CN114626638A - Dynamic scheduling optimization method for battery transportation of shared electric bicycle - Google Patents

Dynamic scheduling optimization method for battery transportation of shared electric bicycle Download PDF

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CN114626638A
CN114626638A CN202210380058.8A CN202210380058A CN114626638A CN 114626638 A CN114626638 A CN 114626638A CN 202210380058 A CN202210380058 A CN 202210380058A CN 114626638 A CN114626638 A CN 114626638A
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杜佳俊
张敏霞
范志强
符杭杭
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Zhejiang University of Technology ZJUT
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Abstract

A dynamic scheduling optimization method for battery transportation of a shared electric bicycle comprises the steps of firstly, designing a strategy for supplementing batteries in a vehicle way according to the actual situation of the shared electric bicycle; then establishing a shared electric bicycle battery transportation dynamic scheduling optimization problem model, and taking the maximum number of replaceable batteries of the vehicles in the specified time as a target, wherein the model comprises the prediction of the number of low-power electric bicycles in a parking spot, the newly increased electric bicycles in the period updating time and the change of the number of the low-power electric bicycles in the non-service parking spot; and finally, a hybrid water wave optimization algorithm based on differential evolution is designed to solve the maximum number of replaced batteries of the vehicle. The invention solves the problem of vehicle path planning of replacing batteries for low-power electric bicycles in the regional shared electric bicycle parking points by operators.

Description

Dynamic scheduling optimization method for transportation of shared electric bicycle batteries
Technical Field
The invention belongs to the field of shared electric bicycle battery transportation scheduling, and relates to a dynamic scheduling optimization method for maximizing the number of replaced electric bicycle batteries.
Background
The shared electric bicycle is a product under the shared economy, and is more time-saving and labor-saving compared with the shared bicycle because the shared electric bicycle is driven by electricity. The shared electric bicycle requires that a user needs to park at a fixed parking spot when using and returning the electric bicycle, and the electric bicycle in the parking spot cannot be automatically charged, so that an operator needs to provide battery replacement service for the electric bicycle with insufficient electric quantity in a manual operation mode. As time goes by, the electric vehicles with low battery in the parking spot change due to the mobility of the electric vehicles in the area, and there is uncertainty about the number of the requested battery replacement. Therefore, a reasonable battery transportation scheduling method is designed to replace the low-power electric bicycle, so that the use satisfaction of users can be improved, the cost of operators can be reduced, and the method is a valuable research problem.
Disclosure of Invention
Aiming at the problems, the invention designs a hybrid water wave optimization algorithm based on differential evolution, provides a dynamic scheduling optimization method for the shared electric single vehicle battery transportation of multiple vehicles, and divides the working time into a plurality of periodic update times for updating the change of the number of low-power electric single vehicles in parking spots and newly-added parking spots needing to be replaced with service, and allocates the parking spots needing to be replaced with the service of the battery to the vehicles to obtain a vehicle service path which maximizes the number of the batteries which can be replaced in the specified working time by the vehicles.
In order to achieve the above object, the present invention provides the following steps:
a method for optimizing the dynamic scheduling of the transportation of shared electric single-vehicle batteries comprises the following steps:
step 1: the current time is represented by t ', and t' is 0 initially; by t*Indicating periodic update times, i.e. every time t*Acquiring the electric bicycle information in the parking spot, and updating the vehicle path again; the current cycle time, i.e. the time after several update cycles, is denoted by t, and at the initial stage t is 0, and S is S { S }1,s2,…,snIndicates that there are n shared electric bicycle parking points in the area,
Figure BDA0003582317100000011
expressed as the current cycle time tstopVehicle spot si(siE is the number of batteries needing to be replaced in S), namely the electric bicycle with low electric quantity; a battery warehouse is denoted by d; by V ═ V1,v2,…,vkRepresents k homogeneous transport vehicles parked in the warehouse, wherein the maximum number of batteries that can be loaded by the vehicle k is bk,max(ii) a The average traveling speed of each vehicle is represented as v, and the number of battery replacements requested per parking spot is satisfied
Figure BDA0003582317100000021
There are m battery stations for the vehicle to be fully charged during service, and H is { H ═ H1,h2,…,hmRepresents; distance between warehouse and parking spot is D1(d,si) Distance between stopping points is D2(si,sj) Distance between battery station and parking spot is D3(hi,si). The average time it takes for a maintenance person to change batteries for a shared electric bicycle is μ, the average time it takes to unload the batteries in the battery station is θ, and the maximum operating time of the vehicle is expressed as
Figure BDA0003582317100000022
Step 2: acquiring the information condition of the vehicle at the current period updating time, wherein the information condition comprises the current position point of the vehicle, the time of the vehicle reaching the position point, the time of the vehicle completing the position point task, the number of the remaining replaceable batteries of the vehicle and the number of the replaced batteries of the vehicle; and adding the parking points waiting for service into the service pool.
And 3, step 3: taking the current position of the vehicle as a starting point, taking the parking point information and each vehicle information in the waiting service pool as input parameters, obtaining a vehicle service path after calculation in steps 19-34, and assuming that the service pool has g parking point waiting distribution paths, obtaining the vehicle v at the current period time tkCan be represented as
Figure BDA0003582317100000023
Wherein n iskIs shown as allocated to vehicle vkThe number of the stop points is increased,
Figure BDA0003582317100000024
expressed as the j (1. ltoreq. j. ltoreq.n) th in the path of the vehicle kk) The individual parking spots provide a replacement service,
Figure BDA0003582317100000025
Figure BDA0003582317100000026
and 4, step 4: by using
Figure BDA0003582317100000027
Denoted as vehicle vkTime of arrival at point j, when vehicle k arrives at the first stop
Figure BDA0003582317100000028
The travel time of (d) is:
Figure BDA0003582317100000029
and 5: by using
Figure BDA00035823171000000210
Indicating a vehicle vkTime to complete the Point j task, vehicle vkChange the first parking spot
Figure BDA00035823171000000211
The time of the medium-low battery is as follows:
Figure BDA00035823171000000212
then the vehicle vkComplete the first parking spot
Figure BDA00035823171000000213
The service task has the time of
Figure BDA00035823171000000214
Step 6: by using
Figure BDA00035823171000000215
Is shown at T* k,jTime of day vehicle vkThe number of replaceable batteries remaining. Then the vehicle v at this timekThe number of replaceable batteries is:
Figure BDA0003582317100000031
and 7: vehicle vkForward to the next parking station
Figure BDA0003582317100000032
Serving it. If it is not
Figure BDA0003582317100000033
The vehicle goes to a nearby appropriate battery station to replenish the battery and then serves the next parking spot
Figure BDA0003582317100000034
Otherwise, the process jumps to the step 10,
vehicle vkAt a parking spot
Figure BDA0003582317100000035
The most suitable battery station for selecting as a supplementary battery
Figure BDA0003582317100000036
Comprises the following steps:
Figure BDA0003582317100000037
and 8: vehicle vkTo a parking spot
Figure BDA0003582317100000038
The time of day was:
Figure BDA0003582317100000039
and step 9: vehicle vkAt a parking spot
Figure BDA00035823171000000310
The time for completing the replacement task is as follows:
Figure BDA00035823171000000311
step 10: vehicle vkGo directly to parking spot
Figure BDA00035823171000000312
To a parking spot
Figure BDA00035823171000000313
The time of day was:
Figure BDA00035823171000000314
step 11: vehicle vkGo directly to parking spot
Figure BDA00035823171000000315
The time for completing the service task of the point replacement is as follows:
Figure BDA00035823171000000316
step 12: when the vehicle vkService-completed parking spot
Figure BDA00035823171000000317
Thereafter, the number of remaining replaceable batteries on the vehicle is updated as:
Figure BDA00035823171000000318
step 13: vehicle vkThe number of electric bicycle batteries that have been replaced at this cycle time t is:
Figure BDA0003582317100000041
where round () denotes round down, ckFor vehicles v by the time t of the current cyclekThe number of stops served on its path;
step 14: when the current time t' is less than or equal to t, turning to the step 7; otherwise, go to step 15;
step 15: if the vehicle is at the stopping point, waiting for the vehicle to finish the task of the point, wherein the current time when the vehicle is finished is t'; if the vehicle is in the way from the parking point i to the parking point j at the moment, waiting for the vehicle to finish the task of the point j, wherein the current time when the vehicle is finished is t'; if the vehicle is at the battery station, waiting for the vehicle to finish the task at the point, wherein the current time when the vehicle finishes is t';
step 16: saving the vehicle v at the current moment tkThe condition information of the vehicle comprises the current position point of the vehicle, the time of the vehicle reaching the position point, the time of the vehicle completing the task of the position point, the number of the residual replaceable batteries of the vehicle and the number of the replaced batteries of the vehicle;
and step 17: setting the period time t as t' + t*Obtaining the periodic update time t*The information condition of the low-power electric bicycles in the parking spots of the local area comprises newly-added parking spots meeting the replacement condition and the parking spots which are not served yet, and the number of the low-power electric bicycles is changed on the way of the vehicles going to the parking spots, so that the battery replacement number is requested according to the condition in the current parking spots
Figure BDA0003582317100000042
Increase delta by a predetermined value
Figure BDA0003582317100000043
And order
Figure BDA0003582317100000044
Namely, the maximum estimation is carried out on the number of the electric bicycles in the parking spot.
Step 18: and (3) obtaining the maximum number of the low-battery electric bicycles of all the vehicles, combining with the mathematical model of the problem in the steps 10, 11 and 13, and expressing as follows:
Figure BDA0003582317100000045
Figure BDA0003582317100000051
step 19: designing a fitness function based on a differential evolution discrete water wave optimization algorithm, and defining the fitness function according to the target problem and the constraint in the step 18
Figure BDA0003582317100000052
Wherein
Figure BDA0003582317100000053
For vehicles vjThe number of the replaced electric bicycle batteries in the period time t;
step 20: coding in a sequencing sequence mode to form a sequencing sequence Pi：
Pi＝(π1,π2,..,πj..,πD) Wherein D is n + k-1, PiIndicates the ith ordered sequence. PijDenotes the sequence number,. pij＝j(j＝1,2,..,D)；
Step 21: randomly generating water waves of population size NP, denoted Pi＝(π1,π2,..,πj..,πD) (i ═ 1, 2.., N) and finding the optimal solution P in the populationi *With a fitness of f (P)i *)；
Step 22: if the algorithm reaches the termination condition, outputting the optimal solution and finishing; otherwise, jumping to step 31;
step 23: propagation operation, for each water wave PiWavelength of cyclic execution
Figure BDA0003582317100000054
Secondly;
step 24: if f (P'i)>f(Pi) And then water wave P'iReplacement of PiOtherwise, jumping to step 28;
step 25: if f (x')>f(Pi *) Then replace P with water wave xi *；
Step 26: wave breaking operation according to the original optimal fitness fmaxDetermining solitary waves with fitness of new optimal solution
Figure BDA0003582317100000055
Wherein k ismaxFor generating the maximum value of the independent wave, epsilon is a minimum positive number so as to avoid the generation of abnormity when the denominator is zero;
step 27: randomly generating a probability number r less than 1 if r>c, randomly selecting the water wave PiExchange two dimensions of; otherwise, randomly selecting a dimension D ═ rand (1, D-1), and exchanging two dimensions of the left dimension and the right dimension of D;
step 28: calculating the population size NP (g) of the current generation according to the following formula
Figure BDA0003582317100000056
Wherein g is the current iteration number, gmaxFor a set maximum number of iterations, NPmaxAnd NPminRespectively representing the upper limit and the lower limit of the population scale;
step 29: if NP > NP (g), NP-NP (g);
step 30: calculating the water wave wavelength according to the following formula
Figure BDA0003582317100000061
Wherein f ismaxAnd fminRespectively representing the maximum fitness and the minimum fitness in the current population, wherein alpha is a wavelength attenuation coefficient, epsilon is a minimum positive number to avoid the generation of abnormity when the denominator is zero, and going to step 23;
step 31: if it is not
Figure BDA0003582317100000062
Entering the next step; otherwise jump to step 2;
step 32: and outputting the final solution to obtain the maximum number of the replaced batteries of the vehicle.
Further, the step 23 includes the following operations;
step 23-1:
Figure BDA0003582317100000063
wherein rand (0,1) is [0,1 ]]F is the variation rate, swap (x) is a random exchange function, and two dimensions in the water wave are randomly selected for exchange;
step 23-2:
Figure BDA0003582317100000064
wherein rand (0,1) is [0,1 ]]The random value between, CR is the crossover rate, PMX (P)m,Pi) Cross policy functions for partial matches;
step 23-3: conflict detection, namely establishing a mapping relation according to the two exchanged sequences to ensure that the formed new subsequence has no conflict;
step 23-4:
Figure BDA0003582317100000065
and selecting the water wave with the optimal adaptive value as a new water wave according to the formula.
The invention has the beneficial effects that: according to the method, a multi-vehicle sharing electric single vehicle battery transportation dynamic scheduling optimization problem model is established, a mixed water wave optimization algorithm based on differential evolution is used for solving, vehicle path optimization of vehicles for replacing low-power electric single vehicle batteries in a parking spot in the maximum working time is maximized, the replacement rate of the batteries is improved, the operation cost of the whole electric single vehicle is reduced, and the user satisfaction degree is improved.
Drawings
FIG. 1 is a schematic diagram of a multi-vehicle service initial path;
fig. 2 is a new path obtained through periodic updating.
Detailed Description
The invention will be further explained with reference to the drawings.
With reference to fig. 1 and fig. 2, a method for optimizing dynamic scheduling of transportation of shared electric single vehicle batteries includes the following steps:
step 1: as in fig. 1, initially t, t' is 0; t is t*The service path is renewed at 1 hour intervals. In the figure there is a warehouse (D), two battery stations (H)1,H2) Two transport vehicles (V)1,V2) And three parking spots (S) to be serviced1,S2,S3) Wherein the maximum amount of the vehicle capable of loading the battery is indicated by a large bracket, the number of low-capacity electric bicycles for which battery replacement is requested at each parking spot is indicated by a small bracket, and the number on the dotted line between the points is indicated as the average time (in hours) for the vehicle to travel between the two points; furthermore, given that the number of batteries in warehouses and battery stations is sufficient for vehicle use in the problem, assuming a maximum number of vehicle-carrying batteries of 100, the average time to change batteries for each electric vehicle is 1 minute, and the average time taken to unload batteries in the battery station is 10 minutes. The maximum working time of the vehicle is expressed as
Figure BDA0003582317100000071
Step 2: acquiring the information condition of the vehicle at the current period updating time, wherein the information condition comprises the current position point of the vehicle, the time of the vehicle reaching the position point, the time of the vehicle completing the position point task, the number of the residual replaceable batteries of the vehicle and the number of the replaced batteries of the vehicle; adding parking spots waiting for service into a service pool;
and step 3: and (5) taking the current position of the vehicle as a starting point, taking the parking point information and each vehicle information in the waiting service pool as input parameters, and obtaining the vehicle service path after calculation in the step 20.
Figure BDA0003582317100000072
Figure BDA0003582317100000073
After periodic updating, as shown in FIG. 2
Figure BDA0003582317100000074
And 4, step 4: by using
Figure BDA0003582317100000075
Denoted as vehicle vkTime when point j is reached, t is 0, when vehicle v is1To the first stop s1The travel time of (d) is:
Figure BDA0003582317100000076
the time is as long as the reaction time is short,
Figure BDA0003582317100000077
the method comprises the following steps of (1) taking minutes;
and 5: by using
Figure BDA0003582317100000078
Indicating a vehicle vkTime to complete the Point j task, vehicle v1Changing the first parking spot s1The time of the medium-low battery is as follows:
Figure BDA0003582317100000079
minute, then vehicle v1The time for completing the first parking spot service task is
Figure BDA00035823171000000710
The method comprises the following steps of (1) taking minutes; vehicle v2Changing the first parking spot s3The time of the medium-low battery is 47 minutes, and the task of finishing the battery is 77 minutes;
step 6: by using
Figure BDA0003582317100000081
Is shown at T* k,jTime of day vehicle vkThe number of remaining replaceable batteries, the vehicle v at this time1The number of replaceable batteries is:
Figure BDA0003582317100000082
vehicle v1The number of replaceable batteries is 53;
and 7: vehicle v1Forward to the next parking station s2Serve it if
Figure BDA0003582317100000083
The vehicle goes to a nearby suitable battery station to replenish the battery and then serves the next parking spot s2Otherwise, jumping to step 10;
vehicle vkAt a parking spot
Figure BDA0003582317100000084
The most suitable battery site to select as a supplementary battery
Figure BDA0003582317100000085
Comprises the following steps:
Figure BDA0003582317100000086
and 8: vehicle vkTo a parking spot
Figure BDA0003582317100000087
The time of day was:
Figure BDA0003582317100000088
and step 9: vehicle vkAt a parking spot
Figure BDA0003582317100000089
The time for completing the replacement task is as follows:
Figure BDA00035823171000000810
step 10: vehicle v1Go directly to parking spot s2To a stop s2The time of day was:
Figure BDA00035823171000000811
the method comprises the following steps of (1) taking minutes;
step 11: vehicle vkGo directly to parking spot
Figure BDA00035823171000000812
The time for completing the service replacement task at this point is as follows:
Figure BDA00035823171000000813
vehicle v1Completion of s2The time of the task of (1) was 119 minutes;
step 12: when the vehicle vkService-off parking spot
Figure BDA00035823171000000814
Thereafter, the number of remaining replaceable batteries on the vehicle is updated to:
Figure BDA0003582317100000091
vehicle v1At service-out stop s2The remaining replaceable battery is 11;
step 13: vehicle vkThe number of electric bicycle batteries that have been replaced at this cycle time t is:
Figure BDA0003582317100000092
where round () represents rounding down, ckFor vehicles v by the time t of the current cyclekThe number of stops served on its path. Vehicle v1The maximum number of replaced batteries is 89, and the vehicle v2The maximum number of replacement batteries is 47;
step 14: when the current time t' is less than or equal to t, turning to the step 7; otherwise, entering the next step;
step 15: if the vehicle is at the stopping point, waiting for the vehicle to finish the task of the point, wherein the current time when the vehicle is finished is t'; if the vehicle is in the way from the parking point i to the parking point j at the moment, waiting for the vehicle to finish the task of the point j, wherein the current time when the vehicle is finished is t'; if the vehicle is at the battery station, waiting for the vehicle to finish the task at the point, wherein the current time when the vehicle finishes is t';
step 16: saving vehicle v at present time tkThe condition information of the vehicle comprises the current position point of the vehicle, the time of the vehicle reaching the position point, the time of the vehicle completing the task of the position point, the number of the residual replaceable batteries of the vehicle and the number of the replaced batteries of the vehicle;
and step 17: setting the period time t as t' + t*Obtaining the periodic update time t*The information condition of the low-power electric bicycles in the parking spots of the local area comprises newly-added parking spots meeting the replacement condition and the parking spots which are not served yet, and the number of the low-power electric bicycles is changed on the way of the vehicles going to the parking spots, so that the battery replacement number is requested according to the condition in the current parking spots
Figure BDA0003582317100000093
Increase delta by an estimated value of
Figure BDA0003582317100000094
And order
Figure BDA0003582317100000095
The maximum estimation is carried out on the number of electric bicycles in the parking spot, and as shown in fig. 2, when the cycle time is 60 minutes, a vehicle service path after re-planning is obtained;
step 18: and (3) solving the maximum number of low-power electric bicycles which can be replaced by all vehicles, and combining the mathematical model of the problem in the step 13 to represent that:
Figure BDA0003582317100000101
Figure BDA0003582317100000102
in this case, the calculated solution value from the objective function is 129, which is the maximum number of replaced batteries;
step 19: and designing a fitness function based on a discrete water wave optimization algorithm of differential evolution. Defining a fitness function based on the objective problem and constraints in step 15
Figure BDA0003582317100000103
Wherein
Figure BDA0003582317100000104
For vehicles vjThe number of the replaced electric bicycle batteries within the period time t;
step 20: coding in a sequencing sequence mode to form a sequencing sequence Pi：
Pi＝(π1,π2,..,πj..,πD) Where D is n + k-1, PiIndicating the sorted sequence of the ith. PijDenotes the sequence number,. pij＝j(j＝1,2,..,D)；
Step 21: randomly generating water waves of population size NP, denoted Pi＝(π1,π2,..,πj..,πD) (i ═ 1, 2.., N) and finding the optimal solution P in the populationi *With a fitness of f (P)i *)；
Step 22: if the algorithm reaches the termination condition, outputting the current optimal solution and jumping to step 32;
step 23: propagation operation, for each water wave PiThe following operations are performed;
step 23-1:
Figure BDA0003582317100000105
wherein rand (0,1) is [0,1 ]]F is the variation rate, swap (x) is a random exchange function, and two dimensions in the water wave are randomly selected for exchange;
step 23-2:
Figure BDA0003582317100000106
wherein rand (0,1) is [0,1 ]]CR is the crossover rate, PMX (P)m,Pi) Cross policy functions for partial matches;
step 23-3: conflict detection, namely establishing a mapping relation according to the two exchanged sequences to ensure that the formed new subsequence has no conflict;
step 23-4:
Figure BDA0003582317100000107
selecting the optimal adaptive value as a new water wave according to the formula;
step 24: if f (P'i)>f(Pi) And then is water wave P'iReplacement of PiOtherwise, jumping to step 28;
step 25: if f (x')>f(Pi *) Then replace P with water wave xi *；
Step 26: wave breaking operation according to the original optimal fitness fmaxDetermining solitary waves with fitness of new optimal solution
Figure BDA0003582317100000111
Wherein k ismaxFor generating the maximum value of the independent wave, epsilon is a minimum positive number so as to avoid the generation of abnormity when the denominator is zero;
step 27: randomly generating a probability number r less than 1 if r>c, randomly selecting the water wave PiExchange two dimensions of; otherwise, randomly selecting a dimension D (1, D-1), exchanging two dimensions of the left and right sides of D, and c (0.3);
step 28: the population size NP (g) of the current generation was calculated according to the following formula
Figure BDA0003582317100000112
Wherein g is the current iteration number, gmaxIs the set maximum number of iterations. NPmaxAnd NPminRespectively indicate speciesUpper and lower limits of population size;
step 29: if NP > NP (g), NP-NP (g);
and step 30: calculating the water wave wavelength according to the following formula
Figure BDA0003582317100000113
Wherein f ismaxAnd fminRespectively representing the maximum fitness and the minimum fitness in the current population, wherein alpha is a wavelength attenuation coefficient, epsilon is a minimum positive number to avoid the generation of abnormity when the denominator is zero, and going to step 23;
step 31: if it is not
Figure BDA0003582317100000114
Then the next step; otherwise, jumping to the step 2;
step 32: and finally, outputting, and obtaining the maximum number of replaced batteries of the vehicle.

Claims (2)

1. A shared electric single vehicle battery transportation dynamic scheduling optimization method is characterized by comprising the following steps:
step 1: the current time is represented by t ', and t' is 0 initially; by t*Indicating periodic update times, i.e. every time t*Acquiring the electric bicycle information in the parking spot, and updating the vehicle path again; the current cycle time is represented by t, namely the time of a plurality of updating cycles, and t is 0 in the initial stage; with S ═ S1,s2,…,snIndicates that there are n shared electric bicycle parking points in the area,
Figure FDA0003582317090000011
expressed as the current cycle time t parking spot si(siThe number of batteries needing to be replaced in the element S), namely the low-power electric bicycle; a battery warehouse is denoted by d; by V ═ V1,v2,…,vkRepresents k homogeneous transport vehicles parked in the warehouse, wherein the maximum number of batteries that can be loaded by the vehicle k is bk,max(ii) a The average traveling speed of each vehicle is denoted by v, and each stop point requests a battery replacementIs in an amount of
Figure FDA0003582317090000012
There are m battery stations for the vehicle to be fully charged during service, and H is { H ═ H1,h2,…,hmRepresents; distance between warehouse and parking spot is D1(d,si) Distance between stopping points is D2(si,sj) Distance between battery station and parking spot is D3(hi,si) The average time taken by the maintenance personnel to replace the battery for a shared electric bicycle is mu, the average time taken to unload the battery in the battery station is theta, and the maximum operating time of the vehicle is expressed as
Figure FDA0003582317090000017
Step 2: acquiring the information condition of the vehicle at the current period updating time, wherein the information condition comprises the current position point of the vehicle, the time of the vehicle reaching the position point, the time of the vehicle completing the position point task, the number of the remaining replaceable batteries of the vehicle and the number of the replaced batteries of the vehicle; adding parking spots waiting for service into a service pool;
and step 3: taking the current position of the vehicle as a starting point, taking the parking point information and the information of each vehicle in the waiting service pool as input parameters, and obtaining a vehicle service path after calculation in steps 19-34; assuming that the service pool has g parking spots waiting for distribution paths, the vehicle v is driven at the current cycle time tkCan be represented as
Figure FDA0003582317090000013
Wherein n iskIs shown as allocated to vehicle vkS 'of parking points'k,jExpressed as the j (1. ltoreq. j. ltoreq.n) th in the path of the vehicle kk) The individual parking spots provide a replacement service,
Figure FDA0003582317090000014
Figure FDA0003582317090000015
and 4, step 4: by using
Figure FDA0003582317090000016
Denoted as vehicle vkTime of arrival at point j, when vehicle k arrives at the first stop
Figure FDA0003582317090000021
The travel time of (c) is:
Figure FDA0003582317090000022
and 5: by using
Figure FDA0003582317090000023
Indicating a vehicle vkTime to complete the Point j task, vehicle vkChanging the first parking spot
Figure FDA0003582317090000024
The time of the medium-low battery is as follows:
Figure FDA0003582317090000025
then the vehicle vkComplete the first parking spot
Figure FDA0003582317090000026
The service task has the time of
Figure FDA0003582317090000027
Step 6: by using
Figure FDA0003582317090000028
Is shown at T* k,jTime of day vehicle vkThe number of remaining replaceable batteries, the vehicle v at this timekThe number of replaceable batteries is:
Figure FDA0003582317090000029
and 7: vehicle vkForward to the next parking station
Figure FDA00035823170900000210
Serve it if
Figure FDA00035823170900000211
The vehicle goes to a nearby appropriate battery station to replenish the battery and then serves the next parking spot
Figure FDA00035823170900000212
Otherwise, jumping to step 10;
vehicle vkAt a parking spot
Figure FDA00035823170900000213
The most suitable battery site to select as a supplementary battery
Figure FDA00035823170900000214
Comprises the following steps:
Figure FDA00035823170900000215
and 8: vehicle vkTo a parking spot
Figure FDA00035823170900000216
The time is as follows:
Figure FDA00035823170900000217
and step 9: vehicle vkAt a parking spot
Figure FDA00035823170900000218
The time for completing the replacement task is as follows:
Figure FDA00035823170900000219
step 10: vehicle vkGo directly to parking spot
Figure FDA00035823170900000220
To a parking spot
Figure FDA00035823170900000221
The time of day was:
Figure FDA00035823170900000222
step 11: vehicle vkGo directly to parking spot
Figure FDA0003582317090000031
The time for completing the service replacement task at this point is as follows:
Figure FDA0003582317090000032
step 12: when the vehicle vkService-off parking spot
Figure FDA0003582317090000033
Thereafter, the number of remaining replaceable batteries on the vehicle is updated to:
Figure FDA0003582317090000034
step 13: vehicle vkThe number of electric bicycle batteries that have been replaced at this cycle time t is:
Figure FDA0003582317090000035
where round () denotes round down, ckFor vehicles v by the time t of the current cyclekThe number of stops served on its path;
step 14: when the current time t' is less than or equal to t, turning to the step 7; otherwise, entering the next step;
step 15: if the vehicle is at the parking point, waiting for the vehicle to finish the task of the point, wherein the current time when the vehicle finishes is t'; if the vehicle is in the way from the parking point i to the parking point j at the moment, waiting for the vehicle to finish the task of the point j, wherein the current time when the vehicle is finished is t'; if the vehicle is at the battery station, waiting for the vehicle to finish the task at the point, wherein the current time when the vehicle finishes is t';
step 16: saving vehicle v at present time tkThe condition information of the vehicle comprises the current position point of the vehicle, the time of the vehicle reaching the position point, the time of the vehicle completing the task of the position point, the number of the residual replaceable batteries of the vehicle and the number of the replaced batteries of the vehicle;
and step 17: setting the period time t as t' + t*Obtaining the periodic update time t*The information conditions of the electric bicycles with low electric quantity in the parking spots of the local area comprise newly-added parking spots meeting the replacement condition and parking spots which are not served; since the number of the low-power electric bicycles is changed while the vehicle travels to the parking spot, the battery replacement number is requested according to the current situation in the parking spot
Figure FDA0003582317090000041
Increase delta by a predetermined value
Figure FDA0003582317090000042
And order
Figure FDA0003582317090000043
Namely, the maximum estimation is carried out on the number of electric bicycles in the parking spot;
step 18: and (3) calculating the maximum number of low-battery electric bicycles of all vehicles, and combining the steps 10, 11 and 13, wherein a mathematical model of the problem is represented as follows:
Figure FDA0003582317090000044
Figure FDA0003582317090000045
step 19: designing a fitness function based on a differential evolution discrete water wave optimization algorithm, and defining the fitness function according to the target problem and the constraint in the step 18
Figure FDA0003582317090000046
Wherein
Figure FDA0003582317090000047
For vehicles vjThe number of the replaced electric bicycle batteries in the period time t;
step 20: coding in a sequencing sequence mode to form a sequencing sequence Pi：Pi＝(π1,π2,..,πj..,πD) Wherein D is n + k-1, piDenotes the ordered sequence of the ith, pijDenotes the sequence number,. pij＝j(j＝1,2,..,D)；
Step 21: randomly generating water waves of population size NP, denoted Pi＝(π1,π2,..,πj..,πD) (i ═ 1, 2.., N) and finding the optimal solution P in the populationi *With a fitness of f (P)i *)；
Step 22: if the algorithm reaches the termination condition, outputting the optimal solution and finishing; otherwise, jumping to step 31;
step 23: propagation operation, for each water wave PiWavelength of cyclic execution
Figure FDA0003582317090000048
Secondly;
step 24: if f (P'i)>f(Pi) And then is water wave P'iReplacement of PiOtherwise, jumping to step 28;
step 25: if f (x')>f(Pi *) Then replace P with water wave xi *；
Step 26: wave breaking operation according to the original optimal fitness fmaxDetermining solitary waves with fitness of new optimal solution
Figure FDA0003582317090000049
Wherein k ismaxFor generating the maximum value of the independent wave, epsilon is a minimum positive number so as to avoid the generation of abnormity when the denominator is zero;
step 27: randomly generating a probability number r less than 1 if r>c, randomly selecting the water wave PiExchange two dimensions of; otherwise, randomly selecting a dimension D (1, D-1) and exchanging two dimensions of the left dimension and the right dimension of D;
step 28: the population size NP (g) of the current generation was calculated according to the following formula
Figure FDA0003582317090000051
Wherein g is the current iteration number, gmaxFor a set maximum number of iterations, NPmaxAnd NPminRespectively representing the upper limit and the lower limit of the population scale;
step 29: if NP > NP (g), NP-NP (g);
step 30: calculating the water wave wavelength according to the following formula
Figure FDA0003582317090000052
Wherein f ismaxAnd fminRespectively expressed as the maximum fitness and the minimum fitness in the current population, alpha is a wavelength attenuation coefficient, epsilon is a minimum positive number to avoid the generation of abnormity when the denominator is zero, and the step 23 is carried out;
step 31: if it is not
Figure FDA0003582317090000053
Entering the next step; otherwise, jumping to the step 2;
step 32: and outputting the final solution to obtain the maximum number of the replaced batteries of the vehicle.
2. The method of claim 1, wherein the step 23 comprises the operations of;
step 23-1:
Figure FDA0003582317090000054
wherein rand (0,1) is [0,1 ]]F is the variation rate, swap (x) is a random exchange function, and two dimensions in the water wave are randomly selected to be exchanged;
step 23-2:
Figure FDA0003582317090000055
wherein rand (0,1) is [0,1 ]]The random value between, CR is the crossover rate, PMX (P)m,Pi) Matching a cross policy function for the portion;
step 23-3: conflict detection, namely establishing a mapping relation according to the two exchanged sequences to ensure that the formed new subsequence has no conflict;
step 23-4:
Figure FDA0003582317090000056
and selecting the water wave with the optimal adaptive value as a new water wave according to the formula.
CN202210380058.8A 2022-04-06 2022-04-06 Dynamic scheduling optimization method for battery transportation of shared electric bicycle Pending CN114626638A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116468257A (en) * 2023-06-20 2023-07-21 宁波小遛共享信息科技有限公司 Evaluation method and server for newly-added parking spots in operation fence

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116468257A (en) * 2023-06-20 2023-07-21 宁波小遛共享信息科技有限公司 Evaluation method and server for newly-added parking spots in operation fence
CN116468257B (en) * 2023-06-20 2023-09-29 浙江小遛信息科技有限公司 Evaluation method and server for newly-added parking spots in operation fence

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