CN117078141A - Multi-vehicle type dangerous goods fully-loaded delivery vehicle scheduling method - Google Patents
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Abstract
The invention provides a multi-vehicle type dangerous goods fully-loaded delivery vehicle dispatching method, and relates to the technical field of navigation and positioning of delivery vehicles. Aiming at the problem of dispatching vehicles with single-dispatching center and multiple-vehicle-type dangerous goods fully loaded, the invention constructs a two-stage optimization model for path optimization and vehicle dispatching by taking vehicle types and transportation paths as decision variables. Aiming at the path optimization model, a three-stage optimization method is designed, wherein a pulse algorithm is adopted in the first stage to obtain Pareto paths from a distribution center to all demand points, the vehicle configuration scheme for transportation of all demand nodes is calculated in the second stage, and a NSGA-II multi-objective optimization is designed in the third stage to obtain a path selection scheme. Aiming at a vehicle dispatching model, a UMDA-based optimization method is adopted to solve, the calculation process of the method is illustrated through calculation, and the effectiveness of the method is verified.
Description
Technical Field
The invention relates to the technical field of navigation and positioning of delivery vehicles, in particular to a multi-vehicle type dangerous goods fully-loaded delivery vehicle scheduling method.
Background
In the actual dangerous goods transportation process, transportation enterprises may have multiple vehicle types, and reasonable selection of vehicle types for assembly and transportation may reduce transportation cost and risk simultaneously, for example, a gas station needs 20m of a certain oil product 3 If a nuclear load of 13.5m is used 3 Two passes of transportation are required for the tank truck of (2), but 29.5m is adopted 3 The tank car of (2) only needs to be transported once, and no matter which target is transported at the cost and risk, 29.5m is adopted 3 The tank truck is more economical to complete in transportation and has smaller average risk, and therefore, the problem of dispatching the full-load dangerous goods transportation vehicles in multiple vehicle types is discussed.
The transportation of multiple combined vehicle types has the advantages of transporting dangerous goods from the transportation cost, transportation risk, required vehicle number, transportation times and total transportation time compared with the transportation of single vehicle types, and transportation enterprises can use the transportation vehicles of different vehicle types in a matched mode through reasonable configuration in production practice, so that the transportation of large vehicles cannot be simply considered in actual transportation, but the transportation of dangerous goods can be optimized by a scientific method after the relationship between the transportation risk and the dangerous goods load is quantified.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-vehicle type dangerous article full-load delivery vehicle dispatching method, and a path optimization and vehicle dispatching model taking vehicle types and transportation paths as decision variables is constructed aiming at the problem of dispatching the multi-vehicle type dangerous article full-load delivery vehicles in a single delivery center.
Including a transportation path plan model P1 and an integer programming model P2,
the specific description of the model P1 is as follows:
minf＝(f1,f2) (1)
s.t.
wherein: (2) As a transport cost objective function, formula (3) is a transport risk objective function, formula (4) indicates that for any demand node, the transport vehicle core capacity needs to meet its demand, and formula (5) (6) is a decision variable.
The specific description of model P2 is as follows:
s.t.
wherein: the formula (7) is an objective function and consists of two parts, wherein the first part is the target with the minimum transportation times, M is an integer large enough to ensure the high priority of the transportation times, the second part is the minimum total nuclear capacity of all vehicles, and the formula (8) is the total nuclear capacity of all vehicles, so that the transportation requirement of the requirement point d is met.
Further, the specific steps are as follows:
s1: the solution process of model P1 can be divided into three phases:
the first stage: and obtaining Pareto paths from the distribution center to all the demand points, calculating the Pareto paths from the distribution center to all the demand points by adopting a pulse algorithm through the transportation cost and risk values of any vehicle type running on all road sections.
And a second stage: the vehicle configuration scheme of each demand node transportation is obtained, the vehicle configuration problem is an integer planning model, and the vehicle model is limited in type, so that the problem is small in scale, and an exhaustive method can be adopted to search the optimal solution.
And a third stage: adopting NSGA-II multi-objective optimization to obtain a path selection scheme, obtaining a Pareto path from a distribution center to each demand node according to a first stage and obtaining a vehicle configuration scheme according to a second stage, and solving the path selection scheme by adopting a NSGA-II multi-objective optimization method
S2: the UMDA-based VRPTW solving method in the model P2 is utilized for solving, and the method is adopted for |V| times circularly, so that the operation time schedule of all vehicle types can be obtained.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in:
according to the multi-vehicle type dangerous goods fully-loaded delivery vehicle dispatching method, multiple combined vehicle types are adopted for transportation, so that the method has the advantages of transporting dangerous goods from a single vehicle type, such as transportation cost, transportation risk, required vehicle number, transportation times and total transportation time, and transportation enterprises can use the transportation vehicles in a matched mode through reasonable configuration of transportation vehicles of different vehicle types in production practice.
Drawings
FIG. 1 is a flow chart of a multi-vehicle type dangerous goods transportation vehicle scheduling problem solving process;
FIG. 2 is a test network of the present invention;
fig. 3 is a Pareto optimal front comparison obtained when a single vehicle model of the present invention is transported with three vehicle models.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In the actual dangerous goods transportation process, transportation enterprises may have multiple vehicle types, and reasonable selection of vehicle types for assembly and transportation may reduce transportation cost and risk simultaneously, for example, a gas station needs 20m of a certain oil product 3 If a nuclear load of 13.5m is used 3 Two passes of transportation are required for the tank truck of (2), but 29.5m is adopted 3 The tank car of (2) only needs to be transported once, and no matter which target is transported at the cost and risk, 29.5m is adopted 3 The tank car of (2) is more economical to complete in transportation and the average risk is smaller. For this reason, the problem of scheduling a full-load hazardous material transportation vehicle in multiple vehicle models is discussed.
The differences in transportation costs and risks of different vehicle types during transportation are extremely complex, and in order to reduce the complexity of such differences in transportation operations of different vehicle types, the following assumptions are made:
(1) All transport vehicles, when returning to the distribution center, have a risk value of 0 when they pass through all road segments.
(2) It is assumed that the traveling speeds of vehicles of different vehicle types on arbitrary road sections are the same.
(3) The transportation cost of vehicles of different types in any road section is kept unchanged by the corresponding (proportional) relation among vehicles of different types. For example: the cost of running a 10 ton dangerous goods-loaded transport vehicle on a road section A is 80 yuan, the cost of running a transport vehicle on a road section B is 90 yuan, if the cost of running a 15 ton dangerous goods-loaded vehicle on the road section A is 120 yuan, the cost of running the vehicle on the road section B is 90 x 120/80=135 yuan, and the proportional relation of the transport risks is also established.
(4) The risk preference for the decision maker is neutral, i.e. not just considering one of the transportation costs or transportation risks but ignoring the other objective.
In the transport network g= (N, E), N represents a set of N nodes, E represents an inter-node road segmentAnd (5) collecting. K represents all vehicle models available for transportation process selection. The risk that v-type vehicles may exist for transporting dangerous goods on road sections (i, j) is r ij (v) The transportation cost is divided into two cases, and the full-load transportation is thatTravel time is +.>The empty driving cost is->Travel time isAssume that m demand points are to be completed +.>Dangerous goods distribution tasks of (1), the demand of each task is q 1 ,q 2 ,…,q m The delivery time window of the demand point v is [ b ] d ,e d ]A plurality of paths P exist between the distribution center o and the demand point d od All vehicles start from the distribution center o to complete the distribution task and return to the distribution center, and the nuclear capacity of the vehicles is g v Average loading time is Deltat 1 (v) The unloading time is delta t 2 (v) A. The invention relates to a method for producing a fibre-reinforced plastic composite Variable->Indicating that the section (i, j) E is on the way from the delivery center o to the demand point d, otherwise +.>The distribution time during transportation, the number of vehicles, the total distance travelled by the vehicles during distribution and the total risk are analyzed as follows.
(1) Vehicle model selection and path planning
For single-vehicle dangerous goods transportation, the cost and risk of transportation of any vehicle under the same path are the same, but for different types of transportation vehicles of |v| (v=1, 2, …, |v|), the transportation task of each demand node can select one or more vehicles for transportation, and the transportation cost and risk value of the vehicles are related to the selection of the vehicle type.
Assuming that the number of v-vehicles required to complete the delivery task of the demand node d isThe transportation cost of all vehicles from the distribution center to the demand point is the sum of the transportation costs of the road sections, namely:
according to the assumption, the route at the time of empty car return is the route with the minimum cost from the distribution center node o to the demand point dThe empty transport cost of all vehicles returning to the distribution center from the demand point is that
Similar to the transportation costs, the total transportation risk of completing the delivery task at the demand point d is:
the transportation path scenario can be described by a model P1:
P1:
minf＝(f 1 ,f 2 ) (4)
s.t.
wherein: equation (5) is a transport cost objective function, equation (6) is a transport risk objective function, equation (7) indicates that for any demand node, the transport vehicle core load needs to meet its demand, and equations (8) (9) are decision variables.
In the case of a given transport path and a known type of transport vehicle, the combination strategy of which type is chosen to minimize the costs or risks in transport is of course most desirable to minimize both. The influence of the vehicle type on the transportation cost and risk in the dangerous goods transportation process is very complex, and has a great relationship with the transported dangerous goods. Taking dangerous goods which can be exploded in transportation as an example, guo Xiaolin and Verma conduct research on transportation tool selection problems, if a decision maker is risk neutral, transportation cost and risk can be simultaneously minimized by minimizing transportation times, and according to the decision maker neutral risk preference of the assumption (4), the following integer programming model (model P2) can be established for vehicle type selection problems in the transportation process.
P2:
s.t.
Wherein: the formula (10) is an objective function and consists of two parts, wherein the first part is the target with the minimum transportation times, M is an integer large enough to ensure the high priority of the transportation times, the second part is the minimum total nuclear capacity of all vehicles, and the formula (11) is the total nuclear capacity of all vehicles, so that the transportation requirement of the requirement point d is met.
(2) Vehicle scheduling
Similar to the scheduling of single-vehicle dangerous goods transportation vehicles, fewer vehicles participate in the distribution task through reasonable scheduling after a decision maker determines a certain path selection scheme according to risk preference. In the same way, in the transportation of dangerous goods of multiple vehicle types, the operation time table of each vehicle type is determined, so that each vehicle type can be reasonably scheduled. Therefore, aiming at the problem of multiple vehicle types, all operations can be continuously numbered according to the demand of each demand node on the vehicle type v for any vehicle type v, and the problem of single vehicle type aiming at the vehicle type v is converted.
The model is a combined optimization problem comprising a plurality of variables, and different types of transport vehicles on the same path travel in the model, so that the transportation cost and risk on the path are converted from constants to variables related to the number of each vehicle type traveling on the path, and the variables cannot be directly solved by a common multi-objective optimization method such as NSGA-II algorithm. Therefore, firstly, the difference between the transportation cost and the risk of the vehicles of different vehicle types is required to be processed, so that the cost of the vehicles of different vehicle types during transportation can still be represented by a constant, and then the cost can be solved by applying a common multi-objective optimization method.
According to the assumption (3), when vehicles of different vehicle types travel on any road section, the corresponding (proportion) relation of the transportation cost and risk among the vehicles of each vehicle type is kept unchanged, so when the vehicles of multiple vehicle types participate in transportation, the vehicles of various vehicle types can be converted into vehicles of a certain vehicle type according to the corresponding relation, calculation is carried out, when the path is optimized, the Pareto path from the distribution center to the demand nodes is irrelevant to the demand quantity of the demand nodes, and is only relevant to the transportation cost and risk value of the vehicles traveling on each road section, therefore, in the solving process of the model, the Pareto path from the distribution center to the demand nodes can be determined through the condition of single vehicle type transportation, the cost and risk of traveling on the paths are determined through the vehicle collocation of the vehicles of different vehicle types, and then the transportation total cost and total risk from the distribution center to all the demand nodes are solved, so that the solution of the model can be obtained.
From the above analysis, the solution process of the model can be divided into three phases:
(1) The first stage: acquiring Pareto paths from a distribution center to various demand points
The transportation cost and risk value of running on all road sections by any vehicle type are adopted by adopting a pulse algorithm [3] And calculating Pareto paths from the distribution center to the various demand points.
(2) And a second stage: vehicle configuration scheme for acquiring transportation of each demand node
The vehicle configuration problem is an integer planning model, and the type of the vehicle model is limited, so that the scale of the problem is smaller, and an exhaustion method can be adopted to search the optimal solution of the problem.
(3) And a third stage: and adopting NSGA-II multi-objective optimization to obtain a path selection scheme.
And obtaining a Pareto path from the distribution center to each demand node according to the first stage and obtaining a vehicle configuration scheme according to the second stage, and solving and obtaining a path selection scheme by adopting an NSGA-II multi-objective optimization method.
VRPTW solving method based on UMDA (unified modeling architecture) of model heart [4] Solving is carried out, and the method is adopted for |V| times circularly, so that the operation time schedule of all vehicle types can be obtained.
A flow chart of the solving method is shown in fig. 1.
As in the test transport network of fig. 2, node 0 is the distribution center and node 1,2,4,7,8 is the demand point. The distance and risk of each road section in the network are shown in table 1, and the demand and time window of each demand point are shown in table 2. The transport vehicle hasThree types: the information of the nuclear capacity and the like corresponding to the type 1 vehicle, the type 2 vehicle and the type 3 vehicle are shown in the table 3, wherein the transportation risk coefficient is 13.5m compared with the nuclear capacity 3 Risk change coefficient of each road section of the vehicle, and running average speed of all vehicles is 45 km.h -1 . By arranging the vehicle transportation scheme, the transportation process achieves the transportation cost and the transportation risk are minimized.
Table 1 shows the length and risk evaluation values of each road section in the test network.
Table 1 lengths of road segments and risk of transport vehicles traveling on the road segments
Table 2 is a window of demand for all demand nodes and time to accept the offload, must arrive within a specified time, and if arriving earlier than the earliest start time, wait is needed. The earliest time the vehicle starts from the delivery center and the latest time it returns to the delivery center are 08:00 and 20:00, respectively. Table 3 cost unit price, risk factor, and loading and unloading duration for various vehicle types.
TABLE 2 demand Point demand and time Window
TABLE 3 cost per unit price, risk factors, and loading and unloading duration for various vehicle types
By adopting the designed solving algorithm to calculate aiming at the upper requirement, a group of Pareto path sets (12 path selection schemes) are obtained, and the total transportation cost and total transportation risk are compared with those of the single-vehicle type transportation dangerous goods, as shown in figure 3. For the minimum risk solution, the transportation risk of a single vehicle type is only about 40% of the same transportation cost, and for the minimum cost solution, the transportation risk is only about 55%. The accuracy of the comparison result depends on whether the ratio evaluation result of the transportation cost and the transportation risk among the vehicle types accords with the actual situation, and verification is needed in the actual transportation situation, but the comparison result can reflect that when the vehicle is fully loaded with dangerous goods for transportation, the combined transportation of a plurality of vehicle types is better than the transportation of a single vehicle type in the aspects of transportation cost and transportation risk under the same driving scheme.
The path schemes of the minimum total transportation cost and the minimum total transportation risk are selected respectively, and a vehicle dispatching scheme (see table 4) under the condition of the minimum total transportation cost and a vehicle dispatching scheme (see table 5) under the condition of the minimum total transportation risk can be obtained by adopting a VRP problem solving method based on UMDA.
Table 4 vehicle schedule with minimum total cost of transportation (scenario 1)
Table 5 vehicle schedule with minimal total risk of transportation (scenario 12)
Under the same requirement and optimization targets, taking the situation that the total transportation cost is minimum and the total transportation risk is minimum as an example, the total dispatching number, the transportation times and the transportation time when the single vehicle type transportation and the three vehicles are combined for transportation are compared with each other as shown in table 6, wherein the calculation of the transportation time is accurate to minutes (min).
Table 6 comparison of single vehicle transport and three vehicle combination transport under the same demand
The following conclusions were drawn by comparison: under the condition of the minimum total transportation cost, the number of vehicles in the two transportation modes is the same, and the total transportation times and the transportation time in the combined transportation of the three vehicle types are 53% and 74% in the transportation of the single vehicle type respectively; under the condition of minimum total risk of transportation, the number of vehicles in the two transportation modes is the same, and the total number of transportation times and total time in the combined transportation of the three vehicle types are 53% and 68% in the transportation of the single vehicle type respectively. The number of vehicles, the number of transportation times and the transportation time required in the vehicle dispatching process are related to the running speeds and the loading and unloading times of different vehicle types, the conclusion is obtained under the condition that the running speeds of the vehicles of the different vehicle types are the same, and meanwhile, the loading and unloading times of the different vehicle types are estimated values in the calculation example, so that the conclusion needs to be verified in the actual situation, but can be reflected that the total number of transportation times and the total time of the combination transportation of multiple vehicle types are better than those of the transportation of a single vehicle type when the dangerous goods are transported in full load.
From the analysis, the transportation of multiple combined vehicle types has great advantages over the transportation of dangerous goods by single vehicle types in terms of transportation cost, transportation risk, required vehicle number, transportation times and total transportation time, and transportation enterprises can use the transportation vehicles of different vehicle types in a matched mode through reasonable configuration in production practice. In this example, the transportation cost and transportation risk are reduced by adopting the 3-type vehicle transportation with larger nuclear load, but the conclusion is obtained under the condition that the transportation risk and the dangerous goods load are in direct proportion, but the relation between the result caused by accident in the dangerous goods transportation and the load is affected by a plurality of factors, not a simple direct proportion relation, and the geometric progression increase with the load after the accident is possible. Therefore, in actual transportation, large-scale vehicle transportation cannot be simply considered, but the relationship between the transportation risk and the dangerous goods load can be quantified, and then the vehicle dispatching is performed after the optimization is performed by adopting a scientific method.
The foregoing description is merely illustrative of the preferred embodiments of the present disclosure and the technical principles applied thereto, and it should be understood by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the features described above, but encompasses other technical solutions formed by any combination of the features described above or their equivalents, such as the features described above and the features disclosed in the embodiments of the present disclosure (but not limited to) having similar functions, being interchanged.
Claims (2)
1. A multi-vehicle type dangerous goods fully loaded delivery vehicle dispatching method is characterized by comprising a transportation path scheme model P1 and an integer programming model P2,
the specific description of the model P1 is as follows:
minf＝(f 1 ,f 2 ) (1)
s.t.
wherein: the formula (2) is a transportation cost objective function, the formula (3) is a transportation risk objective function, the formula (4) shows that for any demand node, the nuclear capacity of the transportation vehicle needs to meet the demand, and the formulas (5) and (6) are decision variables;
the specific description of model P1 is as follows:
s.t.
wherein: the formula (7) is an objective function and consists of two parts, wherein the first part is the target with the minimum transportation times, M is an integer large enough to ensure the high priority of the transportation times, the second part is the minimum total nuclear capacity of all vehicles, and the formula (8) is the total nuclear capacity of all vehicles, so that the transportation requirement of the requirement point d is met.
2. The method for dispatching the multi-vehicle type dangerous goods fully loaded delivery vehicle according to claim 1, which is characterized by comprising the following specific steps:
s1: aiming at the path optimization model P1, a three-stage optimization method is designed:
the first stage: the method comprises the steps of obtaining Pareto paths from a distribution center to all demand points, calculating the Pareto paths from the distribution center to all demand points by adopting a pulse algorithm through transportation cost and risk values of any vehicle type running on all road sections;
and a second stage: the vehicle configuration scheme of each demand node transportation is obtained, the vehicle configuration problem is an integer planning model, and the vehicle type is limited, so that the scale of the problem is smaller, and an exhaustion method can be adopted to search the optimal solution;
and a third stage: adopting NSGA-II multi-objective optimization to obtain a path selection scheme, obtaining a Pareto path from a distribution center to each demand node according to a first stage and obtaining a vehicle configuration scheme according to a second stage, and solving the path selection scheme by adopting a NSGA-II multi-objective optimization method;
s2: the UMDA-based VRPTW solving method in the model P2 is utilized for solving, and the method is adopted for |V| times circularly, so that the operation time schedule of all vehicle types can be obtained.
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