Multi-objective optimization method, medium and equipment for tourist and shipment fusion in rural area

Abstract

The invention discloses a multi-objective optimization method, medium and equipment for passenger and goods mail fusion in rural areas, and the method comprises the steps: S1, building a multi-objective optimization model of urban and rural passenger vehicle operation based on the constraint conditions of urban and rural passenger vehicle operation; s2, acquiring operation parameters of urban and rural passenger vehicles and inputting the operation parameters to the multi-objective optimization model; s3, determining and coding values of decision variables involved in the operation optimization process; s4, searching an optimal solution set by using a multi-target genetic algorithm; and S5, adjusting an urban and rural passenger vehicle operation scheme based on the optimal solution set, and departing the vehicle. The urban and rural passenger transport vehicle operation multi-objective optimization model under the passenger-goods-mail fusion is constructed, the optimal decision of the vehicle operation problem of the subject benefits of all parties is met, and the problem that the transport capacity and the transport volume of the passenger-goods-mail in the rural area are not matched and unbalanced is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent logistics, in particular to a multi-objective optimization method for passenger, freight and mail integration in rural areas. BACKGROUND

[0002] Based on the new development of passenger, freight and mail integration, higher requirements are put forward for passenger vehicle operation, which needs to meet the needs of passenger travel, enterprise profit and two-way flow of goods.

[0003] Currently, the existing passenger vehicle operation optimization method mainly focuses on the matching of transport capacity and vehicle scheduling in urban traffic background, without considering the passenger, freight and mail integration mode and the operation characteristics of urban and rural passenger vehicles, so it cannot fully reflect the interests of different subjects, behavior demands and behavior decisions. At the same time, the traditional single optimization target cannot consider multiple inconsistent or conflicting targets. Therefore, based on the above technical problems, a multi-objective optimization method is needed to construct an optimization method that can quantify the interests of passengers, passenger transport enterprises and logistics enterprises. SUMMARY

[0004] The present application provides a multi-objective optimization method for passenger, freight and mail integration in rural areas, which can quantify the interests of passengers, passenger transport enterprises and logistics enterprises, and can at least solve one of the above technical problems.

[0005] In order to solve the above technical problems, the present application adopts the following technical solutions:

[0006] A multi-objective optimization method for passenger, freight and mail integration in rural areas, comprising the following steps:

[0007] S1, based on the constraint conditions of urban and rural passenger vehicle operation, a multi-objective optimization model of urban and rural passenger vehicle operation is constructed;

[0008] S2, obtaining the parameters of urban and rural passenger vehicle operation and inputting them into the multi-objective optimization model;

[0009] S3, determining and encoding the value of the decision variable involved in the operation optimization process;

[0010] S4, using a multi-objective genetic algorithm to find the optimal solution set;

[0011] S5, adjusting the urban and rural passenger vehicle operation scheme based on the optimal solution set and dispatching the vehicle.

[0012] Further, the S1 further comprises:

[0013] S11, defining the parameters and variables of urban and rural passenger vehicle operation:

[0014] The number of bus types currently equipped on the bus line is m, m = 1, 2,..., M;

[0015] n m represents the number of m-type vehicles;

[0016] β m represents the rated passenger capacity of m-type vehicles, minβ m represents the rated passenger capacity of the smallest vehicle type; maxβ m represents the rated passenger capacity of the largest vehicle type;

[0017] A is the total frequency of all vehicle types on the bus line, and A is an integer;

[0018] FS is the minimum fleet size;

[0019] i and j represent the stations on the bus line, i represents the boarding station, and j represents the alighting station, the set of stations {i, j | i, j = 1, 2,..., N}, and the set of bus services {p | p = 1, 2,..., p};

[0020] represents the loading box capacity of the vehicle type m used by the pth bus service, Q p,j represents the number of passengers boarding at station i in the pth bus service, Q represents the number of passengers alighting from the boarding station i to the alighting station j, represents the cross-sectional passenger flow between stations i and i+1 for the pth bus service in the study period, T is the study period, and K is the bus fare revenue;

[0021] t is the travel time of the bus vehicle, t ij represents the travel time of the vehicle between two stations, t w is the average waiting time of passengers;

[0022] H is the headway value of the bus vehicle on the bus line, H min is the minimum headway, H max is the maximum headway;

[0023] x p,m is a decision variable, representing x p,m = 1 if the pth bus service uses the m-type vehicle, otherwise x

[0024] x p,m = 0;

[0025] D λ is the starting mileage, D ij is the passenger's ride mileage, x ij is another decision variable, x ij = 1 when D λ > D ij=1, otherwise x ij =0;

[0026] V oj The number of express deliveries delivered by express delivery companies to each village-level outlet;

[0027] represents the number of small express deliveries carried to station i by the pth train. represents the number of large express deliveries carried by the p-th train to station i;

[0028] B s is the freight rate for small express delivery, B h The freight rate for large express delivery;

[0029] S12. Calculate the constraints for urban and rural passenger vehicle operations:

[0030] The range of departure frequency A is determined by the maximum cross-sectional passenger flow of the bus line. and the departure interval H, and is calculated as follows:

[0031]

[0032] A min <<A<<A max

[0033] The inverse function method is used to solve the minimum fleet size FS corresponding to the departure frequency A. The calculation method is as follows:

[0034]

[0035] Where d(i, t) represents the net number of vehicle departures minus the number of vehicle arrivals at station i before time t, D(i) represents the maximum value of d(i, t) within the vehicle operating time range, that is, the number of vehicles required by station i under the timetable determined by the departure frequency plan, and FS represents the minimum number of vehicles required to complete all shift tasks;

[0036] The calculation method for the bus departure interval H constraint is as follows:

[0037] H min <<H<<H max (4)

[0038] Capacity demand Q p,j The constraints are calculated as follows:

[0039]

[0040] Urban and rural bus express delivery box capacity V oj The constraints are calculated as follows:

[0041]

[0042] The urban and rural bus fleet size FS constraint is calculated as follows:

[0043]

[0044] During the urban and rural bus operation, any vehicle p can only be executed by one type of vehicle, so x p,m and x ij Two 0-1 variables satisfy the statistical value of 1, and the calculation method is as follows:

[0045]

[0046] S13, construct a multi-objective optimization objective function of urban and rural passenger transport vehicle operation to form a multi-objective optimization model of urban and rural passenger transport vehicle operation.

[0047] Further, the S13 further comprises:

[0048] S131, calculate the passenger travel cost Z1, which includes passenger waiting cost C1 and passenger travel cost C2, and the calculation method is as follows:

[0049] t w = H / 2 (10)

[0050]

[0051] Min Z1 = C1 + C2 (13)

[0052] S132, calculate the maximum profit of urban and rural passenger transport enterprises, which includes bus fare income K, freight operation income and bus operation cost, and the calculation method is as follows:

[0053] 1. The bus fare income K is related to the number of passengers and the fare, and the calculation method is as follows:

[0054]

[0055] Where, U p is the basic fare, CEIL(.) represents the rounding up of the value, and f is the step mileage;

[0056] 2. The express business income E2 is proportional to the freight rate and volume, and is also affected by the volume of the package, and the calculation method is as follows:

[0057]

[0058] 3. The bus operation cost Z2 is related to the operation mileage, vehicle configuration and bus vehicle labor cost, and the calculation method is as follows:

[0059]

[0060] Min Z2 = E1 + E2 - (C3 + C4) (19)

[0061] wherein, D od is the driving distance of the urban and rural public transport from the starting station to the terminal station, θ m represents the unit mileage cost of the public transport vehicle, r2 is the unit time value of the public transport staff, is the time when the vehicle p arrives at the terminal station N, is the time when the vehicle p departs from the county-level starting station;

[0062] 4. The minimum cost Min Z3 of the express enterprise operation is calculated as follows:

[0063]

[0064] Min Z3 = C5 + C6 + C7 (23)

[0065] wherein, C5 represents the storage cost, C6 represents the cost paid to the urban and rural passenger transport enterprises and the village-level service network, C7 represents the express enterprise individual distribution cost, V arrive represents the number of express deliveries arriving at the county-level station on the day, C store represents the warehousing cost required for each express delivery to be stored at the county-level station, Y represents the number of distribution times of the express enterprise per day, θ e represents the unit mileage cost of the distribution vehicle, D j represents the total distance driven after completing the distribution task of all the village-level networks and returning to the county-level station, r3 represents the unit time value of the distribution staff, t j represents the total driving time required for returning to the county-level station after completing the distribution, t h represents the loading and unloading time of each loading and unloading point, x j is a decision variable, x j = 1 when the j station is a village-level service network and the express loading and unloading is performed, otherwise x j = 0, and L is the network operation cost required to be paid by the enterprise when the express network is individually established.

[0066] Further, the S2 further comprises:

[0067] S21, selecting a specific bus route, inputting the bus route and passenger flow data, operation parameters, station data and multi-vehicle data on the bus route, and dividing the operation period of the bus route into two time periods of off-season and peak season according to passenger flow characteristics, and selecting one day in the off-season and peak season as a research time period T for simulation;

[0068] S22, determining a reasonable departure frequency A on the basis of considering passenger travel demand in the off-season and peak season.

[0069] S23, determine the fleet size FS under each frequency A, find the corresponding vehicle combination scheme, meet the constraint conditions of the multi-objective optimization model of urban and rural passenger vehicle operation.

[0070] Further, the S3 further comprises:

[0071] S31, due to the small feasible range of frequency A, all the frequency ranges [A min ,A max ] that meet the existing bus interval and line section passenger flow of urban and rural public transport are listed by enumeration method;

[0072] S32, for different values, the frequency A is taken as a constant value;

[0073] S33, the minimum fleet size FS corresponding to different frequency A is calculated by using the inverse difference function method, and the value is taken as the value of FS in the operation constraint condition formula (7);

[0074] S34, real number coding is adopted by genetic algorithm, the first gene represents the value of A, i.e. the frequency, the second gene represents the value of FS, i.e. the minimum fleet size corresponding to each frequency, and the third to A+2 genes represent the vehicle type m corresponding to the frequency, m represents the use of the m type vehicle.

[0075] Further, the S4 further comprises:

[0076] S41, the NSGA-II algorithm is used to seek the optimal Pareto solution set, i.e. the urban and rural passenger vehicle operation optimization scheme corresponding to each frequency;

[0077] S42, output the optimal solution set, each scheme in the optimal solution set contains the frequency and the vehicle type configuration corresponding to each frequency, and the passenger travel cost, urban and rural passenger enterprise profit and express enterprise operation cost corresponding to the scheme.

[0078] Further, in the S5, according to the target and data output by the optimal solution set, the urban and rural passenger operation optimization scheme analysis is carried out, and the adjustment scheme of the frequency and the corresponding fleet size is provided.

[0079] A computer readable storage medium, storing a computer program, the computer program is executed by a processor, so that the processor executes the steps of the above-mentioned multi-objective optimization method for rural area passenger and cargo and mail integration.

[0080] A computer device comprises a memory and a processor, the memory stores a computer program, when the computer program is executed by the processor, the processor executes the steps of the multi-objective optimization method for rural passenger, freight and mail integration.

[0081] The beneficial effects of the present application are embodied in:

[0082] 1、The present application analyzes the interests of different subjects such as passengers, passenger transport enterprises and express enterprises under the background of passenger, freight and mail integration in rural areas, constructs a multi-objective optimization model of urban and rural passenger transport vehicles under passenger, freight and mail integration, and obtains an optimization scheme through genetic algorithm coding, so as to make optimal decisions for vehicle operation problems that meet the interests of all parties.

[0083] 2、On the basis of traditional passenger transport enterprise operation optimization, the present application adjusts the schemes of passenger vehicle operation such as departure interval, fleet size and vehicle type configuration optimization, effectively solves the problem of unbalanced matching of rural passenger, freight and mail transport capacity and volume, improves the operation effect of urban and rural passenger transport vehicles, and promotes the efficient development of passenger, freight and mail integration mode. BRIEF DESCRIPTION OF DRAWINGS

[0084] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application, the illustrative embodiments of the present application and the description thereof are used to explain the present application, and do not constitute improper limitation on the present application.

[0085] Figure 1 It is a multi-objective optimization method overall flowchart of the embodiment of the present application.

[0086] Figure 2 It is a multi-objective optimization method implementation process block diagram of the embodiment of the present application.

[0087] Figure 3 It is a structure block diagram of the computer device of the embodiment of the present application. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. The embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0089] It should be noted that the meaning of "and / or" appearing throughout the present text includes three parallel solutions, taking "A and / or B" as an example, including A solution, or B solution, or A and B solution. In addition, "multiple" refers to more than two. In addition, in the present application, "multi-objective optimization model" specifically refers to "urban and rural passenger vehicle operation multi-objective optimization model". In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the realization of ordinary skilled personnel in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection required by the present application.

[0090] It should be noted that "passenger and cargo integration" in the present application refers to a fusion development mode that integrates rural passenger transport, freight transport and postal express delivery into one, relying on urban and rural passenger transport network resources and the spare loading space of passenger buses, and can solve the problems of travel, logistics distribution and postal delivery of the masses; "urban and rural passenger transport" refers to various road public passenger transport modes in urban and rural areas, relying on bus stations, bus shelters and rural passenger stations, operating according to the specified line, station and frequency, thereby providing basic travel services for urban and rural residents; "urban and rural freight transport" is a two-way circulation process, which mainly reflects the two processes of uplink of agricultural products and downlink of production and living supplies, and forms a complete closed loop; "multi-objective optimization" refers to considering multiple conflicting objectives in the optimization problem, and finding a set of optimal solutions through optimization algorithm to make all objectives as satisfied as possible.

[0091] Referring to Figure 1 The embodiment of the present application provides a multi-objective optimization method for rural area passenger and cargo integration, comprising the following steps:

[0092] S1, based on the constraint conditions of urban and rural passenger vehicle operation, a multi-objective optimization model of urban and rural passenger vehicle operation is constructed;

[0093] S2, obtaining the parameters of urban and rural passenger vehicle operation and inputting them into the multi-objective optimization model;

[0094] S3, determining and encoding the value of the decision variable involved in the operation optimization process;

[0095] S4, using a multi-objective genetic algorithm to find an optimal solution set;

[0096] S5, adjusting the urban and rural passenger vehicle operation scheme based on the optimal solution set and dispatching the vehicle.

[0097] Referring to Figure 2 In the present embodiment, the S1 further comprises:

[0098] S11, define the parameters and variables of urban passenger vehicle operation:

[0099] The current bus line is equipped with uniform or different passenger capacity bus models m, m = 1, 2,..., M;

[0100] n m represents the number of m type vehicles;

[0101] β m represents the rated passenger capacity of m type vehicles, minβ m represents the rated passenger capacity of the smallest vehicle; maxβ m represents the rated passenger capacity of the largest vehicle;

[0102] A is the total frequency of all vehicle types on the bus line, and A is an integer;

[0103] FS is the minimum fleet size;

[0104] i and j represent the stations on the bus line, i represents the boarding station, j represents the alighting station, the station set {i, j | i, j = 1, 2, Ω, N}, and the set of bus trips {p | p = 1, 2, Ω, p};

[0105] represents the carrying box capacity of the m type vehicle used by the pth bus, Q p,j represents the number of passengers boarding at station i in the pth bus trip, Q represents the number of passengers from the boarding point i to the alighting point j, represents the cross-section passenger flow between stations i and i+1 for bus p in the study period, T is the study period, and K is the bus fare revenue;

[0106] t is the bus travel time, t ij represents the travel time of the vehicle between two stations, t w is the average waiting time of passengers;

[0107] H is the headway value of the bus on the bus line, H min is the minimum headway, H max is the maximum headway;

[0108] x p,m is a decision variable, which represents x p,m = 1 if the pth bus uses m type vehicle, otherwise

[0109] x p,m = 0;

[0110] D λ is the starting mileage, D ij is the passenger's ride mileage, x ij is another decision variable, when Dij >D λ When x ij =1, otherwise x ij =0;

[0111] V oj The number of express deliveries delivered by express delivery companies to each village-level outlet;

[0112] represents the number of small express deliveries carried to station i by the pth train. represents the number of large express deliveries carried by the p-th train to station i;

[0113] B s is the freight rate for small express delivery, B h The freight rate for large express delivery;

[0114] S12. Calculate the constraints for urban and rural passenger vehicle operations:

[0115] The range of departure frequency A is determined by the maximum cross-sectional passenger flow of the bus line. and the departure interval H, and is calculated as follows:

[0116]

[0117] A min <<A<<A max

[0118] The inverse function method is used to solve the minimum fleet size FS corresponding to the departure frequency A. The calculation method is as follows:

[0119]

[0120] Where d(i, t) represents the net number of vehicle departures minus the number of vehicle arrivals at station i before time t, D(i) represents the maximum value of d(i, t) within the vehicle operating time range, that is, the number of vehicles required by station i under the timetable determined by the departure frequency plan, and FS represents the minimum number of vehicles required to complete all shift tasks;

[0121] The calculation method for the bus departure interval H constraint is as follows:

[0122] H min <<H<<H max (4)

[0123] Capacity demand Q p,j The constraints are calculated as follows:

[0124]

[0125] Urban and rural bus express delivery box capacity V ojThe constraint calculation method is as follows:

[0126]

[0127] The constraint calculation method of the urban and rural bus fleet size FS is as follows:

[0128]

[0129] During the urban and rural bus operation, any vehicle p can only be executed by one type of vehicle, so x p,m and x ij The two 0-1 variables satisfy the statistical quantity of 1, and the calculation method is as follows:

[0130]

[0131] S13, constructing an urban and rural passenger vehicle operation multi-objective optimization objective function to form an urban and rural passenger vehicle operation multi-objective optimization model.

[0132] Referring to Figure 2 In this embodiment, the S13 further comprises:

[0133] S131, passenger travel cost Z1 calculation, the composition of the passenger travel cost includes passenger waiting cost C1 and passenger riding cost C2, and the calculation method is as follows:

[0134] t w = H / 2 (10)

[0135]

[0136] Min Z1 = C1 + C2 (13)

[0137] S132, calculation of the maximum profit of the urban and rural passenger transport enterprise, the composition of the maximum profit of the urban and rural passenger transport enterprise includes bus fare income K, freight operation income and bus operation cost, and the calculation method is as follows:

[0138] 1. The bus fare income K is related to the number of passengers and the fare, and the calculation method is as follows:

[0139]

[0140] Wherein, U p is the basic fare, CEIL(.) represents the rounding up of the obtained value, and f is the step mileage;

[0141] 2. The express business income E2 is proportional to the freight rate and the freight volume, and is also affected by the package volume, and the calculation method is as follows:

[0142]

[0143] 3, Bus operation cost Z2 is related to operation mileage, vehicle configuration and bus staff cost, and is calculated as follows:

[0144]

[0145] Min Z2 = E1 + E2 - (C3 + C4) (19)

[0146] Wherein, D od is the driving distance from the starting station to the terminal station of urban and rural buses, θ m represents the unit mileage cost of the bus, r2 is the unit time value of the bus staff, is the time when the vehicle p arrives at the terminal station N, is the time when the vehicle p departs from the county starting station;

[0147] 4, The calculation method of the minimum cost Min Z3 of express enterprise operation is as follows:

[0148]

[0149] Min Z3 = C5 + C6 + C7 (23)

[0150] Wherein, C5 represents storage cost, C6 represents the cost paid to urban and rural passenger transport enterprises and village service network, and C7 represents the separate distribution cost of the express enterprise, V arrive represents the number of express deliveries arriving at the county station on the same day, C store represents the warehousing cost required for each express delivery to be stored in the county station, Y represents the number of distribution times of the express enterprise per day, θ e represents the unit mileage cost of the distribution vehicle, D j represents the total distance driven after completing the distribution task of all village network points and returning to the county station, r3 represents the unit time value of the distribution staff, t j represents the total driving time required for returning to the county station after completing the distribution, t h represents the loading and unloading time of each loading and unloading point, x j is a decision variable, x j = 1 when j station is a village service network point and express loading and unloading is performed, otherwise x j = 0, and L is the network operation cost required to be paid by the enterprise when setting up an express network point.

[0151] Referring to Figure 2 , in the embodiment, the S2 further comprises:

[0152] S21, select a specific bus line, input the bus line and passenger flow data, operation parameters, station data and multi-vehicle data on the bus line, and divide the operation period of the bus line into two time periods of off-season and peak season according to passenger flow characteristics, and select one day in the off-season and peak season as a research time period T for simulation;

[0153] S22, determine a reasonable departure frequency A based on the consideration of passenger travel demand in the off-season and peak season;

[0154] S23, determine the vehicle fleet size FS under each departure frequency A, find the corresponding vehicle type combination scheme, and meet the constraint conditions of the multi-objective optimization model of urban and rural passenger vehicle operation.

[0155] Referring to Figure 2 In this embodiment, the S3 further comprises:

[0156] S31, since the feasible range of the departure frequency A is small, all the departure frequency range [A min ,A max ] that meets the existing departure interval of the urban and rural bus and the passenger flow of the line section is listed by enumeration method;

[0157] S32, for different values, the departure frequency A is taken as a constant value;

[0158] S33, the inverse difference function method is used to calculate the minimum vehicle fleet size FS corresponding to each departure frequency A, and the value is taken as the value of FS in the operation constraint condition formula (7);

[0159] S34, the genetic algorithm real number coding is adopted, the first gene represents the value of A, that is, the departure frequency, the second gene represents the value of FS, that is, the minimum vehicle fleet size corresponding to each departure frequency, and the third to A+2 genes represent the vehicle type m corresponding to the departure frequency, m represents the use of the m type vehicle.

[0160] Referring to Figure 2 In this embodiment, the S4 further comprises:

[0161] S41, the NSGA-II algorithm is used to seek the optimal Pareto solution set, that is, the urban and rural passenger vehicle operation optimization scheme corresponding to each departure frequency;

[0162] S42, output the optimal solution set, each scheme in the optimal solution set contains the departure frequency and the vehicle type configuration corresponding to each vehicle, and the passenger travel cost, the urban and rural passenger enterprise profit and the express enterprise operation cost corresponding to the scheme 3 targets and values.

[0163] Referring to Figure 2In the embodiment, in the S5, target and data output according to the optimal solution set are used to analyze the urban and rural passenger transport operation optimization scheme, and an adjustment scheme of the departure frequency and the corresponding vehicle fleet scale is provided.

[0164] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the multi-objective optimization method for the integration of passenger, freight and mail in rural areas.

[0165] Referring to Figure 3 The embodiment of the present application further provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the multi-objective optimization method for the integration of passenger, freight and mail in rural areas.

[0166] The embodiment of the present application further provides a computer program product comprising instructions which, when executed on a computer, cause the computer to carry out the steps of the multi-objective optimization method for the integration of passenger, freight and mail in rural areas.

[0167] It can be understood that the system, device and storage medium provided by the embodiment of the present application correspond to the method provided by the embodiment of the present application, and the explanation, examples and beneficial effects of the related content can be referred to the corresponding part in the multi-objective optimization method for the integration of passenger, freight and mail in rural areas.

[0168] It should be understood that all or part of the steps in the embodiments of the present application can be implemented by software, hardware, firmware or any combination thereof. When implemented by hardware, all or part of the steps can be implemented in the form of a purchased standard component or a custom component. When implemented by software, all or part of the steps can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available media. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, DVD), or semiconductor media (for example, solid state disk (SSD)) and the like.

[0169] In summary, the present application aims to provide a passenger and cargo and mail integration multi-objective optimization method for rural areas, to improve the operation effectiveness of urban and rural passenger vehicles through scheduling coordination, and to promote the efficient development of passenger and cargo and mail integration. Passenger and cargo and mail integration effectively promotes the integration and development of rural passenger transport, freight transport and postal express delivery by utilizing the rural passenger transport network resources and the spare loading space of passenger buses. Limited by the failure of passenger transport enterprises to reasonably plan traffic capacity and volume, urban and rural passenger vehicles have not yet achieved the best state in terms of operation effectiveness, which in turn affects the efficient development of passenger and cargo and mail integration. Under the premise of fully analyzing the demands of passengers, passenger transport enterprises and logistics enterprises, the present application constructs a multi-objective optimization model, proposes optimization schemes for the driving plan and vehicle configuration of passenger transport enterprises, and solves the problems of urban and rural passenger transport losses and high rural logistics costs on the basis of ensuring urban and rural passenger transport services.

[0170] It should be understood that the examples and embodiments described herein are only for illustration and are not intended to limit the present application, and those skilled in the art can make various modifications or changes based on it, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-objective optimization method for passenger, freight and mail integration in rural areas, characterized by: The following steps are involved: S1. Based on the constraints of urban and rural passenger vehicle operations, a multi-objective optimization model for urban and rural passenger vehicle operations is constructed; S2. Obtaining parameters of urban and rural passenger transport vehicle operations and inputting them into a multi-objective optimization model; S3. Determine and encode the values ​​of the decision variables involved in the operation optimization process; S4, using multi-objective genetic algorithm to find the optimal solution set; S5. Adjust the operation plan of urban and rural passenger vehicles based on the optimal solution set and dispatch them.

2. The multi-objective optimization method for passenger, freight and mail integration in rural areas according to claim 1, characterized in that: Said S1 further comprises: S11. Define the parameters and variables for urban and rural passenger vehicle operations: The number of bus types equipped with the same or different passenger capacities on a current bus route is m, where m = 1, 2, ..., M; n m represents the number of type m vehicles; β m represents the rated passenger capacity of vehicle type m, minβ m Indicates the rated passenger capacity of the smallest vehicle type; maxβ m Indicates the rated passenger capacity of the largest vehicle type; A is the total departure frequency of all bus types on the bus route, and A is an integer; FS is the minimum fleet size; i and j represent the stops on the bus line, i represents the boarding stop, j represents the getting-off stop, the stop set is {i,j|i,j=1,2,…,N}, and the bus number set is {p|p=1,2,…,p}; represents the cargo box capacity of vehicle type m used by the pth train, Q p,j Indicates the number of passengers who boarded the bus at station i during the p-th train operation, Q indicates the number of passengers who boarded the bus at station i and got off at station j. represents the cross-sectional passenger flow of bus number p between stations i and i+1 during the study period, T is the study period, and K is the bus fare revenue; t is the bus travel time, t ij represents the travel time of a vehicle between two stations, t w is the average waiting time for passengers; H is the departure interval of buses on this bus route, min is the minimum departure interval, H max is the maximum departure interval; x p,m is a decision variable, indicating that if the p-th train uses the m-th model, then x p,m =1, otherwise x p,m ＝0； D λ is the starting mileage, D ij is the passenger's mileage, x ij is another decision variable, when D ij >D λ When x ij =1, otherwise x ij =0; V oj The number of express deliveries delivered by express delivery companies to each village-level outlet; represents the number of small express deliveries carried to station i by the pth train. represents the number of large express deliveries carried by the p-th train to station i; B s is the freight rate for small express delivery, B h The freight rate for large express delivery; S12. Calculate the constraints for urban and rural passenger vehicle operations: The range of departure frequency A is determined by the maximum cross-sectional passenger flow of the bus line. and the departure interval H, and is calculated as follows: A min <<A<<A max The inverse function method is used to solve the minimum fleet size FS corresponding to the departure frequency A. The calculation method is as follows: Where d(i, t) represents the net number of vehicle departures minus the number of vehicle arrivals at station i before time t, D(i) represents the maximum value of d(i, t) within the vehicle operating time range, that is, the number of vehicles required by station i under the timetable determined by the departure frequency plan, and FS represents the minimum number of vehicles required to complete all shift tasks; The calculation method for the bus departure interval H constraint is as follows: H min <<H<<H max (4) Capacity demand Q p,j The constraints are calculated as follows: Urban and rural bus express delivery box capacity V oj The constraints are calculated as follows: The calculation method for the FS constraint of urban and rural bus fleet size is as follows: During the operation of urban and rural bus, any trip p can only be operated by one type of vehicle, so x p,m and x ij Two 0-1 variables satisfy the statistic of 1, which is calculated as follows: S13. Construct a multi-objective optimization objective function for urban and rural passenger vehicle operations to form a multi-objective optimization model for urban and rural passenger vehicle operations.

3. The multi-objective optimization method for passenger, freight and mail integration in rural areas according to claim 2, characterized in that: Said S13 further comprises: S131. Calculate the passenger travel cost Z1. The passenger travel cost includes the passenger waiting cost C1 and the passenger riding cost C2. The calculation method is as follows: t w =H / 2(10) Min Z1=C1+C2 (13) S132. Calculation of the maximum profit of urban and rural passenger transport enterprises. The maximum profit of rural passenger transport enterprises consists of bus fare revenue K, freight operation revenue, and bus operation costs. The calculation method is as follows:

1. Bus fare revenue K is related to the number of passengers and the fare, and is calculated as follows: Among them, U p is the base fare, CEIL(.) indicates the value is rounded up, and f is the step mileage; 2. Express delivery revenue E2 is directly proportional to freight rates and freight volume, and is also affected by the volume of packages. It is calculated as follows:

3. The bus operating cost Z2 is related to the mileage, vehicle configuration, and bus personnel costs, and is calculated as follows: Min Z2=E1+E2-(C3+C4) (19) Among them, D od is the travel distance of urban and rural buses from the starting station to the terminal station, θ m represents the unit mileage cost of the bus, r2 is the unit time value of the bus staff, is the time when train number p arrives at the terminal station N, The departure time of train number p from the county-level departure station; 4. The calculation method for the minimum operating cost Min Z3 of express delivery companies is as follows: Min Z3=C5+C6+C7 (23) Among them, C5 represents storage costs, C6 represents fees paid to urban and rural passenger transport companies and village service outlets, C7 represents the delivery costs of express delivery companies, and V arrive Indicates the number of express deliveries arriving at the county-level station on the same day, C store represents the storage cost of each express delivery at the county-level station, Y represents the number of deliveries per day by the express delivery company, θ e Denotes the unit mileage cost of the delivery vehicle, D j represents the total distance traveled to return to the county-level station after completing the delivery tasks of all village-level outlets, r3 represents the unit time value of the delivery personnel, and t j represents the total driving time required to return to the county-level station after completing the delivery, t h represents the loading and unloading time of each loading and unloading point, x j is a decision variable. When station j is a village-level service point and performs express delivery, x j =1, otherwise x j =0, L is the outlet operation fee that an enterprise needs to pay when setting up an express delivery outlet independently.

4. The multi-objective optimization method for passenger, freight and mail integration in rural areas according to claim 2, characterized in that: Said S2 further comprises: S21. Select a specific bus route, input the bus route and its passenger flow data, operating parameters, station data, and multiple bus vehicle data, and divide the bus route's operating period into two time periods, the off-season and the peak season, based on passenger flow characteristics. Select one day each in the off-season and the peak season as the research time period T for the simulation. S22. Determine a reasonable departure frequency A based on the passenger travel demand during off-season and peak seasons; S23. Determine the fleet size FS under each departure frequency A, find the corresponding vehicle model combination plan, and meet the constraints of the multi-objective optimization model of urban and rural passenger vehicle operations.

5. The multi-objective optimization method for passenger, freight and mail integration in rural areas according to claim 2, characterized in that: Said S3 further comprises: S31. Since the feasible range of departure frequency A is small, all departure frequency ranges that meet the existing departure intervals and line section passenger flow of urban and rural public transportation are listed through enumeration method [A min ,A max ]; S32. Traverse different values ​​and take the departure frequency A as a fixed value; S33. Use the inverse function method to calculate the value of the minimum fleet size FS corresponding to different departure frequencies A, and use this value as the value of FS in the operation constraint condition formula (7); S34. Use genetic algorithm real number coding. The first gene represents the value of A, that is, the frequency of departure. The second gene represents the value of FS, that is, the minimum fleet size corresponding to each departure frequency. The third to A+2 genes represent the vehicle model m corresponding to the departure frequency, where m represents the mth type of vehicle.

6. The multi-objective optimization method for passenger, freight and mail integration in rural areas according to claim 1, characterized in that: Said S4 further comprises: S41. Use the NSGA-II algorithm to find the optimal Pareto solution set, that is, the urban and rural passenger vehicle operation optimization plan corresponding to each departure frequency; S42. Output the optimal solution set. Each solution in the optimal solution set includes the departure frequency and the vehicle model configuration for each trip corresponding to the departure frequency, as well as three objectives and values ​​corresponding to the solution: passenger travel cost, urban and rural passenger transport enterprise profit, and express delivery enterprise operating cost.

7. The multi-objective optimization method for passenger, freight and mail integration in rural areas according to claim 1, characterized in that: In S5, based on the objectives and data output by the optimal solution set, an analysis of the optimization plan for urban and rural passenger transport operations is performed, and an adjustment plan for the departure frequency and the corresponding fleet size is provided.

8. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the processor executes the steps of the multi-objective optimization method for passenger, freight and mail integration in rural areas as described in any one of claims 1 to 7.

9. A computer device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the multi-objective optimization method for passenger, freight and mail integration in rural areas as described in any one of claims 1 to 7.

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