Emergency allocation method for engineering materials in complex and hard area

Abstract

The invention discloses an emergency allocation method for engineering materials in a complex and hard area, and belongs to the field of emergency allocation of the engineering materials, and the method comprises the steps: collecting the external environment data of the complex and hard area, and calculating the transportation time of the engineering materials from a supplier to a demand point under the influence of an external environment according to the external environment data; according to the engineering material transportation time, the engineering material demand early warning amount is calculated based on the early warning scene; and constructing an engineering material transportation space-time service network according to the engineering material demand early warning amount, constructing an engineering material emergency allocation optimization model according to the engineering material transportation space-time service network, and solving the engineering material emergency allocation optimization model to obtain an engineering material emergency allocation scheme. The problem that in the prior art, materials are difficult to deploy after supplier transportation is affected under the influence of an external environment in a complex and hard area is not considered.

Description

Technical Field

[0001] The 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] In some areas, due to the lack of local resources and uneven distribution, engineering materials are mainly transported to construction sites through long-distance roads, railways and other transportation methods. At the same time, the environment in these areas is complex and dangerous, the capacity of engineering material transportation lines is weak, and the areas along the lines are mountainous and dangerous, with frequent geological disasters and harsh climates, making the transportation and supply of engineering materials extremely challenging. In response to the demand for engineering material supply in complex and dangerous areas, a corresponding decision-making support system is formed through technical methods such as early warning of engineering material transportation demand and the preparation of engineering material emergency allocation plans to ensure the orderly progress of engineering material transportation and supply.

[0003] Under the influence of the external environment in complex and dangerous areas, there are two scenarios for different types of engineering materials: single supplier and multiple suppliers. In the scenario of multiple suppliers, there are scenarios such as the transportation of engineering materials from a certain supplier being affected and the transportation of engineering materials from multiple suppliers being affected. The calculation methods for early warning of supply guarantee of engineering materials in different scenarios are different, and the existing demand early warning theoretical methods are difficult to adapt to the calculation needs of early warning values ​​in multiple scenarios. In terms of emergency allocation of engineering materials, the existing methods mainly design emergency allocation models and methods for specific risk impact scenarios, such as vehicle emergency path optimization under transportation line interruption, supplier adjustment optimization under supply guarantee conditions, etc. In complex and dangerous areas, there are many possibilities such as vehicle emergency path adjustment, supplier selection adjustment, emergency allocation of engineering materials across work areas and work points, and emergency allocation of engineering materials across bidding sections. The emergency allocation plan for materials in specific scenarios may be difficult to obtain a feasible emergency allocation plan for engineering materials in some scenarios, and it is difficult to efficiently support the effective and high-quality supply of engineering materials in complex and dangerous areas.

[0004] The existing technical methods mainly include:

[0005] In terms of engineering material demand warning, most studies are aimed at predicting and analyzing the demand for emergency rescue materials. The technical methods used include statistical analysis, machine learning, etc., which provide a basis for the allocation of emergency rescue materials. Some studies focus on risk warning, using risk assessment algorithms and neural networks to achieve early warning of internal and external risks, such as supply chain risk warning. There are few studies on engineering material demand warning, and there are very few studies on engineering material demand warning methods for differentiated engineering material supply and demand scenarios in complex and dangerous areas.

[0006] In terms of optimization of emergency deployment of engineering materials, a large number of technical studies focus on the emergency deployment of rescue materials, including the use of network optimization methods to achieve the distribution of post-disaster rescue materials, and the integration and optimization of post-disaster road repair and material distribution. Most technical studies are aimed at specific scenarios, such as emergency vehicle route planning or emergency deployment of reserve materials. There are very few studies that integrate and optimize the emergency deployment of engineering materials in different emergency scenarios.

[0007] Most of the existing technologies are used to predict and analyze the demand for emergency materials. However, there is no technical method available for reference to calculate the early warning value of the demand for engineering materials in scenarios such as when there is a single supplier, when there are multiple suppliers, when there are multiple suppliers, and when there are multiple suppliers. Most of the existing emergency allocation methods for engineering materials are designed for specific risk impact scenarios, such as the optimization of emergency vehicle routes under the interruption of transportation routes, and the adjustment and optimization of suppliers under the condition of guaranteed supply. This approach cannot obtain an integrated emergency allocation plan for engineering materials. Summary of the invention

[0008] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for emergency allocation of engineering materials in complex and dangerous areas, which solves the problem that the prior art does not take into account 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 invention object, 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 environment based on 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: the present invention integrates statistical analysis, network flow and operations optimization methods, and by designing a calculation method for warning values ​​of engineering material transportation demand for different risk impact scenarios, it can determine the emergency demand of specific material demand points under different risk impact scenarios; by constructing a spatiotemporal service network for engineering material transportation and a model for the preparation of engineering material emergency allocation plans, it realizes the integrated preparation of engineering material emergency allocation plans for different categories of materials, different warning scenarios, and different organizational objects. The prepared emergency allocation plan can achieve a better overall situation and provide technical method support for the emergency allocation of engineering materials in complex and dangerous areas.

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

[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] W 1,c =D c +S c -P c ×d t -P c ×m t

[0019] Among them, Q 1,c is the demand warning quantity of engineering material category c at the current demand point when the transportation scenario is a single supplier and the material transportation is affected; W 1,c is the remaining quantity of engineering material category c when the material transportation of the current demand point is affected when the transportation scenario is a single supplier; |·| is the absolute value; D c S is the current storage capacity of engineering material category c at the current demand point; c P is the quantity of engineering material category c that can reach the current demand point during the affected transportation period; c is the average daily consumption of engineering material category c at the current demand point; d t is the minimum material reserve time; m t It is the transportation time of engineering materials from suppliers to current demand points under the influence of external environment.

[0020] 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 and the transportation of engineering materials are affected, and provide support for the preparation of emergency allocation plans for engineering materials.

[0021] 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:

[0022]

[0023] W 2,c =D c +Σ i≠a S i,c m a +S a,c -P c d t -P c m a

[0024] Among them, Q 2,c is the demand warning quantity of engineering material category c at the current demand point when the warning scenario is multiple suppliers and the material transportation of a single supplier is affected; W 2,c is the remaining quantity of engineering material category c at the current demand point when the material transportation of a single supplier is affected when the early warning scenario is multiple suppliers; |·| is the absolute value; D c is the existing storage volume of engineering material category c at the current demand point; i is the index of the engineering material supplier; a is the supplier affected by the material transportation; S i,c is the average daily transportation volume of engineering material category c of supplier i; m a S is the transportation time of engineering materials from supplier a to the current demand point under the influence of the external environment; a,c The quantity of engineering material category c that can be transported by supplier a to the current demand point during the affected period; P c is the average daily consumption of engineering material category c at the current demand point; d t This is the minimum material reserve time.

[0025] 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 and one of the suppliers is affected, providing support for the preparation of emergency allocation plans for engineering materials.

[0026] 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:

[0027]

[0028] Among them, Q 3,cis the demand warning quantity of engineering material category c at the current demand point when the warning scenario is multiple suppliers and the material transportation of a single supplier is affected; W 3,c is the remaining quantity of engineering material category c at the current demand point when the material transportation of a single supplier is affected when the early warning scenario is multiple suppliers; |·| is the absolute value; D c is the existing storage capacity of engineering material category c at the current demand point; i is the index of the engineering material supplier; R is the supplier set of engineering material category c at the current demand point; R c S is the set of suppliers affected by the transportation of engineering materials of the current demand point c; i,c is the average daily transportation volume of engineering material category c of supplier i; maxT is the longest time that the transportation of engineering materials is affected; a is the supplier whose material transportation is affected; S a,c The quantity of engineering material category c that can be transported by supplier a to the current demand point during the affected period; m i P is the transportation time of engineering materials from supplier i to the current demand point under the influence of external environment; c is the average daily consumption of engineering material category c at the current demand point; d t 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 the 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 (T s,1 , O1) to point (T e,1 , D1) of the heavy vehicle arc, point (T s,2 , O2) to point (T e，2 , D2) of the empty arc and point (T s,3 , O3) to point (T e，3 , O3) delay arc, and obtain the space-time network of heavy and empty vehicle transportation service; T s,1 is the time node of the heavy vehicle departure; O1 is the physical node corresponding to the starting position of the heavy vehicle departure; T e,1is the time node when the heavy vehicle arrives at the terminal position; D1 is the physical node corresponding to the heavy vehicle arriving at the terminal position; T s,2 is the time node of the empty car departure; O2 is the physical node corresponding to the starting position of the empty car departure; T e，2 is the time node when the empty vehicle arrives at the terminal position; D2 is the physical node corresponding to the empty vehicle arriving at the terminal position; T s,3 is the time when the vehicle arrives at O3; T e，3 is the time when the vehicle departs from O3; O3 is the physical node corresponding to the vehicle's stop position;

[0033] A virtual starting point and a virtual end point are set outside the nodes of the space-time network of the heavy and empty truck transportation service. 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, and the space-time node corresponding to the end position of the logistics is connected to the virtual end point to construct a virtual arc, and the cargo flow is set on each virtual arc to obtain a transportation 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 space-time 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] min Cost = Cost1 + Cost2 + Cost3

[0038]

[0039]

[0040] Among them, min is the minimum function; Cost is the comprehensive transportation cost; Cost1 is the transportation cost of construction materials; Cost2 is the empty truck transportation cost; Cost3 is the penalty cost for delayed delivery of construction materials; A L is the heavy vehicle arc set; b1 is the heavy vehicle arc index; l b1 is the cost of heavy vehicle transportation through arc b1; F is the set of vehicle types; f is the vehicle type index; is a decision variable, representing the traffic flow of type f on arc b1; A E is the empty arc set; b2 is the empty arc index; e b2 is the cost of transporting an empty vehicle through arc b2; is a decision variable, representing the traffic flow of type f on arc b2; δ is the penalty coefficient for delayed delivery of unit materials; G is the set of engineering material transportation requirements; g is the cargo flow index; m g is the volume of cargo flow g; max is the maximum value function; is a 0-1 decision variable. If the cargo flow g flows through the arc b1, its value is 1, otherwise it is 0; t b1 is the departure time of arc b1; h b1 is the transport time of arc b1; w g is the specified arrival time of cargo flow g; is a collection of cargo flows that transport s types of materials starting from supply point p; To reach the demand point q, transport the cargo flow set of s types of materials; G f A collection of freight flows transported using F-type trucks.

[0041] The beneficial effects of the above further scheme are as follows: the optimization target of emergency allocation of construction materials includes transportation costs, vehicle allocation costs and delay costs of construction materials, 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.

[0042] Furthermore, the constraints of the optimization model for emergency deployment of engineering materials 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:

[0043]

[0044]

[0045] in, is the set of arcs starting from node n; r1 is the index of the arc starting from node n; is the set of arcs that reach node n; r2 is the arc index that reaches node n; is a 0-1 decision variable. If the cargo flow g flows through the link arc r1, its value is 1, otherwise it is 0; is a 0-1 decision variable. If the cargo flow g flows through the link arc r2, its value is 1, otherwise it is 0; N is the set of spatiotemporal nodes in the spatiotemporal service network for engineering material transportation; G is the set of engineering material transportation demand; g is the cargo flow index; V o V is the virtual starting point of the cargo flow; d It is the virtual end point of the cargo flow; is a decision variable, representing the traffic flow of type f on arc r1; is a decision variable, representing the vehicle flow of type f on arc r2; F is the set of vehicle types; f is the vehicle type index; v f is the number of vehicles of type f; U o It is the super starting point of the traffic flow; U d It is the super terminal of the traffic flow; G f A collection of freight flows for transportation using F-type trucks; is a 0-1 decision variable. If the cargo flow g flows through the arc b1, its value is 1, otherwise it is 0; m g is the volume of cargo flow g; is a decision variable, representing the traffic flow of type f on arc b1; d f A is the load capacity of the vehicle of type f; L is the heavy vehicle arc set; b1 is the heavy vehicle arc index; is a decision variable, representing the traffic flow of type f on arc b; b is the index of the union of the set of loaded arcs and the set of empty arcs; C r is the maximum throughput capacity of path r; R' is the transport path set; r is the transport path index; is a decision variable, representing the traffic flow of type f on arc b2; is the stock of type f vehicles at node n in the initial stage; P is the set of supply points; Q is the set of demand points; o b2 Indicates the starting point of arc b2; is a decision variable. When the cargo flow g flows through arc b3, it is 0. b3 is A E ∪A D ∪A U The arc index in A E is the set of empty arcs; A D A is the delayed arc set; U It is a super arc set; is a decision variable. When the cargo flow g flows through arc b4, it is 0 or 1. b4 is A L ∪A V The arc index in A V is a set of virtual arcs; is a decision variable, indicating the traffic flow of type f on arc b5; b5 is the virtual arc index; is a decision variable, representing the traffic flow of type f on arc b6; b6 is A L ∪A E ∪A D ∪A U The arc index in ; Z is a set of integers.

[0046] The beneficial effect of the above further solution is that the optimized design of the constraint conditions can ensure the feasibility of the final deployment solution and reduce the difficulty of solving the problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flow chart of the method.

[0048] Figure 2 A technical roadmap for the emergency deployment plan module for engineering materials in complex and dangerous areas in the embodiment of the present invention.

[0049] Figure 3 Schematic diagram of the space-time service network for heavy and empty vehicle transportation in an embodiment of the present invention.

[0050] 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.

[0051] Figure 5 This is a 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

[0052] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0053] 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:

[0054] 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 environment based on external environmental data;

[0055] According to the transportation time of engineering materials, the warning quantity of engineering material demand is calculated based on the warning scenario;

[0056] 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.

[0057] 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.

[0058] In this embodiment, the present invention is Figure 1 The implementation of the technical solution shown in the figure specifically includes three links: calculation of engineering material transportation time, early warning of engineering material transportation demand, and emergency allocation of engineering materials. Ultimately, it realizes the integrated preparation of emergency allocation plans for engineering materials for different categories, different early warning scenarios, and different organizational objects under different external environmental influence conditions. The method for calculating the transportation time of engineering materials refers to the invention patent "Method for calculating the transportation time of engineering materials in complex and dangerous areas based on risk prediction" (application number: 202310779630.2).

[0059] When the warning scenario is a single supplier and material transportation is affected, the expression of the engineering material demand warning quantity is:

[0060]

[0061] W 1,c =D c +S c -P c ×d t -P c ×m t

[0062] Among them, Q 1,c is the demand warning quantity of engineering material category c at the current demand point when the transportation scenario is a single supplier and the material transportation is affected; W 1,c is the remaining quantity of engineering material category c when the material transportation of the current demand point is affected when the transportation scenario is a single supplier; |·| is the absolute value; D c S is the current storage capacity of engineering material category c at the current demand point; c P is the quantity of engineering material category c that can reach the current demand point during the affected transportation period; c is the average daily consumption of engineering material category c at the current demand point; d t is the minimum material reserve time; m t It is the transportation time of engineering materials from suppliers to current demand points under the influence of external environment.

[0063] 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:

[0064]

[0065] W 2,c =D c +∑ i≠a S i,c m a +S a,c -P c d t -Pc m a

[0066] Among them, Q 2,c is the demand warning quantity of engineering material category c at the current demand point when the warning scenario is multiple suppliers and the material transportation of a single supplier is affected; W 2,c is the remaining quantity of engineering material category c at the current demand point when the material transportation of a single supplier is affected when the early warning scenario is multiple suppliers; |·| is the absolute value; D c is the existing storage volume of engineering material category c at the current demand point; i is the index of the engineering material supplier; a is the supplier affected by the material transportation; S i,c is the average daily transportation volume of engineering material category c of supplier i; m a S is the transportation time of engineering materials from supplier a to the current demand point under the influence of the external environment; a,c The quantity of engineering material category c that can be transported by supplier a to the current demand point during the affected period; P c is the average daily consumption of engineering material category c at the current demand point; d t This is the minimum material reserve time.

[0067] 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:

[0068]

[0069] Among them, Q 3,c is the demand warning quantity of engineering material category c at the current demand point when the warning scenario is multiple suppliers and the material transportation of a single supplier is affected; W 3,c is the remaining quantity of engineering material category c at the current demand point when the material transportation of a single supplier is affected when the early warning scenario is multiple suppliers; |·| is the absolute value; D c is the existing storage capacity of engineering material category c at the current demand point; i is the index of the engineering material supplier; R is the supplier set of engineering material category c at the current demand point; R c S is the set of suppliers affected by the transportation of engineering materials of the current demand point c; i,c is the average daily transportation volume of engineering material category c of supplier i; maxT is the longest time that the transportation of engineering materials is affected; a is the supplier whose material transportation is affected; S a,c The quantity of engineering material category c that can be transported by supplier a to the current demand point during the affected period; m i P is the transportation time of engineering materials from supplier i to the current demand point under the influence of external environment; c is the average daily consumption of engineering material category c at the current demand point; d tThis is the minimum material reserve time.

[0070] like Figure 3 , Figure 4 and Figure 5 As shown, the construction of a spatiotemporal service network for the transportation of construction materials is carried out according to the early warning quantity of the construction material demand, specifically:

[0071] Obtain the location of suppliers, demand points, material storage points and vehicles in transit;

[0072] 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 (T s,1 , O1) to point (T e,1 , D1) of the heavy vehicle arc, point (T s,2 , O2) to point (T e，2 , D2) of the empty arc and point (T s,3 , O3) to point (T e，3 , O3) delay arc, and obtain the space-time network of heavy and empty vehicle transportation service; T s,1 is the time node of the heavy vehicle departure; O1 is the physical node corresponding to the starting position of the heavy vehicle departure; T e,1 is the time node when the heavy vehicle arrives at the terminal position; D1 is the physical node corresponding to the heavy vehicle arriving at the terminal position; T s,2 is the time node of the empty car departure; O2 is the physical node corresponding to the starting position of the empty car departure; T e，2 is the time node when the empty vehicle arrives at the terminal position; D2 is the physical node corresponding to the empty vehicle arriving at the terminal position; T s,3 is the time when the vehicle arrives at O3; T e，3 is the time when the vehicle departs from O3; O3 is the physical node corresponding to the vehicle's stop position;

[0073] A virtual starting point and a virtual end point are set outside the nodes of the space-time network of the heavy and empty truck transportation service. 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, and the space-time node corresponding to the end position of the logistics is connected to the virtual end point to construct a virtual arc, and the cargo flow is set on each virtual arc to obtain a transportation 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;

[0074] 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.

[0075] In this embodiment, the technical route for the preparation of emergency deployment plans for engineering materials in complex and dangerous areas is as follows: Figure 2 As shown in the figure, (1) Data preprocessing. The basic data includes 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 the work points, and the transportation network between the work points. The dynamic data includes the supply, demand, and warning quantity of engineering materials, the storage quantity of different types of engineering materials in different sections / work areas / work points, the location of vehicles in transit (which can distinguish between vehicles affected by transportation and vehicles not affected by transportation) and their carrying capacity.

[0076] The location information of suppliers, demand points, and material storage points in different sections / work areas / work sites is converted into physical nodes in the space-time network. The vehicles (or vehicle sets) in transit are analogous to engineering material suppliers and converted into physical nodes in the space-time network. The physical nodes are discretized to form a space-time service network. The supply volume of engineering material supply points, the storage volume of different types of engineering materials in other sections / work areas / work sites, and the loading volume of vehicles in transit can be converted into supply volume, and the warning volume of engineering materials is converted into demand volume, which become the supply and demand parameters in the space-time service network.

[0077] (2) Establish a spatiotemporal service network for the emergency deployment of engineering materials in complex and dangerous areas.

[0078] The process is divided into three stages.

[0079] ① The first stage is to build a heavy and empty vehicle transportation service network. The construction 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 running 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 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.

[0080] 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 to connect with the time-space service network node corresponding to the physical starting point of the cargo flow to build a virtual arc. Similarly, a virtual end point is added to connect with the time-space service network node corresponding to the physical end point of the cargo flow to build 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.

[0081] ③ In the third stage, super starting and ending points and super arcs are added to the traffic flow. The purpose of adding super starting and ending points and arcs is to enable the model to be solved in any scenario. For each type of vehicle, a set of super starting points and super end points are added to the spatiotemporal service network to represent the starting point and end point of the traffic flow, respectively. Before the decision cycle, a super arc is constructed between the super starting point and the starting point of each node, and at the end of the decision cycle, a super arc is constructed between the super end point and the end point of each node. The traffic flow on each arc can represent the initial empty vehicle stock of each starting point, and the total flow between the super starting point and the corresponding super end point is the total number of vehicles of this type.

[0082] The objective function of the optimization model for emergency deployment of engineering materials is:

[0083] min Cost = Cost1 + Cost2 + Cost3

[0084]

[0085] Among them, min is the minimum function; Cost is the comprehensive transportation cost; Cost1 is the transportation cost of construction materials; Cost2 is the empty truck transportation cost; Cost3 is the penalty cost for delayed delivery of construction materials; A L is the heavy vehicle arc set; b1 is the heavy vehicle arc index; l b1 is the cost of heavy vehicle transportation through arc b1; F is the set of vehicle types; f is the vehicle type index; is a decision variable, representing the traffic flow of type f on arc b1; A E is the empty arc set; b2 is the empty arc index; e b2 is the cost of transporting an empty vehicle through arc b2; is a decision variable, representing the traffic flow of type f on arc b2; δ is the penalty coefficient for delayed delivery of unit materials; G is the set of engineering material transportation requirements; g is the cargo flow index; m g is the volume of cargo flow g; max is the maximum value function; is a 0-1 decision variable. If the cargo flow g flows through the arc b1, its value is 1, otherwise it is 0; t b1 is the departure time of arc b1; h b1 is the transport time of arc b1; w g is the specified arrival time of cargo flow g; is a collection of cargo flows that transport s types of materials starting from supply point p; To reach the demand point q, transport the cargo flow set of s types of materials; G f A collection of freight flows transported using F-type trucks.

[0086] The constraints of the optimization model for emergency deployment of engineering materials 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:

[0087]

[0088] in, is the set of arcs starting from node n; r1 is the index of the arc starting from node n; is the set of arcs that reach node n; r2 is the arc index that reaches node n; is a 0-1 decision variable. If the cargo flow g flows through the link arc r1, its value is 1, otherwise it is 0; is a 0-1 decision variable. If the cargo flow g flows through the link arc r2, its value is 1, otherwise it is 0; N is the set of spatiotemporal nodes in the spatiotemporal service network for engineering material transportation; G is the set of engineering material transportation demand; g is the cargo flow index; V o V is the virtual starting point of the cargo flow; d It is the virtual end point of the cargo flow; is a decision variable, representing the traffic flow of type f on arc r1; is a decision variable, representing the vehicle flow of type f on arc r2; F is the set of vehicle types; f is the vehicle type index; v f is the number of vehicles of type f; U o It is the super starting point of the traffic flow; U d It is the super terminal of the traffic flow; G f A collection of freight flows for transportation using F-type trucks; is a 0-1 decision variable. If the cargo flow g flows through the arc b1, its value is 1, otherwise it is 0; m g is the volume of cargo flow g; is a decision variable, representing the traffic flow of type f on arc b1; d f A is the load capacity of the vehicle of type f; L is the heavy vehicle arc set; b1 is the heavy vehicle arc index; is a decision variable, representing the traffic flow of type f on arc b; b is the index of the union of the set of loaded arcs and the set of empty arcs; C r is the maximum throughput capacity of path r; R' is the transport path set; r is the transport path index; is a decision variable, representing the traffic flow of type f on arc b2; is the stock of type f vehicles at node n in the initial stage; P is the set of supply points; Q is the set of demand points; o b2 Indicates the starting point of arc b2; is a decision variable. When the cargo flow g flows through arc b3, it is 0. b3 is A E ∪A D ∪A U The arc index in A E is the set of empty arcs; A D A is the delayed arc set; U It is a super arc set; is a decision variable. When the cargo flow g flows through arc b4, it is 0 or 1. b4 is A L ∪A V The arc index in A V is a set of virtual arcs; is a decision variable, indicating the traffic flow of type f on arc b5; b5 is the virtual arc index; is a decision variable, representing the traffic flow of type f on arc b6; b6 is A L ∪A E ∪A D ∪A U The arc index in ; Z is a set of integers.

Claims

1. A method for emergency deployment of engineering materials in complex and dangerous areas, characterized in that: 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 environment based on external environmental data; According to the transportation time of engineering materials, the warning quantity of engineering material demand is calculated based on the warning scenario; 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.

2. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: 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.

3. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 2 is characterized by: When the warning scenario is a single supplier and material transportation is affected, the expression of the engineering material demand warning quantity is: W 1,c =D c +S c -P c ×d t -P c ×m t Among them, Q 1,c is the demand warning quantity of engineering material category c at the current demand point when the transportation scenario is a single supplier and the material transportation is affected; W 1,c is the remaining quantity of engineering material category c when the material transportation of the current demand point is affected when the transportation scenario is a single supplier; |·| is the absolute value; D c S is the existing storage capacity of engineering material category c at the current demand point; c P is the quantity of engineering material category c that can reach the current demand point during the affected transportation period; c is the average daily consumption of engineering material category c at the current demand point; d t is the minimum material reserve time; m t It is the transportation time of engineering materials from suppliers to current demand points under the influence of external environment.

4. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 2 is characterized in that: 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: W 2,c =D c +Σ i≠a S i,c m a +S a,c -P c d t -P c m a Among them, Q 2,c is the demand warning quantity of engineering material category c at the current demand point when the warning scenario is multiple suppliers and the material transportation of a single supplier is affected; W 2,c is the remaining quantity of engineering material category c at the current demand point when the material transportation of a single supplier is affected when the early warning scenario is multiple suppliers; |·| is the absolute value; D c is the existing storage volume of engineering material category c at the current demand point; i is the index of the engineering material supplier; a is the supplier affected by the material transportation; S i,c is the average daily transportation volume of engineering material category c of supplier i; m a S is the transportation time of engineering materials from supplier a to the current demand point under the influence of the external environment; a,c The quantity of engineering material category c that can be transported by supplier a to the current demand point during the affected period; P c is the average daily consumption of engineering material category c at the current demand point; d t This is the minimum material reserve time.

5. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 2 is characterized by: 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: Among them, Q 3,c is the demand warning quantity of engineering material category c at the current demand point when the warning scenario is multiple suppliers and the material transportation of a single supplier is affected; W 3,c is the remaining quantity of engineering material category c at the current demand point when the material transportation of a single supplier is affected when the early warning scenario is multiple suppliers; |·| is the absolute value; D c is the existing storage capacity of engineering material category c at the current demand point; i is the index of the engineering material supplier; R is the supplier set of engineering material category c at the current demand point; R c S is the set of suppliers affected by the transportation of engineering materials of the current demand point c; i,c is the average daily transportation volume of engineering material category c of supplier i; maxT is the longest time that the transportation of engineering materials is affected; a is the supplier whose material transportation is affected; S a,c The quantity of engineering material category c that can be transported by supplier a to the current demand point during the affected period; m i P is the transportation time of engineering materials from supplier i to the current demand point under the influence of external environment; c is the average daily consumption of engineering material category c at the current demand point; d t This is the minimum material reserve time.

6. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: The construction of a spatiotemporal service network for the transportation of construction materials according to the early warning quantity of construction 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 space-time network with time nodes as the horizontal axis and physical nodes as the vertical axis; For each freight flow, in the basic space-time network, combined with the vehicle running time, construct a point (T s,1 , O1) to point (T e,1 , D1) of the heavy vehicle arc, point (T s,2 , O2) to point (T e，2 , D2) of the empty arc and point (T s,3 , O3) to point (T e，3 , O3) delay arc, and obtain the space-time network of heavy and empty vehicle transportation service; T s,1 is the time node of the heavy vehicle departure; O1 is the physical node corresponding to the starting position of the heavy vehicle departure; T e,1 is the time node when the heavy vehicle arrives at the terminal position; D1 is the physical node corresponding to the heavy vehicle arriving at the terminal position; T s,2 is the time node of the empty car departure; O2 is the physical node corresponding to the starting position of the empty car departure; T e，2 is the time node when the empty vehicle arrives at the terminal position; D2 is the physical node corresponding to the empty vehicle arriving at the terminal position; T s,3 is the time when the vehicle arrives at O3; T e，3 is the time when the vehicle departs from O3; O3 is the physical node corresponding to the vehicle's stop position; A virtual starting point and a virtual end point are set outside the nodes of the space-time network of the heavy and empty truck transportation service. 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, and the space-time node corresponding to the end position of the logistics is connected to the virtual end point to construct a virtual arc, and the cargo flow is set on each virtual arc to obtain a transportation 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.

7. 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 optimization model for emergency deployment of engineering materials is: min Cost = Cost1 + Cost2 + Cost3 Among them, min is the minimum function; Cost is the comprehensive transportation cost; Cost1 is the transportation cost of construction materials; Cost2 is the empty truck transportation cost; Cost3 is the penalty cost for delayed delivery of construction materials; A L is the heavy vehicle arc set; b1 is the heavy vehicle arc index; l b1 is the cost of heavy vehicle transportation through arc b1; F is the set of vehicle types; f is the vehicle type index; is a decision variable, representing the traffic flow of type f on arc b1; A E is the empty arc set; b2 is the empty arc index; e b2 is the cost of transporting an empty vehicle through arc b2; is a decision variable, representing the traffic flow of type f on arc b2; δ is the penalty coefficient for delayed delivery of unit materials; G is the set of engineering material transportation requirements; g is the cargo flow index; m g is the volume of cargo flow g; max is the maximum value function; is a 0-1 decision variable. If the cargo flow g flows through the arc b1, its value is 1, otherwise it is 0; t b1 is the departure time of arc b1; h b1 is the transport time of arc b1; w g is the specified arrival time of cargo flow g; is a collection of cargo flows that transport s types of materials starting from supply point p; To reach the demand point q, transport the cargo flow set of s types of materials; G f A collection of freight flows transported using F-type trucks.

8. The method for emergency deployment of engineering materials in complex and dangerous areas according to claim 1 is characterized by: The constraints of the optimization model for emergency deployment of engineering materials 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, is the set of arcs starting from node n; r1 is the index of the arc starting from node n; is the set of arcs that reach node n; r2 is the arc index that reaches node n; is a 0-1 decision variable. If the cargo flow g flows through the link arc r1, its value is 1, otherwise it is 0; is a 0-1 decision variable. If the cargo flow g flows through the link arc r2, its value is 1, otherwise it is 0; N is the set of spatiotemporal nodes in the spatiotemporal service network for engineering material transportation; G is the set of engineering material transportation demand; g is the cargo flow index; V o V is the virtual starting point of the cargo flow; d It is the virtual end point of the cargo flow; is a decision variable, representing the traffic flow of type f on arc r1; is a decision variable, representing the vehicle flow of type f on arc r2; F is the set of vehicle types; f is the vehicle type index; v f is the number of vehicles of type f; U o It is the super starting point of the traffic flow; U d It is the super terminal of the traffic flow; G f A collection of freight flows for transportation using F-type trucks; is a 0-1 decision variable. If the cargo flow g flows through the arc b1, its value is 1, otherwise it is 0; m g is the volume of cargo flow g; is a decision variable, representing the traffic flow of type f on arc b1; d f A is the load capacity of the vehicle of type f; L is the heavy vehicle arc set; b1 is the heavy vehicle arc index; is a decision variable, representing the traffic flow of type f on arc b; b is the index of the union of the set of loaded arcs and the set of empty arcs; C r is the maximum throughput capacity of path r; R' is the transport path set; r is the transport path index; is a decision variable, representing the traffic flow of type f on arc b2; is the stock of type f vehicles at node n in the initial stage; P is the set of supply points; Q is the set of demand points; o b2 Indicates the starting point of arc b2; is a decision variable. When the cargo flow g flows through arc b3, it is 0. b3 is A E ∪A D ∪A U The arc index in A E is the set of empty arcs; A D A is the delayed arc set; U It is a super arc set; is a decision variable. When the cargo flow g flows through arc b4, it is 0 or 1. b4 is A L ∪A V The arc index in A V is a set of virtual arcs; is a decision variable, indicating the traffic flow of type f on arc b5; b5 is the virtual arc index; is a decision variable, representing the traffic flow of type f on arc b6; b6 is A L ∪A E ∪A D ∪A U The arc index in ; Z is a set of integers.

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