Method for intelligent optimization of delivery routes

Abstract

The present invention provides a method for intelligent optimization of distribution paths, which belongs to the field of logistics distribution technology, and includes: uploading multiple logistics information to the system, so that the corresponding multiple loading points and unloading points in the multiple logistics information are all marked on the system. Determine the distance between the vehicle starting point and multiple loading points and the distance between each loading point and each unloading point, and the system integrates and determines multiple transportation plans. The system determines the transportation cost and estimated time for completing the transportation plan, and determines the rate of return based on the freight, transportation cost and estimated time of multiple logistics information. Sort and display multiple transportation plans in order of rate of return from large to small. The method for intelligent optimization of distribution paths provided by the present invention allows car owners to choose a plan with a higher rate of return for transportation, and the result is accurate and reliable, ensuring the car owner's income.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics distribution, and more specifically, relates to a method for intelligent optimization of distribution paths. Background Art

[0002] With the advancement of logistics informatization, logistics information systems have developed rapidly in my country. Logistics information platforms gather logistics information through the Internet, reducing the asymmetry between logistics demand and resources. Among the businesses matched through logistics information platforms, transportation (involving vehicle sources and cargo sources) is the most important business.

[0003] In order to reduce transportation costs, existing technologies often optimize factors such as transportation distance and the loading capacity of transportation vehicles. The above optimization can reduce transportation costs to a certain extent. Existing methods usually require vehicle owners to select loading and unloading points through the logistics system based on their own experience. The whole process is cumbersome and some logistics information with higher freight costs is ignored, resulting in lower profits for vehicle owners. Summary of the invention

[0004] The purpose of the present invention is to provide a method for intelligent optimization of delivery routes, aiming to solve the problem of complicated process and low benefits for vehicle owners.

[0005] To achieve the above object, the technical solution adopted by the present invention is: to provide a method for intelligent optimization of distribution paths, including:

[0006] Uploading a plurality of logistics information into the system, so that a plurality of loading points and unloading points corresponding to the plurality of logistics information are marked on the system;

[0007] Determine the distance between the vehicle starting point and the plurality of loading points and the distance between each loading point and each unloading point, and the system integrates and determines a plurality of transportation plans;

[0008] The system determines the transportation cost and estimated time for completing the transportation plan, and determines the rate of return based on the freight, transportation cost and estimated time of the plurality of logistics information;

[0009] The plurality of transport plans are sorted and displayed in descending order of the rate of return.

[0010] In a possible implementation, the determining of the rate of return based on the freight, the transportation cost and the estimated time of the plurality of logistics information includes:

[0011] The freight is subtracted from the transportation cost, and the result of the subtraction is divided by the estimated time to obtain the rate of return.

[0012] In a possible implementation, the step of marking corresponding loading points and unloading points in the plurality of logistics information on the system includes:

[0013] A first search value is set, and a first search range is formed with the starting point as the center and the first search value as the radius, and a plurality of the loading points located in the first search range are retained in the system, and the rest are eliminated.

[0014] In a possible implementation manner, after forming a first search range with the starting point as the center and the first search value as the radius, the method includes:

[0015] Setting the plurality of loading points located in the search range as corresponding plurality of first loading points;

[0016] Set a second search value, form a second search range with the first loading point as the center and the second search value as the radius, and set the first loading points located in the second search range as the corresponding second loading points;

[0017] A third search value is set, a third search range is formed with the second loading point as the center and the third search value as the radius, and a plurality of the second loading points located in the third search range are set as corresponding plurality of third loading points.

[0018] In a possible implementation, the first search value, the second search value and the third search value are respectively determined by the current carrying capacity of the vehicle and the corresponding loading capacity of the first loading point, the loading capacity of the second loading point and the loading capacity of the third loading point.

[0019] In a possible implementation, the system integrating and determining multiple transportation solutions includes:

[0020] Randomly arranging and combining the plurality of loading points and the plurality of unloading points to form a plurality of options;

[0021] According to the principle of loading before unloading, a plurality of the candidate solutions are screened, and the screened candidate solutions are used as the transportation solution.

[0022] In one possible implementation, the system includes map navigation software, and the system uses the map navigation software to determine the minimum distance between the starting point and the loading point, between two loading points, and between the loading point and the unloading point, and determines the transportation cost and the estimated time.

[0023] In a possible implementation, sorting and displaying the plurality of transport solutions in descending order of the yield rate includes:

[0024] The vehicle owner adjusts the corresponding transportation plan;

[0025] The system adjusts the final yields of the multiple transportation plans based on the feedback from the vehicle owners.

[0026] In a possible implementation, the transportation cost includes fuel costs, tolls, accommodation fees, maintenance fees, etc.

[0027] In a possible implementation, the determining of the rate of return based on the freight, the transportation cost and the estimated time of the plurality of logistics information includes:

[0028] Based on the principle that the greater the load, the higher the fuel cost, the fuel cost is adjusted according to the current load capacity of the vehicle.

[0029] The beneficial effect of the method for intelligent optimization of distribution paths provided by the present invention is that: compared with the prior art, the method for intelligent optimization of distribution paths provided by the present invention has multiple logistics information in the system, each of which includes freight, loading point and unloading point. Determine the distance between the starting point of the vehicle and multiple loading points and the distance between each loading point and each unloading point; when the distance is determined, multiple transportation plans will be formed through system integration, each of which takes the starting point as the starting point, and then arrives at the loading point and the corresponding unloading point in sequence, or arrives at multiple loading points in sequence and then unloads.

[0030] Once the transportation plan is determined, the system can determine the freight, transportation cost and estimated time corresponding to multiple logistics information. The estimated time is the time from the starting point to the last unloading point in the transportation plan. The calculated rate of return can determine the profit situation in units of time.

[0031] In this application, the system determines multiple transportation plans based on factors such as the distance and freight rate. The car owner can choose a plan with a higher yield rate for transportation. The results are accurate and reliable, ensuring the car owner's income. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] 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.

[0033] Figure 1 A flow chart of a method for intelligent optimization of delivery routes provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] See also Figure 1 The method for intelligent optimization of the distribution path provided by the present invention is now described. The method for intelligent optimization of the distribution path includes:

[0036] Upload multiple logistics information to the system, so that the corresponding multiple loading points and unloading points in the multiple logistics information are marked on the system.

[0037] Determine the distance between the vehicle starting point and multiple loading points, as well as the distance between each loading point and each unloading point, and the system integrates and determines multiple transportation plans.

[0038] The system determines the transportation cost and estimated time to complete the transportation plan, and determines the rate of return based on the freight, transportation cost and estimated time of multiple logistics information.

[0039] Sort and display multiple transportation plans in descending order of yield.

[0040] The beneficial effect of the method for intelligent optimization of distribution paths provided by the present invention is that: compared with the prior art, the method for intelligent optimization of distribution paths provided by the present invention has multiple logistics information in the system, each of which includes freight, loading point and unloading point. Determine the distance between the starting point of the vehicle and multiple loading points and the distance between each loading point and each unloading point; when the distance is determined, multiple transportation plans will be formed through system integration, each of which takes the starting point as the starting point, and then arrives at the loading point and the corresponding unloading point in sequence, or arrives at multiple loading points in sequence and then unloads.

[0041] Once the transportation plan is determined, the system can determine the freight, transportation cost and estimated time corresponding to multiple logistics information. The estimated time is the time from the starting point to the last unloading point in the transportation plan. The calculated rate of return can determine the profit situation in units of time.

[0042] In this application, the system determines multiple transportation plans based on factors such as the distance and freight rate. The car owner can choose a plan with a higher yield rate for transportation. The results are accurate and reliable, ensuring the car owner's income.

[0043] With the continuous development of network technology, most logistics information is now published on the Internet, and then humans select the appropriate logistics information based on the current location of the vehicle and past experience, and then drive the goods from the loading point to the unloading point.

[0044] Although the above methods can ensure the continuous operation of the logistics system, they do not maximize the interests of the car owners, because the vehicle will incur fuel costs, accommodation costs, meal costs, tolls and vehicle maintenance during the driving process. Although some logistics information indicates higher freight rates, the corresponding driving distance and the number of days spent may also be higher. At present, the receipt of which logistics information is more based on the personal experience of the car owner, which ultimately leads to low profits or even no profit for some car owners.

[0045] The present application aims to provide an intelligent optimization method for logistics routes, which combines the operating costs of vehicles and the time required for transportation and makes comprehensive considerations based on the published logistics information, and finally converts the corresponding logistics information into a benefit rate. The benefit rate can clearly show the possible benefits to the car owner, thereby ensuring the reasonable benefits of the car owner.

[0046] In some embodiments of the method for intelligent optimization of distribution paths provided in the present application, the rate of return determined by freight, transportation cost and estimated time of multiple logistics information includes:

[0047] Subtract the freight from the transportation cost, and divide the result by the estimated time to get the rate of return.

[0048] In order to maximize the benefits of the vehicle owner, if only one logistics task is completed at a time, although it is convenient to operate, the corresponding benefits may be lower, especially when the loading and unloading points of the two logistics tasks are close. In order to solve the above problem, firstly, based on the starting point set by the vehicle owner, the distance, time and corresponding cost of the vehicle to all loading points within the search range are calculated.

[0049] It should be noted that the volume and weight of the cargo corresponding to the loading point should match the type and model of the vehicle. When a loading point can make the vehicle nearly fully loaded, the time to reach the unloading point and the corresponding rate of return are determined based on the distance between the loading point and the unloading point.

[0050] After determining the total cost of running the vehicle from the starting point, loading point, and unloading point, the number of days required and the estimated time to arrive at the unloading point are also required.

[0051] If the vehicle is nearly full by loading at the loading point, the logistics task can be completed by driving to the unloading point after loading. The rate of return can be determined based on the time spent, the freight and operating costs of the logistics task.

[0052] The rate of return is calculated by subtracting the operating cost from the freight and then dividing the difference by the time. The algorithm for the rate of return for completing two or more logistics tasks still follows the above formula, but the corresponding freight is the result of the superposition of multiple logistics tasks.

[0053] In some embodiments of the method for intelligent optimization of distribution paths provided in the present application, the steps of calibrating corresponding loading points and unloading points in the plurality of logistics information on the system include:

[0054] A first search value is set, and a first search range is formed with the starting point as the center and the first search value as the radius. Multiple loading points located in the first search range are retained in the system, and the rest are eliminated.

[0055] First, the vehicle owner determines the current location of the vehicle and related information of the vehicle. The system screens the information uploaded by the vehicle owner and blocks some logistics information that cannot be transported. The vehicle information includes the vehicle's loadable weight, loadable volume, etc.

[0056] According to the information submitted by the vehicle owner, all loading points of suitable logistics information are marked in the system. After the system confirms the starting point set by the vehicle owner, the system sets the first search range with the starting point as the center and the set first search value as the radius, and then retains the loading points in the first search range and eliminates the rest. The system includes map navigation software, which determines the shortest distance between the starting point and multiple loading points.

[0057] Generally, the first search value is set by the car owner and the system together, and in order to ensure the car owner's profit, the system will appropriately amplify the first search value to increase the amount of compared samples and maximize the car owner's profit.

[0058] In some embodiments of the method for intelligent optimization of delivery paths provided in the present application, after forming a first search range with the starting point as the center and the first search value as the radius, the method includes:

[0059] A plurality of loading points within the search range are set as corresponding plurality of first loading points.

[0060] A second search value is set, a second search range is formed with the first loading point as the center and the second search value as the radius, and a plurality of first loading points located in the second search range are set as corresponding plurality of second loading points.

[0061] A third search value is set, a third search range is formed with the second loading point as the center and the third search value as the radius, and multiple second loading points located in the third search range are set as corresponding multiple third loading points.

[0062] When the vehicle is loaded with goods, the heavier the weight of the goods, the more fuel the vehicle consumes. After the vehicle is loaded with goods at the first loading point, the more goods loaded, the higher the cost required for the vehicle to travel for a long time.

[0063] Therefore, the second search value needs to be adjusted accordingly according to the amount of goods loaded at the first loading point. For a more detailed explanation, there are three first loading points within the first search value range through system screening. The goods corresponding to the three first loading points are A, B and C, and A>B>C. Then when A, B and C do not make the vehicle close to full load, it is necessary to search with the three first loading points as the center and the second search value as the radius. However, the sizes of these three second search values ​​decrease. The purpose of this is to prevent the vehicle from carrying goods for a long distance, which leads to an increase in transportation costs.

[0064] In order to improve the efficiency of the entire system, a database is established based on the vehicle model, and the vehicle load and search value are matched and stored in the database. The database stores different vehicle loads and appropriate search values. As the vehicle load increases, the search value will decrease accordingly.

[0065] The establishment of the database can be provided by each car owner, and the original stored data in the database needs to be adjusted according to the actual operation of the vehicle to ensure the maximum benefit for the car owner.

[0066] In some embodiments of the method for intelligent optimization of delivery paths provided in the present application, the first search value, the second search value and the third search value are respectively determined by the current carrying capacity of the vehicle and the corresponding loading capacity of the first loading point, the loading capacity of the second loading point and the loading capacity of the third loading point.

[0067] The first search value is usually determined by the vehicle owner. After the first search value determines the first loading point, if the vehicle can continue to be loaded, the maximum cargo volume and weight that the vehicle can load is used as the maximum threshold to screen suitable logistics tasks.

[0068] Since there may be cargo on the vehicle when the vehicle reaches the second loading point, the second search value will be reduced accordingly, and the third search value will be reduced accordingly compared to the second search value, and so on.

[0069] In some embodiments of the method for intelligent optimization of distribution paths provided in the present application, the system integrates and determines multiple transportation solutions including:

[0070] Multiple loading points and multiple unloading points are randomly arranged and combined to form multiple options.

[0071] According to the principle of loading before unloading, multiple candidate plans are screened and the screened candidate plans are used as transportation plans.

[0072] When the first loading point has less cargo to transport or the vehicle can continue to load other cargo, the current first loading point is taken as the center of the circle, and then all the loading points within the range of the second search value are determined and retained based on the second search value. The difference is made according to the vehicle's carrying capacity and the cargo corresponding to the first loading point, and the result of the difference is compared with the cargo quantity corresponding to each second loading point. When the vehicle can carry the cargo, it is set as the second loading point, otherwise it is eliminated.

[0073] Since the relative positions between the first loading point and the second loading point vary greatly, and the first loading point may correspond to multiple second loading points. In order to determine the most optimized solution, the distance between the first loading point and the second loading point, the distance between the first unloading point and the first loading point, and the distance between the first unloading point and the second unloading point are calculated, so as to determine three solutions. The first solution is to load from the first loading point and the second loading point in turn, and then unload at the first unloading point and the second unloading point in turn; the second solution is to reach the first loading point and the first unloading point first, and then reach the second loading point and the second unloading point; the third solution is to reach the first loading point and the second loading point in turn, and then reach the second unloading point and the first unloading point.

[0074] Since the above three options involve different distances, the required costs and the number of days will be different. Therefore, it is necessary to calculate the operating costs and estimated time of the three options separately, and then determine the three profit values.

[0075] After the three profit values ​​are determined, they are sent to the car owner through the system, and the car owner chooses which plan according to his or her own situation.

[0076] Since some vehicles can carry more cargo or the loading points have less cargo to transport, there is still space for further loading after loading the cargo at two loading points.

[0077] At this time, you need to set the third search value, and determine the third loading point with the second loading point as the center and the third search value as the radius. At this time, a lot of data needs to be calculated. If a very large amount of data needs to be calculated, it is impossible to complete the above work quickly manually, so you need to call the relevant function modules in the system to complete the relevant calculations.

[0078] For more detailed explanation, the first loading point, the second loading point, the third loading point, the first unloading point, the second unloading point and the third unloading point are replaced by A, B, C, D, E and F respectively. Then, with A as the first letter, the other 5 letters are randomly arranged and combined to get 120 plans. Then, unreasonable plans are eliminated from these plans, such as going to E first and then to B and going to F first and then to C.

[0079] After eliminating unreasonable plans, the distances between the locations indicated by the two letters are calculated respectively, and then multiple benefit values ​​are calculated based on the calculation results. The multiple benefit values ​​are then sorted in descending order and sent to the car owner.

[0080] In some embodiments of the method for intelligent optimization of distribution paths provided in the present application, the system includes map navigation software, and the system uses the map navigation software to determine the minimum distance between the starting point and the loading point, between two loading points, and between the loading point and the unloading point, and determines the transportation cost and estimated time.

[0081] The plans for completing only one logistics task at a time are arranged from the highest to the lowest rate of return, the plans for completing two logistics tasks at a time are arranged from the highest to the lowest rate of return, and the plans for completing three logistics tasks at a time are arranged from the highest to the lowest rate of return. When all the arrangements are completed, the car owner will make a choice based on the planned distance and the corresponding estimated time.

[0082] When it is confirmed that the transportation has started, the relevant logistics information will be removed from the system, and the time and route of the next destination will be sent to the vehicle owner for real-time navigation.

[0083] After the owner has started the transportation, the system calculates the estimated arrival time based on the vehicle's speed and informs the driver of the corresponding information.

[0084] In some embodiments of the method for intelligent optimization of distribution paths provided in the present application, multiple transportation plans are sorted and displayed in descending order of yield, including:

[0085] The vehicle owner shall adjust the corresponding transportation plan.

[0086] The system adjusts the final rate of return of multiple transportation plans based on the feedback from vehicle owners.

[0087] Due to the different number of drivers in the vehicle, the driving time of the drivers varies. Therefore, the arrival time determined by the system may be different from the actual arrival time of the vehicle, which may lead to a large difference between the final determined benefit value and the actual benefit situation. In order to solve the above problem, after the system determines the estimated time, the owner of the vehicle confirms the above time and modifies and adjusts the estimated time according to his own situation.

[0088] After receiving the modification feedback from the car owner, the system will re-adjust the benefit value accordingly. When the car owner advances the estimated time of arrival of the vehicle at the corresponding location, the benefit value will be appropriately increased according to the advance time; when the estimated time of arrival of the vehicle at the corresponding location is delayed, the benefit value will be appropriately reduced.

[0089] Since the road information on the map navigation software may not be updated in a timely manner, or the car owner has his own planned route, when the system feedbacks the transportation plan to the car owner, the car owner can modify the estimated path given by the system according to his own situation. Due to the path modification, the corresponding estimated time and benefit value also need to be adjusted accordingly.

[0090] In some embodiments of the method for intelligent optimization of delivery routes provided in this application, the transportation costs include fuel costs, tolls, accommodation costs, and maintenance costs, etc. As the estimated time increases, the transportation costs will increase accordingly, and the fuel costs, tolls, accommodation costs, and maintenance costs are jointly determined by the system and the vehicle owner.

[0091] In some embodiments of the method for intelligent optimization of distribution paths provided in the present application, the rate of return determined by freight, transportation cost and estimated time of multiple logistics information includes:

[0092] Based on the principle that the heavier the load, the higher the fuel cost, the fuel cost will be adjusted according to the vehicle's current load capacity.

[0093] When a vehicle is carrying goods, the fuel consumption during driving will increase accordingly. At this time, if the rate of return is still determined by the fuel consumption per kilometer when the vehicle is empty, the final result will inevitably be different from the actual situation.

[0094] In order to solve the above problem, the vehicle owner needs to upload the corresponding vehicle information, and then determine the vehicle's fuel consumption data at different loads based on the vehicle model. When the vehicle arrives at the loading point and loads the goods, the system will determine the corresponding fuel cost based on the weight of the loaded goods and the distance to the next destination.

[0095] When the vehicle is loaded with goods again or unloads the corresponding goods, the system will make real-time adjustments to the fuel consumption based on the amount of goods carried on the vehicle at this time.

[0096] Through the above adjustments, the final rate of return is guaranteed to be closer to the actual level, and data such as fuel consumption can also be stored in the database in advance to facilitate the system's access at any time.

[0097] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent optimization of distribution paths, characterized in that: include: Uploading a plurality of logistics information into the system, so that a plurality of loading points and unloading points corresponding to the plurality of logistics information are marked on the system; Determine the distance between the vehicle starting point and the plurality of loading points and the distance between each loading point and each unloading point, and the system integrates and determines a plurality of transportation plans; The system determines the transportation cost and estimated time for completing the transportation plan, and determines the rate of return based on the freight, transportation cost and estimated time of the plurality of logistics information; Sorting and displaying the plurality of transport plans in descending order of the rate of return; The step of making the corresponding multiple loading points and unloading points in the multiple logistics information all marked on the system includes: Set a first search value, form a first search range with the starting point as the center and the first search value as the radius, retain the plurality of loading points located in the first search range in the system, and remove the rest; After forming a first search range with the starting point as the center and the first search value as the radius, the method includes: Setting the plurality of loading points located in the search range as corresponding plurality of first loading points; Set a second search value, form a second search range with the first loading point as the center and the second search value as the radius, and set the first loading points located in the second search range as the corresponding second loading points; Set a third search value, form a third search range with the second loading point as the center and the third search value as the radius, and set a plurality of the second loading points located in the third search range as corresponding plurality of third loading points; The first search value, the second search value and the third search value are respectively determined by the current carrying capacity of the vehicle and the corresponding loading capacity of the first loading point, the loading capacity of the second loading point and the loading capacity of the third loading point.

2. The method for intelligent optimization of distribution paths according to claim 1, characterized in that: The rate of return determined by the freight, the transportation cost and the estimated time of the plurality of logistics information includes: The freight is subtracted from the transportation cost, and the result of the subtraction is divided by the estimated time to obtain the rate of return.

3. The method for intelligent optimization of distribution paths according to claim 1, characterized in that: The system integrates and determines multiple transportation solutions including: Randomly arranging and combining the plurality of loading points and the plurality of unloading points to form a plurality of options; According to the principle of loading before unloading, a plurality of the candidate solutions are screened, and the screened candidate solutions are used as the transportation solution.

4. The method for intelligent optimization of distribution paths according to claim 1, characterized in that: The system includes map navigation software, and the system uses the map navigation software to determine the minimum distance between the starting point and the loading point, between two loading points, and between the loading point and the unloading point, and determines the transportation cost and the estimated time.

5. The method for intelligent optimization of distribution paths according to claim 1, characterized in that: The step of sorting and displaying the plurality of transport schemes in descending order of the rate of return comprises: The vehicle owner adjusts the corresponding transportation plan; The system adjusts the final yields of the multiple transportation plans based on the feedback from the vehicle owners.

6. The method for intelligent optimization of distribution paths according to claim 1, characterized in that: The transportation costs include fuel, tolls, accommodation and maintenance costs.

7. The method for intelligent optimization of distribution paths according to claim 6, characterized in that: The rate of return determined by the freight, the transportation cost and the estimated time of the plurality of logistics information includes: Based on the principle that the greater the load, the higher the fuel cost, the fuel cost is adjusted according to the current load capacity of the vehicle.

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