Route planning method, device, and medium for an urban freight hybrid fleet

Abstract

The present invention discloses a route planning method, equipment and medium for a hybrid urban freight fleet, including: setting carbon point incentives for drivers, customers and platforms; wherein the driver carbon point incentive is the product of the sum of the driver's carpooling carbon points and the driver's electric vehicle carbon points for all orders accepted by a driver in one day, and the staggered transportation incentive coefficient; the customer carbon point incentive is the product of the sum of the customer's carpooling carbon points and the customer's electric vehicle carbon points for all orders placed by a customer in one day, and the staggered transportation incentive coefficient; the platform carbon point incentive is the product of the sum of the driver's carbon point incentive and the customer's carbon point incentive and the carbon point reward coefficient issued by the logistics platform; constructing the interests of drivers, customers and platforms based on the driver, customer and platform carbon point incentives; taking the weighted sum of the interests of drivers, customers and platforms as the objective function, setting constraints, solving the objective function according to the constraints, and obtaining hybrid oil-electric freight carpooling routes and charging strategies for electric trucks.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban transportation carbon emissions, and relates to a route planning method, equipment, and medium for an urban freight hybrid fleet. Background Art

[0002] As urbanization accelerates and urban freight demand surges, carbon emissions from traditional fuel trucking continue to rise. Existing carbon reduction strategies primarily focus on passenger transport (such as carpooling points incentives), but lack systematic incentives for freight. While existing technologies, such as the Green Vehicle Routing Problem (GVRP) and the Pickup and Delivery Problem (PDP), can optimize routes, they fail to address the following issues:

[0003] 1. Lack of a tripartite interest coordination mechanism: It is difficult to dynamically balance the costs and benefits of customers, drivers, and the platform;

[0004] 2. Insufficient promotion of electric trucks and ride-sharing solutions: There is a lack of quantitative models to incentivize drivers and customers to actively choose electric trucks and ride-sharing strategies;

[0005] 3. Difficulty in quantifying carbon emission reduction effects: The existing path optimization model is not deeply integrated with the carbon credit incentive mechanism. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a route planning method, equipment and medium for an urban freight hybrid fleet.

[0007] In a first aspect, an embodiment of the present invention provides a route planning method for an urban freight hybrid fleet that incorporates a bilateral incentive mechanism, the method comprising:

[0008] Set up driver carbon point incentives, customer carbon point incentives, and platform carbon point incentives; among which, the driver carbon point incentive is the product of the sum of the driver's carpooling carbon points and the driver's electric vehicle carbon points for all orders undertaken by a driver in one day, and the off-peak transportation incentive coefficient; the customer carbon point incentive is the product of the sum of the customer's carpooling carbon points and the customer's electric vehicle carbon points for all orders placed by a customer in one day, and the off-peak transportation incentive coefficient; the platform carbon point incentive is the product of the sum of the driver carbon point incentive and the customer carbon point incentive, and the carbon point reward coefficient issued by the logistics platform;

[0009] Build driver benefits based on driver carbon point incentives, driver commissions, and truck driving costs; build customer benefits based on customer carbon point incentives and order expenditures; build platform benefits based on order revenue, driver commissions, driver carbon point incentives, and customer carbon point incentives.

[0010] Taking the weighted sum of driver interests, customer interests, and platform interests as the objective function, constraints including path connectivity constraints, vehicle cargo capacity constraints, electric vehicle battery power constraints, time window constraints, and driver workload constraints are set. The objective function is solved according to the constraints to obtain the hybrid freight carpooling routes and charging strategies for electric trucks.

[0011] In a second aspect, an embodiment of the present invention provides an electronic device, including:

[0012] at least one processor; and

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to execute the above-mentioned route planning method for the urban freight hybrid fleet.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the above-mentioned route planning method for a hybrid urban freight vehicle fleet.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned route planning method for a hybrid urban freight fleet.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention provides a route planning method for an urban freight hybrid fleet that integrates a bilateral incentive mechanism, defining carbon credit incentives for drivers, customers, and platforms. The driver carbon credit incentive is the product of the sum of the driver's carpooling carbon credits and the driver's electric vehicle carbon credits for all orders a driver accepts in a day, multiplied by the staggered transportation incentive coefficient. The customer carbon credit incentive is the product of the sum of the customer carpooling carbon credits and the customer's electric vehicle carbon credits for all orders a customer places in a day, multiplied by the staggered transportation incentive coefficient. The platform carbon credit incentive is the product of the sum of the driver carbon credit incentive and the customer carbon credit incentive, multiplied by the carbon credit reward coefficient issued by the logistics platform. The interests of drivers, customers, and the platform are constructed based on the driver, customer, and platform carbon credit incentives. Constraints are set using the weighted sum of the interests of drivers, customers, and the platform as the objective function, and the objective function is solved based on the constraints to obtain hybrid freight carpooling routes and electric truck charging strategies. The present invention implements efficient carpooling route planning on a logistics management platform that simultaneously operates electric trucks and fuel trucks, and maximizes carbon emission reduction through the carbon credit mechanism while balancing the interests of customers, drivers, and the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a schematic diagram of a route planning method for an urban freight hybrid fleet provided by an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] It should be noted that, unless there is any conflict, the features in the following embodiments and implementations may be combined with each other.

[0024] Step S1, obtaining freight data.

[0025] Specifically, the freight data includes node information and vehicle information;

[0026] Node information includes: After a customer submits a freight request, the crowdsourced logistics management platform obtains information such as the origin and destination (nodes) of the freight order, the weight and volume of the cargo, and the loading and unloading time windows. Furthermore, the crowdsourced logistics management platform provides information such as the location and charging power of charging stations (nodes) within the freight coverage area.

[0027] Vehicle information includes: The crowdsourcing logistics management platform provides information on trucks that can be operated in the current period, including vehicle energy type (fuel truck or electric truck), truck weight, maximum cargo capacity, energy storage capacity (oil or electricity), energy consumption rate, vehicle purchase cost, starting price corresponding to different models, and platform revenue and commission expenditure per unit transportation distance.

[0028] Step S2, setting driver carbon point incentives, customer carbon point incentives, and platform carbon point incentives based on freight data; wherein, the driver carbon point incentive is the product of the sum of the driver's carpooling carbon points and the driver's electric vehicle carbon points for all orders undertaken by a driver in one day, and the off-peak transportation incentive coefficient; the customer carbon point incentive is the product of the sum of the customer's carpooling carbon points and the customer's electric vehicle carbon points for all orders placed by a customer in one day, and the off-peak transportation incentive coefficient; the platform carbon point incentive is the product of the sum of the driver's carbon point incentive and the customer's carbon point incentive, and the carbon point reward coefficient issued by the logistics platform.

[0029] Step S2-1, the process of setting up driver carbon credit incentives includes:

[0030] Driver electric vehicle carbon credits are the carbon credits awarded to drivers for completing transport tasks by driving freight electric vehicles. All individual transport orders transported by a truck in a day are traversed, and the product of the distance traveled by the truck between the loading point and the unloading point and the weight of the goods corresponding to each individual transport order is calculated. The product corresponding to all individual transport orders is accumulated and multiplied by the new energy credit coefficient to obtain the driver electric vehicle carbon credits.

[0031] The driver's carpooling carbon points are the carbon points reward earned by the driver for completing the transportation task of the carpooling order; traverse all the carpooling orders transported by a truck in one day, calculate the product of the driving distance of the truck between the loading point and the unloading point and the mass of the goods corresponding to each carpooling order, and accumulate the corresponding products of all carpooling orders as the first cumulative value; traverse each road section, calculate the path distance of each road section, the variable of whether the truck passes through the road section, and the product of the mass of the goods when the truck leaves the starting point of the road section, and accumulate the corresponding products of all road sections as the second cumulative value; calculate the difference between the first cumulative value and the second cumulative value and the carpooling points coefficient to obtain the driver's carpooling carbon points;

[0032] The staggered transport incentive coefficient is a reward for staggered transport during off-peak hours on the road network. The difference between the time a truck leaves the end of a road segment and the time it leaves the first node is calculated, and then multiplied by the variable indicating whether the truck passed through the road segment and the traffic condition coefficient. The product corresponding to each road segment is accumulated across all sections to obtain the staggered transport incentive coefficient.

[0033] The driver's carbon points incentive is calculated by multiplying the sum of the driver's carpooling carbon points and the driver's tram carbon points for all orders undertaken by a driver in a day by the off-peak transportation incentive coefficient.

[0034] Specifically, the expression of driver carbon credit incentive is as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] In the formula, green energy credit represents the driver's electric vehicle carbon credit, pooling credit represents the driver's carpooling carbon credit, and off-peak credit represents the off-peak transportation incentive coefficient; Represents the carbon credits of truck driver k at time t. Represents the point value; Represents the vehicle passing through the road section ( , )’s driving distance, Representative Node The mass of cargo loaded onto the vehicle; Indicates an order shipped separately, represents the set of all individual shipment orders that truck k delivers in one day, Indicates a carpooling order. represents the set of all carpooling orders transported by truck k in one day, A represents the set of road segments, represents the path distance of the road segment (i, j), is a variable reflecting whether vehicle k passes through road section (i, j), . Represents the mass of cargo when truck k leaves node i. Represents the new energy integral coefficient of the s model, Represents the carpooling points coefficient for the s model, represents the traffic condition coefficient during off-peak hours, represents the traffic condition coefficient during peak hours, Time represents the time when truck k leaves node j. Similarly, Time represents the time when truck k leaves node i, represents the duration of the off-peak period when truck k is transporting. Represents the duration of truck k's transportation during peak hours.

[0041] Furthermore, the road section ( , )middle Indicates the starting point of the order, i.e. the loading point. Indicates the end point of the order, i.e., the unloading point; the road segment (i, j) indicates any road segment, where node i may not be the start point of the order, and node j may not be the end point of the order; when node i is the start point of the order and node j is the end point of the order, the road segment ( , ) is equivalent to segment (i, j).

[0042] It should be noted that by setting up driver carbon point incentives, drivers are encouraged to choose electric vehicles for carpooling, while at the same time increasing their enthusiasm for completing transportation tasks during non-peak hours.

[0043] Step S2-2, the process of setting up customer carbon credit incentives includes:

[0044] Customer electric vehicle carbon credits are the carbon credits earned by customers who choose freight electric vehicles to complete their orders. All individual transport orders transported by a truck in a day are traversed, and the product of the distance traveled by the truck between the loading point and the unloading point and the weight of the goods corresponding to each individual transport order is calculated. The product corresponding to all individual transport orders is accumulated and multiplied by the new energy credit coefficient to obtain the customer electric vehicle carbon credits.

[0045] Customer carpooling carbon points are carbon point rewards earned by customers who choose carpooling. All carpooling orders transported by a truck in one day are traversed, and the product of the distance traveled by the truck between the loading point and the unloading point and the mass of the goods corresponding to each carpooling order is calculated. The products corresponding to all carpooling orders are accumulated as the first accumulated value. Each road section is traversed, and the product of the path distance of each road section, the variable indicating whether the truck passes through the road section, and the mass of the goods when the truck leaves the starting point of the road section is calculated. The products corresponding to all road sections are accumulated as the second accumulated value. The difference between the first accumulated value and the second accumulated value is calculated and multiplied by the carpooling points coefficient to obtain the customer carpooling carbon points.

[0046] The turnover coefficient is calculated by calculating the ratio of the product of the distance traveled between the loading point and the unloading point by a truck corresponding to a single transport order and the first empty load ratio to the cumulative value of the product of the distance traveled between the loading point and the unloading point by trucks corresponding to all single transport orders and the second empty load ratio. The first empty load ratio is 1 minus the ratio of the mass of cargo loaded on the truck at the loading point to the truck's maximum load capacity; the second empty load ratio is 1 minus the ratio of the mass of cargo traveled between the loading point and the unloading point to the truck's maximum load capacity.

[0047] Calculate the sum of the customer's electric car carbon points, customer carpooling carbon points and turnover coefficient for all orders placed by a customer in one day, and then multiply it by the off-peak transportation incentive coefficient to obtain the customer carbon points incentive.

[0048] At the same time, in a carpooling order, the numerical relationship between the customer's carbon points and the driver's carbon points is consistent with the ratio of the freight turnover of the customer's order (cargo weight × transportation distance) and the total freight turnover of the driver's carpooling order.

[0049] Specifically, the expression of customer carbon credit incentive is as follows:

[0050]

[0051]

[0052]

[0053]

[0054] Where, represents the carbon credits of customer o with order for vehicle type s at time t. Green energy credit represents the carbon credits of the customer's electric vehicle, pooling credit represents the carbon credits of the customer's carpooling, and off-peak credit represents the incentive coefficient for off-peak transportation. Represents the point value. Represents the vehicle passing through the road section ( , ) driving distance. Representative Node The mass of cargo loaded onto the vehicle, Representatives in the road section ( , ) of all goods quality. represents the maximum loading mass of truck k Represents the mass of cargo when truck k leaves node i. Represents the new energy integral coefficient of the s model, Represents the carpooling points coefficient for the s model. is a variable reflecting whether vehicle k passes through road section (i, j), . Indicates an order shipped separately, represents the set of all individual shipment orders that truck k delivers in one day, Indicates a carpooling order. represents the set of all carpooling orders transported by truck k within one day, and A represents the set of road segments.

[0055] Furthermore, through this customer carbon points incentive mechanism, customers can actively participate in environmental protection and reduce carbon emissions while enjoying logistics services.

[0056] Step S2-3, the process of setting up the platform carbon credit incentive includes:

[0057] The platform carbon credit incentive is the carbon credit reward provided by the crowdsourcing logistics platform to drivers and customers. It is based on the sum of the driver carbon credit incentive and the customer carbon credit incentive multiplied by the carbon credit reward coefficient issued by the crowdsourcing logistics platform. The expression is as follows:

[0058]

[0059] in, represents the carbon credits of customer o who ordered model s at time t, represents the carbon credits of truck driver k at time t; Represents the point value; Represents the carbon credit reward coefficient issued by the crowdsourcing logistics platform.

[0060] Step S3, building driver benefits based on driver carbon point incentives, driver commissions, and truck driving costs; building customer benefits based on customer carbon point incentives and order expenditures; building platform benefits based on order revenue, driver commissions, driver carbon point incentives, and customer carbon point incentives.

[0061] Step S3-1: Build driver benefits based on driver carbon credit incentives, driver commissions, and truck driving costs. The process specifically includes:

[0062] The driver's benefit is the difference between the driver's income and the truck's driving cost. The driver's income includes the driver's commission and the driver's carbon credits. The truck's driving cost includes the fuel truck's driving cost and the electric truck's driving cost.

[0063] The fuel truck driving cost is the sum of the fixed cost of the fuel truck and the fuel truck transportation energy consumption cost. The fuel truck transportation energy consumption cost is calculated by calculating the product of the fuel truck's energy consumption for each road section, the fuel truck's energy price, and the distance traveled between the loading point and the unloading point. Each road section is traversed and the product corresponding to each road section is accumulated to obtain the fuel truck transportation energy consumption cost.

[0064] The electric truck driving cost is the sum of the fixed cost of the electric truck and the transportation energy consumption cost of the electric truck. The calculation process of the electric truck transportation energy consumption cost is as follows: the product of the energy consumption of the electric truck passing through each road section, the energy price of the electric truck, and the distance traveled between the loading point and the unloading point is calculated, and each road section is traversed, and the product corresponding to each road section is accumulated to obtain the electric truck transportation energy consumption cost.

[0065] Among them, the driver carbon points are the accumulated driver carbon points incentive corresponding to each truck and each model.

[0066] Specifically, driver interests The expression is as follows:

[0067]

[0068] The calculation process of truck driving cost includes:

[0069]

[0070]

[0071]

[0072] Where, represents the driving cost of truck k, Represents the distance traveled by the truck between the loading point and the unloading point. represents the energy consumption of a fuel truck of model s passing through the road section (i, j), Represents the energy price of fuel vehicles (unit: yuan / liter), Represents the average cost of purchasing and maintaining an S-type fuel truck. represents the energy consumption of electric truck model s passing through road section (i, j), represents the energy price of electric vehicles (unit: yuan / kWh), Represents the amortized cost of purchasing and maintaining an S-type electric truck. Represents a collection of fuel trucks, Represents a collection of electric trucks.

[0073] Among them, all costs are calculated in yuan.

[0074] The expression of driver carbon credits is as follows:

[0075]

[0076] In the formula, s represents the model, S represents the model set, Represents the set of vehicles of model s.

[0077] Step S3-2: Building customer benefits based on customer carbon credit incentives and order revenue. The process specifically includes:

[0078] The customer benefit is the difference between customer income and customer cost, where customer income includes customer carbon credits and customer cost includes order expenditure and time cost;

[0079] The customer carbon points are accumulated as incentives for each car model and each individual transport order.

[0080] Furthermore, in this example, the customer income may also include overtime compensation; the expression is as follows:

[0081]

[0082] Where, Representing customer interests;

[0083]

[0084]

[0085]

[0086] Where, Indicates that the truck has left the loading point moment, Indicates that the truck has arrived at the unloading point At this moment, VOT represents the customer's time cost. Indicates that the truck exceeds the loading point The duration of the time window (unit: minutes), Indicates that the truck has exceeded the unloading point The duration of the time window (unit: minutes), Represents the amount of overtime compensation (unit: yuan / minute).

[0087] Step S3-3: Building platform benefits based on order revenue, driver commissions, driver carbon credit incentives, and customer carbon credit incentives. The process specifically includes:

[0088] When the total distance traveled does not exceed the starting distance, the order revenue is calculated based on the vehicle's starting price, whether it is a shared ride, and the amount of overtime compensation. When the total distance traveled exceeds the starting distance, the order revenue is calculated based on the vehicle's starting price, the distance traveled from the point where the order exceeds the starting distance to the destination, the price per unit distance of the vehicle order, whether it is a shared ride, and the amount of overtime compensation.

[0089] The platform carbon points are obtained by multiplying the sum of the driver carbon points incentive and the customer carbon points incentive by the platform carbon points reward coefficient;

[0090] The platform's profits are built based on order revenue, driver commissions, and platform carbon credits. The order costs paid by customers are equal to the order revenue received by the platform.

[0091] Specifically, platform interests The expression is as follows:

[0092]

[0093] Among them, when the total driving distance does not exceed the starting distance:

[0094]

[0095] When the total distance traveled exceeds the starting distance:

[0096]

[0097] Where, The starting price of the S model vehicle (unit: yuan), Representative Order The distance traveled by the vehicle from the starting distance to the end point (unit: kilometers), The unit distance price of the order of model S (unit: yuan / km), is the decision variable representing whether order o is a carpool, Represents a discount for carpooling orders. Represents the time the truck exceeds the time window at the loading point, Represents the time the truck exceeds the time window at the unloading point, Represents the amount of overtime compensation (unit: yuan / minute).

[0098]

[0099] Where, represents the unit distance income of the S-type truck driver (unit: yuan / km), Represents the distance traveled by the vehicle through road segment (i, j) (unit: kilometers).

[0100]

[0101] Where, represents the carbon credits of truck driver k (unit: points), Represents the carbon credits of customer o who ordered model s (unit: points), represents the point value (unit: yuan / cent), and x represents the platform's carbon point reward coefficient. The positive or negative value of the reward coefficient determines whether the platform will bear the cost of carbon point rewards for drivers and customers. If the reward coefficient is negative, the platform carbon points are considered a platform cost, and the crowdsourced logistics management platform bears the cost of carbon point rewards for drivers and customers. If the reward coefficient is positive, the platform carbon points are considered platform revenue, and the cost of carbon point rewards for drivers and customers is borne by external organizations. The value of the reward coefficient determines the size of the platform's expenditure or carbon point incentive.

[0102] In step S4, the weighted sum of driver interests, customer interests, and platform interests is used as the objective function. Constraints are set, including path connectivity constraints, vehicle cargo capacity constraints, electric vehicle battery power constraints, time window constraints, and driver workload constraints. The objective function is solved based on the constraints to obtain hybrid freight carpooling routes and charging strategies for electric trucks.

[0103] Step S4-1, takes the weighted sum of driver interests, customer interests, and platform interests as the objective function.

[0104] Step S4-2: setting constraints including path connectivity constraints, vehicle cargo capacity constraints, electric vehicle battery power constraints, time window constraints, and driver workload constraints.

[0105] In this example, the path connectivity constraints ensure that each loading point can only be visited once, each unloading point can and should be visited only once, and the loading and unloading of the same order must be completed by the same truck. At the same time, the connectivity of the vehicle routes is taken into account. The expression is as follows:

[0106]

[0107]

[0108]

[0109]

[0110] In the formula, k represents the vehicle, K represents the set of all vehicles on the platform, is the loading node, It is the unloading node, Indicates whether vehicle k picks up goods from node i, Indicates whether vehicle k delivers to node j, represents the set of all loading points, D represents the set of all unloading points, and Z represents the set of all nodes in the road network; Indicates whether vehicle k passes the road section between the loading point of order o and node z, Indicates whether vehicle k passes the route between node z and the unloading point of order o. Indicates whether vehicle k passes through road segment (i, j).

[0111] In the vehicle cargo capacity constraint, this example ensures that the truck is empty when it departs. The cargo capacity of the truck leaving a node is the sum of the cargo capacity at the previous node and the change in cargo capacity at that node. The cargo capacity of the truck at each node does not exceed the maximum cargo capacity. The expression is as follows:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] Where, Represents the loading mass of the truck at departure, Represents the loading volume of the truck at departure, Indicates whether the vehicle passes the road segment (z, j), represents the loading mass of the truck after loading and unloading at point z, represents the loading and unloading quality of node j, represents the loading mass of the truck after loading and unloading at point j, represents the loading volume of the truck after loading and unloading at point z, represents the loading and unloading volume of node j, represents the loading volume of the truck after loading and unloading at node j, Represents the maximum loading mass of the truck, represents the maximum loading volume of a truck. z represents an arbitrary node.

[0119] In the electric vehicle battery capacity constraint, the electric truck starts with 100% battery capacity. When the electric truck leaves the charging station, the battery capacity is charged up to 80%. The battery capacity of the electric truck is sufficient to reach the next node. The charging time at the charging station is proportional to the required amount of charge. The expression is as follows:

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] Where, Represents the battery capacity of the electric truck when it starts. The maximum battery capacity of the electric truck representing the S model, represents the power consumption of the electric truck passing through the road section (i, j), represents the charging time of car K at node i, Represents the charging rate of the electric truck. represents the set of charging station nodes, Z represents the set of all nodes in the road network, represents the charge of vehicle k at node i.

[0126] In the time window constraint, the departure time from the next node is guaranteed to be later than the departure time from the previous node, the travel time, the loading and unloading time, and the charging time. The loading time is guaranteed to be earlier than the unloading time. The arrival time at the node must be within the loading and unloading time window and 20% of the non-carpooling transportation time (the 20% value can be changed according to actual freight requirements). At the same time, the compensation amount for exceeding the time window is defined, and it is guaranteed that the latest unloading time set by the customer is later than the sum of the earliest loading time and transportation time, and the earliest unloading time set by the customer is earlier than the sum of the latest loading time and transportation time. The expression is as follows:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Where, represents the time when truck k leaves node i, represents the travel time of a vehicle passing through road segment (i, j), represents the parking time of the vehicle at point j, Represents the electric truck collection, Indicates the transportation time from the loading point to the unloading point when order o is transported separately. Represents the earliest time the customer wants the vehicle to leave the loading point. Indicates the latest time the customer wants the vehicle to leave the unloading point. The earliest time the customer wants the vehicle to leave the unloading point. Indicates the latest time the customer wants the vehicle to leave the loading point. represents the time when truck k leaves the loading point, Represents the amount of compensation caused by the truck exceeding the time window at the loading point, represents the time when truck k leaves the unloading point, Represents the amount of compensation caused by the truck exceeding the time window at the unloading point, Represents the penalty per unit time for exceeding the time window.

[0136] Considering the workload and complexity of loading and unloading, a maximum limit on the number of orders that can be placed on a vehicle is set. The expression is as follows:

[0137]

[0138] P represents the set of loading points, and D represents the set of unloading points.

[0139] In step S4-3, the objective function is solved by combining Adaptive Large Neighborhood Search (ALNS) and Simulated Annealing (SA) with constraints such as path connectivity, vehicle cargo capacity, electric vehicle battery charge, time window, and driver workload. This results in hybrid freight carpooling routes and charging strategies for electric trucks.

[0140] The algorithm, combining ALNS and SA, employs the ALNS algorithm to solve the Green Vehicle Routing Problem with Delivery and Loading (GVRP-PD) because ALNS maintains computational efficiency when handling large-scale dynamic problems. A simulated annealing algorithm is used to avoid local optima and enhance the global search capability of the solution. The specific steps are as follows: First, the ALNS algorithm parameters are initialized to generate an initial solution, including the vehicle path and cargo load. Second, during the global search phase, the ALNS algorithm is used to make large-scale adjustments to the path, generating new solutions through removal and insertion operations. Then, during the local optimization phase, the SA algorithm is combined with the SA algorithm to perform small perturbations on the current solution, accepting a certain probability of inferior solutions to escape the local optimum. Then, in each iteration, the quality of the current solution is evaluated using an evaluation function, gradually approaching the optimal solution. Finally, the iteration continues until convergence conditions are met, and the optimal routing solution is output, ensuring that the vehicle travel distance is minimized and all constraints are satisfied.

[0141] In summary, the present invention provides a route planning method for an urban freight hybrid fleet that incorporates a bilateral incentive mechanism, and defines carbon credit incentives for drivers, customers, and platforms; wherein, the driver carbon credit incentive is the product of the sum of the driver's carpooling carbon credits and the driver's electric vehicle carbon credits for all orders a driver undertakes in one day and the staggered transportation incentive coefficient; the customer carbon credit incentive is the product of the sum of the customer's carpooling carbon credits and the customer's electric vehicle carbon credits for all orders a customer places in one day and the staggered transportation incentive coefficient; the platform carbon credit incentive is the product of the sum of the driver carbon credit incentive and the customer carbon credit incentive and the carbon credit reward coefficient issued by the logistics platform; based on the driver, customer, and platform carbon credit incentives, the interests of the driver, customer, and platform are constructed; the weighted sum of the interests of the driver, customer, and platform is used as the objective function, constraints are set, and the objective function is solved according to the constraints to obtain the hybrid freight carpooling route and the charging strategy of the electric truck. The present invention realizes efficient carpooling route planning on a logistics management platform that operates both electric trucks and fuel trucks, and maximizes the carbon emission reduction effect through the carbon credit mechanism while balancing the interests of the three parties: customers, drivers, and the platform.

[0142] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned route planning method for the urban freight hybrid fleet. Figure 2 As shown in the figure, a hardware structure diagram of any device with data processing capability is provided for the route planning method of the urban freight hybrid fleet provided by the embodiment of the present invention. Figure 2In addition to the processor, memory, and network interface shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0143] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the route planning method for a hybrid urban freight vehicle fleet as described above. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0144] The above embodiments are intended only to illustrate the design concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. The scope of protection of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design concepts disclosed in the present invention are within the scope of protection of the present invention.

Claims

1. A route planning method for a city freight hybrid fleet, characterized in that: The method comprises: Set up driver carbon point incentives, customer carbon point incentives, and platform carbon point incentives; among which, the driver carbon point incentive is the product of the sum of the driver's carpooling carbon points and the driver's electric vehicle carbon points for all orders undertaken by a driver in one day, and the off-peak transportation incentive coefficient; the customer carbon point incentive is the product of the sum of the customer's carpooling carbon points and the customer's electric vehicle carbon points for all orders placed by a customer in one day, and the off-peak transportation incentive coefficient; the platform carbon point incentive is the product of the sum of the driver carbon point incentive and the customer carbon point incentive, and the carbon point reward coefficient issued by the logistics platform; Build driver benefits based on driver carbon point incentives, driver commissions, and truck driving costs; build customer benefits based on customer carbon point incentives and order expenditures; build platform benefits based on order revenue, driver commissions, driver carbon point incentives, and customer carbon point incentives. Taking the weighted sum of driver, customer, and platform interests as the objective function, the authors set constraints including path connectivity, vehicle cargo capacity, electric vehicle battery capacity, time window, and driver workload. Based on these constraints, the objective function was solved using adaptive large neighborhood search and simulated annealing to obtain hybrid freight carpooling routes and charging strategies for electric trucks. The process of setting constraints includes: In the path connectivity constraints, each loading point and unloading point is visited only once, the loading and unloading of the same order must be completed by the same truck, and the distance between the starting point and the loading point; In the vehicle cargo capacity constraint, the truck is empty when it departs. The cargo capacity of the truck leaving a node is the sum of the cargo capacity leaving the previous node and the cargo capacity change at the node. The cargo capacity of the truck at each node does not exceed the maximum cargo capacity. In the electric vehicle battery power constraint, the electric truck starts with 100% power. When the electric truck leaves the charging station, the power is charged up to 80%. The electric truck has enough power to reach the next node. The charging time at the charging station is proportional to the amount of power required. In the time window constraint, the time of leaving the next node must be later than the time of leaving the previous node, the time spent on the road, the loading and unloading time, and the charging time. The loading time must be earlier than the unloading time. The arrival time at the node must be within the loading and unloading time window and 20% of the non-carpooling transportation time. The compensation amount for exceeding the time window is defined to ensure that the latest unloading time set by the customer is later than the sum of the earliest loading time and transportation time, and the earliest unloading time set by the customer is earlier than the sum of the latest loading time and transportation time. In the driver workload constraint, set the maximum continuous working time limit for the driver and the maximum number of group orders the driver can share.

2. The route planning method for a city freight hybrid fleet according to claim 1, characterized in that: The process of setting up a driver carbon credit incentive includes: Traverse all the individual transport orders transported by a truck in a day and calculate the product of the distance traveled by the truck between the loading point and the unloading point and the mass of the goods for each individual transport order. Add up the products corresponding to all individual transport orders and multiply them by the new energy credit coefficient to obtain the driver's electric vehicle carbon credits. Traverse all carpooling orders transported by a truck in a day, calculate the product of the distance traveled by the truck between the loading point and the unloading point and the mass of the cargo for each carpooling order, and accumulate the corresponding products of all carpooling orders as the first accumulated value; traverse each road section, calculate the product of the path distance of each road section, the variable indicating whether the truck passes through the road section, and the mass of the cargo when the truck leaves the starting point of the road section, and accumulate the corresponding products of all road sections as the second accumulated value; calculate the difference between the first accumulated value and the second accumulated value and multiply it by the carpooling points coefficient to obtain the driver's carpooling carbon points; Traverse each road section to determine whether the truck has passed through it. If so, calculate the proportion of its transportation time on this section to the entire transportation journey. Multiply this proportion by the traffic status coefficient of the road section to weight it. Finally, add up the weighted proportions of all road sections to obtain the staggered transportation incentive coefficient. The driver's carbon points incentive is calculated by multiplying the sum of the driver's carpooling carbon points and the driver's tram carbon points for all orders undertaken by a driver in a day by the off-peak transportation incentive coefficient.

3. The route planning method for a city freight hybrid fleet according to claim 1, characterized in that: The process of setting up customer carbon credit incentives includes: Traverse all the individual transport orders transported by a truck in a day and calculate the product of the distance traveled by the truck between the loading point and the unloading point and the mass of the goods for each individual transport order. Add up the products corresponding to all individual transport orders and multiply them by the new energy credit coefficient to obtain the customer's electric vehicle carbon credits. Traverse all carpooling orders transported by a truck in a day, calculate the product of the distance traveled by the truck between the loading point and the unloading point and the mass of the goods for each carpooling order, and accumulate the corresponding products of all carpooling orders as the first accumulated value; traverse each road section, calculate the product of the path distance of each road section, the variable indicating whether the truck passes through the road section, and the mass of the goods when the truck leaves the starting point of the road section, and accumulate the corresponding products of all road sections as the second accumulated value; calculate the difference between the first accumulated value and the second accumulated value and multiply it by the carpooling point coefficient to obtain the customer's carpooling carbon points; The turnover coefficient is calculated by calculating the ratio of the product of the distance traveled between the loading point and the unloading point by a truck corresponding to a single transport order and the first empty load ratio to the cumulative value of the product of the distance traveled between the loading point and the unloading point by trucks corresponding to all single transport orders and the second empty load ratio. The first empty load ratio is 1 minus the ratio of the mass of cargo loaded on the truck at the loading point to the truck's maximum load capacity; the second empty load ratio is 1 minus the ratio of the mass of cargo traveled between the loading point and the unloading point to the truck's maximum load capacity. Calculate the sum of the customer's electric car carbon points, customer carpooling carbon points and turnover coefficient for all orders placed by a customer in one day, and then multiply it by the off-peak transportation incentive coefficient to obtain the customer carbon points incentive.

4. The route planning method for a city freight hybrid fleet according to claim 1, characterized in that: The process of building driver benefits based on driver carbon credit incentives, driver commissions, and truck driving costs includes: The driver's benefit is the difference between the driver's income and the truck's driving cost. The driver's income includes the driver's commission and the driver's carbon credits. The truck's driving cost includes the fuel truck's driving cost and the electric truck's driving cost. The fuel truck driving cost is the sum of the fixed cost of the fuel truck and the fuel truck transportation energy consumption cost. The fuel truck transportation energy consumption cost is calculated by calculating the product of the fuel truck's energy consumption for each road section, the fuel truck's energy price, and the distance traveled between the loading point and the unloading point. Each road section is traversed and the product corresponding to each road section is accumulated to obtain the fuel truck transportation energy consumption cost. The electric truck driving cost is the sum of the fixed cost of the electric truck and the transportation energy consumption cost of the electric truck. The calculation process of the electric truck transportation energy consumption cost is as follows: the product of the energy consumption of the electric truck passing through each road section, the energy price of the electric truck, and the distance traveled between the loading point and the unloading point is calculated, and each road section is traversed, and the product corresponding to each road section is accumulated to obtain the electric truck transportation energy consumption cost. Among them, the driver carbon points are the accumulated driver carbon points incentive corresponding to each truck and each model.

5. The route planning method for a city freight hybrid fleet according to claim 1, characterized in that: The process of building customer benefits based on customer carbon credit incentives and order revenue includes: The customer benefit is the difference between customer income and customer cost, where the customer income includes customer carbon credits and the customer cost includes order expenses; The customer carbon points are accumulated as incentives for each customer order.

6. The route planning method for a city freight hybrid fleet according to claim 1, characterized in that: The process of building platform benefits based on order revenue, driver commissions, driver carbon credit incentives, and customer carbon credit incentives includes: When the total distance traveled does not exceed the starting distance, the order revenue is calculated based on the vehicle's starting price, whether it is a shared ride, and the amount of overtime compensation. When the total distance traveled exceeds the starting distance, the order revenue is calculated based on the vehicle's starting price, the distance traveled from the point where the order exceeds the starting distance to the destination, the price per unit distance of the vehicle order, whether it is a shared ride, and the amount of overtime compensation. The platform carbon points are obtained by multiplying the sum of the driver carbon points incentive and the customer carbon points incentive by the platform carbon points reward coefficient; Build platform interests based on order revenue, driver commissions, and platform carbon points.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to execute the route planning method for the urban freight hybrid fleet according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the route planning method for an urban freight hybrid fleet according to any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the route planning method for an urban freight hybrid fleet as described in any one of claims 1 to 6 is implemented.

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