Goods transportation route optimization method and system

Abstract

The invention provides a cargo transportation route optimization method and system, and the method comprises the steps: obtaining a cargo order set and a delivery vehicle set, constructing a delivery network based on the cargo order set and the delivery vehicle set, and determining a corresponding delivery order for each delivery vehicle in the delivery vehicle set based on the delivery network; constructing a route optimization model based on the delivery order and the corresponding delivery vehicle, and determining a constraint condition of the route optimization model; performing interval optimization processing on the constraint condition to obtain an optimized constraint condition; according to the method, factors in all aspects in the transportation process are fully considered, the model solving speed is improved, the optimal route of the delivery order is determined, the transportation efficiency is improved, and the transportation cost is saved.

Description

Technical Field

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

[0002] For current logistics centers or storage centers, their shipping mode usually determines the destination based on the commodity order, and assigns the order to the corresponding delivery person. The delivery person selects the corresponding route based on the designated route or his own experience to complete the transportation process of the commodity or goods. Although there are methods in the prior art for assigning a corresponding route to each order, the process of assigning the route does not take into account factors such as the path, cost, and characteristics of the goods, so that the assigned route cannot meet the optimal requirements, thereby affecting the efficiency and cost of the goods transportation. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method and system for optimizing cargo transportation routes, which are used 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 a cargo transportation route, comprising: 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; The route optimization model is optimized and solved based on the optimization constraints to obtain the optimal route.

[0005] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains a cargo order set and a delivery vehicle set, constructs a delivery network based on the cargo order set and the delivery vehicle set, and determines a corresponding delivery order for each delivery vehicle in the delivery vehicle set based on the delivery network; then constructs a route optimization model based on the delivery order and the corresponding delivery vehicle and determines the constraints of the route optimization model; then performs interval optimization processing on the constraints to obtain optimized constraints; finally, based on the optimized constraints, the route optimization model is optimized and solved to obtain the optimal route. The present invention first determines the best delivery order for each transport vehicle to improve the efficiency of transportation, and then constructs a route optimization model and optimizes the constraints therein, fully considering various factors in the transportation process and improving the speed of model solution, so as to determine the best route for the delivery order, improve transportation efficiency and save transportation costs.

[0006] Preferably, the step 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 comprises: 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.

[0007] Preferably, 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 includes: 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.

[0008] 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 route optimization model is:

[0009] 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.

[0010] 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: ; ; ; ; ; ; ; ; ; ; 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 road 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.

[0011] Preferably, the step of performing interval optimization processing on the constraint condition to obtain the optimized constraint condition 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: ; The second constraint is: ; 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.

[0012] Preferably, 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.

[0013] In a second aspect, the present invention provides the following technical solution, a cargo transportation route optimization system, the system comprising: 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.

[0014] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for optimizing cargo transportation routes when executing the computer program.

[0015] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned method for optimizing a cargo transportation route. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for optimizing a cargo transportation route provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of a cargo transportation route optimization system provided in Embodiment 2 of the present invention; Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0020] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0022] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0023] Embodiment 1 In the first embodiment of the present invention, Figure 1 As shown, a method for optimizing a cargo transportation route includes: S1. Obtain 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; Specifically, for the cargo order set, there are several cargo orders, and for the delivery vehicle set, there are several delivery vehicles. For the delivery vehicles, due to cost and time considerations, some orders may not be suitable for certain delivery orders. Therefore, a delivery network is constructed and suitable delivery orders are allocated to suitable delivery vehicles according to the distribution network.

[0024] Wherein, the step S1 comprises: S11, constructing a node-weighted distribution network based on the goods orders in the goods order set; Specifically, each cargo order is taken as a node to construct a directed graph network, so the distribution network is specifically a directed graph network.

[0025] S12, assigning a delivery vehicle to each node in the delivery network as a distribution set, and arbitrarily selecting a node in the delivery network , the node Neighbor nodes Temporarily assigned to a node In the corresponding allocation set; Among them, a distribution set can contain multiple nodes, that is, a delivery vehicle can correspond to multiple delivery orders.

[0026] S13. 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; Among them, in this embodiment It is 0.88.

[0027] S14. 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; Specifically, when the collection changes value When it is greater than 0, it means that the neighbor node B is placed in the node There will be positive benefits in the corresponding allocation set, so the neighbor node B can be placed in the node In the corresponding distribution set, when the set changes value When it is equal to 0, it means that the neighbor node B is placed in the node The corresponding allocation set will not generate any benefits, so it is treated as an isolated node. When it is equal to 0, it means that the neighbor node B is placed in the node Negative benefits will be generated in the corresponding allocation set, so it will be treated as an isolated node. The isolated node in this step can be used as the adjacent node of the remaining nodes and the above steps are repeated until all nodes are allocated.

[0028] S15, 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; Wherein, the step S15 comprises: S151, identifying a newly added node in the allocation set, and re-establishing a new distribution network based on the newly added node; Specifically, the new distribution network is also a directed graph, but the affiliation relationship between the nodes and the allocation set has changed compared to the distribution network.

[0029] S152, 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; Specifically, this step is the process of updating the connection weights of the nodes in the directed graph.

[0030] S153, 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.

[0031] S154, taking the nodes in the iterated allocation set as iteration nodes, and taking the iteration nodes as the delivery orders of the delivery vehicles corresponding to the allocation set; Specifically, after iterating the above steps, there are a certain number of nodes in each allocation set, so as to allocate the cargo order to the best delivery vehicle.

[0032] S2. 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; Among them, the route optimization model is:

[0033] 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, represents the speed coefficient; Among them, when the delivery vehicle When used, is 1, otherwise it is 0. The speed parameter can be determined according to the vehicle model, which can be expressed as 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.

[0034] Specifically, the route optimization model in this embodiment optimizes the vehicle's driving cost, the vehicle's fixed cost, and the vehicle's carbon emission cost.

[0035] The constraints 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 road 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; Specifically, according to the order of constraints, it respectively constrains vehicle load, constrains access to road network nodes, constrains vehicle cargo height, constrains vehicle load volume, constrains the loop of road network nodes, constrains the frequency of transport vehicles, and constrains the transport time; Among them, for the decision value in the above constraints, when the delivery vehicle From the road network node To the network node hour, is 1, otherwise it is 0. From the road network node To the network node hour, is 1 if the value is true, otherwise it is 0.

[0036] S3, performing interval optimization processing on the constraint conditions to obtain optimized constraint conditions; Among them, for the constraints, there are some constraints with uncertain parameters. Therefore, by performing interval optimization processing on them, making them equivalent to accurate parameter intervals, the speed of model solution can be improved.

[0037] Wherein, the step S3 comprises: S31, identifying an interval constraint in the constraint, wherein the interval constraint includes a first constraint and a second constraint, wherein the first constraint is: ; The second constraint is: .

[0038] S32. Perform interval optimization on the first constraint condition 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; Specifically, the trade-off factor is used to weigh the relationship between the midpoint value of the interval and the interval width. The larger the trade-off factor is, the more the midpoint value of the interval meets the requirements. The preset satisfaction level can be expressed as the satisfaction level of interval optimization.

[0039] S33, performing interval optimization on the second constraint condition to obtain a second optimized constraint condition, wherein the second optimized constraint condition is: ; In the formula, , Respectively The lower and upper limits of .

[0040] S34. Replace the first constraint in the constraint conditions with the first optimization constraint condition and replace the second constraint in the constraint conditions with the second optimization constraint condition to obtain optimization constraint conditions.

[0041] S4. Optimizing and solving the route optimization model based on the optimization constraints to obtain the optimal route.

[0042] Wherein, the step S4 is specifically as follows: Using a genetic algorithm and based on the optimization constraints, the route optimization model is optimized to obtain an optimal route; Specifically, the above genetic algorithm is a commonly used algorithm in the prior art, so it is not described in detail. Through the operators, crossover and mutation of the genetic operators, the optimal individual is obtained, that is, the optimal route is output.

[0043] The cargo transportation route optimization method provided in the first embodiment of the present invention first obtains a cargo order set and a delivery vehicle set, constructs a delivery network based on the cargo order set and the delivery vehicle set, and determines a corresponding delivery order for each delivery vehicle in the delivery vehicle set based on the delivery network; then constructs a route optimization model based on the delivery order and the corresponding delivery vehicle and determines the constraints of the route optimization model; then performs interval optimization processing on the constraints to obtain optimized constraints; finally, optimizes and solves the route optimization model based on the optimized constraints to obtain the optimal route. The present invention first determines the best delivery order for each transport vehicle to improve the efficiency of transportation, then constructs a route optimization model and optimizes the constraints therein, fully considers various factors in the transportation process and improves the speed of model solution, thereby determining the best route for the delivery order, improving transportation efficiency and saving transportation costs.

[0044] Embodiment 2 like Figure 2 As shown, in the second embodiment of the present invention, a system for optimizing cargo transportation routes is provided, and the system includes: Allocation module 1, used for obtaining a cargo order set and a delivery vehicle set, building a delivery network based on the cargo order set and the delivery vehicle set, and determining a corresponding delivery order for each delivery vehicle in the delivery vehicle set based on the delivery network; Construction module 2, 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 3 is used to perform interval optimization processing on the constraint conditions to obtain optimized constraint conditions; The solution module 4 is used to optimize and solve the route optimization model based on the optimization constraint conditions to obtain the optimal route.

[0045] Among them, the allocation module 1 includes: A network construction submodule, used to construct a node-weighted distribution network based on the cargo orders in the cargo order set; The temporary allocation submodule is used to allocate a distribution vehicle as a distribution set to each node in the distribution network. , the node Neighbor nodes Temporarily assigned to a node In the corresponding allocation set; Calculation submodule, used to 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; The decision submodule is used to change the value of the collection 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; The iterative submodule is used to determine an iterative node based on the newly added node, and use the iterative node as a delivery order of the delivery vehicle corresponding to the allocation set.

[0046] The iterator module includes: an identification unit, configured to identify a newly added node in the distribution set and re-establish a new distribution network based on the newly added node; An updating unit, configured to replace 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; an iterative unit, configured to 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 an iterated allocation set; The output unit is used to use the nodes in the iterated distribution set as iteration nodes, and use the iteration nodes as delivery orders of the delivery vehicles corresponding to the distribution set.

[0047] The optimization module 3 comprises: The constraint identification submodule is used to identify the interval constraint conditions in the constraint conditions, wherein the interval constraint conditions include the first constraint condition and the second constraint condition, wherein the first constraint condition is: ; The second constraint is: ; The first interval optimization submodule is used to perform interval optimization on the first constraint condition to obtain a first optimization constraint condition, wherein the first optimization 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 interval optimization submodule is used to perform interval optimization on the second constraint condition to obtain a second optimization constraint condition, wherein the second optimization constraint condition is: ; In the formula, , Respectively The lower and upper limits of The replacement submodule is used to replace the first constraint condition in the constraint conditions with the first optimization constraint condition and to replace the second constraint condition in the constraint conditions with the second optimization constraint condition to obtain the optimization constraint condition.

[0048] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solution: a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, wherein the processor 101 implements the cargo transportation route optimization method as described above when executing the computer program.

[0049] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0050] Among them, the memory 102 may include a large-capacity memory 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 disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, 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 (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can 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.

[0051] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0052] The processor 101 implements the above-mentioned cargo transportation route optimization method by reading and executing the computer program instructions stored in the memory 102.

[0053] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0054] The communication interface 103 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present invention. The communication interface 103 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0055] The bus 100 includes hardware, software or both, and couples the components of the computer device to each other. The bus 100 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a 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 buses or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0056] The computer can execute the cargo transportation route optimization method of the present invention based on the cargo transportation route optimization system obtained, thereby realizing cargo transportation route optimization.

[0057] In some further embodiments of the present invention, in combination with the above-mentioned method for optimizing cargo transportation routes, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned method for optimizing cargo transportation routes when executed by a processor.

[0058] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0059] More specific examples of readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0060] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned 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, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0061] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached 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; The route optimization model is optimized and solved based on the optimization constraints to obtain the optimal route.

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 5, characterized in that: 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: ； The second constraint is: ； 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.

7. 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.

8. A cargo transportation route optimization system, 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.

9. 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 7 is implemented.

10. 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 7 is implemented.

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