CN114492999A - Electric vehicle distribution route generation method considering random demand and time window - Google Patents
Electric vehicle distribution route generation method considering random demand and time window Download PDFInfo
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Abstract
The invention discloses an electric vehicle distribution route generation method considering random demand and a time window based on a self-adaptive large-scale neighborhood search algorithm, which comprises the following steps: giving initial data; defining a removal and insertion operator; constructing an initial solution; performing iterative search on the solution through a self-adaptive large-scale neighborhood search algorithm; and outputting the optimal solution. The method considers the influence of random demands on the vehicle load, can better calculate the energy consumption of the electric vehicle, and reduces the occurrence of the condition of insufficient electric quantity. Meanwhile, the self-adaptive mechanism is adopted to adjust the score of the operator, so that the convergence speed of the solution can be effectively improved. By adopting the specific implementation method, the generated electric vehicle distribution scheme can effectively reduce the number of vehicles required for distribution, and further reduce the distribution cost.
Description
Technical Field
The invention relates to the technical field of vehicle path problem research, in particular to a method for generating an electric vehicle distribution route by considering random requirements and a time window.
Background
The increasing demand for transportation places a great deal of pressure on the environment, and electric vehicles are being widely used in distribution activities because they can provide transportation services with zero exhaust emissions, high efficiency and low noise. However, there are some limitations to the operation of electric vehicles, such as limited driving range, insufficient number of public charging stations, and long charging time. The vehicle routing problem with time windows (VRPTW) is a core optimization model in distribution tasks, electric vehicles need to be charged timely to overcome the defect of limited driving range, and the charging requirement of electric vehicles easily causes violation of the customer time window, so the electric vehicle routing problem with time windows (EVRPTW) is a very challenging combined optimization problem. Meanwhile, in order to avoid the situation that the electric vehicle has insufficient electric quantity in the distribution process, the residual electric quantity of the electric vehicle needs to be accurately estimated. In the existing methods, it is generally assumed that the energy consumption of an electric vehicle is proportional to the driving range and the influence of the vehicle load on the energy consumption is neglected.
However, in the actual distribution process, uncertainty of electric vehicle load is usually caused due to uncertainty of customer demand and limitation of electric vehicle capacity. Since vehicle load is a key factor affecting the energy consumption of an electric vehicle, it is generally not feasible to construct a distribution route by ignoring the uncertainty of customer demand and the effect of vehicle load on the energy consumption of the electric vehicle during actual distribution. Therefore, it is important to make a feasible and less costly distribution scheme under the constraints of random demand and time window, and this is the novelty and inventive step of the present invention. The invention provides an electric vehicle distribution route generation method considering random requirements and a time window, defines a plurality of removal and insertion operators to improve the solution, and adopts a self-adaptive mechanism to adjust the scores of the operators, thereby effectively improving the convergence speed of the solution. Compared with the existing method, the method provided by the patent can calculate the energy consumption of the electric vehicle more accurately, and meanwhile, the number of vehicles is reduced, and the distribution cost is reduced.
Disclosure of Invention
Aiming at the huge complexity of the electric vehicle path problem with the time window, the invention provides the electric vehicle distribution route generation method considering the random demand and the time window to generate the electric vehicle distribution scheme and reduce the distribution cost of enterprises.
The technical scheme for solving the technical problems is as follows:
the embodiment of the invention provides an electric vehicle distribution route generation method considering random requirements and a time window, which comprises the following steps:
step 1: given customer informationCharging station information, a distance matrix between a parking lot, a customer and a charging station and the demand distribution and expected delivery time of the customer are defined, the solution S is a set of distribution routes, each distribution route is a set of ordered customer nodes and charging pile nodes, an objective function is defined as the minimum vehicle number and the total power consumption of the vehicle, and N =0 and NNI=25000，NSR=60，NRR=2000；
And 2, step: the definition removing operation comprises a customer node removing operation, a charging pile node removing operation and a route removing operation, wherein the customer node removing operation comprises a random removing operator: randomly removing K client nodes in the solution; target minimum removal operator: removing K customer nodes in the solution to minimize the objective function value; the highest similarity removal operator: removing the K clients with the highest similarity in the solution; the charging pile node removing operation comprises the following steps: randomly removing sigma charging piles in the solution; shortest path removal operator: removing sigma charging piles in the solution to enable the path of the vehicle to be distributed to be shortest; charge maximum removal operator: removing sigma charging piles with the largest charging times in the solution; the route removal operation includes a random removal operator: randomly removing omega routes in the solution; least-numerous removal operators: removing omega routes with small number of clients in the solution; mileage maximum removal operator: removing omega routes with the longest mileage in the solution; most power consuming removal operator: removing omega routes with the most power consumption in the solution;
and step 3: defining an insertion operation comprising a client node insertion operation set and a charging pile node insertion operation set, wherein the client node insertion operation comprises a random insertion operator: randomly inserting the client into the solution; incremental minimum insertion operator: sequentially inserting the client into a position which enables the increment of the target function to be minimum; energy consumption minimum insertion operator: sequentially inserting the customers into the position with the minimum energy consumption increase; the charging pile node insertion operation comprises a distance minimum insertion operator: firstly, a customer with negative electric quantity when a vehicle arrives is found out, and then a charging station with the minimum distance increase is inserted in front of the customer;
and 4, step 4: constructing an initial solution SinitAnd assigns a current solution ScurThe optimal solution SBRNew solution of Snew；
And 5: if n is>NNIGo to step 14, otherwise continue;
step 6: if N is divided by NSRIf the remainder is 0, randomly selecting a charging pile node removing operator and a charging pile node inserting operator through a roulette algorithm according to the scores of operators in the charging pile node removing operation and the charging pile node inserting operation, and updating S according to the selected operatorscurObtain a temporary solution StempTurning to step 9, otherwise, continuing;
and 7: if N is divided by NRRIf the remainder is 0, randomly selecting a route removal operator and a client node insertion operator by a roulette algorithm according to the scores of the operators in the route removal operation and the client node insertion operation, and updating S according to the selected operatorscurObtain a temporary solution StempTurning to step 9, otherwise, continuing;
and 8: randomly selecting a customer removal operator and a customer node insertion operator by a roulette algorithm according to the scores of the operations in the customer removal operation and the customer node insertion operation, and updating S according to the selected operatorscurObtain a temporary solution Stemp；
And step 9: if S istempIf it is not feasible, then it is determined thattempExecuting the insertion operation of the charging pile nodes;
step 10: if S istempIs greater than SBRThe objective function of (1), then for StempAfter local search, assign SnewOtherwise directly adding StempIs assigned to Snew；
Step 11: if S isnewIs less than SBRIs then SnewIs assigned to SBR；
Step 12: if S isnewIs less than ScurIs then SnewIs assigned to ScurOtherwise, the S is carried out with a certain probabilitynewIs assigned to Scur；
Step 13: updating the operator score, enabling n = n +1, and turning to the step 5;
step 14: outputting the optimal solution SBR。
The embodiment of the invention provides an electric vehicle distribution route generation method considering random requirements and a time window, wherein iterative search is carried out through a self-adaptive large-scale neighborhood search algorithm; the self-adaptive mechanism is adopted to adjust the score of the operator, so that the convergence speed of the solution can be effectively improved; by adopting the specific implementation method, the generated electric vehicle distribution scheme can effectively reduce the number of vehicles required for distribution, and further reduce the distribution cost.
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FIG. 1 is a flow chart illustrating an electric vehicle distribution route generation method with consideration of random demand and time window according to an embodiment of the present invention;
fig. 2 is a schematic view of an initial deconstruction flow according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart illustrating an electric vehicle distribution route generation method considering random demand and a time window according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
step 1: giving customer information, charging station information, distance matrixes among a parking lot, customers and charging stations and demand distribution and expected arrival time of the customers, defining a solution S as a set of distribution routes, wherein each distribution route is a set of ordered customer nodes and charging pile nodes, defining an objective function as the minimum number of used vehicles and the total power consumption of the vehicles, and enabling N =0 and NNI=25000，NSR=60，NRR=2000；
Step 2: the definition removing operation comprises a customer node removing operation, a charging pile node removing operation and a route removing operation, wherein the customer node removing operation comprises a random removing operator: randomly removing K client nodes in the solution; target minimum removal operator: removing K customer nodes in the solution to minimize the objective function value; the highest similarity removal operator: removing the K clients with the highest similarity in the solution; the charging pile node removing operation comprises the following steps: randomly removing sigma charging piles in the solution; shortest path removal operator: removing sigma charging piles in the solution to enable the path of the vehicle to be distributed to be shortest; charge maximum removal operator: removing sigma charging piles with the largest charging times in the solution; the route removal operation includes a random removal operator: randomly removing omega routes in the solution; least-numerous removal operators: removing omega routes with small number of clients in the solution; mileage maximum removal operator: removing omega routes with the longest mean range in the solution; most power consuming removal operator: removing omega routes with the most power consumption in the solution;
and step 3: defining an insertion operation comprising a client node insertion operation set and a charging pile node insertion operation set, wherein the client node insertion operation comprises a random insertion operator: randomly inserting the client into the solution; incremental minimum insertion operator: sequentially inserting the client into a position which enables the increment of the target function to be minimum; energy consumption minimum insertion operator: sequentially inserting the customers into the position with the minimum energy consumption increase; the charging pile node insertion operation comprises a distance minimum insertion operator: firstly, a customer with negative electric quantity when a vehicle arrives is found out, and then a charging station with the minimum distance increase is inserted in front of the customer;
and 4, step 4: constructing an initial solution SinitAnd assigns a current solution ScurThe optimal solution SBRNew solution of Snew；
And 5: if n is>NNIGo to step 14, otherwise continue;
and 6: if N is divided by NSRIf the remainder is 0, randomly selecting a charging pile node removing operator and a charging pile node inserting operator through a roulette algorithm according to the scores of operators in the charging pile node removing operation and the charging pile node inserting operation, and updating the S according to the selected operatorscurObtain a temporary solution StempTurning to step 9, otherwise, continuing;
and 7: if N is divided by NRRIf the remainder is 0, randomly selecting a route removal operator and a client node insertion operator by a roulette algorithm according to the scores of the operators in the route removal operation and the client node insertion operation, and updating S according to the selected operatorscurTo obtainTemporary solution of StempTurning to step 9, otherwise, continuing;
and 8: randomly selecting a customer removal operator and a customer node insertion operator by a roulette algorithm according to the scores of the operations in the customer removal operation and the customer node insertion operation, and updating S according to the selected operatorscurObtain a temporary solution Stemp；
And step 9: if S istempIf not feasible, then pair StempExecuting the insertion operation of the charging pile nodes;
step 10: if S istempIs greater than SBRThe objective function of (1), then for StempAfter local search, assign SnewOtherwise directly adding StempIs assigned to Snew；
Step 11: if S isnewIs less than SBRIs then SnewIs assigned to SBR；
Step 12: if S isnewIs less than ScurIs then SnewIs assigned to ScurOtherwise, the S is carried out with a certain probabilitynewIs assigned to Scur；
Step 13: updating the operator score, enabling n = n +1, and turning to the step 5;
step 14: outputting the optimal solution SBR。
Specifically, the mathematical expression of the objective function in step 1 is
I.e. minimizing the number of vehicles used and minimizing the electricity consumption cost of all vehicles, where xijFor a binary decision variable, take 1 when a vehicle visits arc (i, j), otherwise take 0, x0jRepresenting the departure arc of the vehicle, node 0 representing the yard, Cij(q) represents the amount of power consumed by the vehicle through arc (i, j) ∈ A when customer demand is q, CFAnd CERespectively represent unit vehicle cost and unit power consumption cost. The criterion for determining whether the solution is feasible in step 9 includes: (1) the total transport demand per line in the solution cannot exceed the vehicle capacity; (2) the time window requirements of all client nodes can be met, namely the time of arriving at the client nodes is within the acceptable range of passengers; (3) the upper limit of the electric quantity of the vehicle is not violated, namely the electric quantity of the electric vehicle is not lower than the minimum value. S in step 9tempThe upper limit of the amount of electricity may be violated after the customer node is inserted, and thus it is necessary to make it feasible by performing the charging pile node insertion operation.
Fig. 2 is a schematic view of an initial deconstruction flow of the embodiment of the invention, as shown in fig. 2, including the following steps:
step 41: initializing unallocated customer set CRFor the set of all client nodes, an initial solution SinitIs empty;
step 42: selecting the farthest customer from the yard to be driven from CRRemoving the line and distributing a new line k for the line;
step 43: judgment set CRIf empty, in an idle step 47;
step 44: in set CRIn the network, a customer node c is selected which minimizes the increase in cost of the current linei；
Step 45: judgment ciIf the time window and capacity constraints are met after inserting k, if so, ciInsert k and get it from CROtherwise insert line k into SinitAnd is ciAllocating a new line k = k +1, and turning to step 43;
step 46: judging whether the current line needs to be charged, if not, turning to the step 43, otherwise, inserting a charging pile node into the k, and turning to the step 43;
step 47: output the initial solution Sinit。
Specifically, the time window and the capacity constraint in step 45 respectively refer to: (1) customer node ciAfter inserting route k, the vehicle arrives at ciCannot be earlier than the client ciThe earliest delivery time required, nor can it be later than client ciThe required latest delivery time; (2) Customer node ciAfter the line k is inserted, the maximum demand of all customers on the line cannot exceed the capacity of the electric vehicle. The method is a heuristic method and can provide a feasible initial solution for the algorithm.
Simulation results and analysis
In order to verify the effectiveness of the specific implementation method, experiments are performed in this section on the basis of determining the requirements and considering the E-VRPTW problem of the energy consumption model, and test cases with the scale of 100 are selected as experimental examples. And each test case is operated for 10 times by adopting the same algorithm parameters, and a solution with a small number of corresponding vehicles is preferentially selected. The results of the comparison of the process proposed in this patent with the existing process are shown in table 1. M in the tableICRepresenting the number of conventional fuel-fired vehicles, mERepresenting the number of electric vehicles, fdRepresents the total mileage in kilometers.
TABLE 1 comparison of the embodied method for a case-book of 100 scales with the existing method
As can be seen from table 1, the solution of the present embodiment method uses a smaller number of vehicles than the solution given by the conventional method, but the total mileage of the solution is larger due to the requirement of charging the electric vehicle.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. An electric vehicle distribution route generation method considering random demand and a time window is characterized in that a reasonable electric vehicle distribution route set can be generated according to the demand of a customer and the distribution time demand, and the method comprises the following specific steps:
step 1: giving customer information, charging station information, distance matrixes among a parking lot, customers and charging stations and demand distribution and expected arrival time of the customers, defining a solution S as a set of distribution routes, wherein each distribution route is a set of ordered customer nodes and charging pile nodes, defining an objective function as the minimum number of used vehicles and the total power consumption of the vehicles, and enabling N =0 and NNI=25000,NSR=60，NRR=2000;
Step 2: the definition removing operation comprises a customer node removing operation, a charging pile node removing operation and a route removing operation, wherein the customer node removing operation comprises a random removing operator: randomly removing K client nodes in the solution; target minimum removal operator: removing K customer nodes in the solution to minimize the objective function value; the highest similarity removal operator: removing the K clients with the highest similarity in the solution; the charging pile node removing operation comprises the following steps: randomly removing sigma charging piles in the solution; shortest path removal operator: removing sigma charging piles in the solution to enable the path of the vehicle to be distributed to be shortest; charge maximum removal operator: removing sigma charging piles with the largest charging times in the solution; the route removal operation includes a random removal operator: randomly removing omega routes in the solution; least-numerous removal operators: removing omega routes with small number of clients in the solution; mileage maximum removal operator: removing omega routes with the longest mean range in the solution; most power consuming removal operator: removing omega routes with the most power consumption in the solution;
and step 3: defining an insertion operation comprising a client node insertion operation set and a charging pile node insertion operation set, wherein the client node insertion operation comprises a random insertion operator: randomly inserting the client into the solution; incremental minimum insertion operator: sequentially inserting the client into a position which enables the increment of the target function to be minimum; energy consumption minimum insertion operator: sequentially inserting the customers into the position with the minimum energy consumption increase; the charging pile node insertion operation comprises a distance minimum insertion operator: firstly, a customer with negative electric quantity when a vehicle arrives is found out, and then a charging station with the minimum distance increase is inserted in front of the customer;
and 4, step 4: constructing an initial solution SinitAnd assigns a current solution ScurThe optimal solution SBRNew solution of Snew；
And 5: if n is>NNIGo to step 14, otherwise continue;
step 6: if N is divided by NSRIf the remainder is 0, randomly selecting a charging pile node removing operator and a charging pile node inserting operator through a roulette algorithm according to the scores of operators in the charging pile node removing operation and the charging pile node inserting operation, and updating S according to the selected operatorscurObtain a temporary solution StempTurning to step 9, otherwise, continuing;
and 7: if N is divided by NRRIf the remainder is 0, a route removal operator and a guest are randomly selected by the roulette algorithm according to the scores of the operators in the route removal operation and the guest node insertion operationThe user node inserts an operator, and the S is updated according to the selected operatorcurObtain a temporary solution StempTurning to step 9, otherwise, continuing;
and 8: randomly selecting a customer removal operator and a customer node insertion operator by a roulette algorithm according to the scores of the operations in the customer removal operation and the customer node insertion operation, and updating S according to the selected operatorscurObtain a temporary solution Stemp；
And step 9: if S istempIf not feasible, then pair StempExecuting a charging pile node insertion operator;
step 10: if S istempIs greater than SBRThe objective function of (1), then for StempAfter local search, assign SnewOtherwise directly adding StempIs assigned to Snew；
Step 11: if S isnewIs less than SBRIs then SnewIs assigned to SBR；
Step 12: if S isnewIs less than ScurIs then SnewIs assigned to ScurOtherwise, the S is carried out with a certain probabilitynewIs assigned to Scur；
Step 13: updating the operator score, enabling n = n +1, and turning to the step 5;
step 14: outputting the optimal solution SBR。
2. The method of claim 1, wherein the step of the initial solution construction method comprises:
step 41: initializing unallocated customer set CRFor the set of all client nodes, an initial solution SinitIs empty;
step 42: selecting the farthest customer from the yard to be driven from CRRemoving the line and distributing a new line k for the line;
step 43: judgment set CRWhether it is empty or not, if it is emptyTurning to step 47;
step 44: in set CRSelecting a client node ci which causes the cost increase of the current line to be minimum;
step 45: judgment ciIf the time window and capacity constraints are satisfied after inserting k, if so, ci is inserted into k and is driven from CROtherwise insert line k into SinitAnd is ciAllocating a new line k = k +1, and turning to step 43;
step 46: judging whether the current line needs to be charged, if not, turning to the step 43, otherwise, inserting a charging pile node into the k, and turning to the step 43;
step 47: output the initial solution Sinit。
3. The method of claim 1, wherein the step of updating the operator score comprises:
step 131: if the new solution S is foundnewIs less than the optimal solution SBRThe score of the selected operator is increased by 25;
step 132: if the new solution S is foundnewIs less than the current solution ScurThe score of the selected operator is increased by 21;
step 133: if the new solution S is foundnewIs greater than the optimal solution SBRAnd the current solution ScurBut is still assigned to S by the algorithm chosencurThen the selected operator score is increased by 20.
4. The method of claim 1, wherein the criterion for determining whether the solution is feasible comprises:
(1) the maximum transport demand per line in the solution cannot exceed the vehicle capacity;
(2) the time window requirements of all client nodes can be met, namely the time of arriving at the client nodes is within the acceptable range of passengers;
(3) during the transportation process of the vehicle, the electric quantity cannot be less than the minimum electric quantity.
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| CN114897249B (en) * | 2022-05-19 | 2024-06-25 | 苏州大学 | Method and device applied to solving bulk cargo path planning of electric truck |
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