CN112507506B - Multi-objective optimization method for sharing automobile pricing planning model based on genetic algorithm - Google Patents
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Abstract
The multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm comprises the following steps: firstly, acquiring historical data of the traffic flow of each path sharing vehicle of a road network, and the running speed and the driving duration of the path; carrying out daily prediction and map mapping on the flow of the electric vehicle shared by each time period of each path by a big data method, and then establishing a multi-target model; constructing constraint conditions, solving the multi-objective model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by utilizing a compromise solution method; the invention saves the operation cost for the sharing system, increases the system income, improves the satisfaction degree of users on the use of the sharing service, and reduces the negative influence of the charging behavior of the sharing electric automobile on the stability of the power grid.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a multi-objective optimization method of a shared automobile pricing planning model based on a genetic algorithm, which can be applied to shared electric automobile charging planning and path pricing decision taking time and path price elasticity into consideration.
Background
The electric automobile sharing system is a novel city sharing economy in recent years. As an environment-friendly and low-carbon transportation mode, the electric automobile is combined with the sharing system, so that the geographic service coverage range is greatly improved, and a flexible travel plan is provided for users. So far, research on the aspect of sharing the pricing scheme of the electric automobile at home and abroad is relatively preliminary, and a complete and systematic pricing decision model and optimization method are not formed yet. First, most studies exist that do not consider the inverse decision of customers on different shared price pairs. In fact, the change in the sharing price affects not only the number of users, but also the routing and usage time of the users. Therefore, a space-time distribution model based on price customer path preference and travel request is established, so that the optimization management and profit improvement of operators can be promoted. Meanwhile, when the charging plan and mileage price of the shared electric vehicle are decided, the influence of the electric vehicle on the operation of the external power grid should be considered in the decision process of the shared pricing plan. In addition, the quality of service of the vehicle sharing system is often measured and required in the vehicle sharing price decision process. Unlike conventional car rental services, when the shared electric vehicle battery capacity fails to meet the mileage requirements of the user, the electric vehicle battery needs to be charged and the charging time is often much longer than the refueling time of a conventional vehicle. Current research measures the quality of service of an electric vehicle sharing system by the percentage of customer demand refusal, assuming that if all electric vehicles fail to provide service that meets customer mileage demand, then the customer order will be canceled. However, in actual situations, users often allow and wish to wait a small amount of time until the electric vehicle is recharged to complete the reuse of the shared electric vehicle, which is more practical than directly rejecting the user's needs.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-objective optimization method of a shared automobile pricing planning model based on a genetic algorithm, and simultaneously considers the influence of shared charging behaviors on the stability of a power grid and the maximization of the shared service quality experience of users, and under the condition of meeting relevant constraint conditions, the service quality of an electric automobile sharing system is improved by considering the reduction of waiting charging time of the users.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm comprises the following steps:
firstly, acquiring historical data of the traffic flow of each path sharing vehicle of a road network, and the running speed and the driving duration of the path; carrying out daily prediction and map mapping on the flow of the electric vehicle shared by each time period of each path by a big data method, and then establishing a multi-target model;
and secondly, constructing constraint conditions, solving the multi-objective model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by utilizing a compromise solution method.
Step one, a multi-objective pricing decision model is built, and the multi-objective pricing decision model is three models: the method comprises the steps of maximizing a profit and gain target model of a sharing system, maximizing a satisfaction target model of a user sharing service and minimizing a voltage fluctuation influence target model of a power distribution network system, wherein the method comprises the following specific steps:
maximizing the sharing system profit is equal to the difference between the user travel income RCR and the cost RCS of other electric vehicle sharing systems, and the maximizing the sharing system profit target model WPR is calculated and expressed by the following formula:
wherein: k, j represents the start point and the end point of the path, A1 is the set of the number of the shared electric vehicles on the path kj in the time T, K 'represents the set of the shared charging stations, T' is the time set,the travel time of the route kj; r is R CS The system consists of site maintenance cost, electric automobile depreciation cost, electric automobile charging cost, electric automobile maintenance cost and moving cost;
wherein, user travel income R CR The cost of sharing the system with other electric vehicles is calculated as follows:
wherein:for price decision variables, ++>The route kj car after the price change is represented as t to +.>The shared electric vehicle flow rate of the time period Cv, cre, cmv, cmp represents the depreciated cost, the relocated vehicle cost, the vehicle maintenance cost, and the parking lot maintenance cost of the shared electric vehicle, respectively; zk represents the total number of parking spaces of charging station k, PELt represents the price of electricity purchased from the grid at time t, PCH represents the charge power of the shared electric vehicle, η ch Charge efficiency indicating shared electric vehicle charge, +.>Representing the number of shared electric cars migrating from station k to j at time t, +.>The number of vehicles at a station k at the time t for sharing the electric vehicle;
maximizing user sharing service satisfaction goal W SAT A model, represented by the following formula:
wherein: sharing customer satisfaction factors include sharing prices due to electric vehiclesCustomer dissatisfaction cost R caused by variation PL The method comprises the steps of carrying out a first treatment on the surface of the Unsatisfactory cost of service demand loss R DL The method comprises the steps of carrying out a first treatment on the surface of the Unsatisfactory replacement cost R when a customer uses a taxi as a similar alternative means of transportation AT The method comprises the steps of carrying out a first treatment on the surface of the Time cost R for customers waiting for available shared electric vehicles WC ；
The method for analyzing the shared automobile driving behavior and calculating the waiting time of the user comprises the following steps:
wherein:for the path kj, the waiting queue length of the electric vehicle is shared at time t, < >>For the path kj, sharing the waiting queue length of the electric vehicle at the time t-1, the +.>For the number of the departure of the shared electric vehicles with the battery capacity not lower than e at the t moment of the path kj, the departure flow of the shared electric vehicles is calculated as follows: />
Wherein:
wherein:for sharingThe battery station of the electric vehicle reaches a k point at the time t and the electric quantity is not lower than the number of vehicles of e; />In order to share the number of vehicles with the electric quantity of the electric vehicle at the station k at the time t not lower than e, f is the total charge quantity in the unit time period.
Minimizing power distribution network system voltage fluctuation influence target model W GRID Expressed by the following formula:
minW GRID ＝||J -1 [ΔP,ΔQ]|| 2
wherein: ΔP and ΔQ are vectors sharing the active and reactive power changes caused by electric vehicle charging, J -1 Is the inverse jacobian matrix of the power system.
The constraint conditions constructed in the second step comprise integral constraint of the flow of the shared electric vehicle, constraint of the capacity of the shared charging station and constraint of the capacity of the battery of the shared electric vehicle;
the shared electric vehicle flow integer constraint is as follows:
wherein: e (E) Demand Representing the elastic coefficients of the shared user path and the time price, x0 is the original price of the fixed path,for the originally predicted path traffic.
The shared charging station capacity constraint is as follows:
wherein: z is Z k Indicating chargeThe total number of parking spaces of the power station k,the number of vehicles at a station k at the time t for sharing the electric vehicle;
the battery capacity constraint of the shared electric vehicle is as follows:
wherein: b (B) LT And B is connected with UT Indicating upper and lower limits of the charge capacity of the shared electric automobile,to share the battery capacity percentage of the electric vehicle v at the time t, B v Representing the total battery capacity;
the solving model in the second step is specifically:
s031, inputting simulation data into the multi-target model and constraint, wherein the data comprises a traffic network, a power distribution network and predicted shared electric automobile requirements;
step S032, coding: the price decision variables in the multi-objective model and the traffic flow variables are coded into each individual variable in the genetic algorithm in sequence;
step S033, initializing a population: randomly generating an initial population consisting of N individuals in the value range of the multi-target model variable, and then uniformly distributing the individuals into M sub-populations;
step S034, randomly migrating: in each generation, each individual decides whether to join another sub-population from the current sub-population based on random migration;
step S035, positioning and generalized searching: after sub-population migration decisions are made, each individual located in a certain sub-population performs a location search, and for optimization in each target model, only the fitness value of the individual on all targets is improved, and the change of the value is acceptable;
step S036, selecting and terminating: at the end of each generation, updating the sub-population; when the maximum generation time is reached, the algorithm is terminated; outputting a non-dominant solution;
step S037, decision: after the non-dominant solution is obtained, the compromise solution method is utilized to find the optimal multi-objective shared pricing decision result in all the non-dominant solutions.
Compared with the prior art, the method establishes the multi-objective optimization model by reasonably planning and making pricing decisions of different paths of the shared electric vehicle in different time periods, and based on genetic algorithm calculation, the obtained result of the calculation example shows that: the provided model saves the operation cost for the sharing system, increases the system income, improves the satisfaction degree of users on the use of the sharing service, and reduces the negative influence of the charging behavior of the sharing electric automobile on the stability of the power grid.
Drawings
Fig. 1 is a flow chart of the present invention.
Fig. 2 is a graph of profit gain of the sharing system versus pareto optimality of the sharing quality service.
FIG. 3 is a graph of profit margin versus voltage offset pareto optima for the sharing system.
Fig. 4 is 18:00 and 22: each path PAL value at 00.
Fig. 5 is 18:00 and 22: user demand for each path under non-pricing decisions at 00.
Fig. 6 is 18:00 and 22: at 00, the user demand of each path under pricing decision.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, a genetic algorithm-based multi-objective optimization method for a shared automotive pricing planning model includes the steps of:
firstly, acquiring historical data of the vehicle flow shared by each path of a road network through a big data algorithm Diffusion Convolutional Recurrent Neural Network (DCRNN), wherein the running speed and the driving duration of the path; carrying out daily prediction and map mapping on the electric vehicle flow shared by each time period of each path by a big data method, then establishing a multi-target model, and substituting the daily predicted path flow and a price elastic coefficient equation into the multi-target model;
and secondly, constructing constraint conditions, solving the multi-objective model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by utilizing a compromise solution method.
Step one, a multi-objective pricing decision model is built, and the multi-objective pricing decision model is three models: the method comprises the steps of maximizing a profit and gain target model of a sharing system, maximizing a satisfaction target model of a user sharing service and minimizing a voltage fluctuation influence target model of a power distribution network system, wherein the method comprises the following steps:
maximizing system profit equals user travel income R CR Sharing system cost R with other electric vehicles CS The difference, the maximum share system profit target model WPR, is calculated by:
wherein: k, j represents the start point and the end point of the path, A1 is the set of the number of the shared electric vehicles on the path kj in the time T, K 'represents the set of the shared charging stations, T' is the time set,represents the travel time of the route kj, R CS The system consists of site maintenance cost, electric automobile depreciation cost, electric automobile charging cost, electric automobile maintenance cost and moving cost;
wherein, user travel income R CR The cost of sharing the system with other electric vehicles is calculated as follows:
wherein:for price decision variables, ++>The route kj car after the price change is represented as t to +.>Shared electric vehicle flow in time period, C v 、C re 、C mv 、C mp Representing the depreciation cost, relocation vehicle cost, vehicle maintenance cost and parking lot maintenance cost of the shared electric vehicle every minute respectively; z is Z k Representing the total number of parking spaces of charging station k, PEL t Representing the price of electricity purchased from the grid at time t, P CH Representing the charge power of a shared electric vehicle, eta ch Charge efficiency indicating shared electric vehicle charge, +.>Representing the number of shared electric cars migrating from station k to j at time t, +.>The number of vehicles at a station k at the time t for sharing the electric vehicle;
a maximize user shared services satisfaction objective (WSAT) model expressed by the following formula:
wherein: the shared customer satisfaction factor includes customer dissatisfaction cost R due to variation in shared price of electric vehicles PL The method comprises the steps of carrying out a first treatment on the surface of the Unsatisfactory cost of service demand loss R DL The method comprises the steps of carrying out a first treatment on the surface of the Dissatisfaction when customers use taxis as a similar alternative means of transportationSubstitution cost R AT The method comprises the steps of carrying out a first treatment on the surface of the Time cost R for customers waiting for available shared electric vehicles WC ；
The method for analyzing the shared automobile driving behavior and calculating the waiting time of the user comprises the following steps:
wherein:for the path kj, the waiting queue length of the electric vehicle is shared at time t, < >>For the path kj, sharing the waiting queue length of the electric vehicle at the time t-1, the +.>For the number of the departure of the shared electric vehicles with the battery capacity not lower than e at the t moment of the path kj, the departure flow of the shared electric vehicles is calculated as follows: />
Wherein:
wherein:for sharing the number of vehicles with the battery station of the electric vehicle reaching the k point at the time t and the electric quantity not being equal to e, < >>In order to share the number of vehicles with the electric quantity of the electric vehicle at the station k at the time t not lower than e, f is the total charge quantity in the unit time period.
Minimizing power distribution network system voltage fluctuations affects the target model (W GRID ) Expressed by the following formula:
minW GRID ＝||J -1 [ΔP,ΔQ]|| 2
wherein: Δp and Δq are vectors sharing active and reactive power changes caused by electric vehicle charging. J (J) -1 Is the inverse jacobian matrix of the power system.
The construction constraint conditions of the second step comprise sharing electric vehicle flow integer constraint, sharing charging station capacity constraint and sharing electric vehicle battery capacity constraint;
the shared electric vehicle flow integer constraint is as follows:
wherein: e (E) Demand Representing the elastic coefficients of the shared user path and the time price, x0 is the original price of the fixed path,for the originally predicted path traffic.
Said shared charging station capacity constraint
Wherein: z is Z k Indicating the total number of parking spaces of the charging station k,at time t for sharing electric vehicleNumber of vehicles at station k;
the shared electric vehicle battery capacity constraint
Wherein: b (B) LT And B is connected with UT Indicating upper and lower limits of the charge capacity of the shared electric automobile,to share the battery capacity percentage of the electric vehicle v at the time t, B v Representing the total battery capacity;
the solving model in the second step is specifically:
s031, inputting simulation data into the multi-target model and constraint, wherein the data comprises a traffic network, a power distribution network and predicted shared electric automobile requirements;
step S032, coding: the price decision variables in the multi-objective model and the traffic flow variables are coded into each individual variable in the genetic algorithm in sequence;
step S033, initializing a population: randomly generating an initial population consisting of N individuals in the value range of the multi-target model variable, and then uniformly distributing the individuals into M sub-populations;
step S034, randomly migrating: in each generation, each individual decides whether to join another sub-population from the current sub-population based on random migration;
step S035, positioning and generalized searching: after sub-population migration decisions are made, each individual located in a certain sub-population performs a location search, and for optimization in each target model, only the fitness value of the individual on all targets is improved, and the change of the value is acceptable;
step S036, selecting and terminating: at the end of each generation, updating the sub-population; when the maximum generation time is reached, the algorithm is terminated; outputting a non-dominant solution;
step S037, decision: after the non-dominant solution is obtained, the compromise solution method is utilized to find the optimal multi-objective shared pricing decision result in all the non-dominant solutions.
And (3) calculation simulation:
step S041 example data
The invention uses a drop company automobile sharing demand data set collected in Shenyang from day 2016, 12, 5, to day 2017, 2, 4 to simulate the traffic flow of a sharing electric automobile. The data set includes a path time matrix of the shared service, each data item containing the time and location (latitude and longitude) of the user's respective path's vehicle request. The data set is first preprocessed to manually map the data in the data packet to specific geographic locations and time intervals in sequence. In the calculation example, the invention uses an ieee14 power distribution network node reference system. The shared system pricing operation time simulated by this example was set to 17 to 22pm, with each time step being 1 hour. All shared charging stations are located on different PQ buses. The initial energy of the shared electric vehicle is randomly set within [50%,80% ] of the battery capacity and the battery parameters are set according to the specification of daily wind. The electric vehicle charging power was set to 20kW.
Step S042 example calculation results
The invention applies genetic algorithm to solve the multi-target pricing model proposed by the patent, and the projection of the obtained pareto optimal value on the target 1 and the target 2 is shown in figure 2. It can be observed that user satisfaction decreases as the total profit of the electric car sharing system increases. This is because operators of shared systems increase system profits by lowering shared prices, thereby increasing SEV requirements. Accordingly, the time for the customer to wait for the available electric car service increases, thereby decreasing customer satisfaction. While the increase in demand may result in an increase in the migration costs and toll costs of the shared electric vehicle, the sharing system may still realize a profit by the travel fee revenues paid by the customers. Fig. 3 shows the projection of pareto optima on target 1 and target 3. The results indicate that the profitability and maintenance of the bus voltage are somewhat conflicting objectives. This is mainly because operators reduce the shared use price in order to increase the total profit of the system, resulting in increased demand for electric vehicles and greater pressure on the operation of the grid.
In order to facilitate observation of results, the invention uses PAL value to analyze price change, and the expression isUnder the selected decision, the shared pricing decision model brings 3264 euro profit to the sharing system for the whole operation period, and the total of 2179 trips are taken. Without the pricing decision model, there were 2254 trips and profit of 2760 euros was obtained. Comparing the two cases, the pricing decision model brings a significant profit increase (18.26%) and a demand loss of 3.32%. Fig. 4 shows PAL values for an optimal shared price applied to 30 of a total of 143 path pairs. We selected two typical times for analysis: peak hours (18 points) and off-peak hours (22 points). Fig. 5 and 6 are user demands in a 30-pair path for a no/priced decision model, respectively. As can be seen from fig. 5-6, PAL is approximately inversely proportional to user demand at the optimized shared price. This reflects the fact that customers prefer to drive using a low-priced path shared by electric vehicles.
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During peak hours (18 points), fig. 4 shows that PAL for most paths is higher than static price (i.e., the price at pal=1), and therefore demand under the pricing decision model is reduced. This suggests that the pricing decision model discourages customers from using the shared electric vehicle during peak hours to avoid long waiting times, thereby improving customer satisfaction. At the same time, the reduction of the demand will also reduce the bus voltage variation, thereby alleviating the negative impact of SEV charging on the grid during peak hours. Accordingly, FIG. 4 shows that the PAL values on most paths for off-peak hours (22 pm) are below static prices and the user's demand increases significantly compared to the situation without the pricing decision model. This means that power system planning encourages users to shift the use of shared vehicles from peak to off-peak hours, balancing customer satisfaction and grid bus voltage variations by changing the shared price. The selected solution may reduce the customer waiting cost in all paths over the whole period compared to the case without pricing decision, which is 613 euros, whereas in the case without pricing decision model the customer waiting cost is 741 euros. The pricing scheme selected also achieves smaller voltage variations (3.614% versus 4.802%). The calculation result proves that the pricing decision model provided by the invention optimizes the shared price excitation mechanism, enables the electric automobile sharing system to obtain more profits by changing the service time of customers, and can reduce the bus voltage variation and improve the user satisfaction degree and the power grid operation.
Claims (3)
1. The multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm is characterized by comprising the following steps of:
firstly, acquiring historical data of the traffic flow of each path sharing vehicle of a road network, and the running speed and the driving duration of the path; carrying out daily prediction and map mapping on the flow of the electric vehicle shared by each time period of each path by a big data method, and then establishing a multi-target model;
constructing constraint conditions, solving the multi-objective model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by utilizing a compromise solution method;
step one, a multi-objective pricing decision model is built, and the multi-objective pricing decision model is three models: the method comprises the steps of maximizing a profit and gain target model of a sharing system, maximizing a satisfaction target model of a user sharing service and minimizing a voltage fluctuation influence target model of a power distribution network system, wherein the method comprises the following specific steps:
maximizing the sharing system profit is equal to the difference between the user travel income RCR and the cost RCS of other electric vehicle sharing systems, and the maximizing the sharing system profit target model WPR is calculated and expressed by the following formula:
wherein: k, j represents the starting point and the end point of the path, A1 is the shared electric vehicle quantity set on the path kj in the time t, and K' represents the shared charging stationThe set, T' is the set of times,the travel time of the route kj; r is R CS The system consists of site maintenance cost, electric automobile depreciation cost, electric automobile charging cost, electric automobile maintenance cost and moving cost;
wherein, user travel income R CR The cost of sharing the system with other electric vehicles is calculated as follows:
wherein:for price decision variables, ++>The route kj car after the price change is represented as t to +.>The shared electric vehicle flow rate of the time period Cv, cre, cmv, cmp represents the depreciated cost, the relocated vehicle cost, the vehicle maintenance cost, and the parking lot maintenance cost of the shared electric vehicle, respectively; zk represents the total number of parking spaces of charging station k, PELt represents the price of electricity purchased from the grid at time t, PCH represents the charge power of the shared electric vehicle, η ch Charge efficiency indicating shared electric vehicle charge, +.>Representing the number of shared electric cars migrating from station k to j at time t, +.>The number of vehicles at a station k at the time t for sharing the electric vehicle;
maximizing user sharing service satisfaction goal W SAT A model, represented by the following formula:
wherein: the shared customer satisfaction factor includes customer dissatisfaction cost R due to variation in shared price of electric vehicles PL The method comprises the steps of carrying out a first treatment on the surface of the Unsatisfactory cost of service demand loss R DL The method comprises the steps of carrying out a first treatment on the surface of the Unsatisfactory replacement cost R when a customer uses a taxi as a similar alternative means of transportation AT The method comprises the steps of carrying out a first treatment on the surface of the Time cost R for customers waiting for available shared electric vehicles WC ；
The method for analyzing the shared automobile driving behavior and calculating the waiting time of the user comprises the following steps:
wherein:for the path kj, the waiting queue length of the electric vehicle is shared at time t, < >>For the path kj, sharing the waiting queue length of the electric vehicle at the time t-1, the +.>For the number of the departure of the shared electric vehicles with the battery capacity not lower than e at the t moment of the path kj, the departure flow of the shared electric vehicles is calculated as follows: />
Wherein:
wherein:the method comprises the steps that the number of vehicles with the electric quantity not lower than e reaches k points at t time for sharing battery stations of the electric vehicle; />For sharing the number of vehicles with the electric quantity of the electric vehicle at the station k at the moment t not lower than e, f is the total charge quantity in a unit time period;
minimizing power distribution network system voltage fluctuation influence target model W GRID Expressed by the following formula:
minW GRID ＝||J -1 [ΔP,ΔQ]|| 2
wherein: ΔP and ΔQ are vectors sharing the active and reactive power changes caused by electric vehicle charging, J -1 Is the inverse jacobian matrix of the power system.
2. The genetic algorithm-based multi-objective optimization method of the shared automobile pricing planning model according to claim 1, wherein the constraint conditions constructed in the second step include a shared electric vehicle flow integer constraint, a shared charging station capacity constraint and a shared electric vehicle battery capacity constraint;
the shared electric vehicle flow integer constraint is as follows:
wherein: e (E) Demand Representing the elasticity coefficient of the path and the time price of the shared user, x 0 For a fixed path raw price,the traffic flow of each path is originally predicted;
the shared charging station capacity constraint is as follows:
wherein: z is Z k Indicating the total number of parking spaces of the charging station k,the number of vehicles at a station k at the time t for sharing the electric vehicle;
the battery capacity constraint of the shared electric vehicle is as follows:
wherein: b (B) LT And B is connected with UT Indicating upper and lower limits of the charge capacity of the shared electric automobile,to share the battery capacity percentage of the electric vehicle v at time t,B v indicating the total battery capacity.
3. The genetic algorithm-based multi-objective optimization method of a shared automobile pricing planning model according to claim 1, wherein the solving model in the second step is specifically:
s031, inputting simulation data into the multi-target model and constraint, wherein the data comprises a traffic network, a power distribution network and predicted shared electric automobile requirements;
step S032, coding: the price decision variables in the multi-objective model and the traffic flow variables are coded into each individual variable in the genetic algorithm in sequence;
step S033, initializing a population: randomly generating an initial population consisting of N individuals in the value range of the multi-target model variable, and then uniformly distributing the individuals into M sub-populations;
step S034, randomly migrating: in each generation, each individual decides whether to join another sub-population from the current sub-population based on random migration;
step S035, positioning and generalized searching: after sub-population migration decisions are made, each individual located in a certain sub-population performs a location search, and for optimization in each target model, only the fitness value of the individual on all targets is improved, and the change of the value is acceptable;
step S036, selecting and terminating: at the end of each generation, updating the sub-population; when the maximum generation time is reached, the algorithm is terminated; outputting a non-dominant solution;
step S037, decision: after the non-dominant solution is obtained, the compromise solution method is utilized to find the optimal multi-objective shared pricing decision result in all the non-dominant solutions.
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