A method for emergency deployment of engineering materials in complex and dangerous areas

Abstract

The present invention discloses a method for emergency deployment of engineering materials in complex and hazardous areas, belonging to the field of emergency deployment of engineering materials. The method comprises collecting external environmental data of the complex and hazardous areas, calculating, based on the external environmental data, the transportation time of engineering materials from suppliers to demand points under the influence of the external environment; calculating, based on the engineering material transportation time, an early warning quantity of engineering material demand based on early warning scenarios; constructing a spatiotemporal service network for engineering material transportation based on the early warning quantity of engineering material demand; constructing an emergency deployment optimization model for engineering materials based on the spatiotemporal service network for engineering material transportation, solving the emergency deployment optimization model for engineering materials, and obtaining an emergency deployment plan for engineering materials. The present invention solves the problem that prior art technologies fail to consider the difficulty in deploying materials after supplier transportation is affected by the external environment in complex and hazardous areas.

Description

Technical Field

[0001] The present invention belongs to the field of emergency deployment of engineering materials, and in particular relates to an emergency deployment method of engineering materials in complex and dangerous areas. Background Art

[0002] Due to the scarcity and uneven distribution of local resources in some areas, construction materials are primarily transported to construction sites via long-distance roads and railroads. Furthermore, these areas face complex and hazardous environments, weak construction material transportation routes, and the high mountains and dangerous roads, frequent geological disasters, and harsh climate along the routes make the transportation and supply of construction materials extremely challenging. To meet the demand for construction material supply in these complex and hazardous areas, a decision-making support system has been established through technical methods such as early warning of construction material transportation needs and the development of emergency deployment plans for construction materials, ensuring the orderly progress of construction material transportation and supply.

[0003] Under the influence of external environments in complex and hazardous areas, different types of construction materials face two scenarios: single-supplier and multiple-supplier. In the multiple-supplier scenario, there are scenarios where the transportation of construction materials from a single supplier is affected, or where the transportation of construction materials from multiple suppliers is affected. The calculation methods for early warning of construction material supply guarantees vary in different scenarios, and existing demand warning theoretical methods are not well suited to the calculation of warning values ​​in multiple scenarios. Regarding the emergency allocation of construction materials, existing methods primarily design emergency allocation models and methods for specific risk impact scenarios, such as optimizing vehicle emergency routes under transportation route disruptions and optimizing supplier adjustments under supply guarantee conditions. However, in complex and hazardous areas, there are multiple possibilities, including adjusting vehicle emergency routes, adjusting supplier selection, and emergency allocation of construction materials across work areas and sites, as well as across construction sections. Emergency allocation plans for specific material scenarios may be difficult to implement in some cases, making it difficult to effectively support the efficient and high-quality supply of construction materials in complex and hazardous areas.

[0004] Existing technical methods mainly include:

[0005] In the area of ​​engineering material demand early warning, most research focuses on forecasting and analyzing the demand for emergency relief supplies, using techniques such as statistical analysis and machine learning to provide a foundation for the allocation of emergency relief supplies. Some research focuses on risk early warning, using risk assessment algorithms and neural networks to provide early warnings for internal and external risks, such as supply chain risks. However, research specifically on engineering material demand early warning is limited, and even less research has focused on early warning methods for engineering material demand in complex and hazardous areas with differentiated supply and demand scenarios.

[0006] In the area of ​​optimizing the emergency deployment of construction materials, a significant amount of technical research has focused on the emergency deployment of relief supplies. This includes applying network optimization methods to achieve post-disaster relief material distribution, as well as integrating and optimizing post-disaster road repair and material distribution. Most of this research focuses on specific scenarios, such as emergency vehicle routing or the emergency deployment of reserve supplies. Very few studies have integrated and optimized emergency deployment plans for construction materials across diverse emergency scenarios.

[0007] Existing technologies mostly focus on forecasting and analyzing emergency material demand. However, there are no reference methods for calculating early warning values ​​for different types of construction materials under the influence of complex and hazardous environments, such as those affecting transportation with a single supplier, those affecting transportation with multiple suppliers, or those affecting transportation with multiple suppliers. Existing emergency material allocation methods are mostly designed for specific risk impact scenarios, such as optimizing emergency vehicle routes in the event of transport line disruptions or optimizing supplier adjustments under supply guarantee conditions. This approach fails to produce a comprehensive emergency material allocation plan. Summary of the Invention

[0008] In response to the above-mentioned deficiencies in the prior art, the present invention provides an emergency allocation method for engineering materials in complex and dangerous areas, which solves the problem that the prior art does not consider the difficulty in allocating materials after the supplier's transportation is affected by the external environment in complex and dangerous areas.

[0009] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for emergency deployment of engineering materials in complex and dangerous areas, comprising:

[0010] Collect external environmental data in complex and dangerous areas, and calculate the transportation time of engineering materials from suppliers to demand points under the influence of external environmental data;

[0011] According to the transportation time of engineering materials, the warning quantity of engineering material demand is calculated based on the warning scenario;

[0012] According to the early warning quantity of engineering material demand, a spatiotemporal service network for engineering material transportation is constructed. Based on the spatiotemporal service network for engineering material transportation, an optimization model for emergency allocation of engineering materials is constructed. The optimization model for emergency allocation of engineering materials is solved to obtain an emergency allocation plan for engineering materials.

[0013] The beneficial effects of the present invention are as follows: By integrating statistical analysis, network flow, and operations optimization methods, the present invention designs a method for calculating engineering material transportation demand warning values ​​for different risk impact scenarios, thereby determining the emergency demand for specific material demand points under different risk impact scenarios. By constructing a spatiotemporal service network for engineering material transportation and a model for compiling engineering material emergency allocation plans, the present invention enables the integrated compilation of engineering material emergency allocation plans for different material categories, different warning scenarios, and different organizational targets. The resulting emergency allocation plans achieve overall optimization and provide technical support for the emergency allocation of engineering materials in complex and dangerous areas.

[0014] Furthermore, the warning scenarios include the impact on material transportation when there is only one supplier, the impact on material transportation of a single supplier when there are multiple suppliers, and the impact on material transportation of multiple suppliers when there are multiple suppliers.

[0015] The beneficial effect of the above further scheme is that the warning scenarios are divided into three categories according to the number of affected suppliers, which is conducive to the design of the calculation method of the warning quantity of engineering material demand.

[0016] Furthermore, when the warning scenario is a single supplier and material transportation is affected, the expression of the engineering material demand warning quantity is:

[0017]

[0018] in, When the transportation scenario is a single supplier and the material transportation is affected, the engineering material category at the current demand point Demand warning volume; When the transportation scenario is a single supplier, the engineering material category when the current demand point material transportation is affected the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; To provide engineering materials that can reach current demand points during the period of transportation impact The amount of supplies; Engineering materials categories for current demand points Average daily consumption; Minimum material reserve time; It is the transportation time of engineering materials from suppliers to current demand points under the influence of external environment.

[0019] The beneficial effect of the above further scheme is that it can accurately calculate the early warning quantity of engineering materials when a single supplier is affected and the transportation of engineering materials is affected, providing support for the preparation of emergency allocation plans for engineering materials.

[0020] Furthermore, when the early warning scenario is multiple suppliers and the material transportation of a single supplier is affected, the expression of the early warning quantity of engineering material demand is:

[0021]

[0022]

[0023] in, When the warning scenario is multiple suppliers and the transportation of materials from a single supplier is affected, the engineering material category at the current demand point Demand warning volume; When the warning scenario is multiple suppliers and the transportation of materials from a single supplier is affected, the engineering material category at the current demand point the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; An index of engineering material suppliers; Suppliers affected by the transportation of materials; For suppliers Engineering materials category Average daily transport volume; Suppliers affected by external environment Transportation time of construction materials to the current demand point; For suppliers Engineering material categories that can reach current demand points during the period of transportation impact The amount of supplies; Engineering materials categories for current demand points Average daily consumption; This is the minimum material reserve time.

[0024] The beneficial effect of the above further scheme is that it can accurately calculate the early warning quantity of engineering materials for multiple suppliers, and when the material transportation of one of the suppliers is affected, to provide support for the preparation of emergency allocation plans for engineering materials.

[0025] Furthermore, when the early warning scenario is multiple suppliers and the material transportation of multiple suppliers is affected, the expression of the early warning quantity of engineering material demand is:

[0026]

[0027]

[0028] in, When the early warning scenario is multiple suppliers and the transportation of materials from multiple suppliers is affected, the engineering material category at the current demand point Demand warning volume; When the early warning scenario is multiple suppliers and the transportation of materials from multiple suppliers is affected, the engineering material category at the current demand point the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; An index of engineering material suppliers; Engineering materials categories for current demand points A collection of suppliers; Engineering materials categories for current demand points A collection of suppliers whose material transportation is affected; For suppliers Engineering materials category Average daily transport volume; The longest time that the transportation of construction materials is affected; Suppliers affected by the transportation of materials; For suppliers Engineering material categories that can reach current demand points during the period of transportation impact The amount of supplies; Suppliers affected by external environment Transportation time of construction materials to the current demand point; Engineering materials categories for current demand points Average daily consumption; This is the minimum material reserve time.

[0029] The beneficial effect of the above further scheme is that it can accurately calculate the early warning quantity of engineering materials when the material transportation of multiple suppliers is affected, and provide support for the preparation of emergency allocation plans for engineering materials.

[0030] Furthermore, the construction of a spatiotemporal service network for the transportation of construction materials is carried out according to the early warning quantity of construction material demand, specifically:

[0031] Obtain the location of suppliers, demand points, material storage points, and vehicles in transit;

[0032] The supplier location, demand point location, material storage point location and in-transit vehicle location are converted into physical nodes; the basic space-time network is constructed with time nodes as the horizontal axis and physical nodes as the vertical axis; for each cargo flow, in the basic space-time network, the point ( , ) to point ( , ) of the heavy vehicle arc and point ( , ) to point ( , ) of the empty arc and point ( , ) to point ( , ) delay arc, and obtain the space-time network of heavy and empty vehicle transportation services; The time point for the heavy vehicle to depart; The physical node corresponding to the starting position of the heavy vehicle; The time point when the heavy vehicle arrives at the destination; The physical node corresponding to the destination of the heavy vehicle; The time node for empty vehicle departure; The physical node corresponding to the starting position of the empty vehicle; The time when the empty vehicle arrives at the destination; It is the physical node corresponding to the empty vehicle arriving at the destination; Arrival of vehicle time; For vehicles from Time of departure; The physical node corresponding to the vehicle's parking location;

[0033] A virtual starting point and a virtual end point are set outside the nodes of the space-time network of the transport service of empty and loaded vehicles. For each cargo flow, the space-time node corresponding to the starting position of the cargo flow is connected to the virtual starting point to construct a virtual arc. The space-time node corresponding to the ending position of the logistics is connected to the virtual end point to construct a virtual arc. The cargo flow rate is set on each virtual arc to obtain a transport space-time service network with virtual nodes and connected arcs added. The space-time node is a point with a time node as the horizontal coordinate and a physical node as the vertical coordinate.

[0034] A super starting point and a super end point are set outside the transport space-time service network with added virtual nodes and arcs; a super arc is constructed between the super starting point and each physical node at the start time before the decision cycle, and a super arc is constructed between the super end point and each physical node at the end time at the end of the decision cycle; vehicle flow is set on the super arc before the decision cycle to obtain the construction material transportation space-time service network.

[0035] The beneficial effect of the above further scheme is: through the design of spatiotemporal network, the emergency allocation problem of engineering materials is transformed into a traditional network flow problem, which is conducive to the construction and solution of the optimization model of emergency allocation of engineering materials.

[0036] Furthermore, the objective function of the engineering material emergency deployment optimization model is:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] in, is the minimum function; is the comprehensive transportation cost; The transportation cost of construction materials; Cost of empty truck transportation; Penalty costs for delayed delivery of construction materials; is the heavy vehicle arc set; is the heavy vehicle arc index; Heavy vehicles pass through the arc the cost of transportation; A collection of vehicle types; Index of the type of carrier; is the decision variable, representing the arc The above types are Traffic volume; is the set of empty car arcs; The arc index for the empty car; Empty car passing through the arc the cost of transportation; is the decision variable, representing the arc The above types are Traffic volume; The penalty coefficient for delayed delivery of unit materials; To gather the transportation needs of engineering materials; Index for cargo flow; For cargo flow The size of the transport volume; is the maximum value function; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; For the arc departure time; For the arc transportation time; For cargo flow the stipulated arrival time; From the supply point Departure, transportation A collection of cargo flows of similar materials; To reach the demand point ,transportation A collection of cargo flows of similar materials; For use A collection of freight flows transported by trucks.

[0043] The beneficial effects of the above further plan are: the optimization goals of the emergency allocation of construction materials include transportation costs, vehicle allocation costs and construction material delay costs, which can achieve the coordination of three types of costs. The final emergency allocation plan can reduce the total transportation cost while ensuring the timely delivery of construction materials as much as possible.

[0044] Furthermore, the constraints of the engineering material emergency deployment optimization model include cargo flow conservation constraints, vehicle flow conservation constraints, cargo flow and vehicle flow consistency constraints, path capacity constraints, and initial state empty vehicle distribution constraints:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] in, For slave nodes The set of starting arcs; For slave nodes The starting arc index; To reach the node The set of connected arcs; To reach the node The arc index of is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; It is a collection of space-time nodes in the space-time service network for engineering material transportation; To gather the transportation needs of engineering materials; Index for cargo flow; It is the virtual starting point of the cargo flow; It is the virtual end point of the cargo flow; is the decision variable, representing the arc The above types are Traffic volume; is the decision variable, representing the arc The above types are Traffic volume; A collection of vehicle types; Index of the type of carrier; For the type the number of means of transport; It is a super starting point for traffic flow; It is the super terminal for traffic flow; For use A collection of freight flows transported by trucks; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; For cargo flow The size of the transport volume; is the decision variable, representing the arc The above types are Traffic volume; For the type The load capacity of the means of transport; is the heavy vehicle arc set; is the heavy vehicle arc index; is the decision variable, representing the arc The above types are Traffic volume; is the index of the union of the loaded arc set and the empty arc set; For path Maximum throughput capacity; is a collection of transport paths; The transport path index; is the decision variable, representing the arc The above types are Traffic volume; For nodes In the initial stage The stock of vehicle types; A collection of supply points; is a collection of demand points; Indicates arc connection Starting point; is the decision variable, when the cargo flow Flowing through the arc When , it is 0; for The arc index in ; is the set of empty car arcs; is the delayed arc set; It is a super arc set; is the decision variable, when the cargo flow Flowing through the arc When , it is 0 or 1; for The arc index in ; is a set of virtual arcs; is the decision variable, representing the arc The above types are Traffic volume; is the virtual arc index; is the decision variable, representing the arc The above types are Traffic volume; for The arc index in ; is a set of integers.

[0055] The beneficial effect of the above further solution is that the optimized design of the constraint conditions can ensure the feasibility of the final allocation solution while reducing the difficulty of solving the problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flow chart of the method of the present invention.

[0057] Figure 2 A technical roadmap for the module preparation of emergency deployment plans for engineering materials in complex and dangerous areas in the embodiment of the present invention.

[0058] Figure 3 Schematic diagram of the spatiotemporal service network for heavy and empty vehicle transportation in an embodiment of the present invention.

[0059] Figure 4 This is a schematic diagram of a transport space-time service network with virtual nodes and link arcs added in an embodiment of the present invention.

[0060] Figure 5 Schematic diagram of a transport space-time service network with super nodes and link arcs added in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0062] like Figure 1 As shown, in one embodiment of the present invention, a method for emergency deployment of engineering materials in complex and dangerous areas includes:

[0063] Collect external environmental data in complex and dangerous areas, and calculate the transportation time of engineering materials from suppliers to demand points under the influence of external environmental data;

[0064] According to the transportation time of engineering materials, the warning quantity of engineering material demand is calculated based on the warning scenario;

[0065] According to the early warning quantity of engineering material demand, a spatiotemporal service network for engineering material transportation is constructed. Based on the spatiotemporal service network for engineering material transportation, an optimization model for emergency allocation of engineering materials is constructed. The optimization model for emergency allocation of engineering materials is solved to obtain an emergency allocation plan for engineering materials.

[0066] The warning scenarios include the impact on material transportation when there is only one supplier, the impact on material transportation of a single supplier when there are multiple suppliers, and the impact on material transportation of multiple suppliers when there are multiple suppliers.

[0067] In this embodiment, the present invention is Figure 1 The technical solution illustrated here specifically involves calculating construction material transportation time, early warning of construction material transportation demand, and emergency deployment of construction materials. Ultimately, this allows for the integrated development of emergency deployment plans for construction materials under varying external environmental conditions, tailored to different product categories, warning scenarios, and organizational objectives. The method for calculating construction material transportation time is based on the invention patent "Method for Calculating Construction Material Transportation Time in Complex and Dangerous Areas Based on Risk Prediction" (Application No. 202310779630.2).

[0068] When the warning scenario is a single supplier and material transportation is affected, the expression for the warning quantity of engineering material demand is:

[0069]

[0070] in, When the transportation scenario is a single supplier and the material transportation is affected, the engineering material category at the current demand point Demand warning volume; When the transportation scenario is a single supplier, the engineering material category when the current demand point material transportation is affected the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; To provide engineering materials that can reach current demand points during the period of transportation impact The amount of supplies; Engineering materials categories for current demand points Average daily consumption; Minimum material reserve time; It is the transportation time of engineering materials from suppliers to current demand points under the influence of external environment.

[0071] When the early warning scenario is multiple suppliers and the material transportation of a single supplier is affected, the expression for the early warning quantity of engineering material demand is:

[0072]

[0073]

[0074] in, When the warning scenario is multiple suppliers and the transportation of materials from a single supplier is affected, the engineering material category at the current demand point Demand warning volume; When the warning scenario is multiple suppliers and the transportation of materials from a single supplier is affected, the engineering material category at the current demand point the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; An index of engineering material suppliers; Suppliers affected by the transportation of materials; For suppliers Engineering materials category Average daily transport volume; Suppliers affected by external environment Transportation time of construction materials to the current demand point; For suppliers Engineering material categories that can reach current demand points during the period of transportation impact The amount of supplies; Engineering materials categories for current demand points Average daily consumption; This is the minimum material reserve time.

[0075] When the early warning scenario is multiple suppliers and the material transportation of multiple suppliers is affected, the expression of the early warning quantity of engineering material demand is:

[0076]

[0077]

[0078] in, When the early warning scenario is multiple suppliers and the transportation of materials from multiple suppliers is affected, the engineering material category at the current demand point Demand warning volume; When the early warning scenario is multiple suppliers and the transportation of materials from multiple suppliers is affected, the engineering material category at the current demand point the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; An index of engineering material suppliers; Engineering materials categories for current demand points A collection of suppliers; Engineering materials categories for current demand points A collection of suppliers whose material transportation is affected; For suppliers Engineering materials category Average daily transport volume; The longest time that the transportation of construction materials is affected; Suppliers affected by the transportation of materials; For suppliers Engineering material categories that can reach current demand points during the period of transportation impact The amount of supplies; Suppliers affected by external environment Transportation time of construction materials to the current demand point; Engineering materials categories for current demand points Average daily consumption; This is the minimum material reserve time.

[0079] like Figure 3 、 Figure 4 and Figure 5 As shown, the construction of a spatiotemporal service network for engineering material transportation based on the engineering material demand warning quantity is specifically as follows:

[0080] Obtain the location of suppliers, demand points, material storage points, and vehicles in transit;

[0081] The supplier location, demand point location, material storage point location and in-transit vehicle location are converted into physical nodes; the basic space-time network is constructed with time nodes as the horizontal axis and physical nodes as the vertical axis; for each cargo flow, in the basic space-time network, the point ( , ) to point ( , ) of the heavy vehicle arc and point ( , ) to point ( , ) of the empty arc and point ( , ) to point ( , ) delay arc, and obtain the space-time network of heavy and empty vehicle transportation services; The time point for the heavy vehicle to depart; The physical node corresponding to the starting position of the heavy vehicle; The time point when the heavy vehicle arrives at the destination; The physical node corresponding to the destination of the heavy vehicle; The time node for empty vehicle departure; The physical node corresponding to the starting position of the empty vehicle; The time when the empty vehicle arrives at the destination; It is the physical node corresponding to the empty vehicle arriving at the destination; Arrival of vehicle time; For vehicles from Time of departure; The physical node corresponding to the vehicle's parking location;

[0082] A virtual starting point and a virtual end point are set outside the nodes of the space-time network of the transport service of empty and loaded vehicles. For each cargo flow, the space-time node corresponding to the starting position of the cargo flow is connected to the virtual starting point to construct a virtual arc. The space-time node corresponding to the ending position of the logistics is connected to the virtual end point to construct a virtual arc. The cargo flow rate is set on each virtual arc to obtain a transport space-time service network with virtual nodes and connected arcs added. The space-time node is a point with a time node as the horizontal coordinate and a physical node as the vertical coordinate.

[0083] A super starting point and a super end point are set outside the transport space-time service network with added virtual nodes and arcs; a super arc is constructed between the super starting point and each physical node at the start time before the decision cycle, and a super arc is constructed between the super end point and each physical node at the end time at the end of the decision cycle; vehicle flow is set on the super arc before the decision cycle to obtain the construction material transportation space-time service network.

[0084] In this embodiment, the technical route for the preparation of emergency deployment plan for engineering materials in complex and dangerous areas is as follows: Figure 2 As shown in the figure, (1) Data preprocessing. The basic data consists of static data and dynamic data. The static data includes the location of engineering material suppliers (points), the location of engineering material demand points, the transportation routes between supply and demand points, the location of construction sites, and the transportation network between construction sites. The dynamic data includes the supply, demand, and warning quantities of engineering materials, the storage volume of different types of engineering materials in different sections / work areas / work sites, the location of vehicles in transit (vehicles affected by transportation can be distinguished from those not affected) and their carrying capacity.

[0085] The location information of suppliers, demand points, and material storage points across different sections, work areas, and work sites is converted into physical nodes in the spatiotemporal network. Vehicles (or groups of vehicles) in transit are analogous to construction material suppliers and are also converted into physical nodes in the spatiotemporal network. Discretization of the physical nodes forms a spatiotemporal service network. The supply quantity of construction material supply points, the storage quantity of different types of construction materials in other sections, work areas, and work sites, and the loading capacity of in-transit vehicles can be converted into supply quantity. The amount of construction material warnings is converted into demand quantity, forming the supply and demand parameters of the spatiotemporal service network.

[0086] (2) Establish a spatiotemporal service network for the emergency deployment of engineering materials in complex and dangerous areas.

[0087] The process is divided into three stages.

[0088] ① The first stage is to build a heavy and empty vehicle transportation service network. The engineering material supply nodes, demand nodes, in-transit vehicles or vehicle sets are regarded as physical nodes and discretized, such as using hours as time intervals, to form a basic space-time network. In this space-time network, the time of loading and unloading operations is taken into account in the vehicle operation time, and the processes of empty vehicle loading, heavy vehicle operation, heavy vehicle unloading and empty vehicle movement are simplified into the operation of heavy vehicle arcs and empty vehicle arcs. For each freight demand, all heavy vehicle arcs that meet the starting and end point location requirements are constructed; at each time node, empty vehicle arcs connecting each station with other stations are constructed; and then delay arcs are added between the same logistics nodes in adjacent time periods to form a heavy and empty vehicle transportation service space-time network, such as Figure 3 shown.

[0089] ② In the second stage, virtual starting and ending points of the cargo flow are added. To facilitate flow distribution, for each cargo flow, a virtual starting point is added and connected to the time-space service network node corresponding to the physical starting point of the cargo flow to construct a virtual arc. Similarly, a virtual end point is added and connected to the time-space service network node corresponding to the physical end point of the cargo flow to construct a virtual arc. By setting the cargo flow on each virtual arc, the size of each cargo flow can be represented. At this time, the network structure is as follows: Figure 4 shown.

[0090] ③ In the third phase, superstart and end points and superlink arcs are added to the traffic flow. The addition of superstart and end points and link arcs ensures that the model can be solved in any scenario. For each type of vehicle, a set of superstarts and superend points are added to the spatiotemporal service network to represent the start and end points of the traffic flow, respectively. Before the decision cycle, superlink arcs are constructed between the superstart points and the start points of each node. At the end of the decision cycle, superlink arcs are constructed between the superend points and the end points of each node. The traffic flow on each arc represents the initial empty vehicle inventory at each start point. The total traffic flow between the superstart point and the corresponding superend point is the total number of vehicles of that type.

[0091] The objective function of the engineering materials emergency deployment optimization model is:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] in, is the minimum function; is the comprehensive transportation cost; The transportation cost of construction materials; Cost of empty truck transportation; Penalty costs for delayed delivery of construction materials; is the heavy vehicle arc set; is the heavy vehicle arc index; Heavy vehicles pass through the arc the cost of transportation; A collection of vehicle types; Index of the type of carrier; is the decision variable, representing the arc The above types are Traffic volume; is the set of empty car arcs; The arc index for the empty car; Empty car passing through the arc the cost of transportation; is the decision variable, representing the arc The above types are Traffic volume; The penalty coefficient for delayed delivery of unit materials; To gather the transportation needs of engineering materials; Index for cargo flow; For cargo flow The size of the transport volume; is the maximum value function; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; For the arc departure time; For the arc transportation time; For cargo flow the stipulated arrival time; From the supply point Departure, transportation A collection of cargo flows of similar materials; To reach the demand point ,transportation A collection of cargo flows of similar materials; For use A collection of freight flows transported by trucks.

[0098] The constraints of the engineering material emergency deployment optimization model include cargo flow conservation constraints, vehicle flow conservation constraints, cargo flow and vehicle flow consistency constraints, path capacity constraints, and initial state empty vehicle distribution constraints:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] in, For slave nodes The set of starting arcs; For slave nodes The starting arc index; To reach the node The set of connected arcs; To reach the node The arc index of is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; It is a collection of space-time nodes in the space-time service network for engineering material transportation; To gather the transportation needs of engineering materials; Index for cargo flow; It is the virtual starting point of the cargo flow; It is the virtual end point of the cargo flow; is the decision variable, representing the arc The above types are Traffic volume; is the decision variable, representing the arc The above types are Traffic volume; A collection of vehicle types; Index of the type of carrier; For the type the number of means of transport; It is a super starting point for traffic flow; It is the super terminal for traffic flow; For use A collection of freight flows transported by trucks; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; For cargo flow The size of the transport volume; is the decision variable, representing the arc The above types are Traffic volume; For the type The load capacity of the means of transport; is the heavy vehicle arc set; is the heavy vehicle arc index; is the decision variable, representing the arc The above types are Traffic volume; is the index of the union of the loaded arc set and the empty arc set; For path Maximum throughput capacity; is a collection of transport paths; The transport path index; is the decision variable, representing the arc The above types are Traffic volume; For nodes In the initial stage The stock of vehicle types; A collection of supply points; is a collection of demand points; Indicates arc connection Starting point; is the decision variable, when the cargo flow Flowing through the arc When , it is 0; for The arc index in ; is the set of empty car arcs; is the delayed arc set; It is a super arc set; is the decision variable, when the cargo flow Flowing through the arc When , it is 0 or 1; for The arc index in ; is a set of virtual arcs; is the decision variable, representing the arc The above types are Traffic volume; is the virtual arc index; is the decision variable, representing the arc The above types are Traffic volume; for The arc index in ; is a set of integers.

Claims

1. A method for emergency deployment of engineering materials in complex and dangerous areas, characterized by: include: Collect external environmental data in complex and dangerous areas, and calculate the transportation time of engineering materials from suppliers to demand points under the influence of external environmental data; Calculate the warning quantity of construction material demand based on the transportation time of construction materials and warning scenarios; the warning scenarios include the impact on material transportation when there is only one supplier, the impact on material transportation of a single supplier when there are multiple suppliers, and the impact on material transportation of multiple suppliers when there are multiple suppliers; Based on the early warning quantity of engineering material demand, a spatiotemporal service network for engineering material transportation is constructed. Based on the spatiotemporal service network for engineering material transportation, an optimization model for emergency allocation of engineering materials is constructed. The optimization model for emergency allocation of engineering materials is solved to obtain an emergency allocation plan for engineering materials. The construction of the spatiotemporal service network for engineering material transportation based on the early warning quantity of engineering material demand is specifically as follows: Obtain the location of suppliers, demand points, material storage points, and vehicles in transit; Convert supplier locations, demand point locations, material storage point locations, and in-transit vehicle locations into physical nodes; construct a basic spatiotemporal network with time nodes as the horizontal axis and physical nodes as the vertical axis; For each freight flow, in the basic spatiotemporal network, combined with the vehicle running time, construct a point ( , ) to point ( , ) of the heavy vehicle arc and point ( , ) to point ( , ) of the empty arc and point ( , ) to point ( , ) delay arc, and obtain the space-time network of heavy and empty vehicle transportation services; The time point for the heavy vehicle to depart; The physical node corresponding to the starting position of the heavy vehicle; The time point when the heavy vehicle arrives at the destination; The physical node corresponding to the destination of the heavy vehicle; The time node for empty vehicle departure; The physical node corresponding to the starting position of the empty vehicle; The time when the empty vehicle arrives at the destination; It is the physical node corresponding to the empty vehicle arriving at the destination; Arrival of vehicle time; For vehicles from Time of departure; The physical node corresponding to the vehicle's parking location; A virtual starting point and a virtual end point are set outside the nodes of the space-time network of the transport service of empty and loaded vehicles. For each cargo flow, the space-time node corresponding to the starting position of the cargo flow is connected to the virtual starting point to construct a virtual arc. The space-time node corresponding to the ending position of the logistics is connected to the virtual end point to construct a virtual arc. The cargo flow rate is set on each virtual arc to obtain a transport space-time service network with virtual nodes and connected arcs added. The space-time node is a point with a time node as the horizontal coordinate and a physical node as the vertical coordinate. A super starting point and a super end point are set outside the transport space-time service network with added virtual nodes and arcs; a super arc is constructed between the super starting point and each physical node at the start time before the decision cycle, and a super arc is constructed between the super end point and each physical node at the end time at the end of the decision cycle; vehicle flow is set on the super arc before the decision cycle to obtain the construction material transportation space-time service network.

2. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: When the warning scenario is a single supplier and material transportation is affected, the expression for the warning quantity of engineering material demand is: in, When the transportation scenario is a single supplier and the material transportation is affected, the engineering material category at the current demand point Demand warning volume; When the transportation scenario is a single supplier, the engineering material category when the current demand point material transportation is affected the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; To provide engineering materials that can reach current demand points during the period of transportation impact The amount of supplies; Engineering materials categories for current demand points Average daily consumption; Minimum material reserve time; It is the transportation time of engineering materials from suppliers to current demand points under the influence of external environment.

3. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: When the early warning scenario is multiple suppliers and the material transportation of a single supplier is affected, the expression for the early warning quantity of engineering material demand is: in, When the warning scenario is multiple suppliers and the transportation of materials from a single supplier is affected, the engineering material category at the current demand point Demand warning volume; When the warning scenario is multiple suppliers and the transportation of materials from a single supplier is affected, the engineering material category at the current demand point the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; An index of engineering material suppliers; Suppliers affected by the transportation of materials; For suppliers Engineering materials category Average daily transport volume; Suppliers affected by external environment Transportation time of construction materials to the current demand point; For suppliers Engineering material categories that can reach current demand points during the period of transportation impact The amount of supplies; Engineering materials categories for current demand points Average daily consumption; This is the minimum material reserve time.

4. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: When the early warning scenario is multiple suppliers and the material transportation of multiple suppliers is affected, the expression of the early warning quantity of engineering material demand is: in, When the early warning scenario is multiple suppliers and the transportation of materials from multiple suppliers is affected, the engineering material category at the current demand point Demand warning volume; When the early warning scenario is multiple suppliers and the transportation of materials from multiple suppliers is affected, the engineering material category at the current demand point the remaining amount; is the absolute value; Engineering materials categories for current demand points the existing storage capacity; An index of engineering material suppliers; Engineering materials categories for current demand points A collection of suppliers; Engineering materials categories for current demand points A collection of suppliers whose material transportation is affected; For suppliers Engineering materials category Average daily transport volume; The longest time that the transportation of construction materials is affected; Suppliers affected by the transportation of materials; For suppliers Engineering material categories that can reach current demand points during the period of transportation impact The amount of supplies; Suppliers affected by external environment Transportation time of construction materials to the current demand point; Engineering materials categories for current demand points Average daily consumption; This is the minimum material reserve time.

5. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: The objective function of the engineering materials emergency deployment optimization model is: in, is the minimum function; is the comprehensive transportation cost; The transportation cost of construction materials; Cost of empty truck transportation; Penalty costs for delayed delivery of construction materials; is the heavy vehicle arc set; is the heavy vehicle arc index; Heavy vehicles pass through the arc the cost of transportation; A collection of vehicle types; Index of the type of carrier; is the decision variable, representing the arc The above types are Traffic volume; is the set of empty car arcs; The arc index for the empty car; Empty car passing through the arc the cost of transportation; is the decision variable, representing the arc The above types are Traffic volume; The penalty coefficient for delayed delivery of unit materials; To gather the transportation needs of engineering materials; Index for cargo flow; For cargo flow The size of the transport volume; is the maximum value function; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; For the arc departure time; For the arc transportation time; For cargo flow the stipulated arrival time; From the supply point Departure, transportation A collection of cargo flows of similar materials; To reach the demand point ,transportation A collection of cargo flows of similar materials; For use A collection of freight flows transported by trucks.

6. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: The constraints of the engineering material emergency deployment optimization model include cargo flow conservation constraints, vehicle flow conservation constraints, cargo flow and vehicle flow consistency constraints, path capacity constraints, and initial state empty vehicle distribution constraints: in, For slave nodes The set of starting arcs; For slave nodes The starting arc index; To reach the node The set of connected arcs; To reach the node The arc index of is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; It is a collection of space-time nodes in the space-time service network for engineering material transportation; To gather the transportation needs of engineering materials; Index for cargo flow; It is the virtual starting point of the cargo flow; It is the virtual end point of the cargo flow; is the decision variable, representing the arc The above types are Traffic volume; is the decision variable, representing the arc The above types are Traffic volume; A collection of vehicle types; Index of the type of carrier; For the type the number of means of transport; It is a super starting point for traffic flow; It is the super terminal for traffic flow; For use A collection of freight flows transported by trucks; is a 0-1 decision variable. If the cargo flow Flowing through the arc , its value is 1, otherwise it is 0; For cargo flow The size of the transport volume; is the decision variable, representing the arc The above types are Traffic volume; For the type The load capacity of the means of transport; is the heavy vehicle arc set; is the heavy vehicle arc index; is the decision variable, representing the arc The above types are Traffic volume; is the index of the union of the loaded arc set and the empty arc set; For path Maximum throughput capacity; is a collection of transport paths; The transport path index; is the decision variable, representing the arc The above types are Traffic volume; For nodes In the initial stage The stock of vehicle types; A collection of supply points; is a collection of demand points; Indicates arc connection Starting point; is the decision variable, when the cargo flow Flowing through the arc When , it is 0; for The arc index in ; is the set of empty car arcs; is the delayed arc set; It is a super arc set; is the decision variable, when the cargo flow Flowing through the arc When , it is 0 or 1; for The arc index in ; is a set of virtual arcs; is the decision variable, representing the arc The above types are Traffic volume; is the virtual arc index; is the decision variable, representing the arc The above types are Traffic volume; for The arc index in ; is a set of integers.

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