Green vehicle path planning method and related device

Abstract

The invention discloses a green vehicle path planning method and a related device, and the method comprises the steps: obtaining the order information of a to-be-delivered order and the vehicle information of a delivery vehicle team, and building a cost calculation model based on the order information and the vehicle information, the cost calculation model comprises a vehicle use cost calculation model, a total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total goods damage cost calculation model and a time window penalty cost calculation model, and a cost optimization model is constructed based on the cost calculation model; according to the order information and the vehicle information, determining the cabin capacity, the cargo storage temperature layer and the number of available vehicles of each cold chain transport vehicle, generating constraint conditions based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles, and constructing a green vehicle path planning model according to the cost optimization model and the constraint conditions, vehicle path planning is carried out based on the green vehicle path planning model, the total logistics distribution cost is effectively reduced, and energy consumption and carbon emission are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cold chain logistics distribution, and in particular to a green vehicle path planning method and related devices. Background Art

[0002] The cost of cold chain logistics distribution is high and vehicles cause considerable pollution to the air environment, so the issue of vehicle emissions has received increasing attention. When studying the route problem of cold chain logistics distribution vehicles, its optimization goals should be expanded. Not only should economic costs be considered and service quality improved, but environmental goals should also be paid attention to, and energy conservation and emission reduction in the field of logistics distribution should be promoted. Due to the diversity of storage temperature layers of goods of different customers, the current route planning of cold chain transportation vehicles cannot reduce transportation costs and carbon emission costs while ensuring that customer needs are met, resulting in the inability to achieve the energy conservation and emission reduction needs of cold chain transportation. Summary of the invention

[0003] The main purpose of the present invention is to provide a green vehicle path planning method and related devices, aiming to solve the technical problem that the existing technology cannot reduce transportation costs and carbon emission costs while ensuring that customer needs are met, resulting in the inability to achieve energy conservation and emission reduction needs of cold chain transportation.

[0004] To achieve the above object, the present invention provides a green vehicle path planning method, the method comprising the following steps: Obtaining order information of the order to be delivered and vehicle information of the delivery fleet, wherein the delivery fleet includes a plurality of cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles; Building a cost calculation model based on the order information and the vehicle information, the cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model and a time window penalty cost calculation model; Building a cost optimization model based on the cost calculation model; Determine the cabin capacity, cargo storage temperature layer and number of available vehicles of each cold chain transport vehicle according to the order information and the vehicle information; generating constraints based on the cabin capacity, the cargo storage temperature layer, and the number of available vehicles; A green vehicle path planning model is constructed according to the cost optimization model and the constraint conditions, and vehicle path planning is performed based on the green vehicle path planning model.

[0005] Optionally, the vehicle use cost calculation model is used to calculate the vehicle use cost, and the vehicle use cost includes fixed dispatch cost, vehicle maintenance depreciation cost and driver salary cost; The vehicle use cost calculation model includes: in, Represents the vehicle usage cost, represents the fixed dispatch cost of fuel vehicles, represents the fixed dispatch cost of electric vehicles, Represents the collection of fuel vehicles, Represents a collection of electric vehicles, represents the set of customer points, Represents cold chain transport vehicles, Represents a customer order to be delivered, Represents the starting point, customer point and charging station collection, Represents the terminal, customer point and charging station collection, Represents the labor cost per unit time of the vehicle, Represents the unit time usage cost of a fuel vehicle, Represents the unit time usage cost of electric vehicles, Representative vehicle Whether it passes through the road section ( ) The value of 1 represents the passing section ( ), The value of 0 means that the road section has not been passed ( ), 0 represents the distribution center, and the distribution center is the distribution starting point, Representative vehicle On the road section ( ) driving time, Representative vehicle Order for customers Length of service, Represents an electric vehicle at a charging station Charging time.

[0006] Optionally, the vehicle total energy consumption cost calculation model is used to calculate the vehicle total energy consumption cost, the vehicle total energy consumption cost includes the fuel consumption cost of a fuel vehicle and the electricity consumption cost of an electric vehicle, and the vehicle total energy consumption cost calculation model includes a fuel consumption calculation model and an electricity consumption calculation model; The fuel consumption calculation model includes: in, Represents fuel vehicles Driving on the road section ( )'s fuel consumption rate, and are predefined parameters related to vehicle characteristic parameters, Represents the weight of the fuel vehicle. represents the number of compartments in the vehicle, Represents the vehicle speed, Representative vehicle of Cabin No. is leaving node Loading volume at time; The power consumption calculation model includes: in, represents the air density, Represents the wind-exposed area of ​​the front surface of the vehicle, represents the air resistance coefficient, is the acceleration due to gravity, is the rolling resistance coefficient, , , is the coefficient related to the vehicle characteristic parameters, The energy consumption rate to overcome air resistance, is the energy consumption rate generated by tire friction, The energy consumption rate generated by the transmission system, The energy consumption rate generated by the auxiliary system, is the power of the vehicle air conditioning system. is the total power of the electronic systems in the vehicle; The vehicle total energy consumption cost calculation model includes: in, Represents the total energy consumption cost of the vehicle, represents the fuel price, represents the price of electricity, Representative vehicle On the road ( ) on the route.

[0007] Optionally, the refrigeration cost calculation model is used to calculate the total refrigeration cost generated by the cold storage bin in the cold chain transport vehicle during the refrigeration process, and the refrigeration cost calculation model includes: in, Representative vehicle The time of departure from the distribution center, Representative vehicle Arrival Node moment, Representative vehicle Customer orders when shipped from the distribution center The cargo loading status, represents the total cooling cost, Represents the stratosphere The cooling cost per unit weight per unit time is Order on behalf of customers Preserved temperature layer, Order on behalf of customers The demand, Order on behalf of customers cooling costs.

[0008] Optionally, the total carbon emission cost calculation model is used to calculate the total carbon emission cost of cold chain transport vehicles, the total carbon emission cost includes the carbon emission cost of fuel vehicles, the carbon emission cost of electric vehicles and the carbon emission cost of refrigeration, and the total carbon emission cost calculation model includes the carbon emission cost calculation model of fuel vehicles, the carbon emission cost calculation model of electric vehicles and the carbon emission cost calculation model of refrigeration; The carbon emission cost calculation model of fuel vehicles includes: in, is the fuel carbon emission factor, represents the carbon emission price, Represents the carbon emission cost of fuel vehicles; The electric vehicle carbon emission cost calculation model includes: in, Represents the indirect carbon emissions of electric vehicles for every kilowatt-hour of electricity consumed. represents the carbon emission cost of electric vehicles; The refrigeration carbon emission cost calculation model includes: in, Represents the electricity price, represents the carbon emission cost of refrigeration; The total carbon emission cost calculation model includes: in, Represents the total carbon emission cost.

[0009] Optionally, the total cargo damage cost calculation model is used to calculate the total cargo damage cost of the cargo during transportation, and the total cargo damage cost calculation model includes a freshness function and a value loss function; The freshness function includes: in, Represents the preservation effort factor, represents the time-sensitive factor of freshness, Represents the freshness preservation effort sensitive factor, Represents refrigeration time; The value loss function includes: in, represents the value loss function, represents the freshness function, Represents the freshness of cold chain products; The total cargo damage cost calculation model includes: in, represents the total cargo damage cost, Order on behalf of customers Cost of cargo damage incurred during transportation, Indicates customer The demand, represents the freshness threshold, Order on behalf of customers The unit weight price of the goods, when the customer orders When the freshness of the cold chain products is greater than or equal to the freshness threshold, the customer order is determined No damage costs are incurred when customers place orders When the freshness of the cold chain products is less than the freshness threshold, the customer order is judged The cost of cargo damage is .

[0010] Optionally, the time window penalty cost calculation model is used to calculate a total time window penalty cost, where the total time window penalty cost includes the time window penalty costs of one or more service orders. The time window penalty cost calculation model includes: in, represents the total time window penalty cost, Order on behalf of customers The time window penalty cost is represents the expected delivery time window, Represents the time when the vehicle arrives at the customer point, represents the penalty coefficient for time window violation for early arriving customers, represents the time window violation penalty factor for late arriving customers; The cost optimization model includes: in, represents the target total delivery cost output by the cost optimization model, Represents the target optimization function.

[0011] Optionally, performing vehicle path planning based on the green vehicle path planning model includes: Generate an input information set according to the order information and the vehicle information, the input information set including the number of cabins, cabin capacity, customer demand, expected service time window, business operation information and the service time window of the distribution center; generating a cost information set based on the cost calculation model; The input information set and the cost information set are input into the green vehicle path planning model to calculate the node attraction between each cold chain transport vehicle and each customer order: in, Representation Node With Node The distance between Representation Node With Node The pheromone concentration between Represents a slave node Transfer to Node The time window matching degree after Represents a slave node Transfer to Node The loading rate of the rear vehicle, Represents each node of the vehicle and the node The average distance are weight coefficients, represents the node attraction; generating a plurality of initial delivery plans based on the node attractiveness; Calculate the total delivery cost of each initial delivery plan according to the cost calculation model; Based on the total delivery cost of the plan, candidate delivery plans are selected from the initial delivery plans, and vehicle path planning is performed for each cold chain transport vehicle based on the candidate delivery plans.

[0012] Optionally, selecting candidate delivery plans from the initial delivery plans based on the total delivery cost of the plans, and performing vehicle path planning for each cold chain transport vehicle based on the candidate delivery plans, includes: Update the pheromone of each candidate transportation path based on the total delivery cost of the solution: in, represents the updated pheromone amount of each candidate transportation path, Represents the amount of pheromone before each candidate transportation path is updated, represents the pheromone increment coefficient, is the total cost of the initial delivery plan, is the maximum value of the total distribution cost of each initial distribution plan, is the minimum value of the total distribution cost of each initial distribution plan, Represents the dissipation ratio of pheromones on each candidate transport path after each iteration; Screening out candidate delivery plans from the initial delivery plans based on the pheromone values, and determining candidate transportation routes for each cold chain transport vehicle based on the candidate delivery plans; Performing a variable neighborhood search on each candidate transportation path, wherein the variable neighborhood search includes a swap neighborhood operation, a reverse neighborhood operation, and an insert neighborhood operation; Vehicle path planning is performed for each cold chain transport vehicle based on the variable neighborhood search results.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also proposes a green vehicle path planning device, the green vehicle path planning device comprising: An information acquisition module, used to acquire order information of an order to be delivered and vehicle information of a delivery fleet, wherein the delivery fleet includes a plurality of cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles; A cost calculation module, used to construct a cost calculation model based on the order information and the vehicle information, wherein the cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model, and a time window penalty cost calculation model; A cost optimization module, used for building a cost optimization model based on the cost calculation model; A cargo analysis module, used to determine the cabin capacity, cargo storage temperature layer and number of available vehicles of each cold chain transport vehicle according to the order information and the vehicle information; A constraint analysis module, configured to generate constraint conditions based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles; The path planning module is used to construct a green vehicle path planning model according to the cost optimization model and the constraint conditions, and perform vehicle path planning based on the green vehicle path planning model.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a green vehicle path planning device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the green vehicle path planning method as described above.

[0015] In addition, to achieve the above objectives, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the green vehicle path planning method described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the green vehicle path planning method as described above.

[0017] The present invention obtains order information of orders to be delivered and vehicle information of a delivery fleet, wherein the delivery fleet includes multiple cold-chain transport vehicles, and the cold-chain transport vehicles include fuel vehicles and / or electric vehicles. A cost calculation model is constructed based on the order information and the vehicle information. The cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model, and a time window penalty cost calculation model. A cost optimization model is constructed based on the cost calculation model. The cabin capacity, cargo storage temperature layer, and number of available vehicles of each cold-chain transport vehicle are determined according to the order information and the vehicle information. The present invention generates constraints based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles, builds a green vehicle path planning model according to the cost optimization model and the constraints, and performs vehicle path planning based on the green vehicle path planning model; since the present invention constructs a cost calculation model to analyze the distribution cost and carbon emission cost of the designated distribution point from multiple dimensions, by constructing a cost optimization model and constraints, it is possible to effectively reduce the transportation cost while meeting the storage temperature layer requirements and cargo transportation requirements of the order, thereby accurately planning the green vehicle path, effectively reducing the total cost of logistics distribution, reducing energy consumption and carbon emissions, and realizing the energy conservation and emission reduction needs of cold chain transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1It is a structural diagram of a green vehicle path planning device in a hardware operating environment involved in an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a first embodiment of a green vehicle path planning method according to the present invention; Figure 3 A schematic diagram of a flow chart of a second embodiment of a green vehicle path planning method according to the present invention; Figure 4 A schematic diagram of the neighborhood exchange operation in the second embodiment of the green vehicle path planning method of the present invention; Figure 5 A schematic diagram of a neighborhood reversal operation in the second embodiment of the green vehicle path planning method of the present invention; Figure 6 A schematic diagram of inserting a neighborhood operation in the second embodiment of the green vehicle path planning method of the present invention; Figure 7 This is a structural block diagram of the first embodiment of the green vehicle path planning device of the present invention.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] Reference Figure 1 , Figure 1 A schematic diagram of the structure of a green vehicle path planning device in the hardware operating environment involved in an embodiment of the present invention.

[0023] like Figure 1As shown, the green vehicle path planning device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0024] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the green vehicle path planning device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0025] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a green vehicle path planning program.

[0026] exist Figure 1 In the green vehicle path planning device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the green vehicle path planning device of the present invention can be set in the green vehicle path planning device, and the green vehicle path planning device calls the green vehicle path planning program stored in the memory 1005 through the processor 1001, and executes the green vehicle path planning method provided by the embodiment of the present invention.

[0027] The embodiment of the present invention provides a green vehicle path planning method, referring to Figure 2 , Figure 2 Schematic diagram of the flow chart of the first embodiment of the green vehicle path planning method of the present invention.

[0028] In this embodiment, the green vehicle path planning method includes the following steps: Step S10: Obtain order information of the order to be delivered and vehicle information of the delivery fleet.

[0029] It should be understood that the present embodiment can be applied to a multi-agent cold chain transport vehicle cluster, wherein the cold chain transport vehicle cluster includes a distribution fleet, wherein the distribution fleet includes multiple cold chain transport vehicles, wherein the cold chain transport vehicles include fuel vehicles and / or electric vehicles, wherein the fuel vehicle is a fuel-powered cold chain transport vehicle, and the electric vehicle is an electric-powered cold chain transport vehicle, the distribution fleet can be a cold storage type multi-temperature co-distribution mixed multi-cabin fleet, and the cold chain transport vehicle is a multi-cabin cold storage type multi-temperature co-distribution transport vehicle.

[0030] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a terminal electronic device capable of realizing the above functions, etc. The following takes the green vehicle path planning device (planning device) as an example to illustrate this embodiment and the following embodiments.

[0031] It should be noted that the order to be delivered may be a customer order that requires route planning and delivery planning. The order information may include quantity information, load information, temperature layer requirement information, service time window information, and price information of the delivered goods.

[0032] It should be noted that the above-mentioned temperature layer demand information may include the temperature layers required for cargo storage in each time period of cargo transportation. In some embodiments, the temperature layers required for storage of cold chain transportation goods in different time periods may be different. Therefore, the planning device can switch the temperature layers of the goods in stages based on the temperature layer demand information, so as to ensure the quality and efficiency of cargo transportation.

[0033] It should be noted that the above-mentioned service time window information can be the start time of cargo transportation, transportation duration, service time, delivery time, etc. For example, the service time window of the order to be delivered is the delivery time from time A to time B. If the transportation arrival time is earlier than time A or later than time B, it is determined that the service time window is not met, and thus a time window penalty cost will be incurred.

[0034] It should be noted that the above-mentioned vehicle information may include the number of available vehicles in the delivery fleet, the vehicle type and power type (such as fuel vehicle or electric vehicle) of each available vehicle, the number of cabins of each available vehicle, the vehicle's cruising range, etc.

[0035] Step S20: constructing a cost calculation model based on the order information and the vehicle information.

[0036] It should be noted that the cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model and a time window penalty cost calculation model.

[0037] In some embodiments, the planning device may construct a cost calculation model by considering the temperature difference between the refrigeration temperature and the ambient temperature, the quality of the goods, the electricity consumption cost caused by the transportation time, and the carbon emission cost caused by the electricity production.

[0038] In some embodiments, the planning device may comprehensively consider the impact of vehicle speed, real-time load, and vehicle characteristic parameters on vehicle energy consumption and carbon emissions, and accordingly establish an energy consumption and carbon emission measurement model generated by vehicle driving and refrigeration processes, and calculate vehicle energy consumption costs and carbon emission costs based on the energy consumption and carbon emission measurement model.

[0039] Step S30: constructing a cost optimization model based on the cost calculation model.

[0040] It should be noted that the cost optimization model can be a mathematical model that takes into account constraints such as capacity, temperature layer and number of available vehicles, and takes minimizing the total delivery cost as the optimization goal.

[0041] Furthermore, in order to accurately calculate the vehicle use cost, in some embodiments, the vehicle use cost calculation model is used to calculate the vehicle use cost, and the vehicle use cost includes fixed dispatch cost, vehicle maintenance depreciation cost and driver salary cost; The vehicle use cost calculation model includes: in, Represents the vehicle usage cost, represents the fixed dispatch cost of fuel vehicles, represents the fixed dispatch cost of electric vehicles, Represents the collection of fuel vehicles, Represents a collection of electric vehicles, represents the set of customer points, Represents cold chain transport vehicles, Represents a customer order to be delivered, Represents the starting point, customer point and charging station collection, Represents the terminal, customer point and charging station collection, Represents the labor cost per unit time of the vehicle, Represents the unit time usage cost of a fuel vehicle, Represents the unit time usage cost of electric vehicles, Representative vehicle Whether it passes through the road section ( ) The value of 1 represents the passing section ( ), The value of 0 means that the road section has not been passed ( ), 0 represents the distribution center, and the distribution center is the distribution starting point, Representative vehicle On the road section ( ) driving time, Representative vehicle Order for customers Length of service, Represents an electric vehicle at a charging station Charging time.

[0042] Furthermore, in order to accurately calculate the vehicle energy consumption cost, thereby reducing the vehicle energy consumption during transportation, in some embodiments, the vehicle total energy consumption cost calculation model is used to calculate the vehicle total energy consumption cost, the vehicle total energy consumption cost includes the fuel consumption cost of fuel vehicles and the electricity consumption cost of electric vehicles, and the vehicle total energy consumption cost calculation model includes a fuel consumption calculation model and an electricity consumption calculation model; It should be noted that fuel consumption cost is the purchase cost of fuel, which is one of the main costs of fuel vehicle operation. The planning equipment can use the CMEM model to calculate the fuel consumption of fuel vehicles during the delivery process, mainly considering the impact of load, speed and vehicle characteristic parameters on fuel consumption. )'s fuel consumption rate (L / h) Referring to the following formula, the fuel consumption calculation model includes: in, Represents fuel vehicles Driving on the road section ( )'s fuel consumption rate, and are predefined parameters related to vehicle characteristic parameters, Represents the weight of the fuel vehicle. represents the number of compartments in the vehicle, Represents the vehicle speed, Representative vehicle of Cabin No. is leaving node When loading.

[0043] It should be noted that the planning equipment can use a nonlinear energy consumption model to calculate the energy consumption rate of electric vehicles By comprehensively considering the impact of factors such as vehicle load, driving speed and vehicle characteristic parameters (such as vehicle wind resistance overcoming coefficient, tire friction coefficient, transmission efficiency, auxiliary system energy consumption, etc.) on energy consumption, the total energy consumption rate of the vehicle includes the energy consumption rate of overcoming air resistance. , Energy consumption rate caused by tire friction , energy consumption rate generated by the transmission system , Energy consumption rate generated by auxiliary systems When the vehicle At speed On the road section ( ) when driving on the The calculation refers to the following formula, and the power consumption calculation model shown includes: in, Represents air density , Represents the wind-exposed area of ​​the vehicle's front surface , represents the air resistance coefficient, is the acceleration due to gravity , is the rolling resistance coefficient, , , is the coefficient related to the vehicle characteristic parameters, The energy consumption rate to overcome air resistance, is the energy consumption rate generated by tire friction, The energy consumption rate generated by the transmission system, The energy consumption rate generated by the auxiliary system, is the power of the vehicle air conditioning system , is the total power of the electronic systems in the vehicle , the above formula divided by 3600 means The unit from Convert to ; The vehicle total energy consumption cost calculation model includes: in, Represents the total energy consumption cost of the vehicle, represents the fuel price, represents the price of electricity, Representative vehicle On the road ( ) on the route.

[0044] Furthermore, in order to accurately calculate the refrigeration cost of the cold chain transport vehicle, in some embodiments, the refrigeration cost calculation model is used to calculate the total refrigeration cost generated by the cold storage bin in the cold chain transport vehicle during the refrigeration process.

[0045] It should be noted that in this embodiment, the cold storage cold chain logistics only generates refrigeration costs during the refrigeration process of the cold storage warehouse (cold storage agent), and no additional refrigeration costs are generated during transportation. The refrigeration cost of each refrigeration warehouse is related to the refrigeration temperature zone and is proportional to the weight of the refrigerated goods and the transportation time. The cooling cost is calculated by referring to the following formula. The cooling cost calculation model includes: in, Representative vehicle The time of departure from the distribution center, Representative vehicle Arrival Node moment, Representative vehicle Customer orders when shipped from the distribution center The loading status of the goods (i.e. the customer order when it leaves the distribution center) Whether the cargo is loaded on the vehicle middle), represents the total cooling cost, Represents the stratosphere The cooling cost per unit weight per unit time is Order on behalf of customers Preserved temperature layer, Order on behalf of customers The demand, Order on behalf of customers cooling costs.

[0046] Furthermore, in order to accurately calculate the carbon emission cost, thereby optimizing and reducing the carbon emissions in the transportation route and achieving green environmental protection, in some embodiments, the total carbon emission cost calculation model is used to calculate the total carbon emission cost of the cold chain transport vehicle, the total carbon emission cost includes the carbon emission cost of the fuel vehicle, the carbon emission cost of the electric vehicle and the carbon emission cost of the refrigeration, and the total carbon emission cost calculation model includes the carbon emission cost calculation model of the fuel vehicle, the carbon emission cost calculation model of the electric vehicle and the carbon emission cost calculation model of the refrigeration; It should be noted that the carbon emissions of fuel vehicles are proportional to fuel consumption, so fuel vehicles are driving on roads ( ) Carbon emission costs Referring to the following formula, the carbon emission cost calculation model of fuel vehicles includes: in, is the fuel carbon emission factor, represents the carbon emission price, Represents the carbon emission cost of fuel vehicles; It should be noted that electric vehicles use electricity as a power source and do not directly emit carbon dioxide during use, but the indirect emissions generated by the electricity production process and the power loss during transmission and charging should not be ignored. The electricity carbon emission factor is the indirect carbon emission of electric vehicles for each kilowatt-hour of electricity consumed. It is determined by the electricity carbon emission factor, the power transmission efficiency of the power grid, and the charging efficiency of electric vehicles. For example, the electricity carbon emission factor can be 0.6668 kg / (kw*h). Therefore, electric vehicles Driving on the road section ( ) Generates carbon emission costs Referring to the following formula, the electric vehicle carbon emission cost calculation model includes: in, Represents the indirect carbon emissions of electric vehicles for every kilowatt-hour of electricity consumed. represents the carbon emission cost of electric vehicles; It should be noted that the refrigeration process consumes electricity and also produces indirect emissions. Carbon emissions costs of cooling Referring to the following formula, the refrigeration carbon emission cost calculation model includes: in, Represents the electricity price, represents the carbon emission cost of refrigeration; The total carbon emission cost calculation model includes: in, Represents the total carbon emission cost.

[0047] Furthermore, in order to accurately analyze the cargo damage cost caused by the reduction of freshness of cold chain products during transportation, in some embodiments, the total cargo damage cost calculation model is used to calculate the total cargo damage cost of the goods during transportation, and the total cargo damage cost calculation model includes a freshness function and a value loss function; It should be noted that the freshness of fresh goods will continue to decline over time during transportation. When the freshness of goods drops to a certain level, goods loss occurs. Due to the perishable and perishable characteristics of cold chain products during storage and transportation, losses will occur. The accumulation of storage and transportation time will lead to a decrease in the freshness of cold chain products. The products gradually become stale and eventually rot. Therefore, this article uses the freshness function to quantitatively analyze the freshness of goods. The freshness function includes: in, Represents the preservation effort factor, represents the time-sensitive factor of freshness, Represents the freshness preservation effort sensitive factor, Represents refrigeration time.

[0048] It is understandable that the above preservation effort factor It can indicate the degree of effort put into the preservation process. It reflects the effectiveness of various preservation measures taken during storage and transportation. For example, using higher-performance refrigeration equipment, optimizing transportation routes, and reducing loading and unloading times can all increase the preservation effort factor. Value of preservation effort factor The value range can be , The larger the value, the more effective the preservation measures are.

[0049] It should be noted that the above freshness time-sensitive factor It can indicate the sensitivity of freshness to change over time. It reflects the rate at which the freshness of cold chain products decreases over time during storage and transportation. Freshness time sensitivity factor The value range can be , Larger values ​​indicate that freshness decreases faster over time. For example, some perishable fruits and vegetables may have high values, while some shelf-stable foods may have lower value.

[0050] It should be understood that the freshness preservation effort sensitivity factor It can indicate the sensitivity of freshness to preservation efforts. It reflects the preservation effort factor The degree of influence on freshness. Freshness preservation effort sensitivity factor The value range can be , The larger the value of indicates, the more significant the effect of preservation efforts on improving freshness. For example, some products may be very sensitive to preservation measures, and a slight increase in preservation efforts can significantly improve freshness, while other products may be less sensitive to preservation measures.

[0051] It should be noted that the planning device can use the function To construct and maintain the freshness of cold chain products The relevant value loss function is passed through the freshness function The value loss function of cold chain products Freshness-keeping effort factor and refrigeration time Establish connections and construct value loss functions , the value loss function includes: in, represents the value loss function, represents the freshness function, Represents the freshness of cold chain products.

[0052] It is understandable that when the freshness is high (e.g., ), the customer can accept it, and thinks that there is no damage to the goods, so the customer orders The cargo damage cost incurred during transportation is referred to the following formula. The total cargo damage cost calculation model includes: in, represents the total cargo damage cost, Order on behalf of customers Cost of cargo damage incurred during transportation, Indicates customer The demand, represents the freshness threshold (for example, the freshness threshold can be 0.6), Order on behalf of customers The unit weight price of the goods, when the customer orders When the freshness of the cold chain products is greater than or equal to the freshness threshold, the customer order is determined No damage costs are incurred when customers place orders When the freshness of the cold chain products is less than the freshness threshold, the customer order is judged The cost of cargo damage is .

[0053] Furthermore, in order to accurately analyze the time window penalty costs that may be generated in delivery orders, in some embodiments, the time window penalty cost calculation model is used to calculate the total time window penalty cost, which includes the time window penalty costs of one or more service orders.

[0054] It should be noted that the service time window is a soft time window. The time window can be violated, but a corresponding penalty cost will be incurred for violating the time window. Indicates customer orders If the vehicle arrives earlier than the customer's time window, it needs to wait for delivery and pay the corresponding early penalty cost. If it arrives later than the customer's time window, it needs to pay the corresponding late penalty cost. Delivery time is When customer orders Time window penalty cost Referring to the following formula, the time window penalty cost calculation model includes: in, represents the total time window penalty cost, Order on behalf of customers The time window penalty cost is represents the expected delivery time window, Represents the time when the vehicle arrives at the customer point, represents the penalty coefficient for early arrival of customers at the time window violation. represents the time window violation penalty factor for late arriving customers; It should be noted that the planning device can establish a total cost of vehicle use, total vehicle energy consumption cost, cooling cost, total carbon emission cost, total cargo damage cost and time window penalty cost. A mathematical model with the minimum as the goal (i.e., a cost optimization model), wherein the cost optimization model includes: in, represents the target total delivery cost output by the cost optimization model, Represents the target optimization function.

[0055] In some embodiments, the constraints may include the following formula: Constraint formula (1): Constraint formula (2): Constraint formula (3): Constraint formula (4): Constraint formula (5): Constraint formula (6): Constraint formula (7): Constraint formula (8): Constraint formula (9): Constraint formula (10): Constraint formula (11): Constraint formula (12): Constraint formula (13): Constraint formula (14): Among them, constraint formula (1) indicates that each customer has only one vehicle serving him / her once; constraint formula (2) indicates the relationship between the time when the vehicle departs from the distribution center and the time when it arrives at the first customer node on its path; constraint formula (3) indicates the relationship between the time when the vehicle arrives at the customer node and the time when it arrives at the next node on its path; constraint formula (4) indicates the relationship between the time when the vehicle arrives at the charging station node and the time when it arrives at the next node on its path; constraint formula (5) indicates that each vehicle is dispatched at most once and the vehicle departs from the distribution center; constraint formula (6) indicates that all cabins of the fuel vehicle meet the loading constraints when leaving the distribution center; constraint formula (7) indicates that all cabins of the fuel vehicle meet the loading constraints when leaving the distribution center; constraint formula (8) indicates that all cabins of the fuel vehicle meet the loading constraints when leaving the distribution center; constraint formula (9) indicates that all cabins of the fuel vehicle meet the loading constraints when leaving the distribution center; constraint formula (10) indicates that all cabins of the fuel vehicle meet the loading constraints when leaving the distribution center. ) indicates that all the compartments of the electric vehicle meet the loading constraints when it leaves the distribution center; constraint formula (8) indicates the relationship between the remaining power of the electric vehicle when it arrives at the non-charging station node and the remaining power when it arrives at the next node on its path; constraint formula (9) indicates the relationship between the remaining power of the electric vehicle when it arrives at the charging station node and the remaining power when it arrives at the next node on its path; constraint formula (10) indicates that the battery power of the electric vehicle at any node does not exceed the maximum battery capacity and is non-negative; constraint formula (11) indicates that the time when the vehicle leaves the distribution center and arrives at any node is within the distribution center open cycle; constraint formulas (12) to (14) indicate the value constraints of the decision variables; In some embodiments, the planning device may construct a green vehicle path planning model based on model parameter variables, constraints and a cost optimization model, wherein the meanings of the model parameter variables refer to Table 1-1 and Table 1-2: Step S40: Determine the cabin capacity, cargo storage temperature layer and number of available vehicles of each cold chain transport vehicle based on the order information and the vehicle information.

[0056] It should be noted that in the cold chain distribution scenario, the cabin refers to a loading unit inside the vehicle that is divided into multiple independent physical spaces. Each cabin has independent temperature control capabilities to meet the storage needs of goods in different temperature layers. The above cabin capacity can be the maximum capacity of the cabin in the cold chain transport vehicle that can load goods.

[0057] It should be noted that the above-mentioned cargo storage temperature layer can be a cargo storage level divided by temperature range in cold chain logistics. The above-mentioned number of available vehicles can be the number of cold chain transport vehicles in the distribution fleet that can currently be used to distribute orders to be distributed.

[0058] Step S50: generating constraint conditions based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles.

[0059] It should be noted that the constraints may include cabin capacity, cargo storage temperature layer, and number of available vehicles. For example, the cabin capacity constraint may be that the volume of the cargo does not exceed the cabin capacity of the available vehicle; the cargo storage temperature layer constraint may be that the cabin temperature layer of the available vehicle must meet the storage temperature layer required for the cargo; and the number of available vehicles constraint may be that the number of available vehicles in the delivery fleet must meet the number of transport vehicles required for the orders to be delivered.

[0060] In some embodiments, the constraints may include: (1) for any order, the demand is not greater than the maximum load capacity of the cabin; (2) the vehicle can depart from the distribution center at or after time 0; (3) all vehicles can only depart once within the service cycle of the distribution center; (4) the vehicle maintains a constant speed on all roads; (5) the vehicle does not generate energy consumption and carbon emissions when it stops; (6) the same cabin of the same vehicle can only store goods that need to be stored in the same temperature layer at the same time; (7) fuel vehicles do not consider the endurance constraints of fuel vehicles, while electric vehicles need to consider endurance constraints and charging issues; (8) the battery of electric vehicles is fully charged when departing from the distribution center; (9) electric vehicles are charged using a full charging strategy; (10) the freshness of the goods of each order is the same when departing from the distribution center; (11) the load capacity of any cabin of any vehicle cannot exceed the maximum capacity of the cabin.

[0061] Step S60: constructing a green vehicle path planning model according to the cost optimization model and the constraint conditions, and performing vehicle path planning based on the green vehicle path planning model.

[0062] In some embodiments, the planning device may construct a mathematical model by minimizing the sum of vehicle use cost, energy consumption and carbon emission cost, refrigeration cost, and penalty cost for violating the customer time window as an optimization goal.

[0063] In some embodiments, the planning device may design an Improved Ant Colony Optimization with Variable Neighborhood Algorithm (IACO-VNS) cost optimization model embedded with variable neighborhood search to solve the problem, thereby planning distribution plans for fleets in complex cold chain logistics distribution scenarios, reducing the total cost of logistics distribution, and reducing energy consumption and carbon emissions.

[0064] In some embodiments, the distribution center has a mixed fleet consisting of multiple multi-cabin fuel vehicles with the same specifications and multiple multi-cabin electric vehicles with the same specifications. The fleet needs to deliver cold chain goods of multiple different temperature layers to multiple customers. The number of vehicle cabins is the same as the number of temperature layers where the goods are stored, and the rated insulation temperature of each cabin of each vehicle corresponds to each temperature layer. A customer has only one inseparable order, and the goods of the same order are stored in the same temperature layer. The demand, service time window, service time, storage temperature layer and average price of each order are known. The optimization goal of the cost optimization model is to minimize the total distribution cost while satisfying the constraints of capacity, temperature layer and number of available vehicles.

[0065] This embodiment obtains order information of orders to be delivered and vehicle information of a delivery fleet, wherein the delivery fleet includes multiple cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles. A cost calculation model is constructed based on the order information and the vehicle information. The cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model, and a time window penalty cost calculation model. A cost optimization model is constructed based on the cost calculation model. The cabin capacity, cargo storage temperature layer, and number of available vehicles of each cold chain transport vehicle are determined according to the order information and the vehicle information. The constraints are generated based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles, a green vehicle path planning model is constructed according to the cost optimization model and the constraints, and vehicle path planning is performed based on the green vehicle path planning model; since this embodiment constructs a cost calculation model to analyze the distribution cost and carbon emission cost of the designated distribution point from multiple dimensions, by constructing a cost optimization model and constraints, it is possible to effectively reduce the transportation cost while meeting the storage temperature layer requirements and cargo transportation requirements of the order, thereby accurately planning the green vehicle path, effectively reducing the total cost of logistics distribution, reducing energy consumption and carbon emissions, and realizing the energy conservation and emission reduction needs of cold chain transportation.

[0066] refer to Figure 3 , Figure 3 Schematic diagram of the flow chart of the second embodiment of the green vehicle path planning method of the present invention.

[0067] Based on the above first embodiment, in this embodiment, step S60 further includes: Step S601: Generate an input information set according to the order information and the vehicle information.

[0068] It should be noted that the input information set includes the number of cabins, cabin capacity, customer demand, expected service time window, business operation information and the service time window of the distribution center.

[0069] In some embodiments, the planning device may adapt the Solomon data set to obtain a test case; the basic information set may include: vehicle type, number of vehicle cabins, and fuel vehicle cabin capacity , Electric vehicle cabin capacity ; Input the quantity required by each customer according to the customer's delivery requirements , Desired service time window ; Input the service time of the distribution center according to the actual business system and business cost of the enterprise , vehicle unit fuel consumption cost , Vehicle unit electricity consumption cost , Unit carbon emission cost , Fixed departure costs for fuel vehicles and fixed dispatch charges for electric vehicles , the unit time cost of fuel vehicles and the unit time cost of electric vehicles , Labor cost per unit time of vehicle use , Penalty fees for vehicles arriving early to serve customers , Penalty fees for vehicles arriving late to serve customers And other parameters.

[0070] Step S602: Generate a cost information set based on the cost calculation model.

[0071] It should be noted that the cost information set may include vehicle use cost, vehicle total energy consumption cost, refrigeration cost, total carbon emission cost, total cargo damage cost and time window penalty cost, etc.

[0072] Step S603: Input the input information set and the cost information set into the green vehicle path planning model to calculate the node attraction between each cold chain transport vehicle and each customer order.

[0073] It should be noted that, in this embodiment, an Improved Ant Colony Optimization with Variable Neighborhood Algorithm (IACO-VNS) embedded with variable neighborhood search can be designed to solve the cost optimization model based on the improved ant colony algorithm and constraint conditions, thereby realizing green vehicle path planning.

[0074] In some embodiments, the improved ant colony algorithm may include the following steps: Step 1: Adapt the Solomon dataset to obtain a test case; Step 2: Initialize algorithm parameters. The number of ants is set to , maximum number of iterations ; Set the maximum number of iterations of the variable neighborhood search to ; Step 3: Ant route planning. Plan the route for each ant. Each ant's route represents a feasible solution until all ants' travel plans are found. The above Step 3 includes: Step 3.1: Calculate the attraction of nodes. Calculate the attraction of all remaining optional nodes to the latest node selected by the ant. The attraction calculation formula is as follows: in, Representation Node With Node The distance between Representation Node With Node The pheromone concentration between Represents a slave node Transfer to Node The time window matching degree after Represents a slave node Transfer to Node The loading rate of the rear vehicle, Represents each node of the vehicle and the node The average distance is the weight coefficient, represents the node attraction, are weight coefficients respectively; Step 3.2: Select the next node. According to the attractiveness of each node, the next completed order of the ant is obtained through the transfer probability calculation formula and the roulette method; Step 3.3: Update the node record table. Update the path record table according to the nodes selected in Step 3.2; Step 4: Find the best solution. Decode the ant's walking plan into the fleet's delivery plan, calculate the total cost of each delivery plan, and find the best plan with the lowest total cost among the walking plans planned by the m ants; Step 5: Update the "pheromone" matrix. As time goes by, all pheromones will be proportional to each iteration. Part of it dissipates; the pheromone update calculation formula is as follows: in, represents the updated pheromone amount of each candidate transportation path, Represents the amount of pheromone before each candidate transportation path is updated, represents the pheromone increment coefficient, is the total cost of the initial delivery plan, is the maximum value of the total distribution cost of each initial distribution plan, is the minimum value of the total distribution cost of each initial distribution plan, Represents the dissipation ratio of pheromones on each candidate transport path after each iteration; Step 6: Variable neighborhood search. Perform variable neighborhood search operations on the path of each vehicle in the optimal solution of this round. This includes three neighborhoods: "exchange", "reversal" and "insertion"; Step 6.1: Exchange operation. Select every two orders of each vehicle in turn and exchange their order. If a better result is obtained, the order delivery order after this exchange operation is retained and jump to Step 6.1. The exchange operation process is as follows: Figure 4 As shown; Step 6.2: Reversal operation. Select every two orders of each vehicle in turn and reverse the order of all orders between them. If a better result is obtained, the order delivery order after this reversal operation is retained and jump to Step 6.1. The reversal operation process is as follows: Figure 5 As shown; Step 6.3: Insertion operation. Select every two orders of each vehicle in turn, and insert the first selected order in front of the second order. If a better result is obtained, the order delivery order after this insertion operation is retained and jump to Step 6.1. Otherwise, gen2 is increased by one. The insertion operation process is as follows: Figure 6 As shown; Step 6.4: If , then go to Step 6; otherwise update the optimal solution; Step 7: Clear the path record table. At the same time, gen increases by 1; Step 8: If , then go to Step 3; otherwise, output the optimal solution and the algorithm ends.

[0075] Step S604: Generate multiple initial delivery plans based on the node attractiveness.

[0076] It can be understood that the planning device can calculate the node attraction of each node for each cold chain transport vehicle, and select nodes that meet the attraction threshold for the cold chain transport vehicle based on the node attraction, thereby generating multiple initial delivery plans.

[0077] Step S605: Calculate the total delivery cost corresponding to each initial delivery plan according to the cost calculation model.

[0078] It can be understood that the initial delivery plan includes the correspondence between each cold chain transport vehicle and the fixed order, determines the initial delivery path of each cold chain transport vehicle based on the correspondence, calculates the total delivery cost of all initial delivery paths in each initial delivery plan, and screens the plans based on the total delivery cost.

[0079] Step S606: selecting candidate delivery plans from the initial delivery plans based on the total delivery cost of the plans, and performing vehicle route planning for each cold chain transport vehicle based on the candidate delivery plans.

[0080] It can be understood that the planning device can select a candidate delivery plan or multiple candidate delivery plans with lower costs from the initial delivery plans based on the total delivery cost of the plans.

[0081] Furthermore, in order to accurately plan a low-cost and reasonable-path delivery plan, the above step S606 may include: Step S6061: updating the pheromone of each candidate transportation route based on the total delivery cost of the solution; Step S6062: selecting candidate delivery plans from the initial delivery plans based on the pheromone values, and determining candidate transportation routes for each cold chain transportation vehicle based on the candidate delivery plans; Step S6063: Perform variable neighborhood search on each candidate transportation path; Step S6064: Perform vehicle route planning for each cold chain transport vehicle based on the variable neighborhood search results.

[0082] It should be noted that the pheromone value update formula is as follows: in, represents the updated pheromone amount of each candidate transportation path, Represents the amount of pheromone before each candidate transportation path is updated, represents the pheromone increment coefficient, is the total cost of the initial delivery plan, is the maximum value of the total distribution cost of each initial distribution plan, is the minimum value of the total distribution cost of each initial distribution plan, Represents the dissipation ratio of pheromones on each candidate transport path after each iteration.

[0083] It should be noted that the variable neighborhood search includes a neighborhood exchange operation, a neighborhood reversal operation, and a neighborhood insertion operation.

[0084] The exchange neighborhood operation may include: selecting every two orders of each vehicle in turn, exchanging their order, and if a better result is obtained, then retaining the order of order delivery after this exchange operation, referring to Figure 4 , Figure 4 Schematic diagram of the neighborhood exchange operation.

[0085] The neighborhood reversal operation may include: selecting every two orders of each vehicle in turn, reversing the order of all orders between them as a whole, and if a better result is obtained, then retaining the order delivery order after this reversal operation, referring to Figure 5 , Figure 5 Schematic diagram of the reversal neighborhood operation.

[0086] The insertion neighborhood operation may include: selecting every two orders of each vehicle in turn, inserting the first selected order in front of the second order, and if a better result is obtained, retaining the order of order delivery after this insertion operation, referring to Figure 6 , Figure 6 Schematic diagram of inserting neighborhood operation.

[0087] This embodiment generates an input information set based on the order information and the vehicle information, the input information set including the number of cabins, cabin capacity, customer demand, expected service time window, business information and service time window of the distribution center, generates a cost information set based on the cost calculation model, inputs the input information set and the cost information set into the green vehicle path planning model to calculate the node attraction between each cold chain transport vehicle and each customer order, generates multiple initial delivery plans based on the node attraction, calculates the total delivery cost of each initial delivery plan according to the cost calculation model, selects candidate delivery plans from the initial delivery plans based on the total delivery cost, and performs vehicle path planning for each cold chain transport vehicle based on the candidate delivery plan, thereby accurately planning a low-carbon, environmentally friendly green transportation path, ensuring timely and efficient transportation of ordered goods while reducing energy consumption, costs and carbon emissions.

[0088] In addition, an embodiment of the present invention further proposes a computer-readable storage medium, on which a green vehicle path planning program is stored. When the green vehicle path planning program is executed by a processor, the steps of the green vehicle path planning method described above are implemented.

[0089] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0090] The computer-readable storage medium may be included in the green vehicle path planning device; or may exist independently without being assembled into the green vehicle path planning device.

[0091] In addition, an embodiment of the present invention further proposes a computer program product, including a green vehicle path planning program, which implements the steps of the green vehicle path planning method described above when executed by a processor.

[0092] The specific implementation of the computer program product of the present invention is basically the same as the above-mentioned embodiments of the green vehicle path planning method, and will not be repeated here.

[0093] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the green vehicle path planning device of the present invention.

[0094] like Figure 7 As shown, the green vehicle path planning device proposed in the embodiment of the present invention includes: An information acquisition module 10 is used to acquire order information of an order to be delivered and vehicle information of a delivery fleet, wherein the delivery fleet includes a plurality of cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles; A cost calculation module 20 is used to construct a cost calculation model based on the order information and the vehicle information, wherein the cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model, and a time window penalty cost calculation model; A cost optimization module 30, used to construct a cost optimization model based on the cost calculation model; A cargo analysis module 40, configured to determine the cabin capacity, cargo storage temperature layer, and number of available vehicles of each cold chain transport vehicle according to the order information and the vehicle information; A constraint analysis module 50, for generating constraint conditions based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles; The path planning module 60 is used to construct a green vehicle path planning model according to the cost optimization model and the constraint conditions, and perform vehicle path planning based on the green vehicle path planning model.

[0095] This embodiment obtains order information of orders to be delivered and vehicle information of a delivery fleet, wherein the delivery fleet includes multiple cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles. A cost calculation model is constructed based on the order information and the vehicle information. The cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model, and a time window penalty cost calculation model. A cost optimization model is constructed based on the cost calculation model. The cabin capacity, cargo storage temperature layer, and number of available vehicles of each cold chain transport vehicle are determined according to the order information and the vehicle information. The constraints are generated based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles, a green vehicle path planning model is constructed according to the cost optimization model and the constraints, and vehicle path planning is performed based on the green vehicle path planning model; since this embodiment constructs a cost calculation model to analyze the distribution cost and carbon emission cost of the designated distribution point from multiple dimensions, by constructing a cost optimization model and constraints, it is possible to effectively reduce the transportation cost while meeting the storage temperature layer requirements and cargo transportation requirements of the order, thereby accurately planning the green vehicle path, effectively reducing the total cost of logistics distribution, reducing energy consumption and carbon emissions, and realizing the energy conservation and emission reduction needs of cold chain transportation.

[0096] The green vehicle path planning device provided by the present application adopts the green vehicle path planning method in the above embodiment, which can solve the technical problem of green vehicle path planning. Compared with the prior art, the beneficial effects of the green vehicle path planning device provided by the present application are the same as the beneficial effects of the green vehicle path planning method provided by the above embodiment, and the other technical features in the green vehicle path planning device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0097] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0098] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0099] In addition, for technical details not fully described in this embodiment, reference can be made to the green vehicle path planning method provided in any embodiment of the present invention, and will not be repeated here.

[0100] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0101] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0102] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0103] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A green vehicle path planning method, characterized in that: The green vehicle path planning method comprises: Obtaining order information of the order to be delivered and vehicle information of the delivery fleet, wherein the delivery fleet includes a plurality of cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles; Building a cost calculation model based on the order information and the vehicle information, the cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model and a time window penalty cost calculation model; Building a cost optimization model based on the cost calculation model; Determine the cabin capacity, cargo storage temperature layer and number of available vehicles of each cold chain transport vehicle according to the order information and the vehicle information; generating constraints based on the cabin capacity, the cargo storage temperature layer, and the number of available vehicles; A green vehicle path planning model is constructed according to the cost optimization model and the constraint conditions, and vehicle path planning is performed based on the green vehicle path planning model.

2. The green vehicle path planning method according to claim 1, characterized in that: The vehicle use cost calculation model is used to calculate the vehicle use cost, which includes fixed dispatch cost, vehicle maintenance and depreciation cost, and driver salary cost; The vehicle use cost calculation model includes: in, Represents the vehicle usage cost, represents the fixed dispatch cost of fuel vehicles, represents the fixed dispatch cost of electric vehicles, Represents the collection of fuel vehicles, Represents a collection of electric vehicles, represents the set of customer points, Represents cold chain transport vehicles, Represents customer orders to be delivered, Represents the starting point, customer point and charging station collection, Represents the terminal, customer point and charging station collection, Represents the labor cost per unit time of the vehicle, Represents the unit time usage cost of a fuel vehicle, Represents the unit time usage cost of electric vehicles, Representative vehicle Whether it passes through the road section ( ), The value of 1 represents the passing section ( ), The value of 0 means that the road section has not been passed ( ), 0 represents the distribution center, and the distribution center is the distribution starting point, Representative vehicle On the road section ( ) driving time, Representative vehicle Order for customers Length of service, Represents an electric vehicle at a charging station Charging time.

3. The green vehicle path planning method according to claim 2, characterized in that: The vehicle total energy consumption cost calculation model is used to calculate the vehicle total energy consumption cost, the vehicle total energy consumption cost includes the fuel consumption cost of fuel vehicles and the electricity consumption cost of electric vehicles, and the vehicle total energy consumption cost calculation model includes a fuel consumption calculation model and an electricity consumption calculation model; The fuel consumption calculation model includes: in, Represents fuel vehicles Driving on the road section ( )'s fuel consumption rate, and are predefined parameters related to vehicle characteristic parameters, Represents the weight of the fuel vehicle. represents the number of compartments in the vehicle, Represents the vehicle speed, Representative vehicle of Cabin No. is leaving node Loading volume at time; The power consumption calculation model includes: in, represents the air density, Represents the wind-exposed area of ​​the front surface of the vehicle, represents the air resistance coefficient, is the acceleration due to gravity, is the rolling resistance coefficient, , , is the coefficient related to the vehicle characteristic parameters, The energy consumption rate to overcome air resistance, is the energy consumption rate generated by tire friction, The energy consumption rate generated by the transmission system, The energy consumption rate generated by the auxiliary system, is the power of the vehicle air conditioning system. is the total power of the electronic systems in the vehicle; The vehicle total energy consumption cost calculation model includes: in, Represents the total energy consumption cost of the vehicle, represents the fuel price, represents the price of electricity, Representative vehicle On the road ( ) on the route.

4. The green vehicle path planning method according to claim 3, characterized in that: The refrigeration cost calculation model is used to calculate the total refrigeration cost generated by the cold storage bin in the cold chain transport vehicle during the refrigeration process. The refrigeration cost calculation model includes: in, Representative vehicle The time of departure from the distribution center, Representative vehicle Arrival Node moment, Representative vehicle Customer orders when shipped from a distribution center The cargo loading status, represents the total cooling cost, Represents the stratosphere The cooling cost per unit weight per unit time is Order on behalf of customers Preserved temperature layer, Order on behalf of customers The demand, Order on behalf of customers cooling costs.

5. The green vehicle path planning method according to claim 4, characterized in that: The total carbon emission cost calculation model is used to calculate the total carbon emission cost of cold chain transport vehicles, the total carbon emission cost includes the carbon emission cost of fuel vehicles, the carbon emission cost of electric vehicles and the carbon emission cost of refrigeration, and the total carbon emission cost calculation model includes the carbon emission cost calculation model of fuel vehicles, the carbon emission cost calculation model of electric vehicles and the carbon emission cost calculation model of refrigeration; The carbon emission cost calculation model of fuel vehicles includes: in, is the fuel carbon emission factor, represents the carbon emission price, Represents the carbon emission cost of fuel vehicles; The electric vehicle carbon emission cost calculation model includes: in, Represents the indirect carbon emissions of electric vehicles for every kilowatt-hour of electricity consumed. represents the carbon emission cost of electric vehicles; The refrigeration carbon emission cost calculation model includes: in, Represents the electricity price, represents the carbon emission cost of refrigeration; The total carbon emission cost calculation model includes: in, Represents the total carbon emission cost.

6. The green vehicle path planning method according to claim 5, characterized in that: The total cargo damage cost calculation model is used to calculate the total cargo damage cost of the cargo during transportation, and the total cargo damage cost calculation model includes a freshness function and a value loss function; The freshness function includes: in, Represents the preservation effort factor, represents the freshness time-sensitive factor, Represents the freshness preservation effort sensitive factor, Represents refrigeration time; The value loss function includes: in, represents the value loss function, represents the freshness function, Represents the freshness of cold chain products; The total cargo damage cost calculation model includes: in, represents the total cargo damage cost, Order on behalf of customers Cost of cargo damage incurred during transportation, Indicates customer The demand, represents the freshness threshold, Order on behalf of customers The unit weight price of the goods, when the customer orders When the freshness of the cold chain products is greater than or equal to the freshness threshold, the customer order is determined No damage costs are incurred when customers place orders When the freshness of the cold chain products is less than the freshness threshold, the customer order is judged The cost of cargo damage is .

7. The green vehicle path planning method according to claim 6, characterized in that: The time window penalty cost calculation model is used to calculate the total time window penalty cost, and the total time window penalty cost includes the time window penalty cost of one or more service orders. The time window penalty cost calculation model includes: in, represents the total time window penalty cost, Order on behalf of customers The time window penalty cost is represents the expected delivery time window, Represents the time when the vehicle arrives at the customer point, represents the penalty coefficient for time window violation for early arriving customers, represents the time window violation penalty factor for late arriving customers; The cost optimization model includes: in, represents the target total delivery cost output by the cost optimization model, Represents the target optimization function.

8. The green vehicle path planning method according to any one of claims 1 to 7, characterized in that: The vehicle path planning based on the green vehicle path planning model includes: Generate an input information set according to the order information and the vehicle information, the input information set including the number of cabins, cabin capacity, customer demand, expected service time window, business operation information and the service time window of the distribution center; generating a cost information set based on the cost calculation model; The input information set and the cost information set are input into the green vehicle path planning model to calculate the node attraction between each cold chain transport vehicle and each customer order: in, Representation Node With Node The distance between Representation Node With Node The pheromone concentration between Represents a slave node Transfer to Node The time window matching degree after Represents a slave node Transfer to Node The loading rate of the rear vehicle, Represents each node of the vehicle and the node The average distance are weight coefficients, represents the node attraction; generating a plurality of initial delivery plans based on the node attractiveness; Calculate the total delivery cost of each initial delivery plan according to the cost calculation model; Based on the total delivery cost of the plan, candidate delivery plans are selected from the initial delivery plans, and vehicle path planning is performed for each cold chain transport vehicle based on the candidate delivery plans.

9. The green vehicle path planning method according to claim 8, characterized in that: The method of selecting candidate delivery plans from the initial delivery plan based on the total delivery cost of the plan, and performing vehicle path planning for each cold chain transport vehicle based on the candidate delivery plan, includes: Update the pheromone of each candidate transportation path based on the total delivery cost of the solution: in, represents the updated pheromone amount of each candidate transportation path, Represents the amount of pheromone before each candidate transportation path is updated, represents the pheromone increment coefficient, is the total cost of the initial delivery plan, is the maximum value of the total distribution cost of each initial distribution plan, is the minimum value of the total distribution cost of each initial distribution plan, Represents the dissipation ratio of pheromones on each candidate transport path after each iteration; Screening out candidate delivery plans from the initial delivery plans based on the pheromone values, and determining candidate transportation routes for each cold chain transport vehicle based on the candidate delivery plans; Performing a variable neighborhood search on each candidate transportation path, wherein the variable neighborhood search includes a swap neighborhood operation, a reverse neighborhood operation, and an insert neighborhood operation; Vehicle path planning is performed for each cold chain transport vehicle based on the variable neighborhood search results.

10. A green vehicle path planning device, characterized in that: The green vehicle path planning device comprises: An information acquisition module, used to acquire order information of an order to be delivered and vehicle information of a delivery fleet, wherein the delivery fleet includes a plurality of cold chain transport vehicles, and the cold chain transport vehicles include fuel vehicles and / or electric vehicles; A cost calculation module, used to construct a cost calculation model based on the order information and the vehicle information, wherein the cost calculation model includes: a vehicle use cost calculation model, a vehicle total energy consumption cost calculation model, a refrigeration cost calculation model, a total carbon emission cost calculation model, a total cargo damage cost calculation model, and a time window penalty cost calculation model; A cost optimization module, used for building a cost optimization model based on the cost calculation model; A cargo analysis module, used to determine the cabin capacity, cargo storage temperature layer and number of available vehicles of each cold chain transport vehicle according to the order information and the vehicle information; A constraint analysis module, configured to generate constraint conditions based on the cabin capacity, the cargo storage temperature layer and the number of available vehicles; The path planning module is used to construct a green vehicle path planning model according to the cost optimization model and the constraint conditions, and perform vehicle path planning based on the green vehicle path planning model.

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