CN110135755B - Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling - Google Patents

Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling Download PDF

Info

Publication number
CN110135755B
CN110135755B CN201910432647.4A CN201910432647A CN110135755B CN 110135755 B CN110135755 B CN 110135755B CN 201910432647 A CN201910432647 A CN 201910432647A CN 110135755 B CN110135755 B CN 110135755B
Authority
CN
China
Prior art keywords
time
vehicle
train
model
line
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910432647.4A
Other languages
Chinese (zh)
Other versions
CN110135755A (en
Inventor
姜晓红
过秀成
沈涵瑕
龚小林
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Forestry University
Original Assignee
Nanjing Forestry University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Forestry University filed Critical Nanjing Forestry University
Priority to CN201910432647.4A priority Critical patent/CN110135755B/en
Publication of CN110135755A publication Critical patent/CN110135755A/en
Application granted granted Critical
Publication of CN110135755B publication Critical patent/CN110135755B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G08G1/202—Dispatching vehicles on the basis of a location, e.g. taxi dispatching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Chemical & Material Sciences (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling, which comprises the following steps: constructing a double-layer planning model; based on the upper layer planning assumption condition, establishing an upper layer vehicle scheduling model by taking the minimum bus operation cost as a target; based on lower planning assumption conditions, taking the minimum total travel transfer time and transfer failure penalty of passengers in the zone as targets, and establishing a lower schedule optimization model; solving the lower-layer schedule optimization model, and outputting an optimized feasible task shift set as a satisfactory solution of the lower-layer schedule optimization model; solving an upper vehicle scheduling model, and outputting all task chains for completing a feasible task shift set and the corresponding required vehicle number; and generating an optimal solution of the double-layer planning model, and completing the output of an optimal vehicle scheduling scheme and a corresponding urban and rural bus schedule. The operation plan of the zone line organization can be intuitively reflected, and the convenient transfer requirements of passengers and the effective configuration of enterprise vehicle resources can be further met.

Description

Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling
Technical Field
The invention relates to an optimization method, in particular to a method for compiling comprehensive optimization of urban and rural bus timetables and scheduling vehicles, and belongs to the technical field of optimizing urban and rural bus capacity resources.
Background
Urban and rural buses mainly refer to public transportation connected with rural areas, and comprise urban public transportation trunks connected with urban areas to towns (streets), town public transportation branch lines from towns to villages and town public transportation branch lines between adjacent towns. Compared with the definition of regional buses, the urban and rural buses are slightly smaller in range, and the urban-to-urban main buses are used for positioning the urban and rural main buses, so that a multipoint radial or regional internal ring type network structure can be formed between villages and villages with stronger relevance, and a town-village, village-village line and urban main buses form a single segment. The public transport organization in the district mainly realizes the integration of transport capacity resources in the district by allocating the transport capacity of adjacent lines, and relates to two sub-problems of time table programming and vehicle scheduling in the district. In order to ensure the convenience of passenger transfer and the operation benefit of enterprises, the departure time synchronism of the cooperative transfer points is required in the process of compiling the multi-line schedule of the district, and the multi-line capacity allocation reduces the number of vehicles and the idle running cost as much as possible on the basis of completing the task shift.
Based on the organic connection of the regional bus timetable and the vehicle dispatching, domestic and foreign scholars research the solution of the comprehensive integration optimization problem. There are two existing methods: (1) sequential integration: sequentially solving the schedule or vehicle scheduling sub-problems, wherein the method is likely to discard the overall optimal solution when screening the optimal solution of the preamble sub-problems; (2) complete integration: one model and one algorithm solve two sub-problems at a time, and the related results are as follows: chakroborty et al have studied the optimization problem of the multi-line single transfer point network under the constraint of the number of vehicles; castelli et al have studied to serve more passengers based on minimizing the cost of operation based on vehicle number constraints; the Guihair et al construct a weighted objective function comprising operation cost, number of vehicles, number and intensity of transfer nodes, equidistant departure time and idle running time based on the existing timetable; the Ibarra Rojas et al study defines the multi-line single-yard scheduling optimization of the time window, and the minimum number of vehicles and the maximum synchronous transfer quantity are taken as weighted objective functions; laporte and the like consider passenger travel path selection preference and operation cost limitation, and both adopt a constraint method to solve the pareto optimal solution. Fonseca et al construct a transfer cost and operating cost weighted objective function, which proposes an accurate heuristic solution.
As the urban and rural bus lines have few shifts, passengers have long waiting time once transfer fails, the passenger flow of the trunk lines of the urban and rural buses in the sheet areas and the demand difference of transfer points are obvious, the time for reaching the transfer points is needed to be sequenced, the conventional research references the line numbers of the transfer points to describe the cooperative coefficients, or the transfer quantity is used as one of the optimization targets, but how to better adjust the time sequence from the transfer points to the time according to the transfer quantity is the content to be researched. In addition, the existing modeling is solved by using accurate methods such as mathematics, operation research and the like or a certain heuristic algorithm, and is difficult to operate in engineering practice.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a method for comprehensively optimizing the planning of the bus timetable and the vehicle scheduling in the district and the country, can intuitively reflect the operation plan of the district line organization, realizes the convenient transfer requirement of passengers and the effective configuration of enterprise vehicle resources, and has great industrial utilization value.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling comprises the following steps:
1) Constructing a double-layer planning model;
the double-layer planning model comprises an upper-layer vehicle scheduling model and a lower-layer schedule optimization model, wherein the upper-layer vehicle scheduling model realizes the vehicle time sequence assignment of multiple lines, multiple vehicles and multiple yards aiming at the minimum operation cost of a public transportation enterprise, and the lower-layer schedule optimization model realizes the schedule optimization of multiple urban and rural public transportation lines aiming at the minimum travel transfer total time and transfer failure penalty of passengers in a zone;
1-1) constructing an upper vehicle scheduling model;
based on the upper layer planning assumption condition, establishing an upper layer vehicle scheduling model by taking the minimum bus operation cost as a target;
1-2) constructing a lower-layer schedule optimization model;
based on lower planning assumption conditions, taking the minimum total travel transfer time and transfer failure penalty of passengers in the zone as targets, and establishing a lower schedule optimization model;
2) Solving a lower-layer schedule optimization model;
solving the shift of the urban and rural bus line of the sheet area by adopting an enumeration method;
optimizing a timetable by translating an operation chart on the basis of the existing timetable, and sequentially adjusting a transfer point timetable according to transfer quantity to obtain an optimized feasible task shift set;
outputting the optimized feasible task shift set as a satisfaction solution of the lower-layer schedule optimization model;
3) Solving an upper vehicle scheduling model;
taking the satisfied solution of the outputted lower-layer schedule optimization model as a feasible input solution of the upper-layer vehicle scheduling model, adopting a tabu search algorithm to solve the values of the objective function of the upper-layer vehicle scheduling model in each feasible task shift, and outputting all task chains of a finished feasible task shift set and corresponding required vehicle numbers;
4) Generating an optimal solution of the double-layer planning model;
and selecting the task chain and the number of vehicles corresponding to the minimum bus operation cost from all the task chains and the number of vehicles corresponding to the output completed feasible task shift sets as the optimal solution of the double-layer planning model, and completing the output of the optimal vehicle scheduling scheme and the corresponding bus timetable in the district and the country.
The invention is further provided with: the upper layer planning assumption conditions are: each line running on urban and rural bus lines in the section can be executed according to a schedule and arrives at a station on time; the vehicle types of all vehicles are unified; each line up and down is regarded as an independent study object, and each train number has and only one bus to execute tasks;
the upper vehicle scheduling model specifically comprises the following steps:
Figure GDA0004203934380000031
wherein C is i,j ＝c·t i,j ，C i,M+l ＝c·t i,M+l ，C M+l,j ＝c·t M+l,j (1)
The constraints of the upper vehicle dispatch model are that,
y i,j ∈(0,1)；i,j＝1,2,…,M+K；(i,j)≠(M+l,M+l′)；l,l′＝1,2,…,K (2)
z i,l ∈(0,1)；i＝1,2,…,M；l＝1,2,…,K (3)
Figure GDA0004203934380000032
Figure GDA0004203934380000033
Figure GDA0004203934380000041
y M+l,j -z j,l ≤0；j＝1,2,…,M；l＝1,2,…,K (7)
y i,M+l -z i,l ≤0；i＝1,2,…,M；l＝1,2,…,K (8)
z i,l y i,j -z j,l ≤0；i,j＝1,2,…,M；l＝1,2,…,K (9)
t i +t i+1 +…+t M +t l,i +t i,i+1 +…+t M-1,M +t M,l′ ≤T max ,y l,i ·y i,i+1 …y M-1,M ·y M,l′ ＝1 (10)
in the constraint condition of the upper-layer vehicle scheduling model, the formulas (2) and (3) define y i,j 、z i,l The method comprises the steps of carrying out a first treatment on the surface of the The formula (4) ensures that each bus returns to a yard to wait or execute the next train j after completing the train number i; equation (5) ensures that each train is executed by a vehicle that starts from the yard or starts to execute after the last train is completed; equation (6) ensures that there is only one yard assigned vehicle per lot; the (7) ensures D l Is assigned to the vehicle number j, and j is the first task that the vehicle performs after having been driven out; the (8) ensuresParking lot D l I is the last task that the vehicle performs before returning to the yard; formula (9) ensures that the vehicle directly executes j after completing the number of vehicles i, if i is defined by D l Allocated, j is also defined by D l Distributing; equation (10) is a vehicle duration constraint;
wherein Y is a feasible solution of an upper-layer vehicle scheduling model, mapping S (X) to Y is a feasible input solution of the upper-layer vehicle scheduling model formed by task shifts generated by a lower-layer schedule optimization model according to natural number codes, S (X) is a plurality of feasible task shift sets generated by the lower-layer schedule optimization model, C is a fixed cost of a bus, V is a required vehicle number, M is a total number of shifts, K is a number of yards, C is i,j Running cost from the end point of the train number i to the start point of the train number j, running cost per unit time of c and t i,j For the travel time from the end point of the train number i to the start point of the train number j, C i,M+l For destination i of train number to yard D l Cost of idle running, t i,M+l For destination i of train number to yard D l Travel time of C M+l,j For the yard D l Cost of empty to the start of j, t M+l,j For the yard D l Travel time to start of train number j, T max The maximum continuous driving time of a bus; y is i,j Indicating that the number j of the directly operated vehicles is 1 if the number i of the directly operated vehicles is completed, otherwise, the number j of the directly operated vehicles is 0; y is i,M+l Indicating that if the number of vehicles i is finished, the vehicle returns to D directly l Then 1, otherwise 0; y is M+l,j Indicating that if the number j is D l The first train number of the sent train is 1, otherwise, the first train number of the sent train is 0; z i,l Indicating that if the number of vehicles i is D l The sent vehicle is 1, otherwise, the sent vehicle is 0; z j,l Meaning that if j is D l The sent vehicle is 1, otherwise, the sent vehicle is 0; t is t i For the time of completion of the train number i, i=1, 2, …, M; t is t i,i+1 I=1, 2, …, M-1, which is the travel time from the end point of the train i to the start point of the train i+1; t is t l,i For the yard D l Travel time to start of the number of times i, t M,l′ For the destination of the train number M to the train yard D l' Is set according to the driving time of the vehicle; y is M,l′ Indicating that if the train number M is completed, the train returns to the train yard D l' Then 1, otherwise 0; y is l,i Indicating that if the distance from the yard Dl to the number i is 1,otherwise, 0; y is i,i+1 Indicating that if the vehicle number i is completed, the vehicle number i+1 is directly operated, and if the vehicle number i is not completed, the vehicle number i is 0, i=1, 2, … and M-1;
wherein, the model feasible solution Y is that,
Figure GDA0004203934380000051
y in feasible solution Y in model M,M+K Indicating that if the train number M is completed, the train returns to the train yard D K Then 1 and otherwise 0.
The invention is further provided with: the lower layer planning assumption conditions are as follows: the transfer quantity at any transfer point is known and is not influenced by the departure interval; because urban and rural buses have small passenger flow, passengers at the transfer points can smoothly take vehicles meeting the conditions which arrive first;
the lower schedule optimization model specifically comprises the following steps:
Figure GDA0004203934380000052
the constraints of the underlying schedule optimization model are that,
Figure GDA0004203934380000053
W Mp ≤T；p＝1,2,…,P (13)
Figure GDA0004203934380000054
Figure GDA0004203934380000055
in the constraint condition of the lower schedule optimization model, the formula (12) defines that the maximum departure interval of the line is not exceeded from the starting time of the time period T to the first departure time; w in formula (13) Mp Departure time for last shift of p-th line within period TThe departure time of the last departure shift is within the termination time of the period; formula (14) constrains departure intervals for each line; equation (15) constrains the waiting time of the passenger at the transfer point;
wherein X is the feasible solution of the lower-layer schedule optimization model, the mapping f (X) -S (X) represents the feasible solution generated by the model, S (X) is the feasible task shift set, P is the total number of buses in the patch, M is the total number of shifts, N is the number of transfer points, T is the optimization time period, and,
Figure GDA0004203934380000061
The number of passengers W to be transferred from line p' to line i ip Departure time W for line p train number i in time period T i'p' Departure time T for line p 'train number i' in time period T pn For the travel time, T, from the start station of the line p to the transfer node n p'n Travel time for the start of line p' to transfer node n, ">
Figure GDA0004203934380000062
Maximum departure interval for line p in time period T,/->
Figure GDA0004203934380000063
For minimum departure interval, tw, of line p during time period T max Acceptable maximum transfer waiting time, tw for passengers min The minimum time required for acceptable transfer for the passenger, delta being the penalty factor; />
Figure GDA0004203934380000064
Indicating that if the train number i of the line p and the train number i 'of the line p' can be transferred at the transfer node n, the train number i is 1, otherwise, the train number i is 0; w (W) 1p Departure time W for line p train number 1 in time period T (i+1)p The departure time of the line p train number (i+1) in the time period T;
all the public line numbers P in the patch area are calculated by adopting an uplink and downlink calculation mode.
The invention is further provided with: the public transport operation cost comprises fixed cost of purchasing vehicles and empty driving cost of vehicles between shifts and yards.
The invention is further provided with: when passengers which cannot be successfully transferred are added, the bus operation cost is increased by delta minutes.
The invention is further provided with: the optimization of the schedule in step 2) by translating the running chart, in particular,
step1, selecting a running chart type;
drawing a section line running chart, and marking a first station and a last station and a transfer station by taking a time period as an abscissa and the travel time of each line as an ordinate;
step2, selecting a reference line;
selecting one urban and rural public transportation trunk line with large passenger flow and more transfer nodes as a reference line;
step3, determining a translation line sequence according to the passenger traffic volume and the transfer volume;
step4, sequentially translating urban and rural bus trunk running diagrams;
adjusting the trunk departure interval: if the trunk line starting stations are the same, equal interval departure among the lines is ensured; otherwise, ensuring that the time from the trunk line to the midway transfer station is the same;
the stay time of the tail end of the trunk line is adjusted to ensure that the trunk lines starting from the same site are equally spaced in the return process, or the trunk lines starting from different sites can be synchronously transferred in the return process;
step5, sequentially translating urban and rural bus branch running diagrams;
adjusting the branch departure interval to ensure the time coordination of the branch departure interval and the trunk line at the transfer point;
adjusting the leg end dwell time to ensure that the leg to station time is the same or slightly earlier than the trunk departure time;
step6, calculating the objective function value of the lower-layer schedule optimization model, comparing with the current situation scheme,
judging whether optimization exists or not through comparison, executing the next Step if the optimization exists, and returning to the Step4 if the optimization does not exist;
step7, marking seamless transfer nodes and optimization information to form an optimization scheme, and outputting an optimized operation diagram recorded with the optimization scheme; wherein the optimization information comprises a feasible task shift set, a shift total number and a departure time.
The invention is further provided with: the tabu search algorithm adopted in the step 3) is adopted to solve the values of the objective function of the upper-layer vehicle scheduling model in each feasible task shift, in particular,
3-1) adopting a natural number code represented by a structural body as a basic form of a solution, wherein '0 (x)' represents a parking lot x, and the natural number represents a shift task to be completed;
3-2) generating an initial solution;
sequentially numbering the shift tasks according to natural numbers by taking departure time sequence as sequence, and reordering the natural number sequence in consideration of maximum continuous driving time constraint, shift departure time and operation time; inserting a yard '0 (x)', and screening the optimal yards one by one if the number of yards is limited; merging the possible merged '0 (x)', and connecting the merged '0 (x)' into a feasible task chain, wherein the number of the task chains is the number of the required vehicles;
3-3) calculating by adopting a 2-top neighborhood operation method;
randomly selecting a group of natural number exchange positions each time to form a new sequence, reinserting a car park number according to constraint conditions, and calculating an objective function value; the solution with the largest/smallest objective function value generated by each iteration is used as a tabu object, a candidate set is determined to be a neighborhood space of the solution generated randomly, a rule based on an evaluation value is selected as a scofflaw, and if a target value of one solution appears to be better than any one of the optimal candidate solutions, special privilege is realized; the evaluation function is the difference between the objective function value of the optimal solution and the current solution obtained so far;
3-4) terminating the calculation by adopting a target control principle;
if the current optimal value does not change in the given step number, the calculation is terminated, and the task chain corresponding to the current optimal value and the required vehicle number are output.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of comprehensively optimizing urban and rural bus schedule programming and vehicle scheduling by constructing a double-layer planning model, wherein the urban and rural bus scheduling problem of an upper-layer urban and rural bus is aimed at the minimum comprehensive operation cost of a bus enterprise, realizing the vehicle time sequence assignment of multiple lines, multiple vehicles and multiple yards, and solving by adopting a tabu search algorithm; the urban and rural bus schedule optimization problem in the lower layer area is to realize that the transfer total time and transfer failure penalty of the line at all transfer nodes are minimum, ensure the schedule collaborative sequencing of the transfer nodes, and solve the problem by adopting an operation chart and an enumeration method; on the basis of the current schedule, the lower schedule optimization model generates a group of satisfactory solution upper vehicle scheduling model comparison and selection, and then an optimal vehicle scheduling scheme and a corresponding district urban and rural bus schedule are generated.
The foregoing is merely an overview of the present invention, and for the purpose of providing a better understanding of the present invention, the present invention is further described below with reference to the accompanying drawings.
Drawings
FIG. 1 is a flowchart of method steps according to an embodiment of the present invention;
FIG. 2 is a flow chart of the operation diagram drawing in the method according to the embodiment of the invention;
FIG. 3 is a topology of a bus route in an example application of an embodiment of the present invention;
FIG. 4 is a current operation diagram in an example application of an embodiment of the present invention;
FIG. 5 is a diagram illustrating an example application of an embodiment of the present invention after optimization.
Detailed Description
The invention will be further described with reference to the drawings.
The invention provides a method for compiling comprehensive optimization of urban and rural bus schedules and scheduling vehicles, which is shown in fig. 1 and comprises the following steps:
1) Constructing a double-layer planning model;
the double-layer planning model comprises an upper-layer vehicle scheduling model and a lower-layer schedule optimization model, wherein the upper-layer vehicle scheduling model realizes the vehicle time sequence assignment of multiple lines, multiple vehicles and multiple yards aiming at the minimum operation cost (including the fixed cost of purchasing vehicles and the empty driving cost of vehicles between shifts and yards) of a public transportation enterprise, and the lower-layer schedule optimization model realizes the optimization of multiple urban and rural public transportation line schedules aiming at the minimum travel transfer total time and transfer failure penalty of passengers in a zone.
1-1) constructing an upper vehicle scheduling model;
based on the upper layer planning assumption condition, the bus operation cost is minimum as a target, and an upper layer vehicle scheduling model is established.
The upper layer planning assumption conditions are: each line running on urban and rural bus lines in the section can be executed according to a schedule and arrives at a station on time; the vehicle types of all vehicles are unified; each line up and down is considered as an independent study object, and each train number has and only one bus to perform tasks.
The upper vehicle scheduling model specifically comprises the following steps:
Figure GDA0004203934380000091
wherein C is i,j ＝c·t i,j ，C i,M+l ＝c·t i,M+l ，C M+l,j ＝c·t M+l,j (1)
The constraints of the upper vehicle dispatch model are that,
y i,j ∈(0,1)；i,j＝1,2,…,M+K；(i,j)≠(M+l,M+l′)；l,l′＝1,2,…,K (2)
z i,l ∈(0,1)；i＝1,2,…,M；l＝1,2,…,K (3)
Figure GDA0004203934380000092
Figure GDA0004203934380000093
Figure GDA0004203934380000094
y M+l,j -z j,l ≤0；j＝1,2,…,M；l＝1,2,…,K (7)
y i,M+l -z i,l ≤0；i＝1,2,…,M；l＝1,2,…,K (8)
z i,l y i,j -z j,l ≤0；i,j＝1,2,…,M；l＝1,2,…,K (9)
Figure GDA0004203934380000101
Figure GDA0004203934380000102
in the constraint condition of the upper-layer vehicle scheduling model, the formulas (2) and (3) define y i,j 、z i,l The method comprises the steps of carrying out a first treatment on the surface of the The formula (4) ensures that each bus returns to a yard to wait or execute the next train j after completing the train number i; equation (5) ensures that each train is executed by a vehicle that starts from the yard or starts to execute after the last train is completed; equation (6) ensures that there is only one yard assigned vehicle per lot; the (7) ensures D l Is assigned to the vehicle number j, and j is the first task that the vehicle performs after having been driven out; the parking lot D is ensured by the aid of the formula (8) l I is the last task that the vehicle performs before returning to the yard; formula (9) ensures that the vehicle directly executes j after completing the number of vehicles i, if i is defined by D l Allocated, j is also defined by D l Distributing; equation (10) is a vehicle duration constraint;
wherein Y is a feasible solution of an upper-layer vehicle scheduling model, mapping S (X) to Y is a feasible input solution of the upper-layer vehicle scheduling model formed by task shifts generated by a lower-layer schedule optimization model according to natural number codes, S (X) is a plurality of feasible task shift sets generated by the lower-layer schedule optimization model, C is a fixed cost of a bus, V is a required vehicle number, M is a total number of shifts, K is a number of yards, C is i,j For a vehicleRunning cost from the secondary i end point to the primary j end point of the vehicle number, running cost per unit time of c and t i,j For the travel time from the end point of the train number i to the start point of the train number j, C i,M+l For destination i of train number to yard D l Cost of idle running, t i,M+l For destination i of train number to yard D l Travel time of C M+l,j For the yard D l Cost of empty to the start of j, t M+l,j For the yard D l Travel time to start of train number j, T max The maximum continuous driving time of a bus; y is i,j Indicating that the number j of the directly operated vehicles is 1 if the number i of the directly operated vehicles is completed, otherwise, the number j of the directly operated vehicles is 0; y is i,M+l Indicating that if the number of vehicles i is finished, the vehicle returns to D directly l Then 1, otherwise 0; y is M+l,j Indicating that if the number j is D l The first train number of the sent train is 1, otherwise, the first train number of the sent train is 0; z i,l Indicating that if the number of vehicles i is D l The sent vehicle is 1, otherwise, the sent vehicle is 0; z j,l Meaning that if j is D l The sent vehicle is 1, otherwise, the sent vehicle is 0; t is t i For the time of completion of the train number i, i=1, 2, …, M; t is t i,i+1 I=1, 2, …, M-1, which is the travel time from the end point of the train i to the start point of the train i+1; t is t l,i For the yard D l Travel time to start of the number of times i, t M,l′ For the destination of the train number M to the train yard D l' Is set according to the driving time of the vehicle; y is M,l′ Indicating that if the train number M is completed, the train returns to the train yard D l' Then 1, otherwise 0; y is l,i Indicating if it is from yard D l Directly reaching the train number i, wherein the train number i is 1, otherwise, the train number i is 0; y is i,i+1 Indicating that if the vehicle number i is completed, the vehicle number i+1 is directly operated, and if the vehicle number i is not completed, the vehicle number i is 0, i=1, 2, … and M-1;
wherein, the model feasible solution Y is that,
Figure GDA0004203934380000111
y in feasible solution Y in model M,M+K Indicating that if the train number M is completed, the train returns to the train yard D K Then 1 and otherwise 0.
1-2) constructing a lower-layer schedule optimization model;
based on lower planning assumption conditions, taking the minimum total travel transfer time and transfer failure penalty of passengers in the zone as targets, and establishing a lower schedule optimization model.
The lower layer planning assumption conditions are as follows: the transfer quantity at any transfer point is known and is not influenced by the departure interval; because urban and rural buses have small passenger flow, passengers at the transfer points can smoothly take vehicles meeting the conditions which arrive first.
The lower schedule optimization model specifically comprises the following steps:
Figure GDA0004203934380000112
the constraints of the underlying schedule optimization model are that,
Figure GDA0004203934380000113
W Mp ≤T；p＝1,2,…,P (13)
Figure GDA0004203934380000114
Figure GDA0004203934380000115
in the constraint condition of the lower schedule optimization model, the formula (12) defines that the maximum departure interval of the line is not exceeded from the starting time of the time period T to the first departure time; w in formula (13) Mp The departure time of the last departure shift in the period T is within the termination time of the period; formula (14) constrains departure intervals for each line; equation (15) constrains the waiting time of the passenger at the transfer point;
wherein X is the feasible solution of the lower-layer schedule optimization model, the mapping f (X) -S (X) represents the feasible solution generated by the model, S (X) is the feasible task shift set, P is the number set of all public lines in the patch region,M is the total number of shifts, N is the number of transfer points, T is the optimized time period,
Figure GDA0004203934380000121
The number of passengers W to be transferred from line p' to line i ip Departure time W for line p train number i in time period T i'p' Departure time T for line p 'train number i' in time period T pn For the travel time, T, from the start station of the line p to the transfer node n p'n Travel time for the start of line p' to transfer node n, ">
Figure GDA0004203934380000122
Maximum departure interval for line p in time period T,/->
Figure GDA0004203934380000123
For minimum departure interval, tw, of line p during time period T max Acceptable maximum transfer waiting time, tw for passengers min The minimum time required for the passengers to accept transfer is delta as a penalty factor (when each passenger with no smooth transfer is added, the bus operation cost is increased by delta minutes); />
Figure GDA0004203934380000124
Indicating that if the train number i of the line p and the train number i 'of the line p' can be transferred at the transfer node n, the train number i is 1, otherwise, the train number i is 0; w (W) 1p Departure time W for line p train number 1 in time period T (i+1)p The departure time of the line p train number (i+1) in the time period T;
all the public line numbers P in the patch area are calculated by adopting an uplink and downlink calculation mode.
2) Solving a lower-layer schedule optimization model;
solving the shift of the urban and rural bus line of the sheet area by adopting an enumeration method;
optimizing a timetable by translating an operation chart on the basis of the existing timetable, and sequentially adjusting a transfer point timetable according to transfer quantity to obtain an optimized feasible task shift set;
and outputting the optimized feasible task shift set as a satisfaction solution of the lower schedule optimization model.
As shown in fig. 2, the schedule is optimized by translating the running chart, specifically,
step1, selecting a running chart type;
drawing a section line running chart, and marking a first station and a last station and a transfer station by taking a time period as an abscissa and the travel time of each line as an ordinate;
step2, selecting a reference line;
selecting one urban and rural public transportation trunk line with large passenger flow and more transfer nodes as a reference line;
step3, determining a translation line sequence according to the passenger traffic volume and the transfer volume;
step4, sequentially translating urban and rural bus trunk running diagrams;
adjusting the trunk departure interval: if the trunk line starting stations are the same, equal interval departure among the lines is ensured; otherwise, ensuring that the time from the trunk line to the midway transfer station is the same;
the stay time of the tail end of the trunk line is adjusted to ensure that the trunk lines starting from the same site are equally spaced in the return process, or the trunk lines starting from different sites can be synchronously transferred in the return process;
step5, sequentially translating urban and rural bus branch running diagrams;
adjusting the branch departure interval to ensure the time coordination of the branch departure interval and the trunk line at the transfer point;
adjusting the leg end dwell time to ensure that the leg to station time is the same or slightly earlier than the trunk departure time;
step6, calculating the objective function value of the lower-layer schedule optimization model, comparing with the current situation scheme,
judging whether optimization exists or not through comparison, executing the next Step if the optimization exists, and returning to the Step4 if the optimization does not exist;
step7, marking seamless transfer nodes and optimization information to form an optimization scheme, and outputting an optimized operation diagram recorded with the optimization scheme; wherein the optimization information comprises a feasible task shift set, a shift total number and a departure time.
3) Solving an upper vehicle scheduling model;
and taking the satisfied solution of the outputted lower-layer schedule optimization model as a feasible input solution of the upper-layer vehicle scheduling model, solving the values of the objective function of the upper-layer vehicle scheduling model in each feasible task shift by adopting a tabu search algorithm, and outputting all task chains of the finished feasible task shift set and the corresponding required vehicle number.
The tabu search algorithm solves for, in particular,
3-1) adopting a natural number code represented by a structural body as a basic form of a solution, wherein '0 (x)' represents a parking lot x, and the natural number represents a shift task to be completed; if 0 (1) _1_4_0 (1) _0 (2) _5_9_10_0 (2) shows that two buses respectively start from the parking lot 1 and the parking lot 2, and the buses return to the parking lot after tasks are sequentially executed.
3-2) generating an initial solution;
sequentially numbering the shift tasks according to natural numbers by taking departure time sequence as sequence, and reordering the natural number sequence in consideration of maximum continuous driving time constraint, shift departure time and operation time; inserting a yard '0 (x)', and screening the optimal yards one by one if the number of yards is limited; and merging the possible merged '0 (x)', and connecting the merged '0 (x)' into a feasible task chain, wherein the number of the task chains is the number of the required vehicles.
3-3) calculating by adopting a 2-top neighborhood operation method;
randomly selecting a group of natural number exchange positions each time to form a new sequence, reinserting a car park number according to constraint conditions, and calculating an objective function value; the solution with the largest/smallest objective function value generated by each iteration is used as a tabu object, a candidate set is determined to be a neighborhood space of the solution generated randomly, a rule based on an evaluation value is selected as a scofflaw, and if a target value of one solution appears to be better than any one of the optimal candidate solutions, special privilege is realized; the evaluation function is the difference between the objective function value of the so far obtained optimal solution and the current solution.
3-4) terminating the calculation by adopting a target control principle;
if the current optimal value does not change in the given step number, the calculation is terminated, and the task chain corresponding to the current optimal value and the required vehicle number are output.
4) Generating an optimal solution of the double-layer planning model;
and selecting the task chain and the number of vehicles corresponding to the minimum bus operation cost from all the task chains and the number of vehicles corresponding to the output completed feasible task shift sets as the optimal solution of the double-layer planning model, and completing the output of the optimal vehicle scheduling scheme and the corresponding bus timetable in the district and the country.
Example application: taking five urban and rural bus routes (two main lines and three branch lines) in a south urban area as an example for verification. FIG. 3 is a bus topology, where uplink 1 (D1-B-N1-N3-N4-D3) is the trunk, lines 2 (B-N1-N3-F), 3 (B-N1-N2-A), 4 (D2-N2-N3-E) are all branches, line 5 (C-N4-D3) is the trunk, lines 6-10 are each corresponding to a downlink, and the numbers in FIG. 3 are the inter-node travel times (in minutes). The travel time from each line start point to four transfer points (N1, N2, N3, N4) and three bus yards (D1, D2, D3) are shown in Table 1, table 1 is the operation time length (unit: min) from each line start point to each node, and the transfer amount of each node is shown in Table 5. The current timetable and the running chart are respectively shown in table 2 and fig. 4, and the time period to be optimized is 12:00-15:00. The penalty factor delta is set to 5 by trial and error.
Figure GDA0004203934380000141
TABLE 1
Figure GDA0004203934380000142
TABLE 2
(1) Timetable optimization
Solving two optimization schemes obtained by the lower-layer schedule optimization model firstly realizes seamless transfer of a line with larger transfer quantity requirement, and secondly effectively reduces the waiting time of other transfer nodes; the distinction is only in the order of nodes with small processing transfer, such as 8→ (2) →4 optimized for scheme one, 6→ (1) →3 optimized for scheme two, see in particular table 3 (transfer waiting time comparison before and after schedule optimization).
Figure GDA0004203934380000151
TABLE 3 Table 3
In table 3, "1→ (3) →9 (5, 8)" indicates that the passenger is transferred from line 1 to line 9, () at transfer point (3) and the number indicates the transfer amount at different times; transfer waiting time: positive numbers indicate delay (waiting for transfer), negative numbers indicate advance (transfer failure), 0 is seamless transfer.
(2) Vehicle dispatch
Setting m=40, k=7, c=100, c=1, t in the upper vehicle scheduling model solution max =480 min. The present example analyzes the vehicle schedule over a period of time, assuming virtual yards ( yards 4, 5, 6, 7, respectively) with distances 0 near each of lines 5, 7, 8, 9 to ensure that the vehicle can return to the yard at both start and end. The solving result is shown in table 4 (model calculation result), the vehicle dispatching effect of the first scheme is better, and the optimized timetable, the optimized running chart and the vehicle dispatching optimization scheme are shown in table 5, fig. 5 and table 6 respectively. Note that, the number in () after the departure time in table 4 is a custom shift number, and a black circle in fig. 5 indicates a seamless transfer node. Compared with the current situation, the optimized scheme reduces 3 buses, and the passenger transfer waiting time is reduced by 91.1%.
Figure GDA0004203934380000161
TABLE 4 Table 4
Figure GDA0004203934380000162
TABLE 5
Figure GDA0004203934380000163
TABLE 6
According to the comprehensive optimization method for planning and scheduling the urban and rural bus schedule, which is provided by the invention, a double-layer planning model is built by taking the shortest urban and rural bus transfer time, the smallest transfer failure punishment and the smallest bus operation cost (including the fixed cost for purchasing the vehicle and the idle running cost) as targets, and a certain urban and rural bus multi-line organization is selected for example application, so that the built double-layer planning model can well meet the travel transfer convenience requirement of passengers, and meanwhile, the vehicle resources of an integrated enterprise can be more effectively utilized, and the transfer point to the time can be sequentially adjusted according to the transfer quantity by using an enumeration method and an operation diagram operation method, so that the timetable can be ensured to cooperate with the primary and secondary property, the adopted operation diagram method is easy to operate in engineering practice, and can be visualized and better applied to engineering practice.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. A method for compiling comprehensive optimization of urban and rural bus schedules and scheduling vehicles is characterized by comprising the following steps:
1) Constructing a double-layer planning model;
the double-layer planning model comprises an upper-layer vehicle scheduling model and a lower-layer schedule optimization model, wherein the upper-layer vehicle scheduling model realizes the vehicle time sequence assignment of multiple lines, multiple vehicles and multiple yards aiming at the minimum operation cost of a public transportation enterprise, and the lower-layer schedule optimization model realizes the schedule optimization of multiple urban and rural public transportation lines aiming at the minimum travel transfer total time and transfer failure penalty of passengers in a zone;
1-1) constructing an upper vehicle scheduling model;
based on the upper layer planning assumption condition, establishing an upper layer vehicle scheduling model by taking the minimum bus operation cost as a target;
1-2) constructing a lower-layer schedule optimization model;
based on lower planning assumption conditions, taking the minimum total travel transfer time and transfer failure penalty of passengers in the zone as targets, and establishing a lower schedule optimization model;
2) Solving a lower-layer schedule optimization model;
solving the shift of the urban and rural bus line of the sheet area by adopting an enumeration method;
optimizing a timetable by translating an operation chart on the basis of the existing timetable, and sequentially adjusting a transfer point timetable according to transfer quantity to obtain an optimized feasible task shift set;
outputting the optimized feasible task shift set as a satisfaction solution of the lower-layer schedule optimization model;
3) Solving an upper vehicle scheduling model;
taking the satisfied solution of the outputted lower-layer schedule optimization model as a feasible input solution of the upper-layer vehicle scheduling model, adopting a tabu search algorithm to solve the values of the objective function of the upper-layer vehicle scheduling model in each feasible task shift, and outputting all task chains of a finished feasible task shift set and corresponding required vehicle numbers;
4) Generating an optimal solution of the double-layer planning model;
selecting a task chain and the number of vehicles corresponding to the minimum bus operation cost from all task chains and the number of vehicles corresponding to the output completed feasible task shift set as the optimal solution of the double-layer planning model, and completing the output of an optimal vehicle scheduling scheme and a corresponding bus schedule in a district and country;
the upper layer planning assumption conditions are: each line running on urban and rural bus lines in the section can be executed according to a schedule and arrives at a station on time; the vehicle types of all vehicles are unified; each line up and down is regarded as an independent study object, and each train number has and only one bus to execute tasks;
the upper vehicle scheduling model specifically comprises the following steps:
Figure FDA0004203934370000021
wherein C is i,j ＝c·t i,j ，C i,M+l ＝c·t i,M+l ，C M+l,j ＝c·t M+l,j (1)
The constraints of the upper vehicle dispatch model are that,
y i,j ∈(0,1)；i,j＝1,2,…,M+K；(i,j)≠(M+l,M+l′)；l,l′＝1,2,…,K (2)
z i,l ∈(0,1)；i＝1,2,…,M；l＝1,2,…,K (3)
Figure FDA0004203934370000022
Figure FDA0004203934370000023
Figure FDA0004203934370000024
y M+l,j -z j,l ≤0；j＝1,2,…,M；l＝1,2,…,K (7)
y i,M+l -z i,l ≤0；i＝1,2,…,M；l＝1,2,…,K (8)
z i,l y i,j -z j,l ≤0；i,j＝1,2,…,M；l＝1,2,…,K (9)
t i +t i+1 +…+t M +t l,i +t i,i+1 +…+t M-1,M +t M,l′ ≤T max ,
y l,i ·y i,i+1 …y M-1,M ·y M,l′ ＝1 (10)
constraints for upper vehicle dispatch modelIn the conditions, the formula (2) and the formula (3) define y i,j 、z i,l The method comprises the steps of carrying out a first treatment on the surface of the The formula (4) ensures that each bus returns to a yard to wait or execute the next train j after completing the train number i; equation (5) ensures that each train is executed by a vehicle that starts from the yard or starts to execute after the last train is completed; equation (6) ensures that there is only one yard assigned vehicle per lot; the (7) ensures D l Is assigned to the vehicle number j, and j is the first task that the vehicle performs after having been driven out; the parking lot D is ensured by the aid of the formula (8) l I is the last task that the vehicle performs before returning to the yard; formula (9) ensures that the vehicle directly executes j after completing the number of vehicles i, if i is defined by D l Allocated, j is also defined by D l Distributing; equation (10) is a vehicle duration constraint;
wherein Y is a feasible solution of an upper-layer vehicle scheduling model, mapping S (X) to Y is a feasible input solution of the upper-layer vehicle scheduling model formed by task shifts generated by a lower-layer schedule optimization model according to natural number codes, S (X) is a plurality of feasible task shift sets generated by the lower-layer schedule optimization model, C is a fixed cost of a bus, V is a required vehicle number, M is a total number of shifts, K is a number of yards, C is i,j Running cost from the end point of the train number i to the start point of the train number j, running cost per unit time of c and t i,j For the travel time from the end point of the train number i to the start point of the train number j, C i,M+l For destination i of train number to yard D l Cost of idle running, t i,M+l For destination i of train number to yard D l Travel time of C M+l,j For the yard D l Cost of empty to the start of j, t M+l,j For the yard D l Travel time to start of train number j, T max The maximum continuous driving time of a bus; y is i,j Indicating that the number j of the directly operated vehicles is 1 if the number i of the directly operated vehicles is completed, otherwise, the number j of the directly operated vehicles is 0; y is i,M+l Indicating that if the number of vehicles i is finished, the vehicle returns to D directly l Then 1, otherwise 0; y is M+l,j Indicating that if the number j is D l The first train number of the sent train is 1, otherwise, the first train number of the sent train is 0; z i,l Indicating that if the number of vehicles i is D l The sent vehicle is 1, otherwise, the sent vehicle is 0; z j,l Meaning that if j is D l The sent vehicle is 1, otherwise, the sent vehicle is 0; t is t i For the time of completion of the train number i, i=1, 2, …, M; t is t i,i+1 I=1, 2, …, M-1, which is the travel time from the end point of the train i to the start point of the train i+1; t is t l,i For the yard D l Travel time to start of the number of times i, t M,l′ For the destination of the train number M to the train yard D l' Is set according to the driving time of the vehicle; y is M,l′ Indicating that if the train number M is completed, the train returns to the train yard D l' Then 1, otherwise 0; y is l,i Indicating if it is from yard D l Directly reaching the train number i, wherein the train number i is 1, otherwise, the train number i is 0; y is i,i+1 Indicating that if the vehicle number i is completed, the vehicle number i+1 is directly operated, and if the vehicle number i is not completed, the vehicle number i is 0, i=1, 2, … and M-1;
wherein, the model feasible solution Y is that,
Figure FDA0004203934370000041
y in feasible solution Y in model M,M+K Indicating that if the train number M is completed, the train returns to the train yard D K Then 1, otherwise 0;
the lower layer planning assumption conditions are as follows: the transfer quantity at any transfer point is known and is not influenced by the departure interval; because urban and rural buses have small passenger flow, passengers at the transfer points can smoothly take vehicles meeting the conditions which arrive first;
the lower schedule optimization model specifically comprises the following steps:
Figure FDA0004203934370000042
the constraints of the underlying schedule optimization model are that,
Figure FDA0004203934370000043
W Mp ≤T；p＝1,2,…,P (13)
Figure FDA0004203934370000044
Figure FDA0004203934370000045
in the constraint condition of the lower schedule optimization model, the formula (12) defines that the maximum departure interval of the line is not exceeded from the starting time of the time period T to the first departure time; w in formula (13) Mp The departure time of the last departure shift in the period T is within the termination time of the period; formula (14) constrains departure intervals for each line; equation (15) constrains the waiting time of the passenger at the transfer point;
wherein X is the feasible solution of the lower-layer schedule optimization model, the mapping f (X) -S (X) represents the feasible solution generated by the model, S (X) is the feasible task shift set, P is the total number of buses in the patch, M is the total number of shifts, N is the number of transfer points, T is the optimization time period, and,
Figure FDA0004203934370000051
The number of passengers W to be transferred from line p' to line i ip Departure time W for line p train number i in time period T i'p' Departure time T for line p 'train number i' in time period T pn For the travel time, T, from the start station of the line p to the transfer node n p'n Travel time for the start of line p' to transfer node n, ">
Figure FDA0004203934370000052
Maximum departure interval for line p in time period T,/->
Figure FDA0004203934370000053
For minimum departure interval, tw, of line p during time period T max Acceptable maximum transfer waiting time, tw for passengers min Minimum time required for acceptable transfer for passengers, delta being the penalty factor；/>
Figure FDA0004203934370000054
Indicating that if the train number i of the line p and the train number i 'of the line p' can be transferred at the transfer node n, the train number i is 1, otherwise, the train number i is 0; w (W) 1p Departure time W for line p train number 1 in time period T (i+1)p The departure time of the line p train number (i+1) in the time period T;
all the public line numbers P in the patch area are calculated by adopting an uplink and downlink calculation mode.
2. The method for compiling comprehensive optimization chip district urban bus timetable and scheduling vehicles according to claim 1, wherein the method comprises the following steps: the public transport operation cost comprises fixed cost of purchasing vehicles and empty driving cost of vehicles between shifts and yards.
3. The method for compiling comprehensive optimization chip district urban bus timetable and scheduling vehicles according to claim 1, wherein the method comprises the following steps: when passengers which cannot be successfully transferred are added, the bus operation cost is increased by delta minutes.
4. The method for compiling comprehensive optimization chip district urban bus timetable and scheduling vehicles according to claim 1, wherein the method comprises the following steps: optimizing the schedule by translating the running chart in step 2), in particular,
step1, selecting a running chart type;
drawing a section line running chart, and marking a first station and a last station and a transfer station by taking a time period as an abscissa and the travel time of each line as an ordinate;
step2, selecting a reference line;
selecting one urban and rural public transportation trunk line with large passenger flow and more transfer nodes as a reference line;
step3, determining a translation line sequence according to the passenger traffic volume and the transfer volume;
step4, sequentially translating urban and rural bus trunk running diagrams;
adjusting the trunk departure interval: if the trunk line starting stations are the same, equal interval departure among the lines is ensured; otherwise, ensuring that the time from the trunk line to the midway transfer station is the same;
the stay time of the tail end of the trunk line is adjusted to ensure that the trunk lines starting from the same site are equally spaced in the return process, or the trunk lines starting from different sites can be synchronously transferred in the return process;
step5, sequentially translating urban and rural bus branch running diagrams;
adjusting the branch departure interval to ensure the time coordination of the branch departure interval and the trunk line at the transfer point;
adjusting the leg end dwell time to ensure that the leg to station time is the same or slightly earlier than the trunk departure time;
step6, calculating the objective function value of the lower-layer schedule optimization model, comparing with the current situation scheme,
judging whether optimization exists or not through comparison, executing the next Step if the optimization exists, and returning to the Step4 if the optimization does not exist;
step7, marking seamless transfer nodes and optimization information to form an optimization scheme, and outputting an optimized operation diagram recorded with the optimization scheme; wherein the optimization information comprises a feasible task shift set, a shift total number and a departure time.
5. The method for compiling comprehensive optimization chip district urban bus timetable and scheduling vehicles according to claim 1, wherein the method comprises the following steps: the tabu search algorithm is adopted in the step 3) to solve the values of the objective function of the upper vehicle scheduling model in each feasible task shift, specifically,
3-1) adopting a natural number code represented by a structural body as a basic form of a solution, wherein '0 (x)' represents a parking lot x, and the natural number represents a shift task to be completed;
3-2) generating an initial solution;
sequentially numbering the shift tasks according to natural numbers by taking departure time sequence as sequence, and reordering the natural number sequence in consideration of maximum continuous driving time constraint, shift departure time and operation time; inserting a yard '0 (x)', and screening the optimal yards one by one if the number of yards is limited; merging the possible merged '0 (x)', and connecting the merged '0 (x)' into a feasible task chain, wherein the number of the task chains is the number of the required vehicles;
3-3) calculating by adopting a 2-top neighborhood operation method;
randomly selecting a group of natural number exchange positions each time to form a new sequence, reinserting a car park number according to constraint conditions, and calculating an objective function value; the solution with the largest/smallest objective function value generated by each iteration is used as a tabu object, a candidate set is determined to be a neighborhood space of the solution generated randomly, a rule based on an evaluation value is selected as a scofflaw, and if a target value of one solution appears to be better than any one of the optimal candidate solutions, special privilege is realized; the evaluation function is the difference between the objective function value of the optimal solution and the current solution obtained so far;
3-4) terminating the calculation by adopting a target control principle;
if the current optimal value does not change in the given step number, the calculation is terminated, and the task chain corresponding to the current optimal value and the required vehicle number are output.
CN201910432647.4A 2019-05-23 2019-05-23 Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling Active CN110135755B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910432647.4A CN110135755B (en) 2019-05-23 2019-05-23 Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910432647.4A CN110135755B (en) 2019-05-23 2019-05-23 Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling

Publications (2)

Publication Number Publication Date
CN110135755A CN110135755A (en) 2019-08-16
CN110135755B true CN110135755B (en) 2023-06-20

Family

ID=67572692

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910432647.4A Active CN110135755B (en) 2019-05-23 2019-05-23 Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling

Country Status (1)

Country Link
CN (1) CN110135755B (en)

Families Citing this family (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110851769B (en) * 2019-11-25 2020-07-24 东南大学 Network bearing capacity-based electric bus network reliability evaluation method
CN111260260A (en) * 2020-03-09 2020-06-09 西南交通大学 Method and system for calculating passing capacity of high-speed rail motor car
CN111785025B (en) * 2020-07-22 2021-12-17 北京航空航天大学 Novel flexible bus dispatching method based on automatic driving module bus
CN111897868B (en) * 2020-07-31 2022-06-10 浙江大学 Visual analysis method and system for analyzing and planning bus routes
CN111861279B (en) * 2020-08-04 2024-05-03 北京交通大学 Multi-target high-speed toll station class car scheduling method considering transfer
CN112884216B (en) * 2021-02-04 2023-06-23 国网湖南省电力有限公司 Method for calculating minimum number of vehicles in single bus route
CN113554270B (en) * 2021-06-11 2023-09-01 华设设计集团股份有限公司 Method for determining construction scale of head and tail stations of matched buses
CN113657672B (en) * 2021-08-18 2024-06-18 北京航空航天大学 Comprehensive terminal bus linking method based on dynamic programming
CN113743797B (en) * 2021-09-08 2023-08-04 北京化工大学 Pile-free sharing bicycle operation scheduling strategy optimization method and system based on big data
CN114444965B (en) * 2022-02-11 2024-05-14 吉林大学 Single-yard multi-line electric bus collaborative scheduling method
CN114462869B (en) * 2022-02-11 2024-10-15 吉林大学 Vehicle dispatching and driver dispatching collaborative optimization method for hybrid bus line
CN115472011B (en) * 2022-08-23 2023-09-22 江苏交控智慧城市技术有限公司 Bus intelligent line planning algorithm based on reservation data
CN115271276B (en) * 2022-09-30 2023-02-03 广东工业大学 Combined macro-micro demand response type vehicle scheduling method
CN115952985B (en) * 2022-12-21 2023-08-18 大连理工大学 Mixed scheduling method of module vehicle and bus in multi-line multi-shift traffic system
CN116432386B (en) * 2023-02-20 2023-11-24 湖南大学无锡智能控制研究院 Multi-vehicle type schedule design method and system for intelligent public transport system
CN116702400B (en) * 2023-08-07 2023-10-13 四川国蓝中天环境科技集团有限公司 Mobile city perception optimization method based on buses and mobile sensors
CN117151313B (en) * 2023-10-31 2024-02-02 杭州数知梦科技有限公司 Comprehensive multi-factor bus driving plan optimization method, system and application
CN117391401B (en) * 2023-12-07 2024-03-05 中国民用航空飞行学院 Dispatching method of airport electric ground service vehicle
CN117408857B (en) * 2023-12-13 2024-03-26 深圳市海成智联科技有限公司 Vehicle running chart compiling method based on regional collaboration
CN118504783B (en) * 2024-07-16 2024-10-15 吉林大学 Network connection modularized bus operation schedule and grouping scheme optimization method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106652530A (en) * 2016-11-02 2017-05-10 北京瑞华赢科技发展有限公司 Scheduling timetable generating method and apparatus
CN106845734A (en) * 2017-03-29 2017-06-13 东南大学 Towards the multi-mode public transport timetable optimization method of last park-and-ride demand

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106652530A (en) * 2016-11-02 2017-05-10 北京瑞华赢科技发展有限公司 Scheduling timetable generating method and apparatus
CN106845734A (en) * 2017-03-29 2017-06-13 东南大学 Towards the multi-mode public transport timetable optimization method of last park-and-ride demand

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
区域公交时刻表及车辆调度双层规划模型;刘志刚;《系统工程理论与实践》;20071130;第135-141页 *

Also Published As

Publication number Publication date
CN110135755A (en) 2019-08-16

Similar Documents

Publication Publication Date Title
CN110135755B (en) Method for compiling comprehensive optimization of urban and rural bus timetable and vehicle scheduling
Liao et al. Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling
CN103942948B (en) Based on the generation method of the urban public bus lines network of sectionally smooth join
Enzi et al. Modeling and solving the multimodal car-and ride-sharing problem
CN110458456B (en) Demand response type public transportation system scheduling method and system based on artificial intelligence
CN105678411B (en) Method for compiling passenger train operation scheme diagram
CN107146068A (en) It is a kind of based on it is balanced with train operation daily planning allocate method
CN102169512A (en) Urban mass transit network dynamic accessibility computing method based on time window constraints
Wang et al. Optimization models for high-speed train unit routing problems
CN113947287A (en) Electric bus regional dispatching method and system
CN112949987B (en) Taxi scheduling and matching method, system, equipment and medium based on prediction
Xiaoxue Optimal route planning of parking lot based on Dijkstra algorithm
CN114723125A (en) Intercity vehicle order allocation method combining deep learning and multi-task optimization
Wei et al. Optimal integrated model for feeder transit route design and frequency‐setting problem with stop selection
CN112116203A (en) Public bicycle system layout planning method based on dispatching vehicle
Sun et al. Solving demand-responsive feeder transit service design with fuzzy travel demand: A collaborative ant colony algorithm approach
CN114912659A (en) Method, system, equipment and storage medium for calculating transfer scheme in railway passenger transport
Kumar et al. Mitigation of Complexity in Charging Station Allocation for EVs Using Chaotic Harris Hawks Optimization Charge Scheduling Algorithm
Feng et al. The analysis and application of competition and cooperation between the bus lines
CN113988424B (en) Circulating drop and pull transportation scheduling method
CN113177295B (en) Quick compiling method for subway network train schedule
CN113962599B (en) Urban rail transit networked operation management method and system
CN115729106A (en) Multi-time-constraint running mileage optimized lightweight car pooling scheduling method
CN114537479A (en) Motor train unit operation plan compiling method considering fluctuation within week of train running scheme
Rong et al. Dynamic shared autonomous vehicles dispatching algorithm

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant