Low-altitude logistics distribution method combined with idle transport capacity of public transport

Abstract

The invention discloses a low-altitude logistics distribution method in combination with idle transport capacity of public transport. The method comprises the following steps: collecting basic parameters and user preference parameters of an article transport task input by a user; matching public transport operation data and other global variables according to task basic parameters input by a user, and screening available bus rail shifts and line combinations; inputting the bus rail shifts and line combinations into a multi-target genetic algorithm to solve an optimal low-altitude vehicle joint transportation scheme required by a user; the user confirms the scheme, if the scheme is confirmed, the step S5 is executed, and if the user does not confirm the scheme, the step S6 is executed; s5, updating the database, and issuing the task plan to a business department; and S6, resetting the user preference, and entering the multi-objective genetic algorithm again to solve a new scheme of the user preference. The problem that idle traffic capacity resources are wasted due to the fact that an existing low-altitude logistics distribution method lacks utilization of idle traffic capacity is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics configuration, and in particular, relates to a low-altitude logistics distribution method combining idle transportation capacity of public transportation. Background Art

[0002] At present, most of the personnel or logistics distribution points for the domestic low-altitude economy choose existing buses, rails or hub stations in the city (because they have sufficient ground and above-ground space). How to make good use of the existing idle capacity of public transportation to cooperate with low-altitude transportation lines to achieve more economical and faster operation goals has become an urgent problem to be solved. Summary of the invention

[0003] Aiming at the problem that the existing low-altitude logistics distribution methods lack the utilization of idle transportation capacity, resulting in waste of idle transportation capacity resources, the present invention provides a low-altitude logistics distribution method combining idle transportation capacity of public transportation.

[0004] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0005] A low-altitude logistics distribution method combining idle transportation capacity of public transportation, comprising the steps of:

[0006] S1. Collect basic parameters of the task of transporting items and user preference parameters input by the user;

[0007] S2, matching public transportation operation data and other global variables according to the basic task parameters input by the user, and screening out available bus and rail schedules and line combinations;

[0008] S3, inputting bus-rail schedules and line combinations into a multi-objective genetic algorithm to solve the optimal low-altitude vehicle combined transportation plan required by the user;

[0009] S4, the user confirms the plan. If the user confirms, the process proceeds to step S5. If the user does not confirm, the process proceeds to step S6.

[0010] S5. Update the database and send the task plan to the logistics and public transportation operations departments;

[0011] S6. Reset the user preference and enter the multi-objective genetic algorithm again to find a new solution for the user preference.

[0012] Furthermore, the multi-objective genetic algorithm adopts the NSGA-II algorithm.

[0013] Furthermore, the detailed steps of step S3 include:

[0014] S301, inputting bus track schedules and line combinations into a heuristic algorithm to generate a random initial solution set;

[0015] S302, obtaining a progeny solution set by performing crossover and mutation on a random initial solution set;

[0016] S303, generating a fitness evaluation function according to the time and cost indicator weights set by the user preference parameters input in step S1;

[0017] S304, scoring each solution in step S302 according to the fitness evaluation function generated in step S303, and judging whether an optimal solution is obtained. If an optimal solution is obtained, proceed to step S3; if not, proceed to step S9;

[0018] S305, screening the paternal solution set for the next round of inheritance, and re-entering step S302.

[0019] Furthermore, the public transportation operation data includes at least: details of available bus routes between low-altitude transportation stations, bus departure time plans, estimated arrival times of each bus, and available transportation space data for each bus;

[0020] Other global variables include at least: low-altitude transportation station line data, low-altitude transportation unit mileage cost, single transfer time of air-to-air, air-to-car, and car-to-car, and additional buffer time reserved for sudden traffic situations.

[0021] Furthermore, the basic parameters of the mission include: starting and ending transportation points, starting and ending times, cargo weight and volume;

[0022] Assume the departure place O, the destination D, the i-th low-altitude transport Ki, the i-th public transport Bi, the maximum delivery time T0, and the transit time Tr;

[0023] Filter available bus-rail schedules and line combinations:

[0024] Option 1: If public transport is not considered, the process of one-stage low-altitude transport is: O-K1-D;

[0025] Considering the optimization of logistics deployment, if multi-stage low-altitude transportation is adopted, the process is: O-K1-K2-...-KN-D;

[0026] Solution 2: Considering public transportation, the above process can be optimized as follows: O-K1-K2-...-Kn-B1-B2-...-Bm-D;

[0027] The overall time consumption of Scheme 1 is: T1 = TK1 + TK2 + ... + TKN + transit time, where the transit time is (N-1) * single air-to-air transit time;

[0028] The transportation cost of Option 1 is: C1 = total air transport mileage L1* air transport cost per unit mileage p+transfer cost, where the transfer cost is (N-1)*single air-to-air transfer cost;

[0029] The transportation cost of Option 2 is: C2 = total air transport mileage L2* air transport cost per unit mileage p+ bus transport cost+ transshipment cost;

[0030] The overall time consumption of Scheme 2 is: T2 = TK1 + TK2 + ... + TKn + TB1 + TB2 + ... + TBn + transfer time Tr + additional time Te caused by bus delays and sudden road congestion;

[0031] By traversing all possible route combinations that satisfy L2 less than L1, the time consumption and cost of each combination are evaluated to find all solutions that meet the requirements;

[0032] User preference parameters include: cost priority, time priority, and balanced strategies;

[0033] Transportation time limit: The time required for the plan shall not exceed the maximum delivery time limit required by the user. In the scenario of using public transportation for transfer, the time required for the plan shall not exceed the difference between the maximum delivery time limit required by the user and the surplus time; Transportation cost: On the premise of meeting the transportation time requirements, transportation cost is the most important evaluation criterion in the evaluation function.

[0034] Furthermore, after the fitness evaluation function is determined in step S304, the detailed steps of the NSGA-II algorithm to obtain the optimal solution are as follows:

[0035] S3041, initialization: according to the available lines and vehicles with sufficient idle space in the system, randomly generate an initial population, that is, a set of initial solutions, each solution is a random task plan, and calculate the fitness value of each solution according to the fitness function;

[0036] S3042, Non-dominated sorting: First define the dominance relationship: individual A dominates individual B if and only if A is not worse than B in all objectives and is better than B in at least one objective, and then sort the population according to the dominance relationship;

[0037] S3043, crowding calculation: For each layer of non-dominated solutions, the crowding of individuals is measured by calculating the distance of each individual in the target space;

[0038] S3044, Tournament selection: individuals are selected from the parent generation for reproduction;

[0039] S3045, Crossover and Mutation: Generate new offspring by simulating binary crossover;

[0040] S3046, merge and select new population: first merge the current population and offspring to form a larger population, then re-sort the merged population by non-dominance, retain individuals with low non-dominance levels, and when the number of individuals exceeds the initial population size, give priority to retaining individuals with low non-dominance levels;

[0041] S3047, iteration: repeat steps S3043 to S3046 until the termination condition is met;

[0042] S3048. Output: Finally, a set of non-dominated solutions is output, which constitutes the approximate Pareto optimal solution set of the multi-objective problem.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] Based on low-altitude economic logistics distribution points and routes, we can make full use of the idle capacity of existing bus routes to achieve a lower-cost alternative logistics solution.

[0045] Minor adjustments will be made to optimize the existing bus scheduling mechanism and bus vehicle configuration (such as setting up an item storage area on the bus and an automatic QR code scanning device <automatically scans items when they are put in to enable rapid loading of the bus>) so that it can be compatible with logistics distribution functions without reducing existing bus passenger services. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is an overall flow chart of a low-altitude logistics distribution method combining idle transportation capacity of public transportation in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0048] Public transportation in this embodiment refers to private cars or other transport vehicles that meet the corresponding conditions (such as overlapping routes, being able to guarantee delivery time requirements, being able to conveniently load and deliver goods, having credit, and being willing to accept corresponding prices, etc.), and can also join the logistics distribution chain. For example, some private cars or buses can solve the problem of delivering goods in the last section of the journey in remote areas.

[0049] like Figure 1 As shown, this embodiment provides a low-altitude logistics distribution method combining idle transportation capacity of public transportation, including the steps of:

[0050] S1. Collect basic parameters of the task of transporting items and user preference parameters input by the user;

[0051] S2, matching public transportation operation data and other global variables according to the basic task parameters input by the user, and screening out available bus and rail schedules and line combinations;

[0052] S3, inputting bus-rail schedules and line combinations into a multi-objective genetic algorithm to solve the optimal low-altitude vehicle combined transportation plan required by the user;

[0053] S4, the user confirms the plan. If the user confirms, the process proceeds to step S5. If the user does not confirm, the process proceeds to step S6.

[0054] S5. Update the database and send the task plan to the logistics and public transportation operations departments;

[0055] S6. Reset the user preference and enter the multi-objective genetic algorithm again to find a new solution for the user preference.

[0056] The multi-objective genetic algorithm uses the NSGA-II algorithm. This patent involves a multi-objective optimization algorithm, the purpose of which is to obtain a Pareto frontier solution. At present, the main solution algorithms are mathematical programming methods and genetic algorithms. This article only uses genetic algorithms as examples. Application examples are not limited to genetic algorithms, but should include all applicable multi-objective optimization algorithms.

[0057] The detailed steps of step S3 include:

[0058] S301, inputting bus track schedules and line combinations into a heuristic algorithm to generate a random initial solution set;

[0059] S302, obtaining a progeny solution set by performing crossover and mutation on a random initial solution set;

[0060] S303, generating a fitness evaluation function according to the time and cost indicator weights set by the user preference parameters input in step S1;

[0061] S304, scoring each solution in step S302 according to the fitness evaluation function generated in step S303, and judging whether an optimal solution is obtained. If an optimal solution is obtained, proceed to step S3; if not, proceed to step S9;

[0062] S305, screening the paternal solution set for the next round of inheritance, and re-entering step S302.

[0063] Public transportation operation data shall at least include: details of available bus routes between low-altitude transportation stations, bus departure schedule, estimated arrival time of each bus, and available transportation space data for each bus;

[0064] Other global variables include at least: low-altitude transportation station line data, low-altitude transportation unit mileage cost, single transfer time of air-to-air, air-to-car, and car-to-car, and additional buffer time reserved for sudden traffic situations.

[0065] The basic parameters of the mission include: starting and ending transportation points, starting and ending times, cargo weight and volume;

[0066] Assume the departure place O, the destination D, the i-th low-altitude transport Ki, the i-th public transport Bi, the maximum delivery time T0, and the transit time Tr;

[0067] Filter available bus-rail schedules and line combinations:

[0068] Option 1: If public transport is not considered, the process of one-stage low-altitude transport is: O-K1-D;

[0069] Considering the optimization of logistics deployment, if multi-stage low-altitude transportation is adopted, the process is: O-K1-K2-...-KN-D;

[0070] Option 2: Considering public transportation, the above process can be optimized as follows: O-K1-K2-...-Kn-B1-B2-...-Bm-D; the order of each section K and B can be adjusted alternately without affecting the overall transportation cost and time calculation.

[0071] The overall time consumption of Scheme 1 is: T1 = TK1 + TK2 + ... + TKN + transit time, where the transit time is (N-1) * single air-to-air transit time;

[0072] The transportation cost of Option 1 is: C1 = total air transport mileage L1* air transport cost per unit mileage p+transfer cost, where the transfer cost is (N-1)*single air-to-air transfer cost;

[0073] The transportation cost of Scheme 2 is: C2 = total air transport mileage L2 * air transport cost per unit mileage p + bus transport cost + transfer cost; among them, since bus transport utilizes the extra space of the normal bus schedule, it only increases the fuel consumption cost caused by the extra load and the transfer cost caused by loading and unloading goods. In this method, the extra fuel consumption is ignored, and the transfer cost caused by loading and unloading goods is uniformly included in the transfer cost, so the bus transport cost is approximately 0. Among them, the transfer cost is: single air-to-air transfer cost * number of air-to-air transfers + single car-to-air transfer cost * number of car-to-air transfers + single car-to-car transfer cost * number of car-to-car transfers, and the number of various transfers varies depending on the specific scheme.

[0074] The overall time consumption of Scheme 2 is: T2 = TK1 + TK2 + ... + TKn + TB1 + TB2 + ... + TBn + transfer time Tr + additional time Te caused by bus delays and sudden road congestion; where transfer time Tr = single air-to-air transfer time * number of air-to-air transfers + single vehicle-to-air transfer time * number of vehicle-to-air transfers + vehicle-to-vehicle transfer time Tb, where Tb is affected by the actual operation plan of the bus, and waiting time may occur during the transfer process. The additional time Te caused by bus delays, sudden road congestion, etc. is difficult to predict. It is recommended to leave a certain amount of margin time during production and application. If Te is not considered, the overall time consumption T2 of Scheme 2 should not be greater than the maximum delivery time T0-1 hour.

[0075] By traversing all possible route combinations that satisfy L2 less than L1, the time consumption and cost of each combination are evaluated to find all solutions that meet the requirements.

[0076] User preference parameters include: cost priority, time priority, and balanced strategies;

[0077] Transportation time limit: The time required for the plan shall not exceed the maximum delivery time limit required by the user. In the scenario of using public transportation for transfer, the time required for the plan shall not exceed the difference between the maximum delivery time limit required by the user and the surplus time; Transportation cost: On the premise of meeting the transportation time requirements, transportation cost is the most important evaluation criterion in the evaluation function.

[0078] After the fitness evaluation function is determined in step S304, the detailed steps of the NSGA-II algorithm to obtain the optimal solution are as follows:

[0079] S3041, initialization: according to the available lines and vehicles with sufficient idle space in the system, randomly generate an initial population, that is, a set of initial solutions, each solution is a random task plan, and calculate the fitness value of each solution according to the fitness function;

[0080] S3042, Non-dominated sorting: First define the dominance relationship: individual A dominates individual B if and only if A is not worse than B in all objectives and is better than B in at least one objective, and then sort the population according to the dominance relationship. The algorithm divides the population into multiple levels, with non-dominated individuals in the first level, and the remaining individuals are assigned to other levels according to the degree of domination.

[0081] S3043. Crowding calculation: For each layer of non-dominated solutions, the crowding of individuals is measured by calculating the distance of each individual in the target space. The greater the crowding, the sparser the individuals are distributed in the target space. Crowding is used as a secondary comparison criterion when selecting individuals, giving priority to individuals with large crowding to avoid solutions being concentrated in certain areas.

[0082] S3044, Tournament Selection: Individuals are selected from the parent generation for reproduction. Each time a selection is made, a comparison is made between two individuals, and the individual with a lower non-dominated rank (i.e., the individual closer to the Pareto frontier) is preferred. If the non-dominated ranks of two individuals are the same, the individual with a higher crowding degree (i.e., the individual that is sparser in the space) is selected.

[0083] S3045, Crossover and mutation: Generate new offspring by simulating binary crossover (SBX). SBX can simulate the crossover operation on continuous variables, so that the offspring solutions are distributed around the parent solutions. At the same time, polynomial mutation is used to make small changes to individuals with a certain probability, increasing the diversity of offspring solutions and preventing the algorithm from falling into local optimality.

[0084] S3046, merge and select new population: first merge the current population and offspring to form a larger population, then re-sort the merged population by non-dominance, and retain individuals with low non-dominance levels. When the number of individuals exceeds the initial population size, individuals with low non-dominance levels are retained first. If there are too many individuals in a certain layer, use crowding to select and retain individuals with high crowding.

[0085] S3047, Iteration: Repeat steps 3 to 6 until the termination condition is met (such as reaching the maximum number of iterations or finding a suitable Pareto frontier solution set).

[0086] S3048. Output: Finally, a set of non-dominated solutions is output, which constitutes the approximate Pareto optimal solution set of the multi-objective problem.

[0087] Compared with the prior art, the present invention has the following beneficial effects:

[0088] Based on low-altitude economic logistics distribution points and routes, we can make full use of the idle capacity of existing bus routes to achieve a lower-cost alternative logistics solution.

[0089] Minor adjustments will be made to optimize the existing bus scheduling mechanism and bus vehicle configuration (such as setting up an item storage area on the bus and an automatic QR code scanning device <automatically scans items when they are put in to enable rapid loading of the bus>) so that it can be compatible with logistics distribution functions without reducing existing bus passenger services.

[0090] The above is a detailed introduction to a low-altitude logistics distribution method combined with idle transportation capacity of public transportation provided by the present application. The description of the specific embodiment is only used to help understand the method and its core idea of ​​the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A low-altitude logistics distribution method combining idle transportation capacity of public transportation, characterized in that: Includes steps: S1. Collect basic parameters of the task of transporting items and user preference parameters input by the user; S2, matching public transportation operation data and other global variables according to the basic task parameters input by the user, and screening out available bus and rail schedules and line combinations; S3, inputting bus-rail schedules and line combinations into a multi-objective genetic algorithm to solve the optimal low-altitude vehicle combined transportation plan required by the user; S4, the user confirms the plan. If the user confirms, the process proceeds to step S5. If the user does not confirm, the process proceeds to step S6. S5. Update the database and send the task plan to the logistics and public transportation operations departments; S6. Reset the user preference and enter the multi-objective genetic algorithm again to find a new solution for the user preference.

2. A low-altitude logistics distribution method combining idle transportation capacity of public transportation according to claim 1, characterized in that: The multi-objective genetic algorithm adopts NSGA-II algorithm.

3. A low-altitude logistics distribution method combining idle transportation capacity of public transportation according to claim 2, characterized in that: The detailed steps of step S3 include: S301, inputting bus track schedules and line combinations into a heuristic algorithm to generate a random initial solution set; S302, obtaining a descendant solution set by crossover and mutation of a random initial solution set; S303, generating a fitness evaluation function according to the time and cost indicator weights set by the user preference parameters input in step S1; S304, scoring each solution in step S302 according to the fitness evaluation function generated in step S303, and judging whether an optimal solution is obtained. If an optimal solution is obtained, proceed to step S3; if not, proceed to step S9; S305, screening the paternal solution set for the next round of inheritance, and re-entering step S302.

4. A low-altitude logistics distribution method combining idle transportation capacity of public transportation according to claim 3, characterized in that: Public transportation operation data shall at least include: details of available bus routes between low-altitude transportation stations, bus departure schedules, estimated arrival times of each bus, and available transportation space data for each bus; Other global variables include at least: low-altitude transportation station line data, low-altitude transportation unit mileage cost, single transfer time of air-to-air, air-to-car, and car-to-car, and additional buffer time reserved for sudden traffic situations.

5. A low-altitude logistics distribution method combining idle transportation capacity of public transportation according to claim 4, characterized in that: The basic parameters of the mission include: starting and ending transportation points, starting and ending times, cargo weight and volume; Assume the departure place O, the destination D, the i-th low-altitude transport Ki, the i-th public transport Bi, the maximum delivery time T0, and the transit time Tr; Filter available bus-rail schedules and line combinations: Option 1: If public transport is not considered, the process of one-stage low-altitude transport is: O-K1-D; Considering the optimization of logistics deployment, if multi-stage low-altitude transportation is adopted, the process is: O-K1-K2-...-KN-D; Solution 2: Considering public transportation, the above process can be optimized as follows: O-K1-K2-...-Kn-B1-B2-...-Bm-D; The overall time consumption of Scheme 1 is: T1 = TK1 + TK2 + ... + TKN + transit time, where the transit time is (N-1) * single air-to-air transit time; The transportation cost of Option 1 is: C1 = total air transport mileage L1* air transport cost per unit mileage p+transfer cost, where the transfer cost is (N-1)*single air-to-air transfer cost; The transportation cost of Option 2 is: C2 = total air transport mileage L2* air transport cost per unit mileage p+ bus transport cost+ transshipment cost; The overall time consumption of Scheme 2 is: T2 = TK1 + TK2 + ... + TKn + TB1 + TB2 + ... + TBn + transfer time Tr + additional time Te caused by bus delays and sudden road congestion; By traversing all possible route combinations that satisfy L2 less than L1, the time consumption and cost of each combination are evaluated to find all solutions that meet the requirements; User preference parameters include: cost priority, time priority, and balanced strategies; Transportation time limit: The time required for the plan shall not exceed the maximum delivery time limit required by the user. In the scenario of using public transportation for transfer, the time required for the plan shall not exceed the difference between the maximum delivery time limit required by the user and the surplus time; Transportation cost: On the premise of meeting the transportation time requirements, transportation cost is the most important evaluation criterion in the evaluation function.

6. A low-altitude logistics distribution method combining idle transportation capacity of public transportation according to claim 5, characterized in that: After the fitness evaluation function is determined in step S304, the detailed steps of the NSGA-II algorithm to obtain the optimal solution are as follows: S3041, initialization: according to the available lines and vehicles with sufficient idle space in the system, randomly generate an initial population, that is, a set of initial solutions, each solution is a random task plan, and calculate the fitness value of each solution according to the fitness function; S3042, Non-dominated sorting: First define the dominance relationship: individual A dominates individual B if and only if A is not worse than B in all objectives and is better than B in at least one objective, and then sort the population according to the dominance relationship; S3043, crowding calculation: For each layer of non-dominated solutions, the crowding of individuals is measured by calculating the distance of each individual in the target space; S3044, Tournament selection: individuals are selected from the parent generation for reproduction; S3045, Crossover and mutation: Generate new offspring by simulating binary crossover; S3046, merge and select new population: first merge the current population and offspring to form a larger population, then re-sort the merged population by non-dominance, retain individuals with low non-dominance levels, and when the number of individuals exceeds the initial population size, give priority to retaining individuals with low non-dominance levels; S3047, iteration: repeat steps S3043 to S3046 until the termination condition is met; S3048. Output: Finally, a set of non-dominated solutions is output, which constitutes the approximate Pareto optimal solution set of the multi-objective problem.

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