A method and system for optimizing a freight transportation route

Abstract

The present invention provides a method and system for optimizing a cargo transportation route. The method includes obtaining a set of cargo orders and a set of distribution vehicles, constructing a distribution network based on the set of cargo orders and the set of distribution vehicles, and determining corresponding distribution orders for each distribution vehicle in the set of distribution vehicles based on the distribution network; constructing a route optimization model based on the distribution orders and the corresponding distribution vehicles and determining the constraint conditions of the route optimization model; performing interval optimization processing on the constraint conditions to obtain optimized constraint conditions; and performing optimized solution on the route optimization model based on the optimized constraint conditions to obtain an optimal route. The present invention fully considers various factors in the transportation process and improves the speed of model solution, thereby determining the best route for distribution orders, improving transportation efficiency and saving transportation costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of route optimization, and particularly relates to a method and system for optimizing the goods transportation route. Background Art

[0002] For current logistics centers or warehousing centers, their shipping modes usually determine the destination according to commodity orders, allocate the orders to corresponding deliverymen, and the deliverymen select corresponding routes according to the specified routes or their own experience to complete the transportation process of commodities or goods. Although there are also methods for allocating corresponding routes for each order in the prior art, the process of allocating routes does not consider factors such as paths, costs, and goods characteristics, resulting in the allocated routes not meeting the optimal requirements, thereby affecting the efficiency and cost of goods transportation. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a method and system for optimizing the goods transportation route to solve the technical problems in the prior art.

[0004] On the one hand, the present invention provides the following technical solution. A method for optimizing the goods transportation route includes:

[0005] Obtain a set of goods orders and a set of distribution vehicles, construct a distribution network based on the set of goods orders and the set of distribution vehicles, and determine corresponding distribution orders for each distribution vehicle in the set of distribution vehicles based on the distribution network;

[0006] Construct a route optimization model based on the distribution order and the corresponding distribution vehicle, and determine the constraint conditions of the route optimization model;

[0007] Perform interval optimization processing on the constraint conditions to obtain optimized constraint conditions;

[0008] Perform optimized solution on the route optimization model based on the optimized constraint conditions to obtain the optimal route.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: First, the present invention obtains a set of cargo orders and a set of distribution vehicles, constructs a distribution network based on the set of cargo orders and the set of distribution vehicles, and determines corresponding distribution orders for each distribution vehicle in the set of distribution vehicles based on the distribution network; then constructs a route optimization model based on the distribution orders and the corresponding distribution vehicles and determines the constraint conditions of the route optimization model; then performs interval optimization processing on the constraint conditions to obtain optimized constraint conditions; finally, optimizes and solves the route optimization model based on the optimized constraint conditions to obtain the optimal route. The present invention first determines the best distribution order for each transport vehicle to improve the transport efficiency, and then constructs a route optimization model and optimizes the constraint conditions therein, fully considering various factors in the transport process and improving the speed of model solution, thereby determining the best route for the distribution order, improving the transport efficiency and saving the transport cost.

[0010] Preferably, the step of constructing a distribution network based on the set of cargo orders and the set of distribution vehicles and determining corresponding distribution orders for each distribution vehicle in the set of distribution vehicles based on the distribution network includes:

[0011] Construct a node-weighted distribution network with the cargo orders in the set of cargo orders;

[0012] Assign a distribution vehicle to each node in the distribution network as an assignment set, and arbitrarily select a node in the distribution network , and temporarily assign the neighbor nodes of the node to the assignment set corresponding to the node ;

[0013] Calculate the neighbor node :

[0014] ;

[0015] In the formula, represents the sum of the weights of all links in the distribution network, represents the sum of the weights of the link from the neighbor node to the node , represents the sum of the connection weights related to the neighbor node , represents the fitting parameter, represents the sum of the connection weights of all node edges in the assignment set corresponding to the node ;

[0016] If the set change value is greater than 0, then put the neighbor node into the node In the corresponding allocation set, if the set change value is not greater than 0, then the neighbor nodes are regarded as isolated nodes, and nodes are reselected, and the process of temporarily allocating nodes is cycled to store a number of new nodes in a number of allocation sets;

[0017] Based on the new nodes, iterative nodes are determined, and the iterative nodes are used as the delivery orders of the delivery vehicles corresponding to the allocation sets.

[0018] Preferably, the step of determining iterative nodes based on the new nodes and using the iterative nodes as the delivery orders of the delivery vehicles corresponding to the allocation sets includes:

[0019] Identify the new nodes in the allocation set, and re - establish a new delivery network based on the new nodes;

[0020] Replace the connection weights between the new nodes in the new delivery network with the connection weights between the nodes in the corresponding allocation sets of the new nodes;

[0021] Based on the replaced connection weights, temporarily allocate and calculate the set change value for the new nodes iteratively until the set change value is 0, and output the iterated allocation set;

[0022] Use the nodes in the iterated allocation set as iterative nodes, and use the iterative nodes as the delivery orders of the delivery vehicles corresponding to the allocation sets.

[0023] Preferably, in the step of constructing a route optimization model based on the delivery orders and the corresponding delivery vehicles and determining the constraint conditions of the route optimization model, the route optimization model is:

[0024]

[0025] In the formula, represents the objective function, represents the driving cost, represents the vehicle cost, represents the environmental cost, represents the delivery vehicle 's vehicle unit - distance cost, represents the road network node to the road network node 's distance, represents the delivery vehicle from the road network node to the road network node 's decision value, represents the delivery vehicle 's start - up cost, represents the delivery vehicle The decision value used represents the carbon emission coefficient represents the delivery vehicle 's unit carbon emission cost represents the delivery vehicle 's vehicle speed represents the speed coefficient

[0026] Preferably, in the step of constructing a route optimization model based on the delivery order and the corresponding delivery vehicle and determining the constraint conditions of the route optimization model, the constraint conditions are:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] In the formula, represents the demand of the delivery order represents the delivery vehicle 's maximum load represents the delivery vehicle from the road network node to the road network node 's decision value represents the height and volume of the goods of type in the delivery order , respectively represent the height and volume of the delivery vehicle 's cargo compartment represents the number of delivery orders respectively represent the cargo demands of the paths , , represents the one related to the road network node​ or road network nodes adjacent road network nodes, respectively represent the paths 、 、 the proportion of the goods demand of the distribution orders on the vehicle in all the goods demands of the distribution orders, represents the transportation frequency of the distribution vehicle ; respectively represent the distribution vehicle on the path 、 、 the minimum transportation frequency, respectively represent the distribution vehicle on the path 、 、 the maximum transportation frequency, represents the distribution vehicle at the road network node to the road network node the transportation time between, represents the ideal utilization rate of the distribution vehicle ; represents the distribution vehicle the number of the corresponding vehicle type in all transportation vehicles.

[0038] Preferably, the steps of performing interval optimization on the constraint conditions to obtain optimized constraint conditions include:

[0039] Identifying the interval constraint conditions in the constraint conditions, where the interval constraint conditions include a first constraint condition and a second constraint condition. Among them, the first constraint condition is:

[0040] ;

[0041] The second constraint condition is:

[0042] ;

[0043] Performing interval optimization on the first constraint condition to obtain a first optimized constraint condition. Among them, the first optimized constraint condition is:

[0044] ;

[0045] ;

[0046] In the formula, represents the simplified value, 、 respectively represent The lower limit and upper limit is the preset satisfaction degree represents a trade-off factor;

[0047] Perform interval optimization on the second constraint condition to obtain a second optimized constraint condition, where the second optimized constraint condition is:

[0048] ;

[0049] In the formula, and respectively represent the lower limit and upper limit;

[0050] Replace the first constraint condition in the constraint condition with the first optimized constraint condition and replace the second constraint condition in the constraint condition with the second optimized constraint condition to obtain an optimized constraint condition.

[0051] Preferably, the step of optimizing and solving the route optimization model based on the optimized constraint condition to obtain an optimal route is specifically as follows:

[0052] Use a genetic algorithm and optimize and solve the route optimization model based on the optimized constraint condition to obtain an optimal route.

[0053] In a second aspect, the present invention provides the following technical solution, a goods transportation route optimization system, the system includes:

[0054] An allocation module, configured to obtain a set of goods orders and a set of distribution vehicles, construct a distribution network based on the set of goods orders and the set of distribution vehicles, and determine corresponding distribution orders for each distribution vehicle in the set of distribution vehicles based on the distribution network;

[0055] A construction module, configured to construct a route optimization model based on the distribution order and the corresponding distribution vehicle and determine the constraint conditions of the route optimization model;

[0056] An optimization module, configured to perform interval optimization processing on the constraint conditions to obtain optimized constraint conditions;

[0057] A solution module, configured to optimize and solve the route optimization model based on the optimized constraint conditions to obtain an optimal route.

[0058] In a third aspect, the present invention provides the following technical solution, a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, it implements the goods transportation route optimization method as described above.

[0059] Fourthly, the present invention provides the following technical solution: a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the optimal method for finding a goods transportation route as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0061] Figure 1 It is a flowchart of the optimal method for finding a goods transportation route provided in Embodiment 1 of the present invention;

[0062] Figure 2 It is a structural block diagram of the optimal system for finding a goods transportation route provided in Embodiment 2 of the present invention;

[0063] Figure 3 It is a schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0064] The following will further illustrate the embodiments of the present invention with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary, and are intended to explain the embodiments of the present invention, and should not be construed as a limitation of the present invention.

[0066] In the description of the embodiments of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention.

[0067] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0068] In the embodiments of the present invention, unless otherwise clearly defined and limited, terms such as "installed", "connected", "connected to", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0069] Embodiment 1

[0070] In the first embodiment of the present invention, as Figure 1 shown, a method for optimizing a cargo transportation route includes:

[0071] S1. Obtain a set of cargo orders and a set of distribution vehicles, construct a distribution network based on the set of cargo orders and the set of distribution vehicles, and determine corresponding distribution orders for each distribution vehicle in the set of distribution vehicles based on the distribution network;

[0072] Specifically, in the set of cargo orders, there are several cargo orders, and the same is true for the set of distribution vehicles, where there are several distribution vehicles. For distribution vehicles, considering cost and time, some orders may not be suitable for some distribution vehicles. Therefore, a distribution network is constructed, and suitable distribution orders are assigned to suitable distribution vehicles according to the distribution network.

[0073] Among them, the step S1 includes:

[0074] S11. Construct a node-weighted distribution network with the cargo orders in the set of cargo orders;

[0075] Specifically, each cargo order is used as a node to construct a directed graph network. Therefore, the distribution network is specifically a directed graph network.

[0076] S12. Assign a distribution vehicle to each node in the distribution network as an assignment set, and arbitrarily select a node in the distribution network, and temporarily assign the neighbor nodes of the node in the corresponding allocation set;

[0077] Among them, a single allocation set can contain multiple nodes, that is, a single delivery vehicle can correspond to multiple delivery orders.

[0078] S13. Calculate the neighbor nodes :

[0079] ;

[0080] In the formula, represents the sum of the weights of all the links in the delivery network, represents the neighbor node to the node the sum of the weights of the link, represents the sum of the connection weights related to the neighbor node ; represents the fitting parameter, represents the node the sum of the connection weights of all the node edges in the corresponding allocation set;

[0081] Among them, in this embodiment is 0.88.

[0082] S14. If the set change value is greater than 0, then put the neighbor node into the allocation set corresponding to the node . If the set change value is not greater than 0, then take the neighbor node as an isolated node, reselect the neighbor node of the node and loop through the process of temporary node allocation to store several new nodes in several allocation sets;

[0083] Specifically, when the set change value is greater than 0, it means that putting the neighbor node B into the allocation set corresponding to the node will result in a positive gain. Therefore, the neighbor node B can be put into the allocation set corresponding to the node . When the set change value is equal to 0, it means that putting the neighbor node B into the allocation set corresponding to the node will not generate a gain. Therefore, it is taken as an isolated node. When the set change value is equal to 0, it means that putting the neighbor node B into the allocation set corresponding to the node will result in a negative gain. Therefore, it is taken as an isolated node. The isolated nodes in this step can be used as the adjacent nodes of the remaining nodes and the above steps are repeated until all the nodes are allocated.

[0084] S15. Determine iterative nodes based on the newly added nodes, and use the iterative nodes as the delivery orders of the delivery vehicles corresponding to the allocation sets;

[0085] Among them, the step S15 includes:

[0086] S151. Identify the newly added nodes in the allocation set, and re - establish a new delivery network based on the newly added nodes;

[0087] Specifically, the new delivery network is also a directed graph, but the attribution relationship of the nodes in it compared to the delivery network has changed.

[0088] S152. Replace the connection weights between the newly added nodes in the new delivery network with the connection weights between the nodes in the corresponding allocation set of the newly added nodes;

[0089] Specifically, this step is a process of updating the connection weights of the nodes in the directed graph.

[0090] S153. Based on the replaced connection weights, perform temporary allocation and calculation of the set change value for the newly added nodes iteratively until the set change value is 0, and output the iterated allocation set.

[0091] S154. Use the nodes in the iterated allocation set as iterative nodes, and use the iterative nodes as the delivery orders of the delivery vehicles corresponding to the allocation set;

[0092] Specifically, after the above - mentioned iterative steps, there are a certain number of nodes in each allocation set, and thus the goods orders are allocated to the best delivery vehicles.

[0093] S2. Construct a route optimization model based on the delivery orders and the corresponding delivery vehicles, and determine the constraint conditions of the route optimization model;

[0094] Among them, the route optimization model is:

[0095]

[0096] In the formula, represents the objective function, represents the driving cost, represents the vehicle cost, represents the environmental cost, represents the delivery vehicle 's vehicle unit - distance cost, represents the road network node to the road network node distance, represents the delivery vehicle from the road network node to the road network node The decision value, represents the start-up cost of the delivery vehicle ; represents the decision value of whether the delivery vehicle is in use, represents the carbon emission coefficient, represents the unit carbon emission cost of the delivery vehicle ; represents the vehicle speed of the delivery vehicle ; represents the speed coefficient;

[0097] Among them, when the delivery vehicle is in use, then is 1, otherwise it is 0. The speed parameter can be determined according to the vehicle type, which can represent the conversion relationship between vehicle speed and fuel consumption. The carbon emission coefficient is specifically the conversion coefficient between vehicle fuel consumption and carbon emissions.

[0098] Specifically, the route optimization model in this embodiment specifically optimizes the driving cost of the vehicle, the fixed cost of the vehicle, and the carbon emission cost of the vehicle.

[0099] Among them, the constraint conditions are:

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] In the formula, represents the demand of the delivery order, represents the maximum load of the delivery vehicle ; represents the delivery vehicle from the road network node To the road network node The decision value Indicates the height and volume of the goods of type In the delivery order And Respectively represent the height and volume of the cargo compartment of the delivery vehicle ; Indicates the number of delivery orders Respectively represent the goods demands of the paths And And ; Indicates the road network node adjacent to the road network node Or the road network node ; Respectively represent the proportion of the goods demand of the delivery order on the paths And And To the goods demand of all delivery orders of the delivery vehicle ; Indicates the transportation frequency of the delivery vehicle ; Respectively represent the minimum transportation frequencies of the delivery vehicle On the paths And And ; Respectively represent the maximum transportation frequencies of the delivery vehicle On the paths And And ; Indicates the transportation time of the delivery vehicle Between the road network node And the road network node ; Indicates the ideal utilization rate of the delivery vehicle ; Indicates the number of vehicles of the corresponding vehicle type of the delivery vehicle Among all transport vehicles;

[0111] Specifically, in the order of the constraint conditions, it respectively constrains the vehicle load, the access to the road network node, the loading height of the vehicle, the load volume of the vehicle, the loop of the road network node, the frequency of the transport vehicle, and the transport time;

[0112] Among them, for the decision value in the above constraint conditions, when the delivery vehicle Travels from the road network node To the road network node , is 1, otherwise 0, when the delivery vehicle from the road network node to the road network node at that time, is 1, otherwise 0.

[0113] S3. Perform interval optimization on the constraint conditions to obtain optimized constraint conditions;

[0114] Among them, for the constraint conditions, there are some constraint conditions with uncertain parameters. Therefore, by performing interval optimization on them, they are made equivalent to accurate parameter intervals, which can improve the speed of model solution.

[0115] Among them, the step S3 includes:

[0116] S31. Identify the interval constraint conditions in the constraint conditions. The interval constraint conditions include a first constraint condition and a second constraint condition. Among them, the first constraint condition is:

[0117] ;

[0118] The second constraint condition is:

[0119] .

[0120] S32. Perform interval optimization on the first constraint condition to obtain a first optimized constraint condition. Among them, the first optimized constraint condition is:

[0121] ;

[0122] ;

[0123] In the formula, represents the simplified value, , respectively represent the lower limit and the upper limit of, is the preset satisfaction degree, represents the trade-off factor;

[0124] Specifically, the trade-off factor is used to balance the relationship between the midpoint value and the width of the interval. The larger the trade-off factor, the more the midpoint value of the interval meets the requirements. The preset satisfaction degree can represent the satisfaction level of interval optimization.

[0125] S33. Perform interval optimization on the second constraint condition to obtain a second optimized constraint condition. Among them, the second optimized constraint condition is:

[0126] ;

[0127] In the formula, , respectively represent the lower limit and the upper limit of

[0128] S34. Replace the first constraint condition in the constraint condition with the first optimized constraint condition and replace the second constraint condition in the constraint condition with the second optimized constraint condition to obtain an optimized constraint condition.

[0129] S4. Optimize and solve the route optimization model based on the optimized constraint condition to obtain the optimal route.

[0130] Among them, the specific step S4 is as follows:

[0131] Adopt a genetic algorithm and optimize and solve the route optimization model based on the optimized constraint condition to obtain the optimal route;

[0132] Specifically, the above genetic algorithm is a commonly used algorithm in the prior art, so it will not be elaborated here. Through the operators, crossover and mutation of the genetic operators, the optimal individual is obtained, that is, the optimal route is output.

[0133] The method for optimizing the goods transportation route provided in the first embodiment of the present invention first obtains a goods order set and a distribution vehicle set, constructs a distribution network based on the goods order set and the distribution vehicle set, and determines corresponding distribution orders for each distribution vehicle in the distribution vehicle set based on the distribution network; then constructs a route optimization model based on the distribution order and the corresponding distribution vehicle and determines the constraint conditions of the route optimization model; then performs interval optimization processing on the constraint conditions to obtain an optimized constraint condition; finally, optimizes and solves the route optimization model based on the optimized constraint condition to obtain the optimal route. The present invention first determines the best distribution order for each transport vehicle to improve the transport efficiency, and then constructs a route optimization model and optimizes the constraint conditions therein, fully considering various factors in the transport process and improving the speed of model solution, thereby determining the best route for the distribution order, improving the transport efficiency and saving the transport cost.

[0134] Embodiment 2

[0135] As Figure 2 shown, in the second embodiment of the present invention, a goods transportation route optimization system is provided. The system includes:

[0136] An allocation module 1, configured to obtain a goods order set and a distribution vehicle set, construct a distribution network based on the goods order set and the distribution vehicle set, and determine corresponding distribution orders for each distribution vehicle in the distribution vehicle set based on the distribution network;

[0137] A building module 2, configured to build an optimal route model based on the delivery order and the corresponding delivery vehicle, and determine the constraint conditions of the optimal route model;

[0138] An optimization module 3, configured to perform interval optimization processing on the constraint conditions to obtain optimized constraint conditions;

[0139] A solving module 4, configured to perform optimized solving on the optimal route model based on the optimized constraint conditions to obtain an optimal route.

[0140] Wherein, the allocation module 1 includes:

[0141] A network construction sub-module, configured to build a node-weighted delivery network with the goods orders in the goods order set;

[0142] A temporary allocation sub-module, configured to allocate a delivery vehicle to each node in the delivery network as an allocation set, and arbitrarily select a node in the delivery network , and temporarily allocate the neighbor nodes of the node to the corresponding allocation set of the node;

[0143] A calculation sub-module, configured to calculate the neighbor nodes :

[0144] ;

[0145] In the formula, represents the sum of the weights of all links in the delivery network, represents the sum of the weights of the links from the neighbor node to the node , represents the sum of the connection weights related to the neighbor node , represents the fitting parameter, represents the sum of the connection weights of all node edges in the corresponding allocation set of the node ;

[0146] A decision-making sub-module, configured to, if the set change value is greater than 0, put the neighbor node into the corresponding allocation set of the node, and if the set change value is not greater than 0, regard the neighbor node as an isolated node, re-select the neighbor nodes of the node and loop through the process of temporary node allocation to store several new nodes in several allocation sets;

[0147] An iteration sub-module, configured to determine iteration nodes based on the newly added nodes, and use the iteration nodes as the delivery orders of the delivery vehicles corresponding to the allocation set.

[0148] The iteration sub-module includes:

[0149] An identification unit, configured to identify the newly added nodes in the allocation set, and re-establish a new delivery network based on the newly added nodes;

[0150] An update unit, configured to replace the connection weights between the newly added nodes in the new delivery network with the connection weights between the nodes in the allocation set corresponding to the newly added nodes;

[0151] An iteration unit, configured to perform temporary allocation and set change value calculation on the newly added nodes iteratively based on the replaced connection weights until the set change value is 0, and output the iterated allocation set;

[0152] An output unit, configured to use the nodes in the iterated allocation set as iteration nodes, and use the iteration nodes as the delivery orders of the delivery vehicles corresponding to the allocation set.

[0153] The optimization module 3 includes:

[0154] A constraint identification sub-module, configured to identify the interval constraint conditions in the constraint conditions, where the interval constraint conditions include a first constraint condition and a second constraint condition. Among them, the first constraint condition is:

[0155] ;

[0156] The second constraint condition is:

[0157] ;

[0158] A first interval optimization sub-module, configured to perform interval optimization on the first constraint condition to obtain a first optimized constraint condition. Among them, the first optimized constraint condition is:

[0159] ;

[0160] ;

[0161] In the formula, represents the simplified value, , respectively represent the lower limit and the upper limit, is the preset satisfaction degree, represents the trade-off factor;

[0162] A second interval optimization sub-module, which is used to perform interval optimization on the second constraint condition to obtain a second optimized constraint condition, where the second optimized constraint condition is:

[0163] ;

[0164] In the formula, , respectively represent the lower limit and the upper limit;

[0165] A replacement sub-module, which is used to replace the first constraint condition in the constraint condition with the first optimized constraint condition and replace the second constraint condition in the constraint condition with the second optimized constraint condition to obtain an optimized constraint condition.

[0166] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solution. A computer includes a memory 102, a processor 101, and a computer program stored on the memory 102 and executable on the processor 101. When the processor 101 executes the computer program, the optimal route finding method for cargo transportation as described above is implemented.

[0167] Specifically, the above-mentioned processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present invention.

[0168] Among them, the memory 102 may include a mass storage for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 102 may include removable or non-removable (or fixed) media. In a suitable case, the memory 102 may be inside or outside the data processing device. In a particular embodiment, the memory 102 is a non-volatile memory. In a particular embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). In a suitable case, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. In a suitable case, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0169] The memory 102 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 101.

[0170] The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the above-mentioned optimal route finding method for goods transportation.

[0171] In some of these embodiments, the computer may further include a communication interface 103 and a bus 100. Among them, as Figure 3 shown, the processor 101, the memory 102, and the communication interface 103 are connected through the bus 100 and complete communication with each other.

[0172] The communication interface 103 is used to implement communication between the various modules, devices, units, and / or equipment in the embodiments of the present invention. The communication interface 103 can also implement data communication with other components, such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations, etc.

[0173] Bus 100 includes hardware, software, or both, and couples components of a computer device to each other. Bus 100 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. In a suitable case, Bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate specific buses, the present invention contemplates any suitable bus or interconnect.

[0174] The computer may execute the goods transportation route optimization method of the present invention based on the obtained goods transportation route optimization system, so as to realize the optimization of the goods transportation route.

[0175] In still some other embodiments of the present invention, in combination with the above-mentioned goods transportation route optimization method, embodiments of the present invention provide the following technical solution: a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned goods transportation route optimization method is realized.

[0176] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0177] More specific examples (non-exhaustive list) of the readable medium include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0178] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0179] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0180] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A method for optimizing cargo transportation routes, characterized in that: include: Acquire a cargo order set and a delivery vehicle set, build a delivery network based on the cargo order set and the delivery vehicle set, and determine a corresponding delivery order for each delivery vehicle in the delivery vehicle set based on the delivery network; Building a route optimization model based on the delivery order and the corresponding delivery vehicle and determining the constraint conditions of the route optimization model; Performing interval optimization processing on the constraint conditions to obtain optimized constraint conditions; Optimizing and solving the route optimization model based on the optimization constraints to obtain the optimal route; The step of performing interval optimization processing on the constraint conditions to obtain optimized constraint conditions comprises: An interval constraint in the constraint is identified, wherein the interval constraint includes a first constraint and a second constraint, wherein the first constraint is: ； In the formula, Respectively represent the path , , demand for goods, Represents a road network node or network node Adjacent network nodes, Respectively represent the path , , The cargo demand for delivery orders accounts for the proportion of delivery vehicles The proportion of goods demanded by all delivery orders, Indicates delivery vehicle The maximum load, Indicates delivery vehicle Frequency of transport; The second constraint is: ； In the formula, Indicates delivery vehicle At the network node To the network node The transportation time between Indicates delivery vehicle The ideal utilization rate, Indicates delivery vehicle The number of the corresponding vehicle type among all transport vehicles; The first constraint condition is interval optimized to obtain a first optimized constraint condition, wherein the first optimized constraint condition is: ； ； In the formula, represents the simplified value, , Respectively The lower and upper limits of To set satisfaction, represents the trade-off factor; The second constraint condition is interval optimized to obtain a second optimized constraint condition, wherein the second optimized constraint condition is: ； In the formula, , Respectively The lower and upper limits of The first optimization constraint condition is replaced by the first optimization constraint condition, and the second optimization constraint condition is replaced by the second optimization constraint condition, so as to obtain the optimization constraint condition.

2. The method for optimizing cargo transportation routes according to claim 1, characterized in that: The steps of constructing a distribution network based on the cargo order set and the distribution vehicle set, and determining a corresponding distribution order for each distribution vehicle in the distribution vehicle set based on the distribution network include: Constructing a node-weighted distribution network based on the cargo orders in the cargo order set; Assign a distribution vehicle to each node in the distribution network as a distribution set, and select a node at random in the distribution network. , the node Neighbor nodes Temporarily assigned to a node In the corresponding allocation set; Calculate neighbor nodes : ； In the formula, represents the weight sum of all links in the distribution network, Represents neighbor nodes To Node The weight of the link is Represents the neighboring nodes The associated connection weights and, represents the fitting parameters, Representation Node The sum of the connection weights of all node edges in the corresponding allocation set; If the collection changes value If it is greater than 0, the neighbor node Add a node In the corresponding allocation set, if the set changes value If it is not greater than 0, the neighbor node As an isolated node, reselect the node The neighbor nodes of the node are temporarily allocated in a loop to store several newly added nodes in several allocation sets; An iteration node is determined based on the newly added node, and the iteration node is used as a delivery order of a delivery vehicle corresponding to the allocation set.

3. The method for optimizing cargo transportation routes according to claim 2, characterized in that: The step of determining an iteration node based on the newly added node and using the iteration node as a delivery order of a delivery vehicle corresponding to the allocation set comprises: Identifying newly added nodes in the distribution set, and re-establishing a new distribution network based on the newly added nodes; Replacing the connection weights between the newly added nodes in the new distribution network with the connection weights between the nodes in the distribution set corresponding to the newly added nodes; Iteratively perform temporary allocation and set change value calculation on the newly added nodes based on the replaced connection weights until the set change value is 0, and output the iterative allocation set; The nodes in the iterated allocation set are used as iteration nodes, and the iteration nodes are used as delivery orders of the delivery vehicles corresponding to the allocation set.

4. The method for optimizing cargo transportation routes according to claim 1, characterized in that: In the step of constructing a route optimization model based on the delivery order and the corresponding delivery vehicle and determining the constraint conditions of the route optimization model, the route optimization model is: In the formula, represents the objective function, represents the driving cost, represents the vehicle cost, represents the environmental cost, Indicates delivery vehicle The unit trip cost of the vehicle, Represents a road network node To the network node The distance Indicates delivery vehicle From the road network node To the network node The decision value of Indicates delivery vehicle The startup costs, Indicates delivery vehicle The decision value to be used, represents the carbon emission factor, Indicates delivery vehicle The unit carbon emission cost is Indicates delivery vehicle The speed of the vehicle, Indicates the speed factor.

5. The method for optimizing cargo transportation routes according to claim 4, characterized in that: In the step of constructing a route optimization model based on the delivery order and the corresponding delivery vehicle and determining the constraint conditions of the route optimization model, the constraint conditions are: ； ； ； ； ； ； ； ； ； ； In the formula, represents the demand for delivery orders, Indicates delivery vehicle The maximum load, Indicates delivery vehicle From the road network node To the network node The decision value of Indicates that the order is being delivered Type of cargo height, volume, , Respectively represent delivery vehicles The height and volume of the cargo compartment, Indicates the number of delivery orders. Respectively represent the path , , demand for goods, Represents a road network node or network node Adjacent network nodes, Respectively represent the path , , The cargo demand for delivery orders accounts for the proportion of delivery vehicles The proportion of goods demanded by all delivery orders, Indicates delivery vehicle The frequency of transportation, Respectively represent delivery vehicles On the path , , The minimum transport frequency on Respectively represent delivery vehicles On the path , , The maximum transport frequency on Indicates delivery vehicle At the network node To the network node The transportation time between Indicates delivery vehicle The ideal utilization rate, Indicates delivery vehicle The number of the corresponding vehicle type among all transport vehicles.

6. The method for optimizing cargo transportation routes according to claim 1, characterized in that: The step of optimizing and solving the route optimization model based on the optimization constraint conditions to obtain the optimal route is specifically as follows: The route optimization model is optimized and solved by using a genetic algorithm based on the optimization constraints to obtain the optimal route.

7. A cargo transportation route optimization system, the system adopts the cargo transportation route optimization method according to claim 1, characterized in that: The system comprises: An allocation module, configured to obtain a cargo order set and a delivery vehicle set, construct a delivery network based on the cargo order set and the delivery vehicle set, and determine a corresponding delivery order for each delivery vehicle in the delivery vehicle set based on the delivery network; A construction module, used to construct a route optimization model based on the delivery order and the corresponding delivery vehicle and determine the constraint conditions of the route optimization model; An optimization module, used for performing interval optimization processing on the constraint conditions to obtain optimized constraint conditions; A solution module is used to optimize and solve the route optimization model based on the optimization constraints to obtain the optimal route.

8. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for optimizing the cargo transportation route according to any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for optimizing the freight transportation route according to any one of claims 1 to 6 is implemented.

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