A method for allocating material flow in railway construction based on dynamic balance of supply and demand

Abstract

The present invention provides a method for allocating material flow for railway construction projects based on a dynamic balance of supply and demand. The method comprises: constructing a material transportation network along the railway project that takes into account the roads and railways within and outside the railway project construction area, and constructing a multimodal transport flow allocation model for multiple categories of materials and multiple transport tasks; constructing a multimodal transport flow allocation model for multiple categories of materials that takes into account reserve bases; solving the multimodal transport flow allocation model for multiple categories of materials and multiple transport tasks using a traffic allocation method based on A* and impedance adjustment to obtain a traffic path configuration scheme; and solving the multimodal transport flow allocation model for multiple categories of materials that takes into account reserve bases using a combined heuristic traffic allocation method based on Dijkstra and genetic algorithms to obtain the specific transportation path and mode for each transport task. The present invention distributes the flow of construction material transportation routes, providing a basis for determining daily construction material transportation routes, and ensuring the timely and stable supply of materials for railway construction projects.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering material transportation, and in particular to a method for allocating railway engineering construction material flow based on a dynamic balance of supply and demand. Background Art

[0002] Railway construction typically aims to improve local transportation accessibility and promote economic activity and industrial development. Consequently, during railway construction, key construction materials often require long-distance transportation to reach the construction site. Furthermore, the areas along the railway line are often characterized by high mountains, dangerous roads, frequent geological disasters, harsh climates, and weak transportation infrastructure, making logistics management extremely challenging. Against this backdrop, ensuring the timely delivery of construction materials is crucial during railway construction. This requires considering factors such as diverse material categories, multimodal transport, and the flexible use of material storage bases. Optimizing transportation routes for all construction materials is crucial. Through macro-level traffic allocation and coordinated control, we can better understand material transportation, ensure safe transportation, and guarantee a high rate of timely delivery, thereby improving the management efficiency and economic benefits of railway construction material transportation.

[0003] In existing research literature, the problem of railway network traffic allocation is primarily addressed through the study of railway network traffic routes. Due to the indivisibility of railway traffic transport routes, it can be considered a multi-commodity flow problem, with arc-route and point-arc models being common models. Studies of railway network traffic allocation often focus on single-objective optimization, often minimizing one or a combination of factors such as travel costs, travel time, and transport distance. Multi-objective optimization, on the other hand, integrates the various factors involved in a single objective to establish an objective function, often focusing on the comprehensive optimization of empty and loaded trains. Existing studies often combine train routing with marshaling or operation plan problems. Common constraints include section capacity, maximum traffic detours, indivisibility of traffic, and the principle of tree-shaped routes.

[0004] Since the Wardrop principle was proposed, modeling and solving methods for traffic flow distribution in highway networks have been extensively studied. Existing research on traffic flow distribution problems can be divided into two categories: static flow distribution and dynamic flow distribution. Static flow distribution problems primarily focus on the capacity constraints of road networks. Early research on dynamic flow distribution developed on the foundational technologies of static flow distribution and can be categorized into analytically based dynamic flow distribution and simulation-based dynamic traffic distribution methods. Furthermore, some studies have also examined flow distribution problems in multimodal transportation networks and the shortest path problem in multimodal transportation networks. One proposal proposes a stochastic equilibrium model for multimodal networks that takes into account congestion pricing and the deployment of a bus-based P&R system. This study considers three modes of transportation: cars, buses, and P&R. A Probit-type SUE model was used, along with elastic demand considerations. The model was solved using the fixed-point method. For example, a proposal has been made to use an A*label setting algorithm to solve the restrictive shortest path search problem in a multi-modal transportation network. This study takes into account the time-varying travel time function and schedule information, and uses the A* algorithm and Access-Node method to accelerate the shortest path search. The numerical calculation results show that the algorithm can be effectively applied to the restrictive shortest path search problem in a real multi-modal network.

[0005] Research on intermodal transport route optimization, both domestically and internationally, is primarily categorized into route optimization under deterministic conditions and route optimization under uncertain conditions. Currently, numerous studies address intermodal transport route optimization under deterministic conditions. Most of these studies employ operations research theory to construct mathematical optimization models for relevant transport scenarios and investigate the route optimization problem through solution and parameter analysis. Based on the different objectives for intermodal transport route optimization, these studies can be categorized into single-objective route optimization and multi-objective route optimization. Single-objective optimization approaches the operator's perspective, minimizing cost as the objective function. Some approaches consider the transit time associated with transshipment between different transport modes at transfer stations. By decomposing transfer nodes, the transit time is converted into segment weights, establishing a multimodal transport route optimization model with minimizing both transit and transit time as the single objective. Multi-objective optimization, on the other hand, addresses the needs of both the operator and the shipper, focusing on both cost and time. Other approaches simultaneously minimize the cost of both segment transport and node transshipment, minimizing both transport and transit time, and employing weighted coefficient methods to transform the multi-objective route optimization problem into a single-objective assignment problem. Considering that the entire multimodal transport process is complex and is affected by internal and external environments and prone to uncertainty, it can be divided into three categories: multimodal transport path optimization under uncertain transportation time, multimodal transport path optimization under uncertain transportation demand, and multimodal transport path optimization under multiple uncertain scenarios.

[0006] (1) Exact solution algorithm

[0007] For traffic distribution problems in small road networks and data scales, commercial solvers such as LINGO, GAMS, CPLEX, and Gurobi are generally used. Their built-in column generation, Benders decomposition, and Lagrangian relaxation algorithms can well meet the solution speed and accuracy requirements for small-scale road networks. One proposal studied the multi-commodity network flow problem with capacity constraints and fixed path costs in the context of empty car allocation by railway operators, and designed a Lagrangian-based heuristic algorithm based on dual subgradient search and an original heuristic algorithm based on the path information of the Lagrangian subproblem solution to solve the problem. Another proposal aims to provide decision support for intermodal transport planning, including route and carrier selection in transport service design and emission reduction potential assessment. With greenhouse gas emissions and transportation costs as targets, a capacity multi-commodity network flow model considering multiple objective functions and in-transit inventory is constructed and solved using GAMS; another proposal improves the point-arc model by introducing 0-1 decision variables, and distinguishes between large and small traffic flows. Based on the multi-commodity flow model and the principle of non-dispersion of traffic flows in railway transport organization, constraints are imposed on large traffic flows, line capacity and station capacity, and an improved model for railway network traffic allocation and path optimization is constructed. The example is solved using LINGO.

[0008] Heuristic solution algorithm: For traffic allocation problems under large road networks and data scales, heuristic algorithms are mainly used to solve them. Common algorithms include particle swarm optimization algorithm, ant colony optimization algorithm, neighborhood search algorithm, genetic algorithm and taboo search algorithm.

[0009] To solve the problem of designing multi-commodity networks with fixed costs and capacity constraints, one proposal proposes a path reconnection procedure. This improves the tabu search algorithm by adding a cycle-based neighborhood structure to construct an elite candidate set, and uses the cycle-based neighborhood to construct solution paths based on the elite solution. Another proposal studies transportation planning problems involving multiple modes of transportation, perishable products, and reusable transport items. This approach is extended to the problem of multi-commodity network flow with capacity constraints, constructing a mixed integer programming optimization model and designing an adaptive large neighborhood search for its solution.

[0010] The shortcomings of the aforementioned existing dynamic network flow allocation methods include: They primarily focus on a single network, while some also consider combined transport for flow allocation. However, they fail to systematically consider the impact of factors such as the characteristics of railway construction material transportation, the dynamic balance of supply and demand for ongoing construction material, the layout of facilities along the route, and actual needs on transportation network flow allocation. Consequently, their flow allocation results deviate significantly from actual conditions and are unable to precisely control the routing of railway construction material transportation tasks under specific network conditions. Furthermore, existing algorithm research cannot meet the computational requirements of continuous dynamic flow allocation for large-scale networks, necessitating the design of targeted and efficient solutions.

[0011] Existing dynamic traffic allocation methods for road networks usually carry out traffic allocation work on road networks with a single transportation mode. Most of them use a single road transportation or railway transportation to transport construction materials, which is difficult to meet the long-distance transportation of construction materials in actual situations and lacks practical application effects; some choose multimodal transport to carry out research on the transportation of construction materials, mostly involving ocean transportation, focusing on the conversion of transportation modes between countries, and lack of precise control of the transportation task path of railway construction materials under special road network conditions. Summary of the Invention

[0012] The embodiments of the present invention provide a method for allocating flow of railway engineering construction materials based on a dynamic balance between supply and demand, so as to achieve timely supply and stable guarantee of railway engineering construction materials.

[0013] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions.

[0014] A method for allocating material flow for railway engineering construction based on dynamic balance of supply and demand, comprising:

[0015] Construct a material transportation network along the railway project that takes into account the roads and railways within and outside the railway project construction area;

[0016] Based on the material transportation network along the railway project, a multi-category material and multi-transportation task multi-modal transport flow distribution model is constructed;

[0017] Construct a multi-category multimodal transport flow distribution model considering the reserve base;

[0018] The multimodal transport flow distribution model for multiple categories of materials and multiple transportation tasks is solved by a traffic flow distribution method based on A* and impedance adjustment to obtain the path configuration plan for all traffic flows;

[0019] By using a combined heuristic traffic flow allocation method based on Dijkstra and genetic algorithms, the multi-category material multimodal transport flow allocation model considering the reserve base is solved, and the specific transportation route and transportation mode of each transportation task are obtained.

[0020] Preferably, the construction of a material transportation network along the railway project taking into account the highways and railways within and outside the railway project construction area includes:

[0021] In terms of highway networks, we analyze and screen the road networks along and around railway construction projects to obtain the required basic data for the highway network. We then conduct connectivity and topology checks on the basic data, trim and merge related errors, and construct a highway network topology structure.

[0022] In terms of the railway network, based on the national conventional railway network data, relevant railway stations and railway lines are selected as the basic data for the construction of the railway transportation network. The railway network is then checked for connectivity and topology, and the railway network information is converted into a railway topology structure.

[0023] Based on the conversion tools in the commercial geographic information system software ArcGIS, the road network topology structure and railway topology structure are converted into Coverage structure to complete the construction of the material transportation network along the railway project.

[0024] Preferably, the multimodal transport flow distribution model for multiple categories of materials, multiple transport tasks, and multimodal transport based on the material transport network along the railway project comprises:

[0025] Considering the characteristics of transport capacity, cost, transport time, and material types of different transport modes, and based on the constraints of the number of intermodal transits, the indivisibility of transport task routes, the point-arc connection of transport routes, delivery time, supply point capacity, and point and arc capacity, a multimodal transport flow allocation model for multiple categories of materials and multiple transport tasks is constructed based on the material transport network along the railway project.

[0026] In the multi-category material, multi-transportation task and multimodal transport flow allocation model, based on the demand for different construction materials at various railway construction sites, by considering the supply and demand characteristics, transportation characteristics and storage characteristics of different materials, with the goal of minimizing transportation costs and freight transfer operation costs, different materials are given corresponding weights and constraints are established for flow allocation.

[0027] Preferably, the multimodal transport flow distribution model for multiple categories of materials taking into account the reserve bases includes:

[0028] In order to solve the problem of flow distribution between suppliers and reserve bases and between reserve bases and work sites, with the goal of minimizing total cost, considering the storage capacity limit of reserve bases, the demand of work sites and the transportation time constraint, a multi-category material multimodal transport flow distribution model with reserve bases is constructed, which includes a three-level network and two stages. The three-level network of the multi-category material multimodal transport flow distribution model is suppliers, reserve bases and work sites. The two stages refer to the first stage that the flow distribution plan between suppliers and reserve bases needs to be determined; the second stage needs to determine the flow distribution plan between reserve bases and work sites.

[0029] Preferably, the vehicle flow allocation method based on A* and impedance adjustment is used to solve the multi-category material multi-transportation task multimodal transport flow allocation model to obtain the path configuration scheme for all vehicle flows, including:

[0030] Improve the A* algorithm and use it to find the shortest path between the corresponding ODs for each traffic flow in turn, and adjust the traffic flow allocation plan according to the impedance of the railway network section;

[0031] The specific steps of the shortest path search algorithm based on the A* algorithm include:

[0032] Step 1: Set up two tables: the Open table that stores accessible nodes and the Close table that stores nodes on the optimal path that have been visited. Put the starting point s into the Open table;

[0033] Step 2: Check if the Open table is an empty set. If it is not an empty set, proceed to Step 3. If it is an empty set, stop the algorithm.

[0034] Step 3: Sort the nodes in the Open table according to the evaluation function and obtain the node U with the smallest evaluation function value. If a reset instruction appears, the currently recorded global information must be reset according to U, such as the minimum cost to reach each node from s;

[0035] Step 4: Move node U from the Open table to the Close table;

[0036] Step 5: Determine whether node U is the target node. If yes, stop the algorithm; otherwise, proceed to Step 6.

[0037] Step 6: Each vehicle flow has a delivery time limit. Add the adjacent nodes of U that have not exceeded the delivery time limit to the Open table and go to Step 2. If all adjacent nodes of U have exceeded the delivery time limit, go to Step 2 and return the reset command.

[0038] Step 7: Backtrack the Close table to obtain the shortest path for this branch of traffic.

[0039] The specific steps of the traffic distribution algorithm based on section impedance adjustment include:

[0040] Step 1: Obtain the shortest path for all traffic flows using the A* algorithm;

[0041] Step 2: All traffic flows are distributed to the road network according to the shortest path;

[0042] Step 3: Count the traffic volume on all road sections; determine whether the capacity-limited section exceeds the limit. If not, go to Step 5; if any road section exceeds the capacity limit, go to Step 4;

[0043] Step 4: Block the saturated section or increase the impedance of the saturated section by certain methods, and go to Step 1.

[0044] Step 5: Output the path configuration plan for all traffic flows;

[0045] Step 6: The algorithm ends.

[0046] Preferably, the multimodal transport flow distribution model for multi-category materials considering the reserve base is solved by using a combined heuristic traffic flow distribution method based on Dijkstra and genetic algorithms to obtain the specific transport route and mode for each transport task, including:

[0047] Based on the multi-category material multimodal transport flow distribution model, all construction materials first pass through the transit warehouse before going to the final destination. The Dijkstra algorithm taking into account the delivery time limit is used to generate all feasible paths for each vehicle flow to construct a set of alternative paths. For each vehicle flow, all feasible paths from the supply point to the warehouse are first generated, and then all feasible paths from the warehouse to the construction site are generated. They are combined into complete paths in pairs. Then, by designing crossover operators and mutation operators, the genetic algorithm is used to gradually optimize the path selected by each vehicle flow. Through continuous iteration, the global optimal solution is obtained. The global optimal solution includes the specific transportation path and transportation method for each transportation task. Based on the global optimal solution, all materials are ensured to arrive at the construction site within the specified time limit while meeting the capacity constraints of road sections, nodes and storage bases.

[0048] It can be seen from the technical solution provided by the above-mentioned embodiments of the present invention that the embodiments of the present invention are based on the road network surrounding the railway construction project, taking into account technical factors such as the long railway construction period and the wide variety of engineering materials, and distribute the flow of transportation routes for engineering materials. It can realize the distribution of transportation routes and flows between any two nodes under known transportation conditions (including traffic conditions, weather conditions, and vehicle conditions), provide a basis for determining the daily transportation routes of engineering materials, and realize the timely supply and stable guarantee of railway engineering construction materials.

[0049] Additional aspects and advantages of the present invention will be set forth in part in the following description, will become apparent from the following description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 any creative work.

[0051] Figure 1 A schematic diagram illustrating a method for allocating material flow for railway construction based on a dynamic balance of supply and demand, provided by an embodiment of the present invention;

[0052] Figure 2 A processing flow chart of a method for allocating material flow for railway engineering construction based on dynamic balance of supply and demand provided by an embodiment of the present invention;

[0053] Figure 3 An algorithm flow chart of a traffic flow distribution method based on A* and impedance adjustment provided by an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the process of constructing a material transportation network along a railway project provided by an embodiment of the present invention;

[0055] Figure 5 A schematic diagram of the construction process of a multimodal transport flow distribution model for multiple categories of materials and multiple transport tasks provided by an embodiment of the present invention;

[0056] Figure 6 A schematic diagram of the construction process of a multi-category multimodal transport flow distribution model considering a storage base provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0058] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or couplings. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0059] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such herein, will not be interpreted in an idealized or overly formal sense.

[0060] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0061] The implementation principle of the railway engineering construction material flow distribution method based on the dynamic balance of supply and demand provided by the embodiment of the present invention is as follows: Figure 1 As shown in the figure, this method calculates a transportation plan that minimizes the total cost, including transportation costs, based on the demand for different construction materials at each construction site during railway construction, while meeting project requirements and time windows, taking into account other factors such as the network's capacity and the characteristics of different transportation modes. This achieves the goal of reducing transportation costs, improving the efficiency of construction material transportation, and ensuring the efficiency of material supply. Based on the actual situation of typical railway construction projects, this method constructs a multi-category material multimodal transport flow distribution model and a multi-category material multimodal transport flow distribution model considering reserve bases. Based on the characteristics of the model, a traffic flow distribution method based on A* and impedance adjustment and a heuristic traffic flow distribution method based on Dijkstra and genetic algorithm are designed to solve the model.

[0062] The processing flow chart of a railway engineering construction material flow distribution method based on dynamic balance of supply and demand provided by an embodiment of the present invention is as follows: Figure 2 As shown, the processing steps include the following:

[0063] Step S10: Constructing a material transportation network along the railway project taking into account the roads and railways within and outside the railway project construction area.

[0064] For the highway network, basic data for the required highway network was obtained by analyzing and screening the road networks along and around the railway construction project. Connectivity and topology checks were performed on this basic data, and errors were trimmed and merged to construct the highway network topology. The supply points and construction sites for construction materials were located adjacent to the highways, which were then used in the subsequent path calculation process.

[0065] For the railway network, based on the national conventional railway network data, relevant railway stations and railway lines were selected as the basic data for railway transportation network construction. Connectivity and topology checks were performed on the railway network. The railway network information was converted into a railway topology structure, and the proximity of railway stations to road and rail transportation networks was processed to participate in the subsequent path calculation process.

[0066] Based on the conversion tools in the commercial geographic information system software ArcGIS, the road network topology structure and railway topology structure are converted into a Coverage structure (a feature structure composed of points and arcs), thereby completing the construction of the material transportation network along the railway project.

[0067] Step S20: construct a multimodal transport flow distribution model for multiple categories of materials, multiple transport tasks, and multimodal transport based on the material transport network along the railway project.

[0068] In order to solve the problem of flow distribution of the demand for different material categories at each construction site in the railway construction network, a multi-category, multi-transportation task and multimodal transport flow distribution model was constructed based on the material transportation network along the railway project, with the goal of minimizing the transportation cost and freight transfer operation cost.

[0069] In the multimodal transport flow allocation model for multiple categories of materials and multiple transportation tasks, taking into account the long railway construction period and the wide variety of engineering materials, under special road network conditions, by considering the supply and demand characteristics, transportation characteristics and storage characteristics of different materials, different materials are given corresponding weights and constraints are established for flow allocation, and a multimodal transport flow allocation model for multiple categories of materials is constructed to achieve optimal resource utilization.

[0070] Step S30: Construct a multi-category material multimodal transport flow distribution model that takes into account the reserve base.

[0071] Given that road disruptions along railway construction routes can hinder the timely delivery of goods, consideration is being given to selecting reserve bases along the railway construction routes to ensure smooth material transportation in the event of emergencies. Aiming to address the flow distribution problem between suppliers and reserve bases, and between reserve bases and work sites, and with the goal of minimizing total cost, a multimodal transport flow distribution model for multi-category materials, encompassing three-level networks and two phases, taking into account constraints such as reserve base storage capacity limitations, work site demand, and delivery time, is constructed. This model optimizes material flow distribution, fully utilizes reserve bases, and improves the efficiency of emergency response and material supply.

[0072] The three-level networks of the multimodal transport flow distribution model for multi-category materials are suppliers, reserve bases, and work sites. The two stages refer to the following: in the first stage, the flow distribution plan between suppliers and reserve bases needs to be determined; in the second stage, the flow distribution plan between reserve bases and work sites needs to be determined.

[0073] Step S40: Solve the multimodal transport flow distribution model for multiple categories of materials and multiple transportation tasks by using the traffic distribution method based on A* and impedance adjustment to obtain the path configuration plan for all traffic flows.

[0074] Taking into account that the A* algorithm can use the node valuation function to control the search tendency, the algorithm efficiency is optimized by introducing pruning, using priority queues and other technologies, and the constraints of basic factors such as point and line capacity can be comprehensively considered by introducing the link impedance function. A traffic distribution algorithm based on A* and link impedance adjustment is designed. The shortest path search is performed through A*, and the distribution adjustment algorithm is performed based on the link impedance function to obtain a higher quality solution within a reasonable time.

[0075] The flow allocation algorithm based on A* and link impedance adjustment is based on the shortest path algorithm. It considers the capacity constraints of network points and lines and establishes a flow allocation heuristic method based on link impedance adjustment. Specifically, it can be divided into an A*-based shortest path search algorithm and a flow allocation algorithm based on link impedance adjustment. In this algorithm, each vehicle flow is subject to a time constraint. To ensure that each shortest path found meets this time constraint, the A* algorithm is modified. The modified A* algorithm is applied to each vehicle flow in turn to find the shortest path between the corresponding ODs. Flow allocation heuristic methods typically use the shortest path in the road network as a basic approach, allocating traffic to the network according to a specific allocation principle. Furthermore, they continuously adjust traffic according to specific strategies to meet the capacity constraints of the network points and lines. The flow allocation heuristic solution adjusts the flow allocation scheme based on the impedance of railway network links under a specific vehicle flow order.

[0076] The algorithm flow chart of a vehicle flow distribution method based on A* and impedance adjustment provided by an embodiment of the present invention is as follows: Figure 3 As shown, the processing steps include the following:

[0077] 1. Shortest path search algorithm based on A* algorithm

[0078] Since each traffic flow in this algorithm has a delivery time constraint, to ensure that each shortest path found meets the delivery time constraint, the A* algorithm is improved. The improved A* algorithm is used to find the shortest path between the corresponding ODs for each traffic flow in turn. The specific steps are as follows:

[0079] Step 1: Set up two tables: the Open table, which stores accessible nodes, and the Close table, which stores visited nodes (nodes on the optimal path). Put the starting point s into the Open table.

[0080] Step 2: Check if the Open table is an empty set. If it is not an empty set, proceed to Step 3. If it is an empty set, stop the algorithm.

[0081] Step 3: Sort the nodes in the Open table by the evaluation function, and find the node U with the smallest evaluation function value. If a reset instruction appears, the currently recorded global information must be reset based on U, such as the minimum cost to reach each node from s.

[0082] Step 4: Move U from the Open table to the Close table.

[0083] Step 5: Determine whether U is the target node. If yes, stop the algorithm, otherwise proceed to Step 6.

[0084] Step 6: Add the adjacent nodes of U that have not exceeded the delivery time limit to the Open table and go to Step 2. If all the adjacent nodes of U have exceeded the delivery time limit, go to Step 2 and return the reset instruction.

[0085] Step 7: Backtrack the Close table to obtain the shortest path for this branch of traffic.

[0086] 2. Traffic distribution algorithm based on road section impedance adjustment

[0087] Step 1: Obtain the shortest path for all traffic flows using the A* algorithm.

[0088] Step 2: All traffic flows are assigned to the road network according to the shortest path.

[0089] Step 3: Count the traffic volume on all road sections; determine whether the capacity-limited section exceeds the limit. If not, go to Step 5; if any road section exceeds the capacity limit, go to Step 4.

[0090] Step 4: Block the saturated section or increase the impedance (or pseudo-length) of the saturated section by certain methods, and go to Step 1.

[0091] Step 5: Output flow distribution and routing plan.

[0092] Step 6: The algorithm ends.

[0093] Step S50: Solve the multi-category material multimodal transport flow distribution model considering the reserve base by using a combined heuristic traffic distribution method based on Dijkstra and genetic algorithm to obtain the specific transportation route and transportation mode for each transportation task.

[0094] To efficiently solve a multi-category intermodal transport flow allocation model that considers storage bases, a combined heuristic vehicle flow allocation method based on Dijkstra and genetic algorithms was designed. Considering that all construction materials in this model must first transit through a transit warehouse before reaching their final destination, the algorithm was designed based on the principle of "first constructing a set of alternative routes, then determining the optimal one." The Dijkstra algorithm, which takes into account delivery deadlines, was first used to generate all feasible routes for each vehicle flow to construct a set of alternative routes. Then, by designing crossover and mutation operators, a genetic algorithm was used to gradually optimize the solutions in the solution space. Through continuous iteration, a globally optimal or near-optimal solution was obtained.

[0095] The traffic allocation model considering reserve bases increases its complexity by incorporating reserve bases into the road network to ensure the supply of emergency supplies. Therefore, a combinatorial heuristic algorithm is needed to meet the efficiency requirements of the traffic allocation model considering reserve bases. Given the model's special requirements for material transportation routes, namely that all transported materials must first pass through a transit warehouse before reaching their final destination, an optimization strategy is adopted that first constructs a set of alternative routes and then selects the appropriate one.

[0096] The global optimal solution includes the specific transportation route and transportation method for each transportation task.

[0097] Specifically, the above step S10 includes: a schematic diagram of the construction process of a material transportation network along a railway project provided by an embodiment of the present invention is as follows: Figure 4 As shown in the figure, the specific processing process includes:

[0098] (1) Data extraction of highway transport network for railway construction: For the highway network data in the area along the railway construction line, it is necessary to distinguish between existing roads and planned (construction) roads. Based on the existing roads along the line, combined with some planned (construction) roads that are about to be opened, and excluding roads that are impossible for construction vehicles to pass, the basic data of the highway network along the construction area are obtained. For the highway network outside the construction area, considering its relatively high complexity, it is necessary to screen the road sections and retain the main highways, national and provincial trunk roads, and low-level roads around the source points of engineering materials as the basic data of the peripheral highway network. The data of the highway network along the railway construction area and the peripheral highway network are merged to obtain the basic data of the complete highway transport network.

[0099] (2) Extraction of railway transport network data in railway engineering construction areas: Based on the national conventional railway network data, after excluding railway stations that only handle passenger services, it is used as the basic data for the construction of the railway transport network, including point elements such as railway station name, province, city, longitude and latitude, and line elements such as railway line name.

[0100] (3) Road network connectivity check and topology check: Use commercial geographic information system software ArcGIS software to conduct topology check on the basic data of highway and railway network for railway engineering construction, and eliminate hanging points and pseudo nodes based on the actual road network conditions.

[0101] (4) Construction of the topological structure of the transportation network: The actual road network information is converted into a topological structure. Considering that the source points of engineering materials, construction sites, and railway stations are near the road and railway networks, the relevant point element information needs to be processed in a proximity manner to participate in the subsequent path calculation process. The basic data after inspection and processing and the adjacent points are input as a data set. Considering that the road impedance of different sections of the railway construction highway is different, the basic attributes of the roads in the transportation network are searched and input, including the length of the section and historical traffic information, to set the comprehensive impedance of the section. The network data set is constructed to complete the construction of the topological structure of the transportation network. The specific data includes the section number, starting and ending points, length, impedance, capacity, and the time consumed to pass through the section.

[0102] Specifically, the above step S20 includes: a schematic diagram of the construction process of a multi-category material multi-transportation task multimodal transport flow distribution model provided by an embodiment of the present invention is as follows: Figure 5 As shown in the figure, the specific processing process includes:

[0103] (1) Based on the reference of existing research and taking into account technical factors such as the long railway construction period and the wide variety of engineering materials, the model type is determined to be a multi-category material multi-transportation task multimodal transport flow distribution model. The research objective of the model is to calculate the transportation plan that minimizes the total cost, including transportation costs, based on the demand for different engineering construction materials at each construction site of the railway construction, while meeting the engineering needs and time window.

[0104] (2) Considering the characteristics of transport capacity, cost, transport time, and material types of different transport modes, a model is constructed based on the constraints of factors such as the number of multimodal transport transfers, the indivisibility of transport task routes, the point-arc connection of transport routes, the arrival time, the supply capacity of supply points, and the point and arc capacity.

[0105] (3) By comparing with common shortest path algorithms, the A* algorithm is selected as the shortest path solution algorithm after comprehensively considering factors such as algorithm complexity, applicability, and solution efficiency. Since the change of section impedance can effectively affect the choice of path, the section impedance function is added to consider the constraints of basic factors such as point and line capacity. That is, based on the shortest path algorithm, a flow distribution heuristic method based on section impedance adjustment is established by considering the constraints of road network point and line capacity - a flow distribution algorithm based on A* and section impedance adjustment.

[0106] (4) The Python programming language was used to write the flow distribution algorithm code based on A* and road section impedance adjustment, and the simulation road network examples and actual road network cases were solved. The solution results showed that all transported materials can arrive at the work site within the specified time limit while meeting the road section and node constraints.

[0107] Specifically, the above step S30 includes: a schematic diagram of the construction process of a multi-category material multimodal transport flow distribution model considering a reserve base provided by an embodiment of the present invention is as follows: Figure 6 As shown in the figure, the specific processing process includes:

[0108] (1) Based on previous research, considering the possibility of road network disruptions along the railway construction line, which may result in the inability to deliver goods in a timely manner, in order to ensure the timely arrival of construction materials in emergencies and to arrange the construction materials, the model type is set as a multi-category material multimodal transport flow distribution model considering the reserve base. The model research content is to determine the flow distribution between suppliers and reserve bases and between reserve bases and construction sites with the goal of minimizing total costs.

[0109] (2) Considering the characteristics of transport capacity, cost, transport time, and material types of different transport modes, a model is constructed based on factors such as reserve base allocation, reserve base restrictions, number of multimodal transport transfers, indivisibility of transport task routes, point-arc association of transport routes, arrival time, and point and arc capacity.

[0110] (3) After adding the reserve base, the complexity of the model increases. The traffic allocation method proposed above is difficult to obtain a satisfactory solution in a short time. Therefore, it is necessary to design a heuristic algorithm to solve the model. Considering that all logistics must first pass through the transit warehouse before going to the final destination, the optimization idea of ​​first constructing a set of alternative paths and then selecting a suitable path is adopted. The Dijkstra algorithm considering the delivery time limit generates all feasible paths for each traffic flow to construct a set of alternative paths. For each traffic flow, all feasible paths from the supply point to the warehouse are first generated, and then all feasible paths from the warehouse to the construction point are generated, and they are combined into a complete path. Under the condition of meeting the road network capacity constraints, a genetic algorithm can be designed to optimize the path selected by each traffic flow to select a suitable transportation path.

[0111] (4) The problem is solved by writing the designed heuristic algorithm code in Python programming language. The solution results show that all materials can arrive at the work site within the specified time limit while meeting the capacity constraints of road sections, nodes and reserve bases.

[0112] In summary, the embodiments of the present invention utilize a traffic flow distribution method based on A* and impedance adjustment, an improved genetic algorithm, etc., to obtain road network data and supplier location data surrounding the railway construction project, analyze the possible changes in the surrounding road network under different traffic conditions, weather conditions, and vehicle conditions, and construct a multi-category material multimodal transport flow distribution model; at the same time, in order to ensure the timely supply of construction materials when the road network is not smooth due to special weather conditions, etc., a reserve base is added to the road network, and a multi-category material multimodal transport flow distribution model considering the reserve base is constructed, which includes a three-level network and two stages. By optimizing the material flow distribution, the reserve base is fully utilized, and the efficiency of emergency response and material supply is improved.

[0113] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0114] From the above description of the embodiments, it can be seen that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus the necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention or certain parts of the embodiments.

[0115] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without making any creative efforts.

[0116] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for allocating material flow for railway construction based on dynamic balance of supply and demand, characterized in that: include: Construct a material transportation network along the railway project that takes into account the roads and railways within and outside the railway project construction area; Based on the material transportation network along the railway project, a multi-category material and multi-transportation task multi-modal transport flow distribution model is constructed; Construct a multi-category multimodal transport flow distribution model considering the reserve base; The multimodal transport flow distribution model for multiple categories of materials and multiple transport tasks is solved by a traffic flow distribution method based on A* and impedance adjustment to obtain the path configuration plan for all traffic flows; By using a combined heuristic traffic flow allocation method based on Dijkstra and genetic algorithms, the multi-category multimodal transport flow allocation model considering the reserve base is solved to obtain the specific transportation route and transportation mode for each transportation task; The vehicle flow allocation method based on A* and impedance adjustment is used to solve the multi-category material multi-transportation task multimodal transport flow allocation model to obtain the path configuration plan for all vehicle flows, including: Improve the A* algorithm and use it to find the shortest path between the corresponding ODs for each traffic flow in turn, and adjust the traffic flow allocation plan according to the impedance of the railway network section; The specific steps of the shortest path search algorithm based on the A* algorithm include: Step 1: Set up two tables: the Open table that stores accessible nodes and the Close table that stores nodes on the optimal path that have been visited. Put the starting point s into the Open table; Step 2: Check if the Open table is an empty set. If it is not an empty set, proceed to Step 3. If it is an empty set, stop the algorithm. Step 3: Sort the nodes in the Open table according to the evaluation function and obtain the node U with the smallest evaluation function value. If a reset instruction appears, reset the currently recorded global information according to U; Step 4: Move node U from the Open table to the Close table; Step 5: Determine whether node U is the target node. If yes, stop the algorithm; otherwise, proceed to Step 6. Step 6: Each vehicle flow has a delivery time limit. Add the adjacent nodes of U that have not exceeded the delivery time limit to the Open table and go to Step 2. If all adjacent nodes of U have exceeded the delivery time limit, go to Step 2 and return the reset instruction. Step 7: Backtrack the Close table to obtain the shortest path of the branch traffic flow; The specific steps of the traffic distribution algorithm based on link impedance adjustment include: Step 1: Obtain the shortest path for all traffic flows using the A* algorithm; Step 2: All traffic flows are distributed to the road network according to the shortest path; Step 3: Count the traffic volume on all road sections; determine whether the capacity-limited section exceeds the limit. If not, go to Step 5; if any road section exceeds the capacity limit, go to Step 4; Step 4: Block the saturated section or increase the impedance of the saturated section by certain methods, and then go to Step 1; Step 5: Output the path configuration plan for all traffic flows; Step 6: The algorithm ends; The combined heuristic traffic flow allocation method based on Dijkstra and genetic algorithms is used to solve the multi-category multimodal transport flow allocation model considering the reserve base, and the specific transportation route and transportation mode of each transportation task are obtained, including: Based on the multi-category material multimodal transport flow distribution model, all construction materials first pass through the transit warehouse before going to the final destination. The Dijkstra algorithm taking into account the delivery time limit is used to generate all feasible paths for each vehicle flow to construct a set of alternative paths. For each vehicle flow, all feasible paths from the supply point to the warehouse are first generated, and then all feasible paths from the warehouse to the construction site are generated. They are combined into complete paths in pairs. Then, by designing crossover operators and mutation operators, the genetic algorithm is used to gradually optimize the path selected by each vehicle flow. Through continuous iteration, the global optimal solution is obtained. The global optimal solution includes the specific transportation path and transportation method for each transportation task. Based on the global optimal solution, all materials are ensured to arrive at the construction site within the specified time limit while meeting the capacity constraints of road sections, nodes and storage bases.

2. The method according to claim 1, characterized in that The construction of the material transportation network along the railway project taking into account the highways and railways within and outside the railway project construction area includes: In terms of highway networks, we analyze and screen the road networks along and around railway construction projects to obtain the required basic data for the highway network. We then conduct connectivity and topology checks on the basic data, trim and merge related errors, and construct a highway network topology structure. In terms of the railway network, based on the national conventional railway network data, relevant railway stations and railway lines are selected as the basic data for the construction of the railway transportation network. The railway network is then checked for connectivity and topology, and the railway network information is converted into a railway topology structure. Based on the conversion tools in the commercial geographic information system software ArcGIS, the road network topology structure and railway topology structure are converted into Coverage structure to complete the construction of the material transportation network along the railway project.

3. The method according to claim 1 or 2, characterized in that The multimodal transport flow distribution model for multiple categories of materials, multiple transport tasks, and multimodal transport based on the material transport network along the railway project includes: Considering the characteristics of transport capacity, cost, transport time and material types of different transport modes, and based on the constraints of the number of intermodal transport transfers, the indivisibility of transport task routes, the point-arc connection of transport routes, delivery time, supply point capacity and point and arc capacity, a multimodal transport flow distribution model for multiple categories of materials and multiple transport tasks is constructed based on the material transport network along the railway project. In the multi-category material, multi-transportation task and multimodal transport flow allocation model, based on the demand for different construction materials at various railway construction sites, by considering the supply and demand characteristics, transportation characteristics and storage characteristics of different materials, with the goal of minimizing transportation costs and freight transfer operation costs, different materials are given corresponding weights and constraints are established for flow allocation.

4. The method according to claim 3, characterized in that The multi-category multimodal transport flow distribution model considering the reserve base is constructed, including: In order to solve the problem of flow distribution between suppliers and reserve bases and between reserve bases and work sites, with the goal of minimizing total cost, considering the storage capacity limit of reserve bases, the demand of work sites and the transportation time constraint, a multi-category material multimodal transport flow distribution model with reserve bases is constructed, which includes a three-level network and two stages. The three-level network of the multi-category material multimodal transport flow distribution model is suppliers, reserve bases and work sites. The two stages refer to the first stage that the flow distribution plan between suppliers and reserve bases needs to be determined; the second stage needs to determine the flow distribution plan between reserve bases and work sites.

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