CN111325409A - Method and system for site selection of battery replacement station and route planning of hybrid fleet - Google Patents

Method and system for site selection of battery replacement station and route planning of hybrid fleet Download PDF

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CN111325409A
CN111325409A CN202010158192.4A CN202010158192A CN111325409A CN 111325409 A CN111325409 A CN 111325409A CN 202010158192 A CN202010158192 A CN 202010158192A CN 111325409 A CN111325409 A CN 111325409A
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陈彦如
李得成
张宗成
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Abstract

The invention relates to the technical field of logistics transportation, in particular to a method and a system for site selection of a battery replacement station and route planning of a hybrid fleet. The method comprises the following steps: acquiring alternative data information; constructing an integer planning model for combined decision of power station selection and electric vehicle and fuel vehicle mixed fleet delivery; reconstructing the integer programming model into a main problem model and a sub problem model; the invention designs an accurate branch pricing algorithm with a self-adaptive selection mechanism aiming at the model, and realizes self-adaptive adjustment of operators aiming at different enterprise distribution scenes by designing seven accelerated solving operators so as to accelerate the solving speed. And simultaneously, expanding a complete search solution space by adopting an accurate tag to obtain an optimal solution. The invention can quickly provide the optimal delivery scheme and the optimal station building scheme for the large-scale mixed fleet delivery system of different enterprises under the condition of self-building of the power station, thereby realizing the real-time optimal scheduling of the enterprises.

Description

Method and system for site selection of battery replacement station and route planning of hybrid fleet
Technical Field
The invention relates to the technical field of logistics transportation, in particular to a method and a system for site selection of a battery replacement station and route planning of a hybrid fleet.
Background
With the development of new energy technology and the coming of state-related guidance policies, compared with the traditional fuel vehicles, the electric vehicle has greater advantages in the aspects of financial subsidy, license plate restriction, energy conservation, environmental protection, operation cost and the like, so that more and more logistics enterprises begin to popularize the electric logistics vehicles in urban distribution. However, the electric logistics vehicles need to be charged during distribution due to the limitation of the driving range. However, the construction of the charging infrastructure in China is still in a starting stage at present, the network of the charging infrastructure is not perfect, and the charging time of the battery is generally long (the common slow charging technology generally takes 5-8 hours for full charging, and the quick charging technology generally takes 1-2 hours for full charging). Therefore, in actual operation of enterprises, pure electric vehicles do not completely replace fuel vehicles, and electric vehicle and fuel vehicle mixed fleets are built to complete distribution. Meanwhile, the distribution efficiency of the mixed fleet is improved by building a power exchanging station (the power exchanging time is extremely short and is generally equivalent to the fuel replenishing time of a fuel vehicle).
The electric vehicle and fuel vehicle mixed distribution system under the condition of self-building a power station is completely different from the traditional fuel vehicle distribution system. Under the condition of considering the electricity changing, the addition of the electric vehicle and the electricity changing station changes the input parameters of the original fuel vehicle optimal distribution system, if the path planning is not proper, the economic advantages of the electric vehicle on the operation cost cannot be fully exerted, and the distribution service can be delayed or interrupted due to the limitation of the endurance mileage of the electric vehicle.
The combined decision problem of site selection and path combination of the electric vehicle and the fuel vehicle hybrid fleet based on the battery replacement mode is a complex combined optimization problem, integrates and considers the facility site selection of an enterprise strategic level and the path planning of an operation level, and is the combination of the site selection problem of the battery replacement facility and the distribution path planning problem of the electric vehicle and the fuel vehicle hybrid fleet. Compared with a single optimization site selection model or a path model, the joint decision can be used for comprehensively and integrally optimizing the whole distribution system, so that the distribution cost is optimized to the maximum extent, and meanwhile, the whole distribution system is more complex and more difficult to solve.
The hybrid fleet path planning problem takes into account two distinct vehicles-electric vehicles and fuel vehicles. The electric vehicle has low running cost, but has the limitation conditions of short driving range, battery charging and replacement in the distribution process and the like. The fuel-oil vehicle has longer driving mileage but higher driving cost. Meanwhile, the configuration parameters of the two vehicle types in the aspects of load and the like are different. Therefore, the distribution system needs to synchronously coordinate the path distribution of the two vehicles, the limitation factor is more, and the modeling and solving are more complicated compared with the independent configuration of a pure fuel oil fleet or a pure electric fleet. At present, methods for solving route planning of a hybrid fleet are few, most methods are solved based on traditional heuristic algorithms such as genetic algorithm and neighborhood search, and solving quality and solving stability are not ideal.
The electric vehicle charging and battery replacing facility site selection problem aims to decide the optimal energy supplement position and the optimal number of the station building in a distribution network, so that the electric vehicle with the least station building cost can serve the most, and the bypassing cost of the electric vehicle can be reduced. Most of the existing methods are independent decisions of charging station site selection decisions and fleet delivery path planning, namely, path planning is not considered, and site selection positions are decided only from factors such as service distance, station building cost, station building quantity and the like. And after the facility station building position is determined, independently planning the fleet delivery path. The method breaks the complete distribution system and is difficult to obtain a high-quality decision scheme.
The existing hybrid fleet distribution method under the battery replacement mode mainly has two defects: firstly, integrated decision making is not carried out on the site selection of the swapping station and the route planning of the hybrid fleet, so that the site selection of the swapping station in the obtained decision making scheme is improper, the distribution route is not optimized, the distribution cost of an enterprise is high, and the profit of the enterprise is reduced. In the aspect of algorithm design, although the currently designed accurate algorithm can obtain the optimal solution, the solving efficiency is low, and especially when a large-scale complex distribution system such as site selection-path integration consideration is solved, a feasible solution scheme cannot be provided even within an acceptable time range, so that timely distribution of enterprises is influenced, and the customer satisfaction is reduced.
Disclosure of Invention
The invention aims to provide a method and a system for site selection of a power conversion station and route planning of a hybrid fleet, so as to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
on one hand, the application provides a method for site selection of a power station and route planning of a hybrid fleet, and the method comprises the following steps:
acquiring alternative data information; constructing an integer planning model for combined decision of power station selection and electric vehicle and fuel vehicle mixed fleet delivery; reconstructing the integer programming model into a main problem model and a sub problem model by adopting a Dantzig-Wolfe decomposition method; and solving the main problem model and the sub problem model to obtain a distribution scheme with the lowest total cost and a power station site selection scheme.
Optionally, the alternative data information includes: the number and address of distribution centers; the number of the electric vehicles, the distribution cost of the electric vehicles per kilometer and the maximum driving mileage of the electric vehicles; the number of fuel vehicles, the delivery cost per kilometer of fuel vehicles; the location, demand and service time window of each waiting delivery customer; and the position of each alternative power conversion station.
Optionally, the integer programming model comprises the following formula:
Figure BDA0002404834760000031
Figure BDA0002404834760000032
Figure BDA0002404834760000033
Figure BDA0002404834760000034
Figure BDA0002404834760000035
Figure BDA0002404834760000036
Figure BDA0002404834760000037
Figure BDA0002404834760000038
Figure BDA0002404834760000039
Figure BDA00024048347600000310
Figure BDA00024048347600000311
Figure BDA00024048347600000312
Figure BDA0002404834760000041
Figure BDA0002404834760000042
Figure BDA0002404834760000043
the formula (4-1) in the model is an objective function and consists of three items of cost, wherein the first item is the construction cost of the power change station, the second item is the transportation cost of the electric vehicle, and the third item is the transportation cost of the fuel vehicle; the formula (4-2) shows that each customer point and the power change station can only be accessed by one vehicle at the same time; the formula (4-3) restricts the available number of the delivery vehicles not to exceed the scale of the fleet; the formula (4-4) indicates that the started power change station can provide the power change service; equations (4-5) ensure that each point stream is conserved; formulas (4-6) and (4-7) show that the total distribution task amount of the electric vehicle and the fuel vehicle does not exceed the maximum loading capacity; the formula (4-8) represents the time logical relationship of the vehicle from the point i to the point j; the formula (4-9) represents the time logical relation of the electric vehicle from the power change station f to the point j; equations (4-10) satisfy the service time window constraints for all points; the formula (4-11) satisfies the relation of electric quantity consumption when the electric vehicle runs from the point i to the point j; the formulas (4-12) represent that the electric vehicle has constant electric quantity when arriving at and leaving a customer point; formula (4-13) shows that the electric vehicle starts from the full-charge state of the distribution center and changes the full-charge battery after reaching the power change station; the formula (4-14) restricts the electric quantity of the electric vehicle at any point to be larger than zero, and ensures that the electric vehicle can return to a distribution center; equations (4-15) are 0-1 decision variable constraints.
Optionally, the main problem model comprises the following formula:
Figure BDA0002404834760000044
Figure BDA0002404834760000045
Figure BDA0002404834760000046
Figure BDA0002404834760000047
Figure BDA0002404834760000048
in the formulas (4-16) to (4-20), p is a vehicle driving path, omega is a set of all feasible paths meeting the constraint, and p ∈ omega;
Figure BDA0002404834760000051
a variable of 0-1, indicating whether a vehicle path combination (p, k) is employed in the final solution;
Figure BDA0002404834760000052
a variable 0-1 indicating whether the vehicle path combination (p, k) passes through the arc (i, j);
Figure BDA0002404834760000053
a variable of 0-1, indicating whether the path p passes through the point i;
Figure BDA0002404834760000054
representing the cost of a vehicle path combination (p, k), including the cost of path transportation and the cost of station construction, λkTaking λ according to the type of vehicle keOr λcSaid
Figure BDA0002404834760000055
Can be calculated by the following formula:
Figure BDA0002404834760000056
further, the subproblem model comprises the following formula:
Figure BDA0002404834760000057
Figure BDA0002404834760000058
in the formulas (4 to 21) and (4 to 22), pi ═ pi { [ pi ] }i,πe,πcThe dual variables are respectively corresponding to a formula (4-17), a formula (4-18) and a formula (4-19) in the main problem model;
Figure BDA0002404834760000059
representing the number of tests for each feasible path of Ω in the master problem model.
Further, the solving of the main problem model and the sub problem model includes the following steps:
s41, initializing node information, setting the global upper bound as positive infinity, setting the value of an active node set to be the same as that of a root node, and initializing operator pool information;
s42, judging the termination of calculation, namely judging whether the active node set is empty, if so, determining that the current upper bound is the optimal solution, and terminating the calculation, otherwise, turning to the step S43;
s43, selecting a node from the active node set according to the node selection strategy, and deleting the selected node from the active node set;
s44, solving the node, and if the node is not solved, turning to the step S42; otherwise, the linear relaxation optimal solution of the node is recorded as the local lower bound of the node;
s45, pruning, and if the local lower bound is greater than or equal to the global upper bound, turning to the step S42; otherwise, further judging whether the optimal relaxation solution obtained by the node is a score, if so, turning to the step S46; if the number of the nodes is an integer, updating the global upper bound into a local lower bound, deleting the nodes which are not smaller than the current global upper bound in the active node set, and turning to the step S42;
s46, branching, selecting a branch variable from the relaxation optimal solution of the current node according to a branch strategy to divide a solution space to obtain child nodes, adding the new child nodes into an active node set, and turning to the step S42.
Further, the step S44 includes the following steps:
s4401, constructing a main problem model;
s4402, solving a main problem model;
s4403, transferring dual variables;
s4404, resetting the iteration times recorded in the counter to zero, and updating operator information;
s4405, randomly selecting operators from the operator pool, and solving a subproblem model;
s4406, judging whether a column with a negative check number is generated, if so, entering step S4407, and if not, entering step S4409;
s4407, adding 1 to the iteration number recorded in the counter, adding a score to the operator score, adding a column with a negative inspection number to the main problem model, and simultaneously restoring the operator pool;
s4408, judging the relationship between the iteration times and the small iteration cycles, and if the iteration times are smaller than the small iteration cycles, entering the step S4405; otherwise, go to step S4404;
s4409, adding 1 to the iteration number, and deleting the operator selected in the step S4405 in an operator pool;
s4410, judging whether the operator pool is empty, and if the operator pool is empty, entering the step S4411; otherwise, go to step S4405;
s4411, calling an accurate label extension method to solve a subproblem model;
s4412, judging whether a column with a negative detection number is generated; if a column with a negative check number is generated, adding the column with the negative check number into the main problem model, and entering the step S4402; otherwise, the column generation iteration is terminated.
Further, the operator pool comprises the following 7 operators:
the first operator is used for expanding the current state of the solution to 2 stages in a greedy algorithm to obtain a second-order greedy operator;
and the second operator, namely the current node of the label to be expanded is only expanded to the node of the negative cost arc, and meanwhile, the accurate governing rule is kept unchanged.
And a third operator, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and meanwhile, the accurate governing rule is kept unchanged.
And a fourth operator, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and meanwhile, the constraints of the electric quantity Q of the electric vehicle and the arrival time T of the fuel vehicle in the governing rule are relaxed.
And a fifth operator, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and meanwhile, the constraints of the electric quantity Q of the electric vehicle and the arrival time T of the fuel vehicle in the governing rule are relaxed.
And a sixth operator, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and simultaneously, the constraint of the load W of the electric vehicle and the fuel vehicle in the governing rule is relaxed.
And a seventh operator, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and simultaneously, the constraint of the electric vehicle and the fuel vehicle load W in the governing rule is relaxed.
Further, in step S44, the weight of each operator is updated every time Ct iteration is performed, where Ct is the number of operators, and the operator weight can be calculated by the following formula:
Figure BDA0002404834760000071
in the formula (4-24), theta is a weight parameter and takes the value of theta ∈ [0, 1%]；SiScoring the operator according to SnAnd updating, when the selected operator fails to solve the current subproblem SnOtherwise, the operator is scored according to the following case:
when the operator selected for the 1 st time in the small period can solve the subproblem model, Sn＝10；
When the operator selected at the current time cannot solve the subproblem model and the operator selected at the current time can solve the subproblem model, the operator selected at the current time is not the last operator, Sn＝20；
When the operator selected at the front cannot solve the sub-problem model until the operator selected at the last time can solve the problem, Sn＝30。
In another aspect, the present invention provides a system for site selection of a swapping station and path planning of a hybrid fleet, comprising:
the data acquisition module is used for acquiring alternative data information;
the model construction module is used for constructing an integer planning model for combined decision-making of power station selection and electric vehicle and fuel vehicle mixed fleet delivery;
the model reconstruction module is used for reconstructing the integer programming model into a main problem model and a sub problem model by adopting a Dantzig-Wolfe decomposition method;
and the calculation module is used for solving the main problem model and the sub problem model to obtain a distribution and power station site selection scheme with the lowest total cost.
The invention has the beneficial effects that:
the invention constructs an integer planning model for combined decision of the site selection of the power station and the path of the hybrid fleet, and can autonomously evaluate the solving performance of each operator through seven accelerating operators and a self-adaptive selection mechanism design, thereby flexibly calling the operators according to different enterprise delivery scenes, accelerating the solving speed, and particularly for a large-scale complex delivery system, rapidly obtaining a delivery scheme and realizing the real-time scheduling of enterprises. The invention can simultaneously decide the station building position of the power exchanging station and the path distribution of the electric vehicle and the fuel vehicle in the distribution system, and simultaneously can obtain the optimal path scheme and the station building scheme of the mixed distribution fleet under the condition of self-building the power exchanging station of an enterprise due to the properties of designed branch delimitation and accurate label expansion for completely searching the solution space.
The invention improves the solution of the sub-problem from the traditional label extension method into the solution of a plurality of acceleration operator combinations; the self-adaptive selection mechanism designed for the calling of the multiple solving operators can enable the performance of the operators to be automatically judged through the score condition of the operators in the algorithm, so that the optimal operators are preferentially selected for solving.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for site selection of a swapping station and route planning of a hybrid fleet according to an embodiment of the present invention;
FIG. 2 is a table of corresponding meanings of parameters or variables according to the embodiment of the present invention;
fig. 3 is a detailed flowchart of step S4 according to the embodiment of the present invention;
fig. 4 is a detailed flowchart of step S44 according to the embodiment of the present invention;
FIG. 5 is a schematic diagram of a second-order greedy algorithm according to an embodiment of the present invention;
fig. 6 is a schematic diagram of locations of a warehouse, a customer and a power exchange station according to an embodiment of the present invention;
FIG. 7 is a table of distances between points as described in the embodiments of the present invention;
FIG. 8 is a first sub-problem model solution set list according to an embodiment of the present invention;
FIG. 9 is a second sub-problem model solution set list according to an embodiment of the present invention;
FIG. 10 is a third problem sub-model solution set list according to an embodiment of the present invention;
FIG. 11 is a fourth sub-problem model solution set list according to an embodiment of the present invention;
FIG. 12 is a schematic illustration of a pre-optimization delivery schedule in an embodiment of the present invention;
FIG. 13 is a schematic illustration of an optimized delivery schedule according to an embodiment of the present invention;
fig. 14 is a block diagram of a site selection and hybrid fleet path planning system for a power swapping station according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
In one aspect, as shown in fig. 1, the present embodiment provides a method for site selection of a swapping station and planning a hybrid fleet path, where the method includes step S1, step S2, step S3, and step S4.
S1, acquiring alternative data information;
s2, constructing an integer planning model for a combined decision of power station location selection and electric vehicle and fuel vehicle hybrid fleet delivery;
s3, reconstructing the integer programming model into a main problem model and a sub problem model by adopting a Dantzig-Wolfe decomposition method;
and S4, solving the main problem model and the sub problem model to obtain a distribution scheme and a power station site selection scheme with the lowest total cost.
Optionally, the alternative data information includes: the number and address of distribution centers; the number of the electric vehicles, the distribution cost of the electric vehicles per kilometer and the maximum driving mileage of the electric vehicles; the number of fuel vehicles, the delivery cost per kilometer of fuel vehicles; the location, demand and service time window of each waiting delivery customer; and the position of each alternative power conversion station.
Assuming that a distribution system of a logistics enterprise is provided with a distribution center and a mixed fleet of electric vehicles and fuel vehicles, n customers with known positions and demands wait for distribution, and each customer has a service time window constraint that the vehicles need to serve the customers within a specified time period, wait for the customers in the early period, and not allow the customers to arrive in the late period. Each vehicle is required to return to the starting point after the delivery is completed, each vehicle can only serve one path, and each customer can only be served by one vehicle. Because the electric vehicle has short driving mileage, part of positions can be selected from a given electric power station candidate set to build the electric power stations, and the station building cost is distributed to the calculation of the single delivery cost from the operation perspective. The location of the power exchange station and the distribution path planning of the hybrid fleet need to be jointly decided, so that the distribution system meets the distribution requirements of all customers within the specified time, and the goal of lowest construction cost of the power exchange station and the lowest distribution cost of the hybrid fleet is achieved.
Optionally, the integer programming model comprises the following formula:
Figure BDA0002404834760000111
Figure BDA0002404834760000112
Figure BDA0002404834760000113
Figure BDA0002404834760000114
Figure BDA0002404834760000121
Figure BDA0002404834760000122
Figure BDA0002404834760000123
Figure BDA0002404834760000124
Figure BDA0002404834760000125
Figure BDA0002404834760000126
Figure BDA0002404834760000127
Figure BDA0002404834760000128
Figure BDA0002404834760000129
Figure BDA00024048347600001210
Figure BDA00024048347600001211
the formula (4-1) in the model is an objective function and consists of three items of cost, wherein the first item is the construction cost of the power change station, the second item is the transportation cost of the electric vehicle, and the third item is the transportation cost of the fuel vehicle; the formula (4-2) shows that each customer point and the power change station can only be accessed by one vehicle at the same time; the formula (4-3) restricts the available number of the delivery vehicles not to exceed the scale of the fleet; the formula (4-4) indicates that the started power change station can provide the power change service; equations (4-5) ensure that each point stream is conserved; formulas (4-6) and (4-7) show that the total distribution task amount of the electric vehicle and the fuel vehicle does not exceed the maximum loading capacity; the formula (4-8) represents the time logical relationship of the vehicle from the point i to the point j; the formula (4-9) represents the time logical relation of the electric vehicle from the power change station f to the point j; equations (4-10) satisfy the service time window constraints for all points; the formula (4-11) satisfies the relation of electric quantity consumption when the electric vehicle runs from the point i to the point j; the formulas (4-12) represent that the electric vehicle has constant electric quantity when arriving at and leaving a customer point; formula (4-13) shows that the electric vehicle starts from the full-charge state of the distribution center and changes the full-charge battery after reaching the power change station; the formula (4-14) restricts the electric quantity of the electric vehicle at any point to be larger than zero, and ensures that the electric vehicle can return to a distribution center; equations (4-15) are 0-1 decision variable constraints.
The meanings of the parameters or variables in the formulas (4-1) to (4-15) are shown in the comparison table in FIG. 2.
Optionally, the main problem model comprises the following formula:
Figure BDA0002404834760000131
Figure BDA0002404834760000132
Figure BDA0002404834760000133
Figure BDA0002404834760000134
Figure BDA0002404834760000135
in the formulas (4-16) to (4-20), p is a vehicle driving path, omega is a set of all feasible paths meeting the constraint, and p ∈ omega;
Figure BDA0002404834760000136
a variable of 0-1, indicating whether a vehicle path combination (p, k) is employed in the final solution;
Figure BDA0002404834760000137
a variable 0-1 indicating whether the vehicle path combination (p, k) passes through the arc (i, j);
Figure BDA0002404834760000138
a variable of 0-1, indicating whether the path p passes through the point i;
Figure BDA0002404834760000139
representing the cost of a vehicle path combination (p, k), including the cost of path transportation and the cost of station construction, λkTaking λ according to the type of vehicle keOr λcSaid
Figure BDA00024048347600001310
Can be calculated by the following formula:
Figure BDA00024048347600001311
according to the principle of the simplex method, when the integer programming model solves the minimum target, a column with negative inspection number in the solution space needs to be found, and the column is added into the main problem model for continuous iteration until all the inspection numbers are positive.
Further, the subproblem model comprises the following formula:
Figure BDA0002404834760000141
Figure BDA0002404834760000142
in the formulas (4 to 21) and (4 to 22), pi ═ pi { [ pi ] }i,πe,πcThe dual variables are respectively corresponding to a formula (4-17), a formula (4-18) and a formula (4-19) in the main problem model;
Figure BDA0002404834760000143
representing the number of tests for each feasible path of Ω in the master problem model.
Further, as shown in fig. 3, the step S4 may include the following steps:
s41, initializing node information, setting the global upper bound as positive infinity, setting the value of an active node set to be the same as that of a root node, and initializing operator pool information;
s42, judging the termination of calculation, namely judging whether the active node set is empty, if so, determining that the current upper bound is the optimal solution, and terminating the calculation, otherwise, turning to the step S43;
s43, selecting a node from the active node set according to the node selection strategy, and deleting the selected node from the active node set;
s44, solving the node, and if the node is not solved, turning to the step S42; otherwise, the linear relaxation optimal solution of the node is recorded as the local lower bound of the node;
s45, pruning, and if the local lower bound is greater than or equal to the global upper bound, turning to the step S42; otherwise, further judging whether the optimal relaxation solution obtained by the node is a score, if so, turning to the step S46; if the number of the nodes is an integer, updating the global upper bound into a local lower bound, deleting the nodes which are not smaller than the current global upper bound in the active node set, and turning to the step S42;
s46, branching, selecting a branch variable from the relaxation optimal solution of the current node according to a branch strategy to divide a solution space to obtain child nodes, adding the new child nodes into an active node set, and turning to the step S42.
In fig. 3, GUB is the global upper bound, that is, the current optimal integer solution objective function value of the original problem, and the smaller GUB is, the more effective pruning and fast operation can be promoted; GLB is the global lower bound, namely the objective function value of the root node linear relaxation solution; LLBiThe local lower bound is the objective function value of the linear relaxation solution of the current child node i; i is an active node set, namely a node set to be solved; the node is a partial solution space subproblem of the original integer programming problem and only comprises a partial solution space of a feasible domain of the original problem.
Further, as shown in fig. 4, the step S44 includes the following steps:
s4401, constructing a main problem model;
s4402, solving a main problem model;
s4403, transferring dual variables;
s4404, resetting the iteration times recorded in the counter to zero, and updating operator information;
s4405, randomly selecting operators from the operator pool, and solving a subproblem model;
s4406, judging whether a column with a negative check number is generated, if so, entering step S4407, and if not, entering step S4409;
s4407, adding 1 to the iteration number recorded in the counter, adding a score to the operator score, adding a column with a negative inspection number to the main problem model, and simultaneously restoring the operator pool;
s4408, judging the relationship between the iteration times and the small iteration cycles, and if the iteration times are smaller than the small iteration cycles, entering the step S4405; otherwise, go to step S4404;
s4409, adding 1 to the iteration number, and deleting the operator selected in the step S4405 in an operator pool;
s4410, judging whether the operator pool is empty, and if the operator pool is empty, entering the step S4411; otherwise, go to step S4405;
s4411, calling an accurate label extension method to solve a subproblem model;
s4412, judging whether a column with a negative detection number is generated; if a column with a negative check number is generated, adding the column with the negative check number to the main problem model, and proceeding to step S4402; otherwise, the column generation iteration is terminated.
In step S41 initialization, the operator number H needs to be giveniInitial weight WiAnd the like and stored in the operator pool. When entering a column generation process with a self-adaptive selection mechanism, the Count is used as a counter to record the iteration times, the iteration times are recorded once each time a subproblem is solved, and the selected times N of operators are recorded at the same timeiSetting a small iteration cycle Ct, and updating the weight W of each operator every time the Ct is iterated and calculatediAnd making each operator selected for a number of times NiAnd resetting the counter to 0 again, and restoring the number of the operators in the operator pool to the initial number of the operators. Initial weight W of given operator0In the subsequent algorithm execution, the weight update of each operator is influenced by the operator selection times and whether the current subproblem can be solved during selection.
In FIG. 4, RMP is the main questionA question model; count is the number of iterations recorded by the counter; hi is the selected operator; siScoring the operator; ct is the number of operators; SP is a sub-problem model.
Further, the operator pool comprises the following 7 operators:
and the first operator H1 enlarges the current state of the solution to 2 stages in the greedy algorithm to obtain a second-order greedy operator, as shown in FIG. 5, by taking a reference to a strategy that the current state of the solution is influenced by a plurality of stages in the k-order regret value algorithm, the current state of the solution can be enlarged to k stages in the greedy algorithm, and when the value of k is 2, the second-order greedy operator is obtained. In the second-order greedy operator, the current state of the solution needs to be updated in two stages according to the current node after expansion. Taking fig. 3 as an example, when the starting point o expands backward, only the cost of (o, i)/(o, j) cannot be considered, but a state that o expands to i and j and then expands from i and j is further considered, that is, the cost of (o, i, j)/(o, i, k)/(o, j, i)/(o, j, k)/(o, j, o ') needs to be considered, and the current solution is updated to be (o, j, o') with the minimum cost.
And a second operator H2, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and meanwhile, the accurate governing rule is kept unchanged.
And a third operator H3, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and meanwhile, the accurate governing rule is kept unchanged.
And a fourth operator H4, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and meanwhile, the constraints of the electric quantity Q of the electric vehicle and the arrival time T of the fuel vehicle in the governing rule are relaxed.
And a fifth operator H5, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and meanwhile, the constraints of the electric quantity Q of the electric vehicle and the arrival time T of the fuel vehicle in the governing rule are relaxed.
And a sixth operator H6, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and simultaneously, the constraint of the load W of the electric vehicle and the fuel vehicle in the governing rule is relaxed.
And a seventh operator H7, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and simultaneously, the constraint of the load W of the electric vehicle and the fuel vehicle in the governing rule is relaxed.
Each operator can solve the subproblem independently, the solving speed of the method is higher than that of the precise label extension method, however, the subproblems of different data are different in solving space, meanwhile, the column generation algorithm is a process of continuously iterating and solving limited main problems and subproblems, the limited main problems can continuously add new columns along with the solving of the subproblems, the subproblems can be updated along with the change of dual variables of the limited main problems, the solving space of the subproblems changes in each iteration, and therefore the solving performance of each heuristic operator under different data cannot be determined artificially, and therefore a fixed operator solving and calling sequence cannot be given directly. Based on the self-adaptive large-scale neighborhood search concept, the self-adaptive selection mechanism is designed for selecting the subproblem solving operators, the performance of the operators is scored through the self-adaptive selection mechanism, and the operators are automatically selected and inspired in the column generation algorithm to solve.
Further, in step S44, the weight of each operator is updated every time Ct iteration is performed, where Ct is the number of operators, and the operator weight can be calculated by the following formula:
Figure BDA0002404834760000171
in the formula (4-24), theta is a weight parameter and takes the value of theta ∈ [0, 1%]；SiScoring the operator according to SnAnd updating, when the selected operator fails to solve the current subproblem SnOtherwise, the operator is scored according to the following case:
when the operator selected for the 1 st time in the small period can solve the subproblem model, Sn＝10；
When the operator selected at the current time cannot solve the subproblem model and the operator selected at the current time can solve the subproblem model, the operator selected at the current time is not the last operator, Sn＝20；
When the operator selected at the front cannot solve the sub-problem model until the operator selected at the last time can solve the problem, Sn＝30。
As shown in fig. 6, it is assumed that the logistics enterprise has a distribution center and 3 electric logistics vehicles (vehicle numbers 1-3) and 3 fuel logistics vehicles (vehicle numbers 4-6), and the distribution cost per kilometer of the electric vehicles is λ e1, fuel vehicle distribution cost per kilometer is lambdac1.57, there are 5 customers with known locations and demands waiting for delivery, and the vehicle can only service the customer for each customer-specified time period. Each vehicle is required to return to the distribution center after the distribution is completed, each vehicle can only serve one path, and each customer can only be served by one vehicle. Because the endurance mileage of the electric vehicle is short (the maximum driving mileage is 77.75), an enterprise can select part of positions in a given swapping station candidate set to build a swapping station, and the cost lambda generated by building the swapping station isf38.875, the electric vehicle can replace the full-charge battery in the established power station, and how the enterprise should make a station-building scheme and a distribution scheme to minimize the total distribution cost of the system. Distance d between pointsijAs shown in fig. 7.
And constructing a main problem model and a sub problem model.
First, a feasible initial delivery scheme is given:
k40-2-5-0, corresponding to the variables
Figure BDA0002404834760000181
Figure BDA0002404834760000182
k50-4-3-1-0, corresponding to the variables
Figure BDA0002404834760000183
Figure BDA0002404834760000184
The total cost of the distribution scheme is station building cost 0+ driving cost 356.39 is 356.39;
secondly, initializing an operator pool, wherein 7 operators such as H1, H2, … H7 and the like are shared, and each operator has an initial weight WiAre all provided withAnd 10, each operator is selected with the same probability at the beginning, and the small iteration period Ct is set to be 3.
The column generates the first iteration, count 1:
(1) constructing an RMP, and solving the current RMP:
Figure BDA0002404834760000191
Figure BDA0002404834760000192
Figure BDA0002404834760000193
Figure BDA0002404834760000194
Figure BDA0002404834760000195
Figure BDA0002404834760000196
Figure BDA0002404834760000197
the optimal solution that can solve the current RMP is
Figure BDA0002404834760000198
Solving the current dual variable as pi1＝218.23；π2＝138.16；π3＝π4＝π5＝0；πc＝0。
(2) The arc costs are re-priced according to the dual variables, resulting in a solution set list of the first sub-problem model, as shown in FIG. 8.
(3) Roulette selects operators from the operator pool, and, assuming H2 is selected, solves the SP with H2, solving for a path with a negative cost of-70.16 as follows:
k10-2-0, corresponding to the variable
Figure BDA0002404834760000199
Figure BDA00024048347600001910
The recording operator H2 is selected for 1 time, and the score is added by 10;
the column generates the second iteration, count 2:
(4) constructing an RMP, and solving the current RMP:
Figure BDA00024048347600001911
Figure BDA00024048347600001912
Figure BDA00024048347600001913
Figure BDA00024048347600001914
Figure BDA00024048347600001915
Figure BDA00024048347600001916
Figure BDA00024048347600001917
Figure BDA00024048347600001918
the optimal solution that can solve the current RMP is
Figure BDA00024048347600001919
Solving the current dual variable as pi1＝218.23；π2＝68；π3＝π4＝0；π5＝70.16；πe＝πc＝0
(5) The arc costs are re-priced according to the dual variables, resulting in a solution set list for the second sub-problem model, as shown in FIG. 9.
(6) The roulette selects an operator from the operator pool, and if H1 is selected, H1 is used for solving SP, and a negative cost path is not solved;
h1 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H2 is selected, the SP is solved by H2, and the negative cost path is not solved;
h1 is removed from the operator pool, the roulette is selected from the remaining operators in the operator pool, and assuming H6 is selected, the SP is solved using H6, and a path with negative cost-44.355 is solved as follows:
k20-3-6-1-0, corresponding to the variables
Figure BDA0002404834760000201
Figure BDA0002404834760000202
The recording operator H6 is selected for 1 time, and the score is added by 20;
the column generates the third iteration, count 3:
(7) constructing an RMP, and solving the current RMP:
Figure BDA0002404834760000203
Figure BDA0002404834760000204
Figure BDA0002404834760000205
Figure BDA0002404834760000206
Figure BDA0002404834760000207
Figure BDA0002404834760000208
Figure BDA0002404834760000209
Figure BDA00024048347600002010
the optimal solution that can solve the current RMP is
Figure BDA00024048347600002011
Solving the current dual variable as:
π1＝173.875；π2＝68；π3＝0；π4＝44.355；π5＝70.16；πe＝πc＝0
(8) the arc costs are re-priced according to the dual variables, resulting in a solution set list for the third sub-problem model, as shown in FIG. 10.
(9) The roulette selects an operator from the operator pool, and if H2 is selected, H2 is used for solving SP, and a negative cost path is not solved;
h2 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H5 is selected, the SP is solved by H5, and the negative cost path is not solved;
h5 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H3 is selected, the SP is solved by H3, and the negative cost path is not solved;
h3 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H4 is selected, the SP is solved by H4, and the negative cost path is not solved;
h4 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H7 is selected, the SP is solved by H7, and the negative cost path is not solved;
h7 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H1 is selected, the SP is solved by H1, and the negative cost path is not solved;
h1 is removed from the operator pool, the roulette is selected from the remaining operators in the operator pool, and assuming H6 is selected, the SP is solved using H6, and two paths with negative cost of-25.515 are solved as follows:
k60-4-5-0, corresponding to the variables
Figure BDA0002404834760000211
Figure BDA0002404834760000212
k30-4-5-0, corresponding to the variables
Figure BDA0002404834760000213
Figure BDA0002404834760000214
The recording operator H6 is selected for 2 times in total, and 30 points are added;
at this time, the count ≧ Ct is 3, the operator information is updated:
h1: selecting 0 times, scoring 0, weight unchanged, W1＝10；
H2: 1 hit, score 10, weight increase, W2＝10+10＝20；
H3: selecting 0 times, scoring 0, weight unchanged, W3＝10；
H4: selecting 0 times, scoring 0, weight unchanged, W4＝10；
H5: selecting 0 times, scoring 0, weight unchanged, W5＝10；
H6: choose 2 times, score 20+ 30-50, weight increase, W6＝10+50＝60；
H7: selecting 0 times, scoring 0, weight unchanged, W7＝10；
Reset count to 1.
Column generates the fourth iteration, count 1:
(10) constructing an RMP, and solving the current RMP:
Figure BDA0002404834760000221
Figure BDA0002404834760000222
Figure BDA0002404834760000223
Figure BDA0002404834760000224
Figure BDA0002404834760000225
Figure BDA0002404834760000226
Figure BDA0002404834760000227
Figure BDA0002404834760000228
the optimal solution that can solve the current RMP is
Figure BDA0002404834760000229
Solving the current dual variable as:
π1＝214.35；π2＝108.47；π3＝0；π4＝-3.89；π5＝111.47；πe＝-29.69；πc＝0
(11) the arc costs are re-priced according to the dual variables, resulting in a fourth sub-problem model solution set list, as shown in FIG. 11.
(12) Roulette selects operators from the operator pool, and, assuming H6 was selected (the probability of H6 being selected is greatest), solves the SP with H6, and does not solve the negative cost path;
removing H6 from the operator pool, selecting the roulette from the remaining operators in the operator pool, and assuming H2 is selected (probability of H2 being selected is second), solving for SP with H2 without solving for a negative cost path;
h2 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H3 is selected, the SP is solved by H3, and the negative cost path is not solved;
h3 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H4 is selected, the SP is solved by H4, and the negative cost path is not solved;
h4 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H7 is selected, the SP is solved by H7, and the negative cost path is not solved;
h7 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H1 is selected, the SP is solved by H1, and the negative cost path is not solved;
h1 is deleted from the operator pool, the roulette chooses from the rest operators in the operator pool, H5 is selected, the SP is solved by H5, and the negative cost path is not solved;
and (4) judging that the algorithm pool is empty (namely all operators are deleted), further calling the precision DP to solve, and if the precision DP does not solve the negative cost path, ending the column generation process.
Further judging the solution obtained by the current RMP
Figure BDA0002404834760000231
Is an integer solution, no further branching or delimitation is required, thenBranch pricing ends.
The optimal distribution scheme of the distribution system is finally obtained as follows:
1. the station building scheme comprises the following steps: establishing a battery swapping station at a candidate battery swapping station S6;
2. path scheme: selecting 3 electric vehicles to serve 5 customer points;
electric vehicle k1A driving path: 0-2-0;
electric vehicle k2A driving path: 0-3-6-1-0;
electric vehicle k3A driving path: 0-4-5-0;
3. the total delivery cost 38.875+ travel cost 274 is 312.875.
The delivery schedule before optimization is shown in FIG. 12, total delivery cost before optimization 356.39;
the optimized delivery schedule is shown in FIG. 13, and the optimized total delivery cost 312.875.
On the other hand, the embodiment provides a system for site selection of a swapping station and path planning of a hybrid fleet, which includes a data acquisition module 51, a model construction module 52, a model reconstruction module 53, and a calculation module 54.
A data obtaining module 51, configured to obtain alternative data information;
the model construction module 52 is used for constructing an integer planning model for the combined decision of the battery replacement station site selection and the electric vehicle and fuel vehicle mixed fleet delivery;
a model reconstruction module 53, configured to reconstruct the integer programming model into a main problem model and a sub problem model by using a Dantzig-Wolfe decomposition method;
and the calculation module 54 is used for solving the main problem model and the sub problem model to obtain a distribution scheme and a power station location selection scheme with the lowest total cost.
The site selection of the swapping station and the route planning system of the hybrid fleet provided by the embodiment of the invention have the same implementation principle and technical effect as the embodiments of the site selection of the swapping station and the route planning method of the hybrid fleet, and for the sake of brief description, the corresponding contents in the embodiments of the site selection of the swapping station and the route planning method of the hybrid fleet are referred to.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for site selection of a power change station and path planning of a hybrid fleet is characterized by comprising the following steps:
acquiring alternative data information;
constructing an integer planning model for combined decision of power station selection and electric vehicle and fuel vehicle mixed fleet delivery;
reconstructing the integer programming model into a main problem model and a sub problem model;
and solving the main problem model and the sub problem model to obtain a distribution scheme with the lowest total cost and a power station site selection scheme.
2. The method for power station location selection and hybrid fleet path planning as set forth in claim 1, wherein said alternative data information comprises: the number and address of distribution centers; the number of the electric vehicles, the distribution cost of the electric vehicles per kilometer and the maximum driving mileage of the electric vehicles; the number of fuel vehicles, the delivery cost per kilometer of fuel vehicles; the location, demand and service time window of each waiting delivery customer; and the position of each alternative power conversion station.
3. The method of site selection for a swapping station and hybrid fleet path planning of claim 1, wherein said integer programming model comprises the following formula:
Figure FDA0002404834750000011
Figure FDA0002404834750000012
Figure FDA0002404834750000013
Figure FDA0002404834750000014
Figure FDA0002404834750000015
Figure FDA0002404834750000021
Figure FDA0002404834750000022
Figure FDA0002404834750000023
Figure FDA0002404834750000024
Figure FDA0002404834750000025
Figure FDA0002404834750000026
Figure FDA0002404834750000027
Figure FDA0002404834750000028
Figure FDA0002404834750000029
Figure FDA00024048347500000210
the formula (4-1) in the model is an objective function and consists of three items of cost, wherein the first item is the construction cost of the power change station, the second item is the transportation cost of the electric vehicle, and the third item is the transportation cost of the fuel vehicle; the formula (4-2) shows that each customer point and the power change station can only be accessed by one vehicle at the same time; the formula (4-3) restricts the available number of the delivery vehicles not to exceed the scale of the fleet; the formula (4-4) indicates that the started power change station can provide the power change service; equations (4-5) ensure that each point stream is conserved; formulas (4-6) and (4-7) show that the total distribution task amount of the electric vehicle and the fuel vehicle does not exceed the maximum loading capacity; the formula (4-8) represents the time logical relationship of the vehicle from the point i to the point j; the formula (4-9) represents the time logical relation of the electric vehicle from the power change station f to the point j; equations (4-10) satisfy the service time window constraints for all points; the formula (4-11) satisfies the relation of electric quantity consumption when the electric vehicle runs from the point i to the point j; the formulas (4-12) represent that the electric vehicle has constant electric quantity when arriving at and leaving a customer point; formula (4-13) shows that the electric vehicle starts from the full-charge state of the distribution center and changes the full-charge battery after reaching the power change station; the formula (4-14) restricts the electric quantity of the electric vehicle at any point to be larger than zero, and ensures that the electric vehicle can return to a distribution center; equations (4-15) are 0-1 decision variable constraints.
4. The site selection and hybrid fleet path planning method for a power swapping station of claim 1, wherein: the main problem model comprises the following formula:
Figure FDA0002404834750000031
Figure FDA0002404834750000032
Figure FDA0002404834750000033
Figure FDA0002404834750000034
Figure FDA0002404834750000035
in the formulas (4-16) to (4-20), p is a vehicle driving path, omega is a set of all feasible paths meeting the constraint, and p ∈ omega;
Figure FDA0002404834750000036
a variable of 0-1, indicating whether a vehicle path combination (p, k) is employed in the final solution;
Figure FDA0002404834750000037
a variable 0-1 indicating whether the vehicle path combination (p, k) passes through the arc (i, j);
Figure FDA00024048347500000313
a variable of 0-1, indicating whether the path p passes through the point i;
Figure FDA0002404834750000038
representing the cost of a vehicle path combination (p, k), including the cost of path transportation and the cost of station construction, λkTaking λ according to the type of vehicle keOr λcSaid
Figure FDA0002404834750000039
Can be calculated by the following formula:
Figure FDA00024048347500000310
5. the site selection and hybrid fleet path planning method for a power swapping station of claim 4, wherein: the subproblem model comprises the following formula:
Figure FDA00024048347500000311
Figure FDA00024048347500000312
in the formulas (4 to 21) and (4 to 22), pi ═ pi { [ pi ] }i,πe,πcThe dual variables are respectively corresponding to a formula (4-17), a formula (4-18) and a formula (4-19) in the main problem model;
Figure FDA0002404834750000041
representing the number of tests for each feasible path of Ω in the master problem model.
6. The method for power station site selection and hybrid fleet path planning as set forth in claim 5, wherein said solving a main problem model and a sub problem model comprises the steps of:
s41, initializing node information, setting the global upper bound as positive infinity, setting the value of an active node set to be the same as that of a root node, and initializing operator pool information;
s42, judging the termination of calculation, namely judging whether the active node set is empty, if so, determining that the current upper bound is the optimal solution, and terminating the calculation, otherwise, turning to the step S43;
s43, selecting a node from the active node set according to the node selection strategy, and deleting the selected node from the active node set;
s44, solving the node, and if the node is not solved, turning to the step S42; otherwise, the linear relaxation optimal solution of the node is recorded as the local lower bound of the node;
s45, pruning, and if the local lower bound is greater than or equal to the global upper bound, turning to the step S42; otherwise, further judging whether the optimal relaxation solution obtained by the node is a score, if so, turning to the step S46; if the number of the nodes is an integer, updating the global upper bound into a local lower bound, deleting the nodes which are not smaller than the current global upper bound in the active node set, and turning to the step S42;
s46, branching, selecting a branch variable from the relaxation optimal solution of the current node according to a branch strategy to divide a solution space to obtain child nodes, adding the new child nodes into an active node set, and turning to the step S42.
7. The method for site selection and hybrid fleet path planning for a power conversion station according to claim 6, wherein said step S44 comprises the steps of:
s4401, constructing a main problem model;
s4402, solving a main problem model;
s4403, transferring dual variables;
s4404, resetting the iteration times recorded in the counter to zero, and updating operator information;
s4405, randomly selecting operators from the operator pool, and solving a subproblem model;
s4406, judging whether a column with a negative check number is generated, if so, entering step S4407, and if not, entering step S4409;
s4407, adding 1 to the iteration number recorded in the counter, adding a score to the operator score, adding a column with a negative inspection number to the main problem model, and simultaneously restoring the operator pool;
s4408, judging the relationship between the iteration times and the small iteration cycles, and if the iteration times are smaller than the small iteration cycles, entering the step S4405; otherwise, go to step S4404;
s4409, adding 1 to the iteration number, and deleting the operator selected in the step S4405 in an operator pool;
s4410, judging whether the operator pool is empty, and if the operator pool is empty, entering the step S4411; otherwise, go to step S4405;
s4411, calling an accurate label extension method to solve a subproblem model;
s4412, judging whether a column with a negative detection number is generated; if a column with a negative check number is generated, adding the column with the negative check number into the main problem model, and entering the step S4402; otherwise, the column generation iteration is terminated.
8. The method for power station site selection and hybrid fleet path planning as claimed in claim 7, wherein said operator pool comprises the following 7 operators:
the first operator is used for expanding the current state of the solution to 2 stages in a greedy algorithm to obtain a second-order greedy operator;
and the second operator, namely the current node of the label to be expanded is only expanded to the node of the negative cost arc, and meanwhile, the accurate governing rule is kept unchanged.
And a third operator, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and meanwhile, the accurate governing rule is kept unchanged.
And a fourth operator, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and meanwhile, the constraints of the electric quantity Q of the electric vehicle and the arrival time T of the fuel vehicle in the governing rule are relaxed.
And a fifth operator, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and meanwhile, the constraints of the electric quantity Q of the electric vehicle and the arrival time T of the fuel vehicle in the governing rule are relaxed.
And a sixth operator, wherein the current node of the label to be expanded is only expanded to the node of the negative cost arc, and simultaneously, the constraint of the load W of the electric vehicle and the fuel vehicle in the governing rule is relaxed.
And a seventh operator, wherein the current node of the label to be expanded is only expanded to the node of the cost reduction arc, and simultaneously, the constraint of the electric vehicle and the fuel vehicle load W in the governing rule is relaxed.
9. The method for power station location and hybrid fleet path planning according to claim 7, wherein in step S44, the weight of each operator is updated every Ct iteration, where Ct is the number of operators and the operator weight can be calculated by the following formula:
Figure FDA0002404834750000061
in the formula (4-24), theta is a weight parameter and takes the value of theta ∈ [0, 1%]；SiScoring the operator according to SnAnd updating, when the selected operator fails to solve the current subproblem SnOtherwise, the operator is scored according to the following case:
when the operator selected for the 1 st time in the small period can solve the subproblem model, Sn＝10；
When the operator selected at the current time cannot solve the subproblem model and the operator selected at the current time can solve the subproblem model, the operator selected at the current time is not the last operator, Sn＝20；
When the operator selected at the front cannot solve the sub-problem model until the operator selected at the last time can solve the problem, Sn＝30。
10. A system for site selection of a battery replacement station and path planning of a hybrid fleet of vehicles, the system comprising:
the data acquisition module is used for acquiring alternative data information;
the model construction module is used for constructing an integer planning model for combined decision-making of power station selection and electric vehicle and fuel vehicle mixed fleet delivery;
the model reconstruction module is used for reconstructing the integer programming model into a main problem model and a sub problem model;
and the calculation module is used for solving the main problem model and the sub problem model to obtain a distribution scheme and a power station site selection scheme with the lowest total cost.
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