijDistance between node i and node j, SikService time of the electric vehicle k at the charging node i;
Tikthe moment when the electric vehicle k reaches the node i; t isi early、Ti delayRepresents the standard earliest, latest service time of client i; m isijRepresenting the energy consumed by the electric vehicle on node i to node j; x is the number ofijk: variable 0-1, xijkA value of 1 indicates that the electric vehicle k directly drives to the point j when reaching the node i, and otherwise, the value is 0; y isik: variable 0-1, yikA value of 1 indicates that the electric vehicle k arrives at the node i to be charged, and otherwise is 0.
The charging cost in the objective function is equal to the cost for compensating the energy consumption of the battery of the electric logistics vehicle, namely, the load of the goods is introduced, and the influence of the load of the vehicle on the energy consumption of the battery is mainly considered;
energy consumption formula: m isij＝(αij(ω+fij)lij+βvij 2lij)/efSubstitution into
Wherein, αij＝a+gsinθij+gCrcosθijIs a specific constant, β ═ 0.5CdA ρ represents a vehicle specific constant, efRepresenting engine efficiency, fijRepresenting the load of the electric vehicle k on the node i and the node j; v. ofijRepresenting the distribution speed of the electric vehicle at the node i and the node j; ω represents vehicle weight at full (tons), a represents acceleration (m/s2), g represents gravitational constant (m/s2), θ represents road angle, A represents frontal area of vehicle (m2), ρ represents air density (kg/m3), and CrDenotes the coefficient of rolling resistance, CdThe rolling resistance coefficient is shown.
The expression for the charging cost function is found as follows:
the objective function also takes into account soft time window constraints, assuming that each electric vehicle has a delivery time window Ti early,Ti delay]When the vehicle is delivered in the time window, no penalty cost is paid, if the vehicle isWhen the user arrives early or late, a certain punishment time cost is generated, the punishment time cost is in a linear relation with the time of arriving the client point early or late, and a punishment cost function is obtained as follows:
can be expressed as
Wherein, CaRepresents the cost per unit time, C, of the early arrivalbRepresents the cost per unit time, T, of late arrivali early、Ti delayIndicating the standard earliest, latest service time for client i.
Further, the objective function setting constraint conditions of the improved electric vehicle path optimization model are as follows:
in the above constraints:
equations (1) (2) represent the assurance that each subscribing client is accessible;
the formula (3) represents a flow conservation criterion, and the electric vehicle can leave after reaching a certain node;
the formulas (4) and (5) show that the electric vehicle starts from the distribution center and returns to the distribution center after completing the task;
formula (6) represents that the number of electric vehicles for the distribution task is less than or equal to the total number of electric vehicles in the distribution center;
equation (7) represents the load capacity when the electric vehicle leaves the distribution center;
equation (8) represents that the load capacity of the electric vehicle when leaving the distribution center is less than the rated load capacity;
the expressions (9) and (10) are expressed from the access node i to the nodeThe consumption of the remaining battery at j should be reduced by mij；
Equation (11) represents a time calculation equation of the next access node j;
equation (12) represents that the remaining capacity of the electric vehicle at each node is greater than 0;
the formulae (13) and (14) represent yik、xijkThe 0-1 variable of (1);
variables and parameter symbols in each formula define:
in the formula, Wmax、WokRespectively representing the rated load capacity of the electric vehicle and the load capacity when leaving the distribution center; diDemand of node i; q: battery capacity of the electric vehicle; y isijk: the remaining capacity of the electric vehicle k from the node i to the node j.
Further, the step2 is to solve the objective function of the electric vehicle path optimization model by using a basic genetic algorithm, and comprises the following steps:
(1) chromosomal coding
The method adopts a natural number coding form, wherein a chromosome consists of N nodes, and then the coding length is N, and the genes are randomly arranged by integers from 1 to N;
(2) population initialization
Randomly generating a random permutation of length N, let Wii+1Represents the load capacity of the vehicle leaving the ith customer point to the (i + 1) th customer point in the chromosome, and judges Wii+1Whether or not it is greater than Q, when Vi-1，i≤Wmax、Wi,i+1＞WmaxWhen the gene is inserted, 0 is inserted in front of the i position of the chromosome; restart calculation of W from the position after insertion of 0ii+1Values, repeated until the sequence ends; meanwhile, calculating consumed electric quantity from a first node, judging that when the node i is reached, if the residual electric quantity cannot enable the transport vehicle to reach the next node or cannot reach the nearest charging station from the next node, inserting the number of the charging station nearest to the node behind the node i, wherein the number of the node is N +1, N +2, … and N + F, and circulating to the last node; then, inserting 0 into the head and the tail of the chromosome respectively to finally form an initial chromosome; finally, the above process is repeatedGenerating a plurality of chromosomes to form an initial population;
(3) a fitness function, which is set as a target function CiThe inverse of (d), i.e. the fitness function, is: fi＝1/Ci；
(4) Selection operation
Selecting individuals with high fitness of chromosomes by adopting a roulette mode, and using the individuals as parents to be inherited into the next generation;
(5) crossover operation
There are many methods for interleaving natural number codes, such as sequential interleaving, circular interleaving, etc. These intersection methods are applicable to the TSP problem and are not well suited to the vehicle path optimization problem for multiple delivery vehicles and multiple sub-routes. In order to improve the optimizing capability of the algorithm in the later period of evolution, a new improved crossover operator is adopted, and the excellent sub-paths of the parent generation are reserved to the greatest extent.
(6) Mutation operation
And carrying out mutation operation on the gene sequence according to a set mutation probability Pm by adopting two-point reciprocity.
(7): and (5) evolution reversion operation. Firstly, generating random natural numbers r1 and r 2; and then all nodes between the two points are numbered in reverse. This operation is only valid if fitness increases, and then proceeds to the next generation until the maximum number of iterations is reached.
Further, the specific steps of the interleaving operation in step (5) include:
step1, selecting two parent chromosomes, and randomly selecting a path on the parent chromosomes;
step2, leading the selected sub-road section;
step3, taking the sub-path A of the parent chromosome 1 as a part of the child chromosome 1, adding the codes which the sub-path A does not have in the parent chromosome 2 to the back of the sub-path A according to the sequence in the parent chromosome 2, and adding the code 0 at the end of the chromosome;
step4, for the offspring chromosome 1, adding 1 code 0 at any one of 7 positions after the path A, and then generating 7 chromosomes, and calculating the fitness of the 7 chromosomes, wherein the maximum fitness value is the offspring chromosome 1; other daughter chromosomes are obtained in the same way.
Further, the mutation operation of step (6): and (3) performing mutation operation on the gene sequence by adopting two-point reciprocity according to a set mutation probability Pm.
The invention has the following advantages and beneficial effects:
the existing research mainly considers the constraint conditions of low carbon, driving range, charging strategy and the like for an electric logistics distribution route optimization model, only considers the influence of the transport distance on the electric energy consumption of a vehicle on the electric energy consumption, although the influence of other factors such as the position of the electric vehicle on the electric energy consumption is rarely considered in the recent research, the influence of cargo load on the electric energy consumption of the electric vehicle is not considered, so that the actual electric energy consumption of the vehicle during running deviates from the theory, the condition of electric energy consumption of the vehicle midway occurs, and the scientificity of the electric vehicle distribution route is further influenced. Meanwhile, most studies rarely consider path optimization under the constraint of a soft time window when studying the electric vehicle path problem, which is not consistent with the actual delivery situation. Therefore, on the basis of the existing electric vehicle path optimization, the influence of the vehicle load on the battery energy consumption is mainly considered, the soft time window constraint is also considered, an electric vehicle path optimization model with the aim of minimizing the distribution cost (including the vehicle-out cost, the driver cost, the charging cost and the punishment time window cost) is established, and then a genetic algorithm is adopted for solving to obtain a distribution path optimization scheme. In the process of solving the electric vehicle path optimization model, the distribution path meeting the actual requirement can be quickly obtained.
Compared with the prior art, the method can realize the path optimization of the electric vehicle considering the cargo load and the soft time window, and has important guiding significance for improving the distribution and transportation efficiency, improving the economic benefit of enterprises and reducing the distribution cost.
Drawings
FIG. 1 is a preferred embodiment electric vehicle distribution route provided by the present invention;
FIG. 2 is a flow chart of the present invention for solving a model using a genetic algorithm;
FIG. 3 is a diagram of the operation of the genetic algorithm crossover strategy of the present invention;
fig. 4 is a flowchart of a vehicle distribution route optimization method based on cargo load and soft time window constraints according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
as shown in fig. 4, the method for optimizing the distribution route of the electric vehicle, wherein the step of establishing the optimization model of the route of the electric vehicle with the aim of minimizing the cost comprises the following steps:
(1) in order to minimize distribution cost, various factors such as vehicle capacity, transportation time and transportation cost need to be comprehensively considered.
Specifically, according to the actual conditions of the electric vehicle travel constraint, the cargo load and the time constraint, an electric vehicle path optimization model with minimized distribution cost is constructed, and an objective function is as follows:
Wherein V is the set of all nodes, V is O ∪ C ∪ F ∪ O', itThe system consists of a customer set C, a charging station set F, a distribution center O and a virtual distribution center O'. K ═ 1, 2.., K } represents a set of electric vehicles. C1、C2、C3、C4Respectively representing the use cost, the driver cost, the charging cost and the punishment cost of the vehicle; c0、Cl、CeThe unit vehicle use cost, the unit running cost and the unit electricity price are represented; sikService time, S, of electric vehicle k at customer node iikService time of the electric vehicle k at the charging node i; lijDistance between node i and node j, SikService time of the electric vehicle k at the charging node i;
Tikthe moment when the electric vehicle k reaches the node i; t isi early、Ti delayRepresents the standard earliest, latest service time of client i; m isijRepresenting the energy consumed by the electric vehicle on node i to node j; x is the number ofijk: variable 0-1, xijkA value of 1 indicates that the electric vehicle k directly drives to the point j when reaching the node i, and otherwise, the value is 0; y isik: variable 0-1, yikA value of 1 indicates that the electric vehicle k arrives at the node i to be charged, and otherwise is 0.
The charging cost function specifies:
the charging cost is equal to the cost generated by compensating the energy consumed by the battery of the electric logistics vehicle, namely, the cargo load is introduced, and the influence of the vehicle load on the energy consumption of the battery is mainly considered.
Energy consumption formula: m isij＝(αij(ω+fij)lij+βvij 2lij)/efSubstitution into
Wherein, αij＝a+gsinθij+gCrcosθijIs a specific constant, β ═ 0.5CdA ρ represents a vehicle specific constant, efRepresenting engine efficiency, fijRepresenting the load of the electric vehicle k on the node i and the node j; v. ofijIndicating electric vehiclesDelivery rates at node i and node j; ω represents vehicle weight at full (tons), a represents acceleration (m/s2), g represents gravitational constant (m/s2), θ represents road angle, A represents frontal area of vehicle (m2), ρ represents air density (kg/m3), and CrDenotes the coefficient of rolling resistance, CdThe rolling resistance coefficient is shown.
The expression for the charging cost function is found as follows:
the penalty function specifies:
suppose that each electric vehicle has a delivery time window Ti early,Ti delay]When the vehicle is delivered in the time window, no penalty cost is paid, if the vehicle arrives early or late, a certain penalty time cost is generated, and the magnitude of the penalty time cost is in a linear relation with the time of arriving at the customer point early or late. The penalty cost function is obtained as:
can be expressed as
Wherein, CaRepresents the cost per unit time, C, of the early arrivalbRepresents the cost per unit time, T, of late arrivali early、Ti delayIndicating the standard earliest, latest service time for client i.
(2) Constraint conditions
s.t
The above constraints are illustrated as follows:
equations (1) (2) represent the assurance that each subscribing client is accessible;
the formula (3) represents a flow conservation criterion, and the electric vehicle can leave after reaching a certain node;
the formulas (4) and (5) show that the electric vehicle starts from the distribution center and returns to the distribution center after completing the task;
formula (6) represents that the number of electric vehicles for the distribution task is less than or equal to the total number of electric vehicles in the distribution center;
equation (7) represents the load capacity when the electric vehicle leaves the distribution center;
equation (8) represents that the load capacity of the electric vehicle when leaving the distribution center is less than the rated load capacity;
the expressions (9) and (10) indicate that the consumption of the remaining battery should be reduced by m when accessing the nodes i to jij；
Equation (11) represents a time calculation equation of the next access node j;
equation (12) represents that the remaining capacity of the electric vehicle at each node is greater than 0;
the formulae (13) and (14) represent yik、xijkThe 0-1 variable of (a).
In the formula, Wmax、WokRespectively representing the rated load capacity of the electric vehicle and the load capacity when leaving the distribution center; diDemand of node i; q: battery capacity of the electric vehicle; y isijk: the remaining capacity of the electric vehicle k from the node i to the node j.
2. Solving is carried out by using a genetic algorithm, and referring to fig. 2, the basic design idea is as follows:
(1) chromosomal coding
In the form of natural number coding, where a chromosome is composed of N nodes, the coding length is N, and the genes are randomly arranged in integers from 1 to N, e.g., 8 demand points, (23156487) is a chromosome.
(2) Population initialization
Randomly generating a random permutation of length N, let Wii+1Indicating that the vehicle leaves the ith customer site in the chromosome to go to the (i + 1) th customerWeight of load at the time of point, Wii+1Whether or not it is greater than Q, when W isi-1，i≤Wmax、Wi,i+1＞WmaxWhen the gene is inserted, 0 is inserted in front of the i position of the chromosome; restart calculation of W from the position after insertion of 0ii+1Values, repeated until the sequence ends; meanwhile, calculating consumed electric quantity from a first node, judging that when the node i is reached, if the residual electric quantity cannot enable the transport vehicle to reach the next node or cannot reach the nearest charging station from the next node, inserting the number of the charging station nearest to the node behind the node i, wherein the number of the node is N +1, N +2, … and N + F, and circulating to the last node; then, a 0 is inserted into the head and the tail of the chromosome respectively, and finally an initial chromosome is formed. For example, if there are 8 demand points, 3 electric vehicles, visit 2 charging stations during distribution, then (02358940171060) is a legal chromosome; finally, the above process is repeated to produce several chromosomes constituting the initial population.
(3) A fitness function. The objective function in this document is to solve the minimum problem, so the fitness function in this document can be set as the objective function CiThe inverse of (d), i.e. the fitness function, is: fi＝1/Ci。
(4) Selection operation
Individuals with high fitness chromosomes are selected by roulette and inherited as parents into the next generation.
(5) Crossover operation
In order to reserve the excellent sub-paths of the parent, the crossover operator is improved, and the specific process is as shown in fig. 3:
step1, selecting two parent chromosomes, and randomly selecting a path on the parent chromosomes;
step2, leading the selected sub-road section;
step3, taking the child path A of the parent chromosome 1 as a part of the child chromosome 1, adding the codes which the child path A does not have in the parent chromosome 2 to the back of the child path A according to the sequence in the parent chromosome 2, and adding the code 0 at the end of the chromosome;
step4, for the offspring chromosome 1, adding 1 code 0 at any one of 7 positions after the path A, and then generating 7 chromosomes, and calculating the fitness of the 7 chromosomes, wherein the maximum fitness value is the offspring chromosome 1; other daughter chromosomes are obtained in the same way.
(6) Mutation operation
And carrying out mutation operation on the gene sequence according to a set mutation probability Pm by adopting two-point reciprocity.
(7) Operation of evolution reversion
Firstly, generating random natural numbers r1 and r 2; and then all node numbers between the two points are reversed. This operation is only valid if fitness increases, and then proceeds to the next generation until the maximum number of iterations is reached.
In summary, the invention constructs a path optimization model of the electric vehicle based on the general path optimization of the electric vehicle, considering the soft time window constraint and the influence of the vehicle load on the battery energy consumption. In the running process of the vehicle, the load of the goods can influence the running mileage of the electric vehicle and further influence the whole distribution path, so that the model introduces an energy consumption formula into an objective function, the influence of a soft time window and the load of the vehicle on the energy consumption of the battery is mainly considered, and finally, a reasonable running route of the distribution vehicle is designed, so that the whole distribution cost is minimum.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (6)
1. A vehicle distribution path optimization method based on cargo load and soft time window limitation is characterized by comprising the following steps:
step1, acquiring actual conditions of travel constraint, cargo load and time constraint of an electric vehicle, and constructing an improved electric vehicle path optimization model with minimized distribution cost, wherein the distribution cost comprises departure cost, driver cost, charging cost and penalty time window cost, an introduced energy consumption formula is introduced into an objective function of the improved electric vehicle path optimization model, the influence of a soft time window and vehicle load on battery energy consumption is added into energy consumption, and constraint conditions are set for the objective function of the improved electric vehicle path optimization model;
and 2, designing a genetic algorithm coding scheme by adopting a natural number coding mode, fusing the genetic algorithm coding scheme and an objective function into a fitness function of the algorithm because the genetic algorithm coding scheme cannot completely reflect the constraint conditions of the model, and adopting a new improved crossover operator to retain excellent parent sub-path information as much as possible in crossover operation and retain and inherit the excellent sub-paths in parent chromosomes into the child chromosomes in order to improve the optimizing capability of the algorithm in the later evolution stage. And (3) solving an objective function of the improved electric vehicle path optimization model in the step1 by using an improved genetic algorithm to obtain an optimal distribution path.
2. The method as claimed in claim 1, wherein the objective function of the step1 is an objective function of the improved electric vehicle distribution route optimization objective model
Wherein, V is the set of all nodes, V ═ O ∪ C ∪ F ∪ O ', which is composed of the customer set C, the charging station set F, the distribution center O and the virtual distribution center O' together, K ═ {1, 2.., K } represents the set of electric vehicles1、C2、C3、C4Respectively representing the use cost, the driver cost, the charging cost and the punishment cost of the vehicle; c0、Cl、CeThe unit vehicle use cost, the unit running cost and the unit electricity price are represented; sikService time, S, of electric vehicle k at customer node iikService time of the electric vehicle k at the charging node i; lijDistance between node i and node j, SikService time of the electric vehicle k at the charging node i;
Tikthe moment when the electric vehicle k reaches the node i; t isi early、Ti delayRepresents the standard earliest, latest service time of client i; m isijRepresenting the energy consumed by the electric vehicle on node i to node j; x is the number ofijk: variable 0-1, xijkA value of 1 indicates that the electric vehicle k directly drives to the point j when reaching the node i, and otherwise, the value is 0; y isik: variable 0-1, yikA value of 1 indicates that the electric vehicle k arrives at the node i to be charged, and otherwise is 0.
The charging cost in the objective function is equal to the cost for compensating the energy consumption of the battery of the electric logistics vehicle, namely, the load of the goods is introduced, and the influence of the load of the vehicle on the energy consumption of the battery is mainly considered;
energy consumption formula: m isij＝(αij(ω+fij)lij+βvij 2lij)/efSubstitution into
Wherein, αij＝a+gsinθij+gCrcosθijIs a specific constant, β ═ 0.5CdA ρ represents a vehicle specific constant, efRepresenting engine efficiency, fijIndicating that the electric vehicle k is at the node i and the nodeThe load at point j; v. ofijRepresenting the distribution speed of the electric vehicle at the node i and the node j; ω represents vehicle weight at full (tons), a represents acceleration (m/s2), g represents gravitational constant (m/s2), θ represents road angle, A represents frontal area of vehicle (m2), ρ represents air density (kg/m3), and CrDenotes the coefficient of rolling resistance, CdRepresents a rolling resistance coefficient;
the expression for the charging cost function is found as follows:
the objective function also takes into account soft time window constraints, assuming that each electric vehicle has a delivery time window Ti early,Ti delay]When the vehicle delivers goods in the time window, no penalty cost is paid, if the vehicle arrives early or late, a certain penalty time cost is generated, the magnitude of the penalty time cost is in a linear relation with the time of arriving at the customer point early or late, and the penalty cost function is obtained as follows:
can be expressed as
Wherein, CaRepresents the cost per unit time, C, of the early arrivalbRepresents the cost per unit time, T, of late arrivali early、Ti delayIndicating the standard earliest, latest service time for client i.
3. The method of claim 2, wherein the objective function of the improved electric vehicle path optimization model sets constraints as follows:
in the above constraints:
equations (1) (2) represent the assurance that each subscribing client is accessible;
the formula (3) represents a flow conservation criterion, and the electric vehicle can leave after reaching a certain node;
the formulas (4) and (5) show that the electric vehicle starts from the distribution center and returns to the distribution center after completing the task;
formula (6) represents that the number of electric vehicles for the distribution task is less than or equal to the total number of electric vehicles in the distribution center;
equation (7) represents the load capacity when the electric vehicle leaves the distribution center;
equation (8) represents that the load capacity of the electric vehicle when leaving the distribution center is less than the rated load capacity;
the expressions (9) and (10) indicate that the consumption of the remaining battery should be reduced by m when accessing the nodes i to jij；
Equation (11) represents a time calculation equation of the next access node j;
equation (12) represents that the remaining capacity of the electric vehicle at each node is greater than 0;
the formulae (13) and (14) represent yik、xijkThe 0-1 variable of (1);
in the formula, Wmax、WokRespectively representing the rated load capacity of the electric vehicle and the load capacity when leaving the distribution center; diDemand of node i; q: battery capacity of the electric vehicle; y isijk: the remaining capacity of the electric vehicle k from the node i to the node j.
4. The method for optimizing vehicle distribution path based on cargo load and soft time window limit as claimed in claim 3, wherein said step2 is to solve the objective function of said electric vehicle path optimization model by using basic genetic algorithm, comprising the following steps:
(1) chromosomal coding
The method adopts a natural number coding form, wherein a chromosome consists of N nodes, the coding length is N, and the genes are randomly arranged by integers from 1 to N;
(2) population initialization
Randomly generating a random permutation of length N, let Wii+1Represents the load capacity of the vehicle leaving the ith customer point to the (i + 1) th customer point in the chromosome, and judges Wii+1Whether or not it is greater than Q, when W isi-1，i≤Wmax、Wi,i+1＞WmaxWhen the gene is inserted, 0 is inserted in front of the i position of the chromosome; restart calculation of W from the position after insertion of 0ii+1Values, repeated until the sequence ends; meanwhile, calculating consumed electric quantity from a first node, judging that when the node i is reached, if the residual electric quantity cannot enable the transport vehicle to reach the next node or cannot reach the nearest charging station from the next node, inserting the number of the charging station nearest to the node behind the node i, wherein the number of the node is N +1, N +2, … and N + F, and circulating to the last node; then, inserting 0 into the head and the tail of the chromosome respectively to finally form an initial chromosome; finally, repeating the above process to generate a plurality of chromosomes to form an initial population;
(3) a fitness function, which is set as a target function CiThe inverse of (d), i.e. the fitness function, is: fi＝1/Ci；
(4) Selection operation
Selecting individuals with high fitness of chromosomes by adopting a roulette mode, and using the individuals as parents to be inherited into the next generation;
(5) crossover operation
There are many methods for interleaving natural number codes, such as sequential interleaving, circular interleaving, etc. These intersection methods are applicable to the TSP problem and are not well suited to the vehicle path optimization problem for multiple delivery vehicles, multiple sub-routes. In order to improve the optimizing capability of the algorithm in the later period of evolution, a new improved crossover operator is adopted, and the excellent sub-paths of the parent generation are reserved to the greatest extent.
(6) Mutation operation
And carrying out mutation operation on the gene sequence according to a set mutation probability Pm by adopting two-point reciprocity.
(7): and (5) evolution reversion operation. Firstly, generating random natural numbers r1 and r 2; and then all node numbers between the two points are reversed. This operation is only valid if fitness increases, and then proceeds to the next generation until the maximum number of iterations is reached.
5. The method for optimizing a vehicle distribution path based on cargo load and soft time window limit as claimed in claim 4, wherein the step (5) of interleaving comprises the specific steps of:
step1, selecting two parent chromosomes, and randomly selecting a path on the parent chromosomes;
step2, leading the selected sub-road section;
step3, taking the sub-path A of the parent chromosome 1 as a part of the child chromosome 1, adding the codes which the sub-path A does not have in the parent chromosome 2 to the back of the sub-path A according to the sequence in the parent chromosome 2, and adding the code 0 at the end of the chromosome;
step4, for the offspring chromosome 1, adding 1 code 0 at any one of 7 positions after the path A, and then generating 7 chromosomes, and calculating the fitness of the 7 chromosomes, wherein the maximum fitness value is the offspring chromosome 1; other daughter chromosomes are obtained in the same way.
6. The cargo load and soft time window limit-based vehicle distribution path optimization method of claim 4, wherein the (6) mutation operation: and (3) performing mutation operation on the gene sequence by adopting two-point reciprocity according to a set mutation probability Pm.
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