CN115526492A - Optimization method for multi-vehicle type network drop-and-drop transport scheduling - Google Patents
Optimization method for multi-vehicle type network drop-and-drop transport scheduling Download PDFInfo
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Abstract
The invention relates to the technical field of transportation scheduling, and particularly discloses an optimization method for multi-vehicle type network drop-off and hang-off transportation scheduling, which is applied to the network drop-off and hang-off transportation of a highway port formed by a common semi-trailer, a two-axle highway port semi-trailer and a three-axle highway port semi-trailer and comprises the following steps: establishing a network throwing and hanging scheduling target optimization model; (2) Obtaining task quantity segmentation of ordinary semi-trailer transportation through dynamic planning; (3) Obtaining an initial solution of the target optimization model in the step (1) by a mileage-saving method; (4) And optimizing the initial solution by improving a simulated annealing algorithm to obtain an optimal solution. The optimization method for the multi-vehicle type network drop and pull transportation scheduling can optimize the drop and pull transportation scheduling of the multi-vehicle type so as to reduce the use number of tractors and reduce the transportation cost.
Description
Technical Field
The invention relates to the technical field of transportation scheduling, in particular to an optimization method for multi-vehicle type network drop-and-pull transportation scheduling.
Background
At present, the tractor and the semitrailer are researched less, and related theories need to be further perfected. The academia generally considers that the scheduling problem of the automobile train is complex and is an NP hard problem.
Researchers have made a lot of studies on the Tractor-Trailer (TSRP) vehicle dispatching problem, but only assume that there is a specific amount of customer demand, i.e., the trailer needs to visit the customer only once to meet the demand of cargo delivery or garbage collection. However, in the drop and pull transportation network for highway harbors, drop and pull transportation work is performed between highway harbors, which are not only drop and pull work sites, but also customer demand points. Different from the requirements in the common drop and pull transportation practical application, the highway harbor drop and pull requirements use trailers as units, the required quantity can be a plurality of trailers, and the requirements of two drop and pull ends are possibly unequal, namely, the situations that multiple drop and pull transportation exist between two highway harbors and the requirements of the two drop and pull ends are unbalanced exist, and the complexity of drop and pull transportation scheduling optimization is increased.
The patent document with the publication number of CN111709570A discloses an optimization method for network drop and hang transportation scheduling, which solves a model by establishing a full network drop and hang scheduling optimization model and designing a heuristic algorithm, and verifies the effectiveness of the model by using an example, so that the use number of traction vehicles can be reduced by carrying out drop and hang transportation optimization in a network, and the overall transportation cost is reduced. And in the network drop and hang transportation, scheduling optimization is carried out in a pure network range, so that the use number of traction vehicles can be more effectively reduced, and the transportation cost can be reduced. However, the patent only studies on a single type of drop and pull transport vehicle, but in actual network drop and pull transport, multiple types of drop and pull transport vehicles are usually required to perform cooperative operation, as shown in fig. 1, it is assumed that 5 35 tons of van semi-trailers are required from the highway port 1 to the highway port 2, 3 three-axle 40-foot highway port semi-trailers are required from the highway port 2 to the highway port 1,2 37 tons of van semi-trailers, 4 two-axle 40-foot highway port semi-trailers and 4 two-axle 40-foot highway port semi-trailers are required from the highway port 3 to the highway port 1, 4 40 tons of van semi-trailers and 2 three-axle 40-foot highway port semi-trailers are required. If different vehicle types can not be subjected to cross swing and hanging, only the van type semi-trailer is taken as an example, the whole highway port network at least needs 5 van type semi-trailers with 35 tons, 2 van type semi-trailers with 37 tons and 4 van type semi-trailers with 40 tons, and the return strokes are empty driving. However, if different vehicle types are allowed to be crossed, thrown and hung, in continuous working time, after every transportation task of the 40-ton van semi-trailer between the highway port 1 and the highway port 3, the transportation task between the highway port 3 and the highway port 4 can be continuously completed, all the van semi-trailer tasks in the network are completed according to the transportation task, the use of two 35-ton van semi-trailers can be reduced, the empty driving rate in return trip is reduced, the transportation efficiency is further improved, and the transportation cost is reduced. In the actual operation process, the situation that both a van semi-trailer and a highway port semi-trailer are required in one direction can exist, and the cargo weight can also have difference, namely the van semi-trailers or the highway port semi-trailers of different types are required to be matched. At this time, the difficulty of drop-off transport and dispatch of the drop-off transport at the highway port is greatly increased. However, the existing research on the problem of multi-vehicle drop-and-drop transport of highway ports in a network mode is not mentioned yet, and for the practical situation of a highway port drop-and-drop transport enterprise, the problem of optimization of drop-and-drop transport scheduling of the multi-vehicle drop-and-drop transport of the highway ports needs to be analyzed to solve the practical problem of the enterprise.
Disclosure of Invention
The invention aims to solve at least one of the technical problems, and provides an optimization method for multi-vehicle type network drop and hang transportation scheduling, which can optimize the drop and hang transportation scheduling of the multi-vehicle type so as to reduce the use number of tractors and reduce the transportation cost.
In order to achieve the purpose, the invention adopts the technical scheme that: a multi-vehicle type network drop-off transport scheduling optimization method is applied to road port network drop-off transport composed of a common semi-trailer, a two-axle road port semi-trailer and a three-axle road port semi-trailer, and comprises the following steps:
(1) Establishing a network throwing and hanging scheduling target optimization model;
(2) Obtaining task amount segmentation of common semi-trailer transportation through dynamic planning;
(3) Obtaining an initial solution of the target optimization model in the step (1) by a mileage-saving method;
(4) And optimizing the initial solution by improving a simulated annealing algorithm to obtain an optimal solution.
Preferably, the objective optimization model in step (1) is:
objective function 1:
setting network operation cost to be heavy-hanging driving cost, empty driving cost, fixed cost and punishmentThe cost is formed, and the heavy-hanging running cost is
The empty running cost is as follows:
the fixed cost of using the vehicle on the same day is as follows:
the penalty cost is:
the objective is that the network operation cost is expressed minimally as:
the objective function 2:
the minimum number of tractors invested is expressed as:
min Z 2 ＝K z (2)
the constraint conditions are as follows:
in the formula (I), the compound is shown in the specification,
n: representing a set of all road ports, having N road ports, N = {1,2, …, N };
i, j, l: the number of the road port, i, j, l belongs to N;
m: representing a set of tractor types;
m: representing the type of a tractor, wherein M belongs to M, wherein M =1 is a common semi-trailer tractor, and M =2 is a highway port semi-trailer tractor;
m k : represents the m type tractor K, K belongs to {1,2, …, K };
K z : the total number of tractors required for the road network is represented,
d ij : represents the distance between highway ports i, j, in units: km;
q ijm : the drop and hang transportation requirements of m types of tractors required by the highway ports i to j are represented, the common van type drop and hang is represented by cargo tonnage, and the drop and hang transportation of the highway ports is represented by times. Since the requirements are not necessarily balanced, q ijm Is not necessarily equal to q jim ；
t: the time of the continuous work of the tractors is represented, and the time of the continuous work of all the tractors every day is regulated to be equal;
v m1 ,v m2 : representing m types of tractionThe speed of the towing vehicle for towing heavy hanging and empty running is as follows: km/h;
k 1 ,k 2 : respectively representing the states of heavy suspension and idle running of the tractor, wherein K belongs to the fields of 1,2, … and K;
preferably, the dynamic planning in step (2) includes:
establishing a mathematical model
Setting a constraint condition:
X i ≥0,X i ∈Z,i∈I (9)
in the above formula, the first and second carbon atoms are,
F Q -total cost of transportation demand Q;
x i -the number of uses of each tractor;
i, a tractor set, wherein three vehicle types are provided;
C i -fixed use costs for each tractor;
d, distance between highway ports;
P i -the cost of variation when each tractor is heavily towed;
L i -maximum load capacity of each tractor;
q-one-way freight volume between highway ports;
z is a set of integers.
Preferably, the mileage-saving method in the step (3) includes: firstly, one vehicle is enabled to serve only one node, then the distance saved by combining any two lines under the condition of meeting the constraint is calculated to obtain the odometer, the combined line with the largest saved mileage is updated according to the odometer, and the odometer is updated until any two lines cannot be combined or cannot save the mileage.
Preferably, the improved simulated annealing algorithm comprises the following steps:
stepl, acquiring basic data, wherein the required basic data comprises a drop-and-hang transport network topological structure, distances among road ports, vehicle running speeds, heavy driving costs, idle driving costs and fixed costs of three common semitrailers and two road port semitrailers, and drop-and-hang demands of the common semitrailers and the road port semitrailers;
step2, setting algorithm parameters including initial temperature t of algorithm s End of algorithm temperature t f Temperature decay parameter α, and Markov chain length (maximum number of constant temperature iterations) m k ；
Step3, generating a road port drop-and-hang transport vehicle scheduling scheme { X } according to a mileage-saving method, calculating an evaluation function value F of the scheme, and enabling the current temperature to be t ← t- s Currently, constant-temperature iteration number n ← 1;
step4, rootRandomly generating a neighborhood solution { X ] of the current vehicle dispatching scheme X according to a neighborhood solution construction method neigh And calculating the evaluation function value F thereof neigh ；
Step5, executing Metroplis selection, calculating a neighborhood solution evaluation function value F 'and a function difference value delta F = F' -F of the two solutions, and selecting to accept the original solution or the neighborhood solution as the current solution according to the following method:
(1) if delta F is less than 0, receiving a neighborhood solution as a current solution, and performing constant temperature iteration times n ← n +1;
(2) if delta F is more than 0 and exp (delta F/t) > rand (0,1), receiving a receiving domain solution as a current solution, and carrying out constant temperature iteration times n ← n +1;
(3) under other conditions, accepting the original receiving solution as the current solution, and carrying out constant temperature iteration times n ← n +1;
step6, constant temperature iteration judgment, if the current constant temperature iteration times is less than the length of the Markov chain, namely n is less than or equal to m k Turning to Step5, otherwise, turning to Step7;
step7, cooling and annealing, namely lowering the system temperature t ← alpha.t according to the set temperature attenuation parameters;
step8, judging the termination of the algorithm, if the current temperature is greater than the algorithm ending temperature, namely t is greater than t f Turning to Step4, otherwise, terminating the algorithm, and inputting the current solution as the optimal solution
Preferably, the comprehensive cost of swing hanging transportation is set as Z 1 And the investment number of the tractors is Z 2 Obtaining a neighborhood solution evaluation function Z = omega 1 ·Z’ 1 +ω 2 ·Z’ 2 Wherein
Preferably, the neighborhood solution constructing method includes:
resetting, randomly selecting one position of the current sequence, and randomly assigning the serial number of the position to another position;
exchanging, namely randomly selecting two positions in the current sequence, and exchanging the numbers of the two positions in the next sequence;
2-opt, randomly selecting two positions in the current sequence, and turning over the sequence between the two positions.
Preferably, the improved simulated annealing algorithm further comprises constraint processing, wherein for the common semi-trailer transportation, the constraint conditions are as follows: the method comprises the following steps of vehicle type constraint, load constraint and time constraint, wherein the constraint conditions for the semi-trailer transportation of the two-axis and three-axis highway harbor are as follows: load constraints, time constraints.
The beneficial effects are that: compared with the prior art, the optimization method for multi-vehicle type network drop and pull transportation scheduling obtains the initial solution of the target optimization model by establishing the network drop and pull scheduling target optimization model and adopting a mileage-saving method, optimizes the initial solution by improving a simulated annealing algorithm to obtain the optimal solution, and verifies the effectiveness of the optimization method through examples, so that the using number and the operation cost of tractors for multi-vehicle type drop and pull transportation are integrally lower, and compared with the traditional transportation mode, the cost can be saved in a larger optimization space and the application efficiency of the tractors can be improved.
Drawings
The following detailed description of embodiments of the invention is provided in conjunction with the appended drawings, in which:
FIG. 1 is a schematic diagram of a multi-vehicle drop-and-pull transport in a conventional network type highway port;
fig. 2 is a flow chart of an algorithm design idea of multi-vehicle network drop and pull transportation according to the application.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or there can be intervening components, and when a component is referred to as being "disposed in the middle," it is not just disposed in the middle, so long as it is not disposed at both ends, but rather is within the scope of the middle. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The determination of the drop and pull transport organization mode is the premise and the basis of the drop and pull transport vehicle scheduling and is also the core of the determination of the drop and pull transport vehicle scheduling scheme, the drop and pull transport organization mode design is scientifically and reasonably carried out, and then the drop and pull transport organization mode can be fully exerted by fully considering the problem of the drop and pull transport vehicle scheduling. Therefore, on the basis of considering different drop and pull transportation mode characteristics, a network type drop and pull transportation organization mode is provided by combining the actual investigation condition of an enterprise and aiming at the characteristic of carrying out drop and pull transportation at a highway port.
According to 3 sets of road drop and pull transportation recommended vehicle types published by the transportation department, the road drop and pull transportation tractor vehicle types are divided into common semi-trailer tractors and road port semi-trailer tractors, mainly have driving forms of 4 multiplied by 2, 6 multiplied by 4 and the like, the corresponding maximum mass of the quasi-trailer tractor is generally 35t, 37t and 40t, but the maximum mass of the quasi-trailer is also 34.8t, 36t, 37.3t, 37.5, 39.8t and the like. Semitrailers mainly comprise van semitrailers and highway port transport semitrailers, and various maximum allowable total masses exist.
Therefore, the application provides an optimization method for multi-vehicle type network drop-off transport scheduling, which is applied to the network drop-off transport of a highway port formed by a common semi-trailer, a two-axle highway port semi-trailer and a three-axle highway port semi-trailer and comprises the following steps:
(1) Establishing a network throwing and hanging scheduling target optimization model;
(2) Obtaining task quantity segmentation of ordinary semi-trailer transportation through dynamic planning;
(3) Obtaining an initial solution of the target optimization model in the step (1) by a mileage-saving method;
(4) And optimizing the initial solution by improving a simulated annealing algorithm to obtain an optimal solution.
Specifically, according to the characteristics of the road port drop and pull transportation, the following assumptions are made:
(1) All tractors and trailers in the network belong to recommended highway drop and pull transportation vehicles, but the models are inconsistent, and the tractors can only draw semitrailers which are not more than the maximum standard tractor quality, and the tractor type corresponds to the semitrailer type;
(2) Enough semi-trailers are arranged in each highway port in the network, namely the condition of empty trailer dispatching does not exist, and the assumption can be achieved by integrating social resources by highway port drop-and-pull transport enterprises;
(3) The operation time of the tractor for throwing and hanging the semitrailer is not considered, the number of the trailers is enough, and the tractor is not required to wait for the loading and unloading operation of the trailer;
(4) The distance and time of the back-and-forth driving between the two highway ports are the same;
(5) The throwing and hanging requirements are generated in the network every day, the daily requirements fluctuate, an enterprise can arrange scheduling according to the daily actual requirements, and all the requirements must be met. Because some nodes are far away from each other, drop and hang transportation can not be completed in one day, and therefore it is assumed that the drop and hang tasks which are not completed in the previous day do not influence subsequent scheduling.
According to the actual operation of the drop and pull transport at a highway port, the network type operation cost of the drop and pull transport enterprises is minimum, the number of tractors which are put into the drop and pull transport enterprises is minimum while the requirement is met, and therefore a target optimization model can be obtained as follows:
objective function 1:
setting network operation cost to be heavy-hanging running cost and idle running costFixed cost and punishment cost, the heavy-duty running cost is
The empty running cost is as follows:
the fixed cost of using the vehicle on the same day is as follows:
the penalty cost is:
the objective is that the network operation cost is expressed minimally as:
the objective function 2:
the minimum number of tractors invested is expressed as:
min Z 2 ＝K z (2)
the constraint conditions are as follows:
in the formula (I), the compound is shown in the specification,
n: represents a set of all road ports, N road ports total, N = {1,2, …, N };
i, j, l: the number of the road port, i, j, l belongs to N;
m: representing a set of tractor types;
m: representing the type of the tractor, wherein M belongs to M, wherein M =1 is a common semi-trailer tractor, and M =2 is a highway harbor semi-trailer tractor;
m k : represents the m type tractor K, K is epsilon {1,2, …, K };
K z : the total number of tractors required for the road network is represented,
d ij : represents the distance between the road ports i, j, in units: km;
q ijm : the drop and hang transportation requirements of m types of tractors needed by the highway ports i to j are represented, the common van type drop and hang is represented by the tonnage of goods, and the drop and hang transportation of the highway ports is represented by the times. Since the requirements are not necessarily balanced, q ijm Is not necessarily equal to q jim ；
t: the continuous working time of all tractors is regulated to be equal every day;
v m1 ,v m2 : the speed of the m-type tractor for towing heavy suspension and empty driving is expressed by the unit: km/h;
k 1 ,k 2 : respectively representing the states of heavy suspension and idle running of the tractor, wherein K belongs to the fields of 1,2, … and K;
equation (1) represents that the total network operation cost is minimal; the formula (2) represents that the number of the thrown tractors is minimum; the formula (3) shows that the throwing and hanging requirements between the highway ports are met; equation (4) represents the tractor continuous operation time limit; the formula (5) represents that the quantity of the road network tractors is equal to the sum of the quantity of the tractors for completing the task; equation (6) represents the penalty cost.
Subsequently, the solution is performed by using dynamic programming, a mileage-saving method and an improved simulated annealing algorithm. The specific solving process is as follows:
the two types of vehicle types adopted in the application are respectively a common semi-trailer tractor and a highway port semi-trailer tractor, wherein the common semi-trailer tractor has various vehicle types, and the common semi-trailer transportation needs to meet load restraint and needs to be dragged by the common semi-trailer tractor; the highway port semi-trailer tractor has two-axis and three-axis models, the transportation of the highway port semi-trailer needs to meet the axle number constraint and the highway port semi-trailer needs to be pulled by the highway port semi-trailer tractor, so all scheduling tasks can be divided into three types, namely a common semi-trailer, a two-axis highway port semi-trailer and a three-axis highway port semi-trailer in sequence, and the problem complexity is reduced by independently solving the tasks.
For ordinary semi-trailer transportation, the transportation amount between the highway ports is distributed to a specific dispatching vehicle, and the two decisions are divided into two decisions, namely, the transportation amount is divided into the weight which can be borne by a single trailer, and the weight is converted into one or more dispatching tasks; and the second mode is to reasonably splice the scheduling tasks, thereby achieving the purposes of vehicle and cost reduction. If an intelligent heuristic algorithm is adopted to optimize the two decisions at the same time, the complexity of the whole problem is very high, and a satisfactory solution is difficult to obtain within a limited time. The method comprises the steps of sequentially and separately processing two problems, wherein the first problem is to process the traffic segmentation problem on the premise of not considering task splicing through dynamic planning, and the second problem is to provide an initial solution for an improved simulated annealing algorithm through a mileage-saving method and then optimize the solution.
For the two-axis and three-axis highway port semi-trailer transportation, the problem of traffic division does not need to be processed, and the problem of reasonable splicing of scheduling tasks only needs to be considered, the algorithm design of the problem is consistent with the second problem processing of the common semi-trailer transportation, and the specific algorithm design idea is shown in fig. 2.
There is no fixed processing method in the dynamic planning of the ordinary semi-trailer transportation, and the main idea is to decompose the problem to be solved into a plurality of sub-problems, solve the optimal solution of the sub-problems first, and then obtain the optimal solution of the original problem from the solutions of the sub-problems. The problem of ordinary semi-trailer traffic segmentation (similar to a plate cutting problem) on the premise of not considering task splicing is solved through the algorithm, the problem belongs to an integer linear programming problem, and a segmentation model is constructed for two highway port one-way traffic. From this, the following mathematical model can be proposed:
constraint conditions are as follows:
X i ≥0,X i ∈Z,i∈I (9)
description of the symbols
F Q -total cost of transportation demand Q;
x i -the number of uses of each tractor;
i, a tractor set, wherein three vehicle types are provided;
C i -fixed use costs of each tractor;
d, distance between highway ports;
P i -the cost of variation when each tractor is heavily towed;
L i -maximum load capacity of each tractor;
q is the one-way freight volume between highway ports;
set of Z-integers
Equation (7) represents the minimum total cost; equation (8) represents that the transportation demand is equal to or less than the maximum load capacity of the tractor used; the formula (9) indicates that the usage amount of the tractor is an integer. The task amount segmentation of the common semi-mounted transportation can be obtained through the mathematical model, and the following solving steps can be specifically adopted for more clearly describing the algorithm logic:
step1, inputting the traffic Q;
step2 assigns Q 0 ＝1；
Step3 when Q 0 If < Q, the following steps are executed:
(1) Value assigned F (Q) 0 )＝inf；
(2) Judging whether a vehicle type can directly bear the weight of the bicycle 0 If so, calculating the vehicle model cost F of the minimum cost 1 ；
(3) Let F (Q) 0 )＝min[F(1),F(Q 0 )]；
(4)ForM＝1,Q 0 ＝1
F(Q 0 )＝min[F(Q 0 ),F(M)+F(Q 0 -M]；
End
(5)Q 0 ＝Q 0 +1；
And Step4, outputting the optimal segmentation scheme when the traffic volume is V, and converting the optimal segmentation scheme into a scheduling task.
Solving the dispatching of the transport vehicles for the port-drop and drop-off of the highway by improving the simulated annealing algorithm relates to a plurality of algorithm design core problems of how to design a solution space structure, how to generate an initial solution, how to design an evaluation function, how to generate a neighborhood solution, how to terminate the algorithm and the like, and the following detailed description is carried out on the key problems:
(1) Solution space structure design
For any model, solving through a heuristic algorithm requires designing a corresponding solution space structure design mode to express a scheme of uniqueness and completeness. According to the method, the common highway port semi-trailer transportation is converted into a plurality of scheduling tasks, and the scheduling task splicing problem is described through an integer sequence section taking the scheduling tasks as numbers. If M is the number of scheduling tasks, the coding length is coded as M, where M =8 is assumed, and the specific solution space structure is as follows:
(2) Generation of an initial solution
The execution of the simulated annealing algorithm requires an initial solution to start its local search process, the selection of the initial solution has a certain influence on the final convergence result of the simulated annealing algorithm, and the scheduling tasks are merged by using the idea of saving the mileage algorithm in this document, thereby forming the initial solution.
The mileage-saving method is a classic algorithm for solving the VRP problem approximate solution, the algorithm firstly enables a vehicle to serve only one node, then obtains an odometer by calculating the combined saved distance of any two lines under the condition of meeting the constraint, updates the combined line with the maximum saved mileage according to the odometer, and updates the odometer until any two lines cannot be combined or the mileage cannot be saved.
For the common semi-trailer transportation type, S is set as a scheduling task set obtained through dynamic planning, O (S), D (S), L (S) and C (S), S belongs to S and is respectively the starting road port, the target road port, the transportation volume and the selected vehicle type load of each task, and the specific algorithm steps are as follows:
step1, inputting a scheduling task set S and related parameters, O, D, L and C (meaning as above), and maximum scheduling time TM;
step2, calculating the transportation time T, the cost F and the route road of each task served by a specified tractor;
step3 initializes G =0, and executes the following loop and determination
Lens = size (S), yielding the number of tasks
Fori＝1:Lens
Foj＝1:Lens
Calculating the increased running time T of the tractor for completing the task i and then executing the task j 1
IfC(i)≥C(j)andT(i)+T 1 +T(i)≤TM
Calculating the reduced cost F after the two tasks are merged 1
end
F m (i,j)＝max(F 1 ,0)
End
End
IfmaxF m ＞0
Calculate the task combination with the greatest cost saving i 1 ,j 1
Updating route road (i) 1 ) Cost F (i) 1 ) Time T (i) 1 )
Delete line road (i) 1 ) Cost F (i) 1 ) Time T (i) 1 )
Else
G＝1
End
Step4 output line road, cost F, time T
For the two-axis and three-axis highway port semi-trailer transportation, the calculation modes of the two-axis and three-axis highway port semi-trailer transportation are consistent in the part, compared with the common highway port semi-trailer transportation, the method only has the difference in vehicle type judgment, and the algorithm steps of the two-axis highway port semi-trailer transportation are given here:
step1, inputting a scheduling task set S and related parameters, a starting point O, a destination point D and maximum scheduling time TM;
step2, calculating the transportation time T, the cost F and the route road of each task served by a tractor
Step3 initializes G =0, and executes the following loop and determination
Lens = size (S), yielding the number of tasks
Fori＝1:Lens
Foj＝1:Lens
Calculating the increased running time T of the tractor for completing the task i and then executing the task j 1
IfT(i)+T 1 +T(i)≤TM
Calculating the reduced cost F after the merging of two tasks 1
end
F m (i,j)＝max(F 1 ,0)
End
End
IfmaxF m ＞0
The task combination with the largest cost saving i is calculated 1 ,j 1
Updating route road (i) 1 ) Cost F (i) 1 ) Time T (i) 1 )
Delete line road (i) 1 ) Cost F (i) 1 ) Time T (i) 1 )
Else
G＝1
End
Step4 outputs line road, cost F, time T.
(3) Evaluation function design
The evaluation function is an evaluation formula for determining whether the current solution and the neighborhood solution are received, and is commonly used for facilitating understanding and fully reflecting objectivityThe evaluation function of (a) is typically an objective function or some linearization of an objective function. Two objective functions exist in the road harbor drop and pull transport vehicle scheduling optimization model researched by the application, namely the drop and pull transport comprehensive cost Z 1 And the number of tractor drops Z 2 Since the difference between the two is large in dimension and magnitude, the maximum value appears in the iterative process of the algorithm
And minimum value
After the data is processed in a normalization mode, the weighting processing is carried out:
Z＝ω 1 ·Z’ 1 +ω 2 ·Z’ 2 (12)
(4) Constraint processing
In the model herein, for normal semi-trailer transport constraints are: the method comprises the following steps of vehicle type constraint, load constraint and time constraint, wherein the constraint conditions for the semi-trailer transportation of the two-axis and three-axis highway harbor are as follows: load constraints, time constraints.
The following is specifically described by decoding:
(1) ordinary semi-trailer transportation
Step1, inputting a scheduling task programming sequence G = [8 2 5 143 7 ], starting road port O (S), destination road port D (S), traffic L (S), selected vehicle type load C (S), transportation time T (S) and F (S), wherein S belongs to Ss and belongs to S, and the maximum scheduling time TM;
step2, setting a route number n =1 and a sequence number m =1, transporting a scheduling task G (m) by corresponding vehicle types, forming a route Road (n) = { O [ G (m) ], D [ G (m) ] }, calculating transportation time, selecting vehicle type load RC (n) = C [ G (m) ], and cost RF (n) = F [ G (m) ];
step3, updating m = m +1, and calculating the increased driving time T of the tractor for continuously finishing the dispatching task G (m) i If RC (n)>＝C[G(m)]And RT (n) + T 1 +T[G(m)]TM is less than or equal to TM, task splicing is carried out, and a route load (n) = { route (n), O [ G (m)],D[G(m)]}, transport time RT (n) = RT (n) + T 1 +T[G(m)]And cost, entering the fifth step; and if the condition is not met, entering the fourth step.
Step4, updating n = n +1, transporting the scheduling task G (m) by the corresponding vehicle model, forming a route Road (n) = { O [ G (m) ], D [ G (m) ], calculating a transportation time RT (n) = T [ G (m) ], selecting a vehicle model load RC (n) = C [ G (m) ], and obtaining a cost RF (n) = F [ G (m) ];
and Step5, if m is more than or equal to Numel (G), outputting information such as lines, and otherwise, returning to Step4.
(2) Two-axle and three-axle highway port semi-trailer transportation
Compared with the common semi-trailer transportation, the method has no vehicle type restriction, and in the decoding process, judgment is not needed on the strip, so that surplus is avoided. In the process of decoding the scheduling tasks, the transportation of each scheduling task is subjected to related constraint calculation, so that the feasibility of each coding scheme is ensured.
(5) Neighborhood solution construction method
The neighborhood function is a technical method for generating a neighborhood solution from a current solution in a simulated annealing algorithm, and the construction strategy of the neighborhood function needs to be designed by combining the characteristics of a research problem.
Based on two practical theories of "the local optimal solution of one neighborhood structure is not necessarily the local optimal solution of the other neighborhood structure" and "the global optimal solution is the local optimal solution of all possible neighborhoods", the probability of solving the optimal solution is improved by searching through three neighborhood structures, and the specific neighborhood structures are as follows:
assume that the current scheduling task order is:
| 8 | 2 | 5 | 1 | 4 | 3 | 7 | 6 |
the neighborhood solution construction method comprises the following three ways:
(1) reset (relocation), arbitrarily choose one position of the current order species, and randomly assign the number of the position to another position. If the third position is selected and assigned to the sixth position, the new order is:
| 8 | 2 | 1 | 4 | 3 | 5 | 7 | 6 |
(2) exchanging (exchange), arbitrarily selecting two positions in the current sequence, exchanging the numbers of the two positions in the next sequence, and if the third and sixth positions are selected, the new sequence is:
| 8 | 2 | 3 | 1 | 4 | 5 | 7 | 6 |
(3) 2-opt, randomly selecting two positions in the current sequence, and turning over the sequence between the two positions. If the third position is selected and assigned to the sixth position, the new order is:
| 8 | 2 | 3 | 4 | 1 | 5 | 7 | 6 |
(6) Criterion of algorithm termination
The simulated annealing algorithm comprises a cycle divided into an inner layer and an outer layer, so that the simulated annealing algorithm comprises an inner termination criterion and an outer termination criterion, wherein
(1) Internal cycle termination criteria-sampling the number of steps associated with the problem size, i.e. when the number of internal cycles reaches a certain number of steps corresponding to the road network size multiplied by a certain coefficient value, the algorithmic internal cycle at that temperature is terminated;
(2) outer loop termination criteria-a method of setting a termination temperature threshold is used, i.e. when the temperature drops to a certain set threshold, the algorithm terminates and outputs the result.
In summary, the improved simulated annealing algorithm specifically includes:
loading Stepl basic data, wherein the required basic data comprises a drop and pull transport network topological structure, distances among road ports, vehicle running speeds, three common semi-trailers and two road port semi-trailers, the heavy-driving cost, the idle driving cost and the fixed cost, and data such as drop and pull demand of the common semi-trailers and the road port semi-trailers;
setting parameters of Step2 algorithm, including initial temperature t of algorithm s End of algorithm temperature t f Temperature decay parameter α, and Markov chain length (maximum number of constant temperature iterations) m k ；
Step3, initializing, generating a road port drop-and-hang transport vehicle scheduling scheme { X } according to the mileage-saving method provided by the previous text, calculating an evaluation function value F of the road port drop-and-hang transport vehicle scheduling scheme, and enabling the current temperature to be t ← t } s Currently, constant-temperature iteration number n ← 1;
step4 randomly generating a neighborhood solution { X ] of the current vehicle dispatching scheme X according to the neighborhood solution construction method designed in the previous Step neigh And calculating the evaluation function value F thereof neigh ；
Step5, executing Metroplis selection, calculating a neighborhood solution evaluation function value F 'and a function difference value delta F = F' -F of the two solutions, and selecting to accept the original solution or the neighborhood solution as the current solution according to the following method:
(1) if the delta F is less than 0, accepting the neighbor solution as the current solution, and carrying out constant temperature iteration times n ← n +1;
(2) if delta F is more than 0 and exp (delta F/t) > rand (0,1), receiving a receiving domain solution as a current solution, and carrying out constant temperature iteration times n ← n +1;
(3) under other conditions, accepting the original receiving solution as the current solution, and carrying out constant temperature iteration times n ← n +1;
step6 constant temperature iteration judgment, if the current constant temperature iteration times is less than the length of the Markov chain, namely n is less than or equal to m k Turning to Step5, otherwise, turning to Step7;
step7, cooling and annealing, namely lowering the system temperature t ← alpha.t according to the set temperature attenuation parameters;
step8 algorithm termination judgment, if the current temperature is greater than the algorithm termination temperature, namely t is greater than t f And turning to Step4, otherwise, terminating the algorithm, and inputting the current solution as the optimal solution.
Finally, the above optimization method can be demonstrated by way of example. Specifically, the logistics enterprise L depends on drop and hang transportation at a certain highway port, has drop and hang transportation points in 30 cities, and arranges vehicle scheduling in a drop and hang network, so that the lowest cost of the whole network is the technical problem that enterprise managers want to solve.
According to 3 batch highway drop-hang transportation recommended vehicle types published by the transportation department, the logistics enterprise L can integrate the used tractor vehicles and the heavy-hang empty-hang running cost and the fixed cost, as shown in Table 1:
TABLE 1 model of L drop-and-pull transport vehicle for logistics enterprises
Assuming that the same type of tractor with different models runs at the same speed, the speed of heavy hanging is 50km/m, the speed of empty running is 70km/m, the continuous working time per day T =16 hours, and the direct distance d between the highway and the port ij Values were found according to Google maps.
The values are obtained using the randint function in Matlab2017a software at [0,200]And 30 drop and hang transportation tasks among the highway ports are generated, and all the drop and hang transportation tasks are integers.
According to the solving method, the task data and the known parameters are substituted into the MATLAB2010a for programming calculation, and after multiple runs, the optimal quality value is taken as a final result.
For 11438 tasks in the example, 2428 common semi-trailer tractors, 4080 two-axis semi-trailer tractors and 4055 three-axis semi-trailer tractors are required to complete a given task by adopting the scheduling optimization method of the application through calculation, and the number and the running route of each tractor are obtained as shown in table 2. And for a certain tractor, starting from the highway port of the city to which the tractor belongs, and leaving the tractor in the highway port after belonging to the task in the schedule according to the time sequence of task execution and the constraint of continuous working time.
Table 2 comparison table of calculation results
From table 2, the ordinary semi-trailer transportation throwing-hanging ratio of the enterprise is calculated by a design model to be 1. The network type scheduling optimization has better operation performance, and can optimize the operation of enterprises to the maximum extent, reduce cost and improve efficiency; meanwhile, due to the relatively high purchase cost of the vehicles, the determination of the optimal proportion can also avoid the situation that the enterprises purchase the vehicles blindly, so that the fixed assets are idle and the capital burden is caused.
As can be seen from table 2, the network type swing-hanging mode uses fewer tractors and has lower cost than the conventional mode. The required tractor quantity and the total cost of the network type multi-vehicle type swing-hang transportation are reduced to different degrees compared with the traditional fixed-hang transportation, wherein: the common semi-trailer transportation is reduced by 2.39%, the cost is saved by 7.54%, the two-shaft semi-trailer transportation is reduced by 2.45%, the cost is saved by 7.42%, the three-shaft semi-trailer transportation is reduced by 2.66%, and the cost is saved by 7.95%. Compared with the traditional mode, in the network type throwing and hanging, the tractor is allowed to pull a plurality of trailer tasks under possible conditions, so the number of the used tractors is necessarily reduced. And when the cost increased by the empty running of the tractors is less than the fixed cost saved due to the reduction of the number of the tractors, the two drop-off transportation costs show a descending trend.
Therefore, compared with the traditional transportation mode, the optimization method for the multi-vehicle type network drop and pull transportation scheduling obtains the initial solution of the target optimization model by establishing the network drop and pull scheduling target optimization model and adopting the mileage-saving method, optimizes the initial solution by improving the simulated annealing algorithm to obtain the optimal solution, and verifies the effectiveness of the optimization method through examples, so that the use number and the operation cost of tractors for the multi-vehicle type drop and pull transportation are integrally lower.
The above embodiments are only for illustrating the technical solutions of the present invention and are not limited thereto, and any modification or equivalent replacement without departing from the spirit and scope of the present invention should be covered within the technical solutions of the present invention.
Claims (8)
1. A multi-vehicle type network drop-off transport scheduling optimization method is applied to road port network drop-off transport composed of a common semi-trailer, a two-axle road port semi-trailer and a three-axle road port semi-trailer, and comprises the following steps:
(1) Establishing a network drop-hang scheduling target optimization model;
(2) Obtaining task quantity segmentation of ordinary semi-trailer transportation through dynamic planning;
(3) Obtaining an initial solution of the target optimization model in the step (1) by a mileage-saving method;
(4) And optimizing the initial solution by improving a simulated annealing algorithm to obtain an optimal solution.
2. The method for optimizing the network drop-off transport scheduling of multiple vehicle types according to claim 1, wherein the target optimization model in the step (1) is as follows:
objective function 1:
the network operation cost is composed of a heavy-load running cost, an empty running cost, a fixed cost and a punishment cost, and the heavy-load running cost is
The empty running cost is as follows:
the fixed cost of using the vehicle on the same day is as follows:
the penalty cost is:
the objective is to express the network operation cost minimum as:
the objective function 2:
the minimum number of tractors invested is expressed as:
minZ 2 ＝K z (2)
the constraint conditions are as follows:
in the formula (I), the compound is shown in the specification,
n: represents a set of all road ports, N road ports total, N = {1,2, …, N };
i, j, l: the number of the highway port is represented, i, j, l belongs to N;
m: representing a set of tractor types;
m: representing the type of a tractor, wherein M belongs to M, wherein M =1 is a common semi-trailer tractor, and M =2 is a highway port semi-trailer tractor;
m k : represents the m type tractor K, K belongs to {1,2, …, K };
K z : the total number of tractors required for the road network is represented,
d ij : represents the distance between the road ports i, j, in units: km;
q ijm : the drop and hang transportation requirements of m types of tractors needed by the highway ports i to j are represented, the common van type drop and hang is represented by the tonnage of goods, and the drop and hang transportation of the highway ports is represented by the times. Since the requirements are not necessarily balanced, q ijm Is not necessarily equal to q jim ；
t: the continuous working time of all tractors is regulated to be equal every day;
v m1 ,v m2 : the speed of the m-type tractor for towing heavy suspension and empty driving is expressed by the unit: km/h;
k 1 ,k 2 : respectively representing the states of heavy suspension and idle running of the tractor, wherein K belongs to the fields of 1,2, … and K;
3. the method as claimed in claim 1, wherein the dynamic planning in step (2) comprises:
establishing a mathematical model
Setting a constraint condition:
X i ≥0,X i ∈Z,i∈I (9)
in the above formula, the first and second carbon atoms are,
F Q -total cost of transportation demand Q;
x i -the number of uses of each tractor;
i, a tractor set, wherein three vehicle types are provided;
C i -fixed use costs for each tractor;
d, distance between highway ports;
P i -the cost of variation when each tractor is heavily towed;
L i -maximum load capacity of each tractor;
q-one-way freight volume between highway ports;
z is a set of integers.
4. The method as claimed in claim 1, wherein the mileage-saving method in step (3) comprises: firstly, one vehicle is enabled to serve only one node, then the odometer is obtained by calculating the combined saved distance of any two lines under the condition of meeting the constraint, the combined line with the maximum saved mileage is updated according to the odometer, and the odometer is updated until any two lines cannot be combined or the mileage cannot be saved.
5. The method for optimizing multi-vehicle type network drop-off transport scheduling as claimed in claim 1, wherein the improved simulated annealing algorithm comprises the following steps:
stepl, acquiring basic data, wherein the required basic data comprises a drop-and-hang transport network topological structure, distances among road ports, vehicle running speeds, heavy driving costs, idle driving costs and fixed costs of three common semitrailers and two road port semitrailers, and drop-and-hang demands of the common semitrailers and the road port semitrailers;
step2, setting algorithm parameters including initial temperature t of algorithm s End of algorithm temperature t f Temperature decay parameter α, and Markov chain length (maximum number of constant temperature iterations) m k ；
Step3, generating a road port drop-and-hang transport vehicle scheduling scheme { X } according to a mileage-saving method, calculating an evaluation function value F of the scheme, and enabling the current temperature to be t ← t- s Currently, constant-temperature iteration number n ← 1;
step4, randomly generating neighborhood solution { X ] of current vehicle dispatching scheme X according to neighborhood solution construction method neigh And calculating the evaluation function value F thereof neigh ；
Step5, executing Metroplis selection, calculating a neighborhood solution evaluation function value F 'and a function difference value delta F = F' -F of the two solutions, and selecting to accept the original solution or the neighborhood solution as the current solution according to the following method:
(1) if the delta F is less than 0, accepting the neighbor solution as the current solution, and carrying out constant temperature iteration times n ← n +1;
(2) if delta F is more than 0 and exp (delta F/t) > rand (0,1), receiving a receiving domain solution as a current solution, and carrying out constant temperature iteration times n ← n +1;
(3) under other conditions, accepting the original receiving solution as the current solution, and carrying out constant temperature iteration times n ← n +1;
step6, constant temperature iteration judgment, if the current constant temperature iteration times is less than the length of the Markov chain, namely n is less than or equal to m k Turning to Step5, otherwise, turning to Step7;
step7, cooling and annealing, namely lowering the system temperature t ← alpha.t according to the set temperature attenuation parameters;
step8, judging the termination of the algorithm, if the current temperature is greater than the algorithm ending temperature, namely t is greater than t f And turning to Step4, otherwise, terminating the algorithm, and inputting the current solution as the optimal solution.
6. The method for optimizing the network drop and hang transportation dispatching of the multiple vehicle types according to claim 5, wherein the drop and hang transportation comprehensive cost is set as Z 1 And the investment number of the tractors is Z 2 Obtaining a neighborhood solution evaluation function Z = omega 1 ·Z′ 1 +ω 2 ·Z′ 2 Wherein
7. The method of claim 5, wherein the neighborhood solution construction method comprises:
resetting, randomly selecting one position of the current sequence, and randomly assigning the serial number of the position to another position; exchanging, namely randomly selecting two positions in the current sequence, and exchanging the numbers of the two positions in the next sequence;
2-opt, arbitrarily selecting two positions in the current sequence, and turning the sequence between the two positions.
8. The method of claim 5, wherein the improved simulated annealing algorithm further comprises constraint processing, wherein for common semi-trailer transportation, the constraint conditions are as follows: the method comprises the following steps of vehicle type constraint, load constraint and time constraint, wherein the constraint conditions for the semi-trailer transportation of the two-axis and three-axis highway harbor are as follows: load constraints, time constraints.
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