Scheduling method for solving multimodal transport routing with time window

Abstract

The invention discloses a scheduling method for solving a multimodal transport route with a time window, and relates to the technical field of transport management. The method comprises the following steps: S1, constructing a multimodal routing scheduling model with a time window; S2, carrying out optimization solution through a genetic algorithm; according to the method, the multimodal transport routing scheduling model with the time window is constructed, the genetic algorithm is used for optimization solution, and the genetic algorithm serves as a meta-heuristic algorithm, has strong global search capability, can find an approximate optimal solution in a short time, is suitable for large-scale and multi-constraint multimodal transport scheduling problems, and has good application prospects. The genetic algorithm is high in adaptability, parameter adjustment and improvement can be carried out according to the characteristics of specific problems, the solving efficiency and the solution quality are improved, the mixed integer programming model is constructed, multiple transportation modes and time window constraints are organically combined, high expandability is achieved, and the method can be applied to different types of multimodal transportation problems.

Description

Technical Field

[0001] The invention relates to the technical field of transportation management, and in particular to a method for solving multimodal transport routing scheduling with a time window. Background Art

[0002] With the acceleration of globalization and the rapid development of international trade, multimodal transport, as an efficient mode of logistics and transportation, plays an increasingly important role in modern supply chain management. Multimodal transport can make full use of the advantages of different modes of transportation (such as road, rail, water, etc.) by organically combining them, improving transportation efficiency and reducing logistics costs. However, with the increasing complexity of the transportation network and the diversification of transportation needs, the scheduling and routing optimization problems of multimodal transport have become more complex, especially when considering time window constraints; The Time Windows Multi-modal Intermodal Routing Problem (TW-MIP) is a typical combinatorial optimization problem. The core of the problem is how to optimize the transportation path and scheduling plan to minimize the total transportation cost or total transportation time while satisfying the time window constraints of each transportation mode. The complexity of this problem is mainly reflected in the following aspects: 1. Coordination of multiple modes of transport: Multimodal transport involves multiple modes of transport, each of which has different cost structures, time requirements and capacity limitations, and needs to be optimized by comprehensively considering various factors; 2. Time window constraints: In the actual transportation process, the loading and unloading and transportation time of goods are often subject to strict time window constraints. How to perform optimal scheduling while meeting these time windows is an important challenge; 3. Large-scale data processing: Multimodal transport networks usually involve a large number of transport nodes and routes. How to efficiently process and calculate these data is the key to solving this problem; However, existing technologies still have some defects: At present, some studies have proposed different solutions to the multimodal transport routing scheduling problem with time windows, such as exact algorithms, heuristic algorithms, and meta-heuristic algorithms. However, these methods often face problems such as high computational complexity, long solution time, and difficulty in ensuring the quality of the solution when dealing with large-scale instances. Specifically: 1. Exact algorithms: such as branch-and-bound method and dynamic programming, although they can find the global optimal solution, they are difficult to handle large-scale instances in practical applications due to their high computational complexity; 2. Heuristic algorithms: such as greedy algorithms and simulated annealing, which have fast calculation speeds but are prone to falling into local optimality and it is difficult to ensure the quality of the solution; 3. Meta-heuristic algorithms: such as genetic algorithms and particle swarm optimization, which have strong global search capabilities, but the parameter settings are complex and rely heavily on the specific characteristics of the problem; In view of the above problems, the inventors propose a method for solving multimodal transport routing scheduling with a time window to solve the above problems. Summary of the invention

[0003] In order to solve the above problems, the present invention aims to provide a method for solving multimodal transport routing scheduling with a time window.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: a method for solving multimodal transport routing scheduling with a time window: S1. Construct a multimodal transport routing scheduling model with time windows; S2. Optimize and solve through genetic algorithm.

[0005] Preferably, the specific steps of constructing the multimodal transport routing scheduling model with time windows are as follows: A1: Constructing a multimodal transport network diagram The network is defined as a directed acyclic multigraph;

[0006] in is a node set, is a set of edges. In this network graph, edges are defined as ,in and is a node (starting point, end point or intermodal transfer station) and , yes and The mode of transport used between The end point, or the goal, is defined as ,and ; gather Indicates the mode of transportation, and ; Description of parameters involved in the network model: Fixed point set of network graph , ; Starting point in network diagram , ; Target point in network diagram , ; Transport mode collection in network diagram , ; Edges in a network graph ; A collection of containers or trucks in a network diagram , ; A2: Decision variables : 0 or 1; if container Selected route ,but , otherwise ; : 0 or 1; if the transport mode ,but ; : At the node Waiting time before starting transportation; : At the target node The number of days of delayed arrival at the location; : Arrival Node Time (hours); : Arrival Node time (converted hours); : integer, Arrival Node Time (days); : 0 or 1, At the node Arrive before opening hours. ,otherwise ; At this time node It is a multi-connection terminal or terminal; : 0 or 1, At the node Arrived after closing, ,otherwise, ; At this time node It is a multi-connection terminal or terminal; : slack variables, At the node Arrival node after closing ,at this time ,otherwise , at this time node It is a multi-connection terminal or terminal; : Edge (route) transportation costs; :node Mode of transport from Convert to Costs; : At the node Transportation Mode Time cost; : The cost of being late every day; : infinite number; : Road section The duration of a transport; :node Slave Mode Convert to service time; :route Traffic volume; : The number of days to expiration at the destination node; : Maximum number of transport mode changes between origin and destination; :node Development time; :node Closing time; A3: Objective Function and Constraints The goal is to minimize the sum of total transportation cost + transshipment cost + multi-terminal delay cost + arrival time delay cost:

[0007] Constraints: Each route has flow conservation and demand definition constraints, which make the transportation flow distributed from the starting point To destination On multiple routes:

[0008] Set the capacity of each mode of transport on each transport route, and set an upper limit on the flow of each transport route: When the transport mode is switched at an intermodal terminal or station, the utilization rate of the station is On the route , and both lines are utilized, and , and when the transport mode is switched, ,Right now, When passing through a multimodal transport station and the mode of transport does not change, all relevant factors of this node are not included in the calculation;

[0009] make sure Passing Node The transport mode can be changed at most once; limit The upper limit of the number of mode changes during the entire transportation process from the origin to the destination. This condition is based on business objectives. Too many mode changes will lead to unnecessary cost increases; Calculate each The arrival time at each node and ensure that the transportation time to the destination is Earliest arrival time;

[0010]

[0011] Among all the routes, only the routes involved in transportation have arrival time at the node, otherwise the arrival time at the node is 0; Define the start time of the start node: Convert arrival time to days and hours: like No node , then use slack variables to relax the constraints:

[0012] Additional delays when arriving outside intermodal terminal opening hours:

[0013] Preferably, the specific steps of the genetic algorithm for optimization and solution are as follows: B1: Initialization; B2: Evaluation; B3: Termination condition; B4: Select; B5: Crossover mutation.

[0014] Preferably, the specific steps of the initialization are as follows: The initial population can be generated randomly, and infeasible paths can be gradually deleted through the influence of the objective function; The specific steps of the evaluation are as follows: The fitness evaluation function uses the MIP objective function set; Fitness evaluation function = transportation cost + transshipment cost + delay cost + overdue cost; In order to solve the problem of infeasible paths in the previous step, during the calculation of this evaluation function, the cost is set to infinity when the target path is not connected; The specific steps of the termination condition are as follows: Set the maximum number of runs or reach the predetermined value of the evaluation function; The specific steps of the selection are as follows: Select 10% chromosomes; The specific steps of the crossover mutation are as follows: 5% of chromosomes undergo crossover and 5% have a chance of mutation.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, a multimodal transport routing scheduling model with a time window is constructed, and a genetic algorithm is used for optimization and solution. As a meta-heuristic algorithm, the genetic algorithm has a strong global search capability and can find an approximate optimal solution in a short time. It is suitable for large-scale, multi-constrained multimodal transport scheduling problems. The genetic algorithm has strong adaptability and can adjust and improve parameters according to the characteristics of specific problems, thereby improving the solution efficiency and the quality of the solution. 2. In the present invention, a mixed integer programming model is constructed to organically combine multiple modes of transportation and time window constraints, which has strong scalability and can be applied to different types of multimodal transport problems; 3. The present invention has important practical application value in improving transportation efficiency, reducing logistics costs, ensuring transportation reliability, etc., and provides effective technical support for the optimal scheduling of multimodal transport systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The present invention is a flow chart of a method for solving multimodal transport routing scheduling with a time window.

[0018] Figure 2 The present invention is a flowchart of the genetic algorithm for optimizing the solution. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example: Figure 1-2 As shown, the present invention provides a method for solving multimodal transport routing scheduling with a time window: S1. Construct a multimodal transport routing scheduling model with time windows; S2. Optimize and solve through genetic algorithm.

[0021] The specific steps of constructing a multimodal transport routing scheduling model with time windows are as follows: A1: Constructing a multimodal transport network diagram The network is defined as a directed acyclic multigraph;

[0022] in is a node set, is a set of edges. In this network graph, edges are defined as ,in and is a node (starting point, end point or intermodal transfer station) and , yes and The mode of transport used between The end point, or the goal, is defined as ,and ; gather Indicates the mode of transportation, and ; Description of parameters involved in the network model: Fixed point set of network graph , ; Starting point in network diagram , ; Target point in network diagram , ; Transport mode collection in network diagram , ; Edges in a network graph ; A collection of containers or trucks in a network diagram , ; A2: Decision variables : 0 or 1; if container Selected route ,but , otherwise ; : 0 or 1; if the transport mode ,but ; : At the node Waiting time before starting transportation; : At the target node The number of days of delayed arrival at the location; : Arrival Node Time (hours); : Arrival Node time (converted hours); : integer, Arrival Node Time (days); : 0 or 1, At the node Arrive before opening hours. ,otherwise ; At this time node It is a multi-connection terminal or terminal; : 0 or 1, At the node Arrived after closing, ,otherwise, ; At this time node It is a multi-connection terminal or terminal; : slack variables, At the node Arrival node after closing ,at this time ,otherwise , at this time node It is a multi-connection terminal or terminal; : Edge (route) transportation costs; :node Mode of transport from Convert to Costs; : At the node Transportation Mode Time cost; : The cost of being late every day; : infinite number; : Road section The duration of a transport; :node Slave Mode Convert to service time; :route Traffic volume; : The number of days to expiration at the destination node; : Maximum number of transport mode changes between origin and destination; :node Development time; :node Closing time; A3: Objective Function and Constraints The goal is to minimize the sum of total transportation cost + transshipment cost + multi-terminal delay cost + arrival time delay cost:

[0023] Constraints: Each route has flow conservation and demand definition constraints, which make the transportation flow distributed from the starting point To destination On multiple routes:

[0024] Set the capacity of each mode of transport on each transport route, and set an upper limit on the flow of each transport route: When the transport mode is switched at an intermodal terminal or station, the utilization rate of the station is On the route , and both lines are utilized, and , and when the transport mode is switched, ,Right now, When passing through a multimodal transport station and the mode of transport does not change, all relevant factors of this node are not included in the calculation;

[0025] make sure Passing Node The transport mode can be changed at most once; limit The upper limit of the number of mode changes during the entire transportation process from the origin to the destination. This condition is based on business objectives. Too many mode changes will lead to unnecessary cost increases; Calculate each The arrival time at each node and ensure that the transportation time to the destination is Earliest arrival time;

[0026]

[0027] Among all the routes, only the routes involved in transportation have arrival time at the node, otherwise the arrival time at the node is 0; Define the start time of the start node: Convert arrival time to days and hours: like No node , then use slack variables to relax the constraints:

[0028] Additional delays when arriving outside intermodal terminal opening hours:

[0029] The specific steps of genetic algorithm optimization solution are as follows: B1: Initialization, the initial population can be generated randomly, and infeasible paths can be gradually deleted through the influence of the objective function; B2: Evaluation, the fitness evaluation function uses the MIP objective function set; Fitness evaluation function = transportation cost + transshipment cost + delay cost + overdue cost; In order to solve the problem of infeasible paths in the previous step, during the calculation of this evaluation function, the cost is set to infinity when the target path is not connected; B3: Termination condition, setting the maximum number of runs or reaching the predetermined value of the evaluation function; B4: selection, select 10% chromosomes; B5: Crossover mutation, 5% of chromosomes undergo crossover and 5% probability of mutation.

[0030] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for solving multimodal transport routing scheduling with a time window, characterized by: S1. Construct a multimodal transport routing scheduling model with time windows; S2. Optimize and solve through genetic algorithm.

2. A method for solving multimodal transport routing scheduling with a time window as claimed in claim 1, characterized in that: The specific steps of constructing the multimodal transport routing scheduling model with time windows are as follows: A1: Constructing a multimodal transport network diagram The network is defined as a directed acyclic multigraph; in is a node set, is a set of edges. In this network graph, edges are defined as ,in and is a node (starting point, end point or intermodal transfer station) and , yes and The mode of transport used between The end point, or the goal, is defined as ,and ; gather Indicates the mode of transportation, and ; Description of parameters involved in the network model: Fixed point set of network graph , ; Starting point in network diagram , ; Target point in network diagram , ; Transport mode collection in network diagram , ; Edges in a network graph ; A collection of containers or trucks in a network diagram , ; A2: Decision variables : 0 or 1; if container Selected route ,but , otherwise ; : 0 or 1; if the transport mode ,but ; : At the node Waiting time before starting transportation; : At the target node The number of days of delayed arrival at the location; : Arrival Node Time (hours); : Arrival Node time (converted hours); : integer, Arrival Node Time (days); : 0 or 1, At the node Arrive before opening hours. ,otherwise ; At this time node It is a multi-connection terminal or terminal; : 0 or 1, At the node Arrived after closing, ,otherwise, ; At this time node It is a multi-connection terminal or terminal; : slack variables, At the node Arrival node after closing ,at this time ,otherwise , at this time node It is a multi-connection terminal or terminal; : Edge (route) transportation costs; :node Mode of transport from Convert to Costs; : At the node Transportation Mode Time cost; : The cost of being late every day; : infinite number; : Road section The duration of a transport; :node Slave Mode Convert to service time; :route Traffic volume; : The number of days to expiration at the destination node; : Maximum number of transport mode changes between origin and destination; :node Development time; :node Closing time; A3: Objective Function and Constraints The goal is to minimize the sum of total transportation cost + transshipment cost + multi-terminal delay cost + arrival time delay cost: Constraints: Each route has flow conservation and demand definition constraints, which make the transportation flow distributed from the starting point To destination On multiple routes: Set the capacity of each mode of transport on each transport route, and set an upper limit on the flow of each transport route: When the transport mode is switched at an intermodal terminal or station, the utilization rate of the station is On the route , and both lines are utilized, and , and when the transport mode is switched, ,Right now, When passing through a multimodal transport station and the mode of transport does not change, all relevant factors of this node are not included in the calculation; make sure Passing Node The transport mode can be changed at most once; limit The upper limit of the number of mode changes during the entire transportation process from the origin to the destination. This condition is based on business objectives. Too many mode changes will lead to unnecessary cost increases; Calculate each The arrival time at each node and ensure that the transportation time to the destination is Earliest arrival time; Among all the routes, only the routes involved in transportation have arrival time at the node, otherwise the arrival time at the node is 0; Define the start time of the start node: Convert arrival time to days and hours: like No node , then use slack variables to relax the constraints: Additional delays when arriving outside intermodal terminal opening hours:

3. The method for solving multimodal transport routing scheduling with a time window as claimed in claim 1, characterized in that: The specific steps of the genetic algorithm for optimization and solution are as follows: B1: Initialization; B2: Evaluation; B3: Termination condition; B4: Select; B5: Crossover mutation.

4. A method for solving multimodal transport routing scheduling with a time window as claimed in claim 3, characterized in that: The specific steps of the initialization are as follows: The initial population can be generated randomly, and infeasible paths can be gradually deleted through the influence of the objective function; The specific steps of the evaluation are as follows: The fitness evaluation function uses the MIP objective function set; Fitness evaluation function = transportation cost + transshipment cost + delay cost + overdue cost; In order to solve the problem of infeasible paths in the previous step, during the calculation of this evaluation function, the cost is set to infinity when the target path is not connected; The specific steps of the termination condition are as follows: Set the maximum number of runs or reach the predetermined value of the evaluation function; The specific steps of the selection are as follows: Select 10% chromosomes; The specific steps of the crossover mutation are as follows: 5% of chromosomes undergo crossover and 5% have a chance of mutation.

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