CN108399464A - A kind of multimodal transport method for optimizing route and system - Google Patents
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Abstract
The invention discloses a kind of multimodal transport method for optimizing route and systems, are optimized with self-adapted genetic algorithm to sending path with charge free and sending mode with charge free, reduce the no-load ratio and operating range of vehicle, reduce Enterprise Transportation cost, improve transport timeliness.Its technical solution is：The mathematical model of multiple constraint distribution vehicle scheduling is established according to dispatching problem, it sends efficiency with charge free in order to improve and proposes a kind of self-adapted genetic algorithm, consider distribution cost and distribution time establishes optimization object function, optimizing is carried out to sending path with charge free and sending mode with charge free with self-adapted genetic algorithm.
Description
Technical Field
The invention relates to a multi-type intermodal transportation and path planning technology in the logistics industry, in particular to a path optimization method based on a bionic intelligent algorithm.
Background
With the prosperity and development of electronic commerce and online shopping, the traffic volume of the logistics industry is rapidly increased, and the package delivery cost is the most important one of the logistics costs. Meanwhile, in order to improve the service quality, each large express enterprise successively releases time-efficient delivery services, such as city-on-day, provincial-off-day, and the like. Therefore, the method effectively controls the operation cost and improves the dispatching efficiency, and has important significance on the sustainable development of logistics enterprises. The path optimization and dispatching mode in the current dispatching process are mostly realized in a manual experience mode according to operation data, and the mode presents great limitation to the large-scale dispatching problem. Therefore, factors such as cost, time and the like are integrated, a vehicle scheduling sequence is reasonably arranged, an optimal transportation path is set, and various dispatching modes are selected, so that the no-load rate and the driving distance of the vehicle can be effectively reduced, and the method has important significance in the aspects of reducing the transportation cost of enterprises, improving the profits of the enterprises, enhancing the distribution timeliness and the user satisfaction and the like.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to solve the problems and provides a multi-type combined transport path optimization method and a multi-type combined transport path optimization system.
The technical scheme of the invention is as follows: the invention discloses a multi-type intermodal transport path optimization method, which comprises the following steps:
step 1: constructing a mathematical model of multi-constraint delivery vehicle scheduling;
step 2: and optimizing the distribution route and the dispatching mode based on an adaptive genetic algorithm.
According to an embodiment of the multimodal transportation path optimization method of the present invention, the mathematical model for the multi-constraint delivery vehicle scheduling is constructed in step 1 based on: the logistics distribution network of the multi-mode intermodal transportation is fixed, and all nodes and transportation routes which can pass through the whole logistics distribution process are given; the freight rates, the transportation time and the transportation capacity of different transportation modes are different among nodes; transit can occur at each node, but each node can only occur once at most; the two nodes can only select one transportation mode for cargo transportation; the delivered goods cannot be transported simultaneously in multiple modes in the transportation process; the transfer of goods can only occur at nodes, but not in the transportation process, and uncertain factors including weather changes, road conditions and manual operations are ignored in the whole process.
According to an embodiment of the multimodal transportation path optimization method of the present invention, the constructing the mathematical model in step 1 further includes:
setting P as a node set; s is a transportation mode set, which comprises four aspects of road transportation, railway transportation, air transportation and waterway transportation;a decision variable is used for representing that a transportation mode k is selected from a node i to a node j for transportation;is a decision variable, which represents that the transportation mode is converted from k to l at the node j;the method comprises the steps that the k transportation mode transportation time adopted between the ith node and the j node in the multi-mode combined transportation process is shown;represents a unit conversion time from the transportation mode k to the transportation mode l at the node j; t represents the maximum service time required by the client;respectively representing the unit cost of the transportation of the mode k selected from the node i to the node j in the multi-mode intermodal transportation process; di,jRepresents the distance from node i to node j;respectively representing the conversion cost of the k-th transportation mode to the l-th transportation mode at the node j; q is the total weight of the goods to be transported;the transportation capacity of selecting a transportation mode k from the node i to the node j for transportation is represented;
the mathematical model for multi-constraint delivery vehicle scheduling is as follows:
the constraints are as follows:
according to an embodiment of the multimodal transportation path optimization method of the present invention, the step 2 further comprises:
step a: initializing adaptive genetic algorithm parameters, including population size NpAdaptive cross probability Pc,iAdaptive mutation probability PamMaximum iteration number G;
step b: encoding each dispatching point and dispatching mode in the optimized path;
step c: generating NpSelecting cross position according to self-adaptive cross probability Pc,iPerforming a crossover operation;
step d: for N in the populationpThe individual chromosomes are mutated according to the self-adaptive mutation probability PamPerforming a mutation operation;
step e: calculating an objective function value based on the mathematical model in the step 1, executing selection operation, and reserving an optimal individual;
step f: and (c) judging whether the iteration times g are needed, if so, returning to the step (c), and if not, terminating the algorithm optimization.
The invention also discloses a computer readable storage medium, wherein a computer program is stored on the medium, and the computer program executes the multi-type joint transport path optimization method after running.
The invention also discloses a computer system, which comprises a processor and a storage medium, wherein the storage medium is stored with a computer program, and the processor reads and runs the computer program from the storage medium to execute the multimodal transport path optimization method.
The invention discloses a multi-type intermodal transport path optimization system, which comprises:
the multi-constraint delivery vehicle scheduling model building module is used for building a mathematical model for scheduling the multi-constraint delivery vehicles;
and the distribution route and delivery mode optimization module is used for optimizing the distribution route and the delivery mode based on the adaptive genetic algorithm.
According to an embodiment of the multimodal transportation path optimization system of the present invention, the mathematical model for constructing the multi-constraint delivery vehicle scheduling in the multi-constraint delivery vehicle scheduling model construction module is based on: the logistics distribution network of the multi-mode intermodal transportation is fixed, and all nodes and transportation routes which can pass through the whole logistics distribution process are given; the freight rates, the transportation time and the transportation capacity of different transportation modes are different among nodes; transit can occur at each node, but each node can only occur once at most; the two nodes can only select one transportation mode for cargo transportation; the delivered goods cannot be transported simultaneously in multiple modes in the transportation process; the transfer of goods can only occur at nodes, but not in the transportation process, and uncertain factors including weather changes, road conditions and manual operations are ignored in the whole process.
According to an embodiment of the multimodal transportation path optimization system of the present invention, the mathematical model constructed by the multi-constraint delivery vehicle scheduling model construction module further includes:
setting P as a node set; s is a transportation mode set, which comprises four aspects of road transportation, railway transportation, air transportation and waterway transportation;a decision variable is used for representing that a transportation mode k is selected from a node i to a node j for transportation;is a decision variable, which represents that the transportation mode is converted from k to l at the node j;the method comprises the steps that the k transportation mode transportation time adopted between the ith node and the j node in the multi-mode combined transportation process is shown;represents a unit conversion time from the transportation mode k to the transportation mode l at the node j; t represents the maximum service time required by the client;respectively representing the unit cost of the transportation of the mode k selected from the node i to the node j in the multi-mode intermodal transportation process; di,jRepresents the distance from node i to node j;respectively representing the conversion cost of the k-th transportation mode to the l-th transportation mode at the node j; q is the total weight of the goods to be transported;the transportation capacity of selecting a transportation mode k from the node i to the node j for transportation is represented;
the mathematical model for multi-constraint delivery vehicle scheduling is as follows:
the constraints are as follows:
according to an embodiment of the multimodal transportation path optimization system of the present invention, the delivery route and delivery method optimization module further comprises:
a parameter initialization unit for initializing parameters of the adaptive genetic algorithm, including the population size NpAdaptive cross probability Pc,iAdaptive mutation probability PamMaximum iteration number G;
the encoding unit is used for encoding each delivery point and delivery mode in the optimized path;
a cross operation unit for generating NpSelecting cross position according to self-adaptive cross probability Pc,iPerforming a crossover operation;
a mutation operation unit for N in the populationpThe individual chromosomes are mutated according to the self-adaptive mutation probability PamPerforming a mutation operation;
the selection operation unit is used for calculating an objective function value based on the mathematical model constructed in the multi-constraint delivery vehicle scheduling model construction module, executing selection operation and reserving an optimal individual;
and a termination judging unit for judging whether the iteration times gG are required, if so, returning to the step c, and otherwise, terminating the algorithm optimization.
Compared with the prior art, the invention has the following beneficial effects: the invention establishes a mathematical model of multi-constraint delivery vehicle scheduling according to delivery problems, provides an adaptive genetic algorithm for improving delivery efficiency, establishes an optimization objective function by comprehensively considering delivery cost and delivery time, and optimizes a delivery path and a delivery mode by using the adaptive genetic algorithm.
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The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
FIG. 1 illustrates a flow diagram of an embodiment of a multimodal transport path optimization method of the present invention.
Fig. 2 shows a detailed flowchart of step S2 in the multimodal transport path optimization method shown in fig. 1.
Fig. 3 shows a schematic diagram of an embodiment of the multimodal transport path optimization system of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.
Fig. 1 shows a flow of an embodiment of the multimodal transport path optimization method of the present invention. Referring to fig. 1, the method of the present embodiment includes the following steps.
Step S1: and constructing a mathematical model of multi-constraint delivery vehicle scheduling.
In the step, according to the user requirements, the logistics distribution network of the multi-mode intermodal transport is fixed, and all nodes and transport routes which may pass through the whole logistics distribution process are given; the freight rates, the transportation time and the transportation capacity of different transportation modes are different among nodes; transit can occur at each node, but each node can only occur once at most; the two nodes can only select one transportation mode for cargo transportation; the delivered goods cannot be transported simultaneously in multiple modes in the transportation process; the transfer of goods can only occur at nodes, but not in the transportation process, and uncertain factors including weather changes, road conditions and manual operations are ignored in the whole process.
Setting P as a node set; s is a transportation mode set, which comprises four aspects of road transportation, railway transportation, air transportation and waterway transportation;is a decision variable, represents a slave nodeSelecting a transportation mode k from the point i to the node j for transportation;is a decision variable, which represents that the transportation mode is converted from k to l at the node j;the method comprises the steps that the k transportation mode transportation time adopted between the ith node and the j node in the multi-mode combined transportation process is shown;represents a unit conversion time from the transportation mode k to the transportation mode l at the node j; t represents the maximum service time required by the client;respectively representing the unit cost of the transportation of the mode k selected from the node i to the node j in the multi-mode intermodal transportation process; di,jRepresents the distance from node i to node j;respectively representing the conversion cost of the k-th transportation mode to the l-th transportation mode at the node j; q is the total weight of the goods to be transported;the transportation capacity of selecting a transportation mode k from the node i to the node j for transportation is represented;
the mathematical model for multi-constraint delivery vehicle scheduling is as follows:
the constraints are as follows:
step S2: and optimizing the distribution route and the dispatching mode based on an adaptive genetic algorithm.
The refinement of this step is shown in fig. 2 and described in detail below.
Step S21: initializing adaptive genetic algorithm parameters, such as population size NpSet to 50, adaptive crossover probability Pc,iAdaptive mutation probability PamThe maximum number of iterations G is set to 1000.
Step S22: encoding each dispatch point and dispatch mode in the optimized path.
In this embodiment, a two-stage natural coding scheme is adopted, where the first stage of the coding represents a distribution route, and the second stage represents a transportation scheme for each stage of distribution. In dispatch route coding, for example, it is required to dispatch from point 0 to point 7, and the road network composed of points 0 to 7 includes 6 points in total, and the numbers thereof are divided into numbers 1 to 6. First, numbers 1 to 6 are randomly arranged, such as [1, 6, 4, 3, 2, 5], 0 is added at the forefront and 7 is added at the end, and then [0, 1, 6, 4, 3, 2, 5, 7] is shown as being distributed according to the path 0 → 1 → 2 → 4 → 3 → 2 → 5 → 7. In the code of the dispatching transportation mode, according to four transportation modes of road transportation, railway transportation, air transportation and waterway transportation, 1, 2, 3 and 4 are respectively adopted to represent road transportation, railway transportation, air transportation and waterway transportation. In the above example, one distribution route has 8 distribution points, each point is called a transportation route, and there are 7 distribution patterns, so that 7 distribution patterns, for example, [1, 1, 2, 3, 1, 4, 1], are randomly generated.
Step S23: generating Np(e.g., 50) chromosomes, the cross-over positions being selected according to an adaptive cross-over probability PciA crossover operation is performed.
Wherein the adaptive cross probability:f(xi) For the current i-th individual adaptive function value, fmin(x) And fmax(x) Respectively a minimum fitness function value and a maximum fitness function value in population individuals.
In this embodiment, a partial mapping intersection method is adopted, starting from the first chromosome of the population, grouping every two chromosomes into a group, and randomly generating the number of chromosomes of each group if the self-adaptive intersection probability P is less than or equal toc,iIf the numbers at the two ends of the first section of chromosome are not moved, a random section in the middle is taken for cross exchange; and randomly selecting a section of the second section of chromosome for interchange, otherwise, not performing crossover operation on the group of chromosomes. For example:
step S24: for N in the populationpThe individual chromosomes are mutated according to the self-adaptive mutation probability PamPerforming mutation operation.
Wherein,Pm10.05 is the initial variation probability, G is the maximum number of iterations, G is the current number of iterations, G is the initial variation probability0G/2 is the point of evolution.
The mutation operation is to randomly generate a number of the adaptive mutation probability P for each chromosome of the populationamIf the numbers at the two ends of the first segment chromosome are not moved, randomly takingTwo numbers of the chromosome are exchanged, and a new chromosome is formed; and (4) exchanging any two digits of the second chromosome segment to form a new chromosome, otherwise, carrying out mutation operation on the chromosome. For example,
step S25: and (4) calculating an objective function value based on the mathematical model in the step (1), executing selection operation and reserving the optimal individual.
Here, the objective function is taken as an adaptive function, and the model has two adaptive functions, i.e., time required to complete the entire distribution and cost required to complete the entire distribution. The objective function value corresponding to the individual is the adaptive value of the individual. The embodiment divides the customer demands into two types, one is a common demand and the other is an emergency demand. For the common requirements, a scheme with the shortest time is sought from a distribution route with the lowest cost; for urgent needs, the least costly solution is to be found from the shortest distribution route.
Step S26: and (c) judging whether the iteration times G is less than G, if so, returning to the step c, and if not, terminating the algorithm optimization.
Fig. 3 shows the principle of an embodiment of the multimodal transportation path optimization system of the present invention, please refer to fig. 3, the system of the embodiment includes a multi-constraint delivery vehicle scheduling model building module, a delivery route and a delivery manner optimization module.
The multi-constraint delivery vehicle scheduling model building module builds a mathematical model of multi-constraint delivery vehicle scheduling.
The basis for constructing the mathematical model for the multi-constraint delivery vehicle scheduling in the multi-constraint delivery vehicle scheduling model construction module is as follows: the logistics distribution network of the multi-mode intermodal transportation is fixed, and all nodes and transportation routes which can pass through the whole logistics distribution process are given; the freight rates, the transportation time and the transportation capacity of different transportation modes are different among nodes; transit can occur at each node, but each node can only occur once at most; the two nodes can only select one transportation mode for cargo transportation; the delivered goods cannot be transported simultaneously in multiple modes in the transportation process; the transfer of goods can only occur at nodes, but not in the transportation process, and uncertain factors including weather changes, road conditions and manual operations are ignored in the whole process.
The mathematical model constructed by the multi-constraint delivery vehicle scheduling model construction module further comprises:
setting P as a node set; s is a transportation mode set, which comprises four aspects of road transportation, railway transportation, air transportation and waterway transportation;a decision variable is used for representing that a transportation mode k is selected from a node i to a node j for transportation;is a decision variable, which represents that the transportation mode is converted from k to l at the node j;the method comprises the steps that the k transportation mode transportation time adopted between the ith node and the j node in the multi-mode combined transportation process is shown;represents a unit conversion time from the transportation mode k to the transportation mode l at the node j; t represents the maximum service time required by the client;respectively representing the unit cost of the transportation of the mode k selected from the node i to the node j in the multi-mode intermodal transportation process; di,jRepresents the distance from node i to node j;respectively representing the conversion cost of the k-th transportation mode to the l-th transportation mode at the node j; q is the total weight of the goods to be transported;the transportation capacity of selecting a transportation mode k from the node i to the node j for transportation is represented;
the mathematical model for multi-constraint delivery vehicle scheduling is as follows:
the constraints are as follows:
and the distribution route and delivery mode optimization module optimizes the distribution route and the delivery mode based on the adaptive genetic algorithm. The module further comprises: the device comprises a parameter initialization unit, a coding unit, a cross operation unit, a mutation operation unit, a selection operation unit and a termination judgment unit.
The parameter initialization unit initializes the parameters of the adaptive genetic algorithm, including the population size NpAdaptive cross probability Pc,iAdaptive mutation probability PamThe maximum number of iterations G. The encoding unit encodes each dispatch point and dispatch manner in the optimized path. Cross operation unit generation NpSelecting cross position according to self-adaptive cross probability Pc,iA crossover operation is performed. Variant operation unit pair N in populationpThe individual chromosomes are mutated according to the self-adaptive mutation probability PamPerforming mutation operation. The selection operation unit calculates an objective function value based on a mathematical model constructed in the multi-constraint delivery vehicle scheduling model, executes selection operation, and retains an optimal individual. And the termination judging unit judges whether the iteration times G is less than G, if so, the step c is returned, and if not, the algorithm optimization is terminated.
In addition, the invention also discloses a computer readable storage medium, on which a computer program is stored, and the computer program executes the method for optimizing the multimodal transport path as described in the foregoing embodiments. .
The invention also discloses a computer system, which comprises a processor and a storage medium, wherein the storage medium is stored with a computer program, and the processor reads and runs the computer program from the storage medium to execute the multimodal transport path optimization method described in the foregoing embodiment.
Since the multimodal transport path optimization method has been described in detail in the foregoing, it will not be described in detail herein.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A multimodal transport path optimization method is characterized by comprising the following steps:
step 1: constructing a mathematical model of multi-constraint delivery vehicle scheduling;
step 2: and optimizing the distribution route and the dispatching mode based on an adaptive genetic algorithm.
2. The multimodal transport path optimization method of claim 1 wherein the mathematical model for the multi-constraint delivery vehicle scheduling in step 1 is based on: the logistics distribution network of the multi-mode intermodal transportation is fixed, and all nodes and transportation routes which can pass through the whole logistics distribution process are given; the freight rates, the transportation time and the transportation capacity of different transportation modes are different among nodes; transit can occur at each node, but each node can only occur once at most; the two nodes can only select one transportation mode for cargo transportation; the delivered goods cannot be transported simultaneously in multiple modes in the transportation process; the transfer of goods can only occur at nodes, but not in the transportation process, and uncertain factors including weather changes, road conditions and manual operations are ignored in the whole process.
3. The multimodal transport path optimization method of claim 2 wherein the step 1 of constructing a mathematical model further comprises:
setting P as a node set; s is a transportation mode set, which comprises four aspects of road transportation, railway transportation, air transportation and waterway transportation;a decision variable is used for representing that a transportation mode k is selected from a node i to a node j for transportation;is a decision variable, which represents that the transportation mode is converted from k to l at the node j;the method comprises the steps that the k transportation mode transportation time adopted between the ith node and the j node in the multi-mode combined transportation process is shown;represents a unit conversion time from the transportation mode k to the transportation mode l at the node j; t represents the maximum service time required by the client;respectively representing the unit cost of the transportation of the mode k selected from the node i to the node j in the multi-mode intermodal transportation process; di,jRepresents the distance from node i to node j;respectively representing the conversion cost of the k-th transportation mode to the l-th transportation mode at the node j; q is the total weight of the goods to be transported;the transportation capacity of selecting a transportation mode k from the node i to the node j for transportation is represented;
the mathematical model for multi-constraint delivery vehicle scheduling is as follows:
the constraints are as follows:
4. the multimodal transport path optimization method of claim 3 wherein step 2 further comprises:
step a: initializing adaptive genetic algorithm parameters, including population size NpAdaptive cross probability Pc,iIs adaptive toProbability of variation PamMaximum iteration number G;
step b: encoding each dispatching point and dispatching mode in the optimized path;
step c: generating NpSelecting cross position according to self-adaptive cross probability Pc,iPerforming a crossover operation;
step d: for N in the populationpThe individual chromosomes are mutated according to the self-adaptive mutation probability PamPerforming a mutation operation;
step e: calculating an objective function value based on the mathematical model in the step 1, executing selection operation, and reserving an optimal individual;
step f: and (c) judging whether the iteration times G is less than G, if so, returning to the step c, and if not, terminating the algorithm optimization.
5. A computer-readable storage medium, on which a computer program is stored, the computer program being operative to perform the multimodal transport path optimization method as claimed in any one of claims 1 to 4.
6. A computer system comprising a processor, a storage medium having a computer program stored thereon, the processor reading the computer program from the storage medium and executing the computer program to perform the multimodal transport path optimization method as claimed in any one of claims 1 to 4.
7. A multimodal transport path optimization system comprising:
the multi-constraint delivery vehicle scheduling model building module is used for building a mathematical model for scheduling the multi-constraint delivery vehicles;
and the distribution route and delivery mode optimization module is used for optimizing the distribution route and the delivery mode based on the adaptive genetic algorithm.
8. The multimodal transport path optimization system of claim 7 wherein the mathematical model for the construction of the multi-constraint delivery vehicle dispatch in the multi-constraint delivery vehicle dispatch model construction module is based on: the logistics distribution network of the multi-mode intermodal transportation is fixed, and all nodes and transportation routes which can pass through the whole logistics distribution process are given; the freight rates, the transportation time and the transportation capacity of different transportation modes are different among nodes; transit can occur at each node, but each node can only occur once at most; the two nodes can only select one transportation mode for cargo transportation; the delivered goods cannot be transported simultaneously in multiple modes in the transportation process; the transfer of goods can only occur at nodes, but not in the transportation process, and uncertain factors including weather changes, road conditions and manual operations are ignored in the whole process.
9. The multimodal transport path optimization system of claim 8 wherein the mathematical model constructed by the multi-constraint delivery vehicle dispatch model construction module further comprises:
setting P as a node set; s is a transportation mode set, which comprises four aspects of road transportation, railway transportation, air transportation and waterway transportation;a decision variable is used for representing that a transportation mode k is selected from a node i to a node j for transportation;is a decision variable, which represents that the transportation mode is converted from k to l at the node j;the method comprises the steps that the k transportation mode transportation time adopted between the ith node and the j node in the multi-mode combined transportation process is shown;represents a unit conversion time from the transportation mode k to the transportation mode l at the node j; t represents the maximum service time required by the client;respectively representing the unit cost of the transportation of the mode k selected from the node i to the node j in the multi-mode intermodal transportation process; di,jRepresents the distance from node i to node j;respectively representing the conversion cost of the k-th transportation mode to the l-th transportation mode at the node j; q is the total weight of the goods to be transported;the transportation capacity of selecting a transportation mode k from the node i to the node j for transportation is represented;
the mathematical model for multi-constraint delivery vehicle scheduling is as follows:
the constraints are as follows:
10. the multimodal transport path optimization system of claim 9 wherein the delivery route and dispatch means optimization module further comprises:
parameter initialization unit, initialization adaptationShould be inherited with algorithm parameters including population size NpAdaptive cross probability Pc,iAdaptive mutation probability PamMaximum iteration number G;
the encoding unit is used for encoding each delivery point and delivery mode in the optimized path;
a cross operation unit for generating NpSelecting cross position according to self-adaptive cross probability Pc,iPerforming a crossover operation;
a mutation operation unit for N in the populationpThe individual chromosomes are mutated according to the self-adaptive mutation probability PamPerforming a mutation operation;
the selection operation unit is used for calculating an objective function value based on the mathematical model constructed in the multi-constraint delivery vehicle scheduling model construction module, executing selection operation and reserving an optimal individual;
and a termination judgment unit for judging whether the iteration times G is less than G, if so, returning to the step c, and otherwise, terminating the algorithm optimization.
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