Two-stage robust optimization method for emergency supplies in passenger hub with internal and external supply sources

Abstract

This invention discloses a two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply sources. The method constructs a dual-source supply guarantee system integrating internal reserves and external replenishment, and divides the system into a two-stage time-series decision-making framework. It standardizes the definition of model sets, decision variables, and operating parameters, builds a two-stage robust optimization model that considers both delivery timeliness and overall cost, and configures full-dimensional constraints. A column and constraint generation algorithm is used to decompose the nested structure of the model, iteratively traversing and filtering the worst-case disaster scenario to obtain the optimal supply configuration and allocation scheme. This invention achieves coordinated scheduling of internal and external supplies, effectively responding to various uncertain emergency scenarios, balancing the economic efficiency of supply reserves with the robustness of disaster response, and boasts high solution efficiency and strong applicability. It is suitable for the planning, storage, and dynamic scheduling of emergency supplies in various large-scale integrated passenger transport hubs.

Description

Technical Field

[0001] This invention belongs to the field of information security technology and relates to the tracing of source of robust and high-quality large model generated content. Specifically, this invention relates to a watermarking method for tracing the source of large model generated content based on content-aware injection. Background Technology

[0002] Large-scale integrated passenger transport hubs, characterized by high passenger flow and complex functional zoning, are prone to various emergencies such as fires, floods, earthquakes, and mass incidents. The reserve and allocation of emergency supplies are core safeguards for the safe operation of these hubs. Currently, the management of emergency supplies in domestic passenger transport hubs generally suffers from the following problems: The supply model is singular, with most hubs relying solely on internal warehouses as the only source of supplies. They have not established a supply system that links with external material centers. When major disasters occur and internal inventory is depleted, supplies cannot be replenished in a timely manner, which can easily lead to the interruption of rescue efforts. The decision-making process was chaotic, failing to distinguish between the two major work phases of pre-disaster static configuration and post-disaster dynamic allocation. The adoption of a unified static planning model made it difficult to adapt to the dynamic changes in demand locations, demand quantities, and channel status after a disaster. The ability to handle uncertainty is insufficient. Traditional deterministic optimization models do not quantify three typical disturbances: shifts in demand locations, fluctuations in material demand, and damage to emergency access routes. The solutions are only applicable to a single scenario and have poor robustness under extreme disasters, which can easily lead to problems such as material shortages and blocked routes. The model is difficult to solve. The two-stage robust optimization model for multiple scenarios has a nested min-max extreme value structure, which cannot be directly calculated by conventional solvers. Traditional traversal algorithms are prone to the curse of dimensionality and have low computational efficiency.

[0003] In the prior art, CN115718981B discloses a two-stage robust optimization method for emergency facility site selection and route optimization based on second-order cone optimization. First, a two-stage high-order nonlinear robust optimization model considering traffic flow is constructed. Then, the order of the high-order nonlinear function is reduced. Next, the main problem of the two-stage high-order nonlinear robust optimization model and its sub-problems are constructed. Finally, the column and constraint generation algorithm is iterated until the upper and lower bounds of the original problem meet the requirements, and a feasible solution is output. This invention can improve the robustness of emergency facility site selection results; the high-order nonlinear expression describes congestion time, which is closer to the actual situation.

[0004] This technical solution adopts a single-source emergency facility site selection and capacity configuration, does not involve an internal and external dual-source supply system, does not divide the time-series decision-making framework into pre-disaster static configuration and post-disaster dynamic allocation, does not construct a discrete disaster scenario set for the three types of uncertainties: demand location, material demand, and emergency channel status, and does not configure internal and external supply linkage constraints. The application scenario is general emergency facility site selection rather than passenger transport hubs.

[0005] In the prior art, CN121707219A discloses a method for dispatching emergency supplies for power grids under rain, snow, and ice disasters based on a robust optimization algorithm. This method includes: acquiring meteorological data and transportation facility information under rain, snow, and ice disasters, and constructing a line fault probability prediction model; generating disaster scenarios using Monte Carlo simulation, and obtaining a set of typical disaster scenarios through scenario reduction technology; constructing a joint dispatch optimization mathematical model considering emergency supply allocation and emergency equipment deployment, including a pre-disaster pre-deployment stage and a dynamic response stage during the disaster; using an improved column and constraint generation algorithm to solve a two-stage robust optimization problem, gradually converging to the robust optimal solution, outputting the optimal dispatch scheme for emergency supplies and emergency repair teams, determining the repair objectives and optimal travel routes of the repair teams, and generating a collaborative recovery strategy for the power grid and transportation system after the disaster, thereby achieving rapid post-disaster recovery and rational allocation of emergency resources, and improving the emergency response speed and recovery capability of the power grid under rain, snow, and ice disasters.

[0006] This technical solution adopts a single-source resource pre-positioning point model, without involving an internal and external dual-source supply system. The uncertainties are weather and line failures rather than demand points, material demand and emergency channel status. The application scenario is power grid emergency rather than passenger transport hubs.

[0007] The prior art, CN120450323A, discloses a method and related apparatus for dispatching emergency supplies within and outside the city under uncertain conditions, relating to the field of material dispatching technology. The method includes: extracting each boundary entry point as an external material input point from the city's basic road network model, embedding them and known material points within the city into the basic road network model as nodes; subsequently, establishing a four-level emergency material dispatching network model based on complex network theory using dual topology; and solving for the optimal material delivery path between nodes under uncertain material supply conditions based on a multi-objective function that minimizes path complexity and the difference in global demand satisfaction exponent. Finally, emergency material dispatching within and outside the city is carried out accordingly. This application's scheme comprehensively considers the hierarchical dispatching relationship between materials within and outside the city and the uncertainty of external material delivery, ensuring the synergy and efficiency of material dispatching within and outside the city, and improving the robustness and adaptability of emergency material dispatching, as well as the overall efficiency and risk resistance of the network.

[0008] This technical solution does not employ a two-stage robust optimization framework, does not use a column and constraint generation algorithm, and does not distinguish between the two-stage decision-making process of pre-disaster static configuration and post-disaster dynamic allocation. Its division between urban and rural areas is based on geographical boundaries rather than the attributes of the supply entity, and its technical approach is completely different from that of this invention.

[0009] In summary, the current material management model based solely on internal reserves and static planning is insufficient to meet the emergency support needs of large-scale integrated passenger transport hubs under conditions of multiple disasters and high risks. Therefore, there is an urgent need for a two-stage robust optimization method that combines internal reserves and external supplies, adapting to time-series decision-making and multidimensional uncertainties, to improve the scientific rigor and stability of emergency material allocation and distribution.

[0010] In view of the above-mentioned shortcomings of the existing technology, the present invention is proposed. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of existing passenger transport hub emergency material replenishment models, such as simplistic approaches, unreasonable timing decisions, insufficient uncertainty characterization, weak robustness, and difficulty in solving nested models. This invention provides a two-stage robust optimization method for passenger transport hub emergency material replenishment based on both internal and external sources. The invention establishes a dual replenishment system consisting of an internal warehouse and an external material center, divides decision-making into pre-disaster and post-disaster stages, quantifies multi-dimensional uncertainties and constructs a robust optimization model, and employs a column and constraint generation algorithm for efficient solution. Under the premise of ensuring sufficient material supply, it balances reserve costs and emergency timeliness, comprehensively improving the emergency material support capacity of passenger transport hubs.

[0012] To achieve the above objectives, the present invention adopts the following technical solution, including: A two-stage robust optimization method for emergency supplies in passenger transport hubs with both internal and external supply sources includes the following steps: Step S1: Establishing an internal and external dual-source system, dividing decision-making into two stages, and constructing scenarios to characterize uncertainty; Step S10: Establish an internal and external dual-source system. Designate multiple fixed warehouses within the hub as internal reserve warehouses, with the internal reserve warehouses of the hub as the internal source; designate the urban emergency material reserve center and surrounding area rescue warehouses as external material centers, with the external material centers as the external source, forming a two-level material supply network with internal and external sources. Step S11 involves a two-stage decision-making process, dividing emergency supplies management into a pre-disaster static configuration stage and a post-disaster dynamic allocation stage. The pre-disaster static configuration stage involves determining the maximum storage capacity and regular inventory levels of each internal warehouse, as well as the initial supply chain from the warehouse to potential demand points in waiting areas, platforms, and equipment areas, based on historical risk data from the hub, before a disaster occurs. The dynamic allocation stage involves identifying disaster-stricken locations, material demand, and damage to transportation routes in real time after an emergency occurs; prioritizing the allocation of internal inventory for rescue efforts; if inventory is insufficient to cover demand, initiating cross-regional replenishment from external material centers and adjusting transportation routes in real time based on the status of transportation routes. Step S12, constructing a scenario to depict uncertainty; The uncertainties and disruptive factors identified are: shifts in demand locations, fluctuations in the amount of materials needed, and changes in the accessibility of emergency channels. By combining historical risk data, including but not limited to fire, flooding, and equipment failure, a discrete disaster scenario set is constructed as the basis for model validation and optimization.

[0013] Step S2, Standardization definition of model elements: Define the model set, phased decision variables, and model operating parameters in a unified manner; Step S3: Build a two-stage robust optimization model: Construct a two-stage multi-objective objective function, including a static configuration objective function in the first stage and a dynamic allocation objective function in the second stage, and configure full-dimensional constraints on inventory, supply and demand, transportation, variable attributes, and internal and external supply linkage. Step S4, iterative solution of the model: The nested extreme value structure of the robust model is decomposed by column and constraint generation algorithm. The worst disaster scenario is screened by iteratively selecting the main problem and sub-problems. After convergence, the optimal allocation of materials and the internal and external linkage coordination scheme are output.

[0014] The preferred technical solution provided by this invention is as follows: In step S2, the model set includes an internal reserve warehouse set, an external material center set, a discrete disaster scenario set, a pre-disaster potential demand point set, a post-disaster actual demand point set, and a two-stage supply and demand chain set.

[0015] The preferred technical solution provided by the present invention is as follows: In step S2, the phased decision variables are first-stage static decision variables and second-stage dynamic decision variables; wherein, The first-stage static decision variables include the internal reserve point material storage quantity, the initial supply chain 0-1 variable, and the pre-positioned material allocation quantity. The dynamic decision variables in the second stage include the activation of external materials (0-1 variable), the transportation volume of external materials, the post-disaster internal material allocation (0-1 variable), and the post-disaster internal material transportation volume.

[0016] Another preferred technical solution provided by the present invention is as follows: In step S3, the objective function of the first stage static configuration stage aims to minimize the internal material storage cost and the comprehensive cost of material pre-positioning; the objective function of the second stage dynamic allocation stage aims to minimize the material delivery time, internal transportation cost, and external supply transportation cost; the overall optimization objective is to minimize the comprehensive cost and optimize the material delivery time under the worst disaster scenario.

[0017] A further preferred technical solution provided by the present invention is as follows: In step S3, the constraints include warehouse capacity constraints, sufficient material supply constraints, two-stage supply relationship inheritance constraints, link and transportation volume linkage constraints, 0-1 variable constraints, non-negative material flow constraints, internal inventory call-up limit constraints, and internal and external replenishment linkage constraints.

[0018] Another preferred technical solution provided by the present invention is: In step S4, the execution steps of the column and constraint generation algorithm are as follows: Step S41: Initialize the algorithm's convergence accuracy, disaster scenario parameters, and iteration upper and lower bounds, and construct the initial master problem containing only basic constraints; Step S42: Solve the main problem to obtain the static resource allocation scheme, and update the lower bound of the iteration. Step S43: Iterate through all discrete disaster scenarios to solve subproblems, select the worst-case perturbation scenario with the highest comprehensive cost, and update the upper bound of the iteration. Step S44: Generate the cutting plane and new constraints based on the worst-case scenario, and feed the constraints and variables back to the main problem to complete the model update; Step S45: Repeat the iterative steps from S41 to S45. Stop iterating when the difference between the upper and lower bounds is less than the preset convergence accuracy, and output the globally optimal configuration allocation scheme.

[0019] Another preferred technical solution provided by the present invention is as follows: The main problem corresponds to the pre-disaster static configuration stage, which is used to determine long-term fixed decisions such as internal warehouse inventory and initial supply chain; the sub-problems correspond to the post-disaster dynamic allocation stage, which are used to verify the feasibility and economy of internal and external material allocation schemes under different disaster scenarios.

[0020] This invention also provides a robust optimization device for a two-stage robust optimization method for emergency supplies in passenger transport hubs that enables dual-source replenishment from both internal and external sources, comprising: The system and scenario construction module is used to build an internal and external dual-source material support system, divide a two-stage decision-making architecture, identify uncertain factors, and construct a set of discrete disaster scenarios. The model definition module is used to uniformly define the model set, phased decision variables, and model running parameters; The robust modeling module is used to construct a two-stage multi-objective objective function, configure full-dimensional constraints, and complete the construction of a robust optimization model. The iterative solution module is used to run column and constraint generation algorithms, decompose the main problem and sub-problems to complete iterative solutions, and output the optimal resource allocation and internal and external linkage coordination scheme.

[0021] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0022] The present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described above.

[0023] Beneficial effects: Dual-source supply enhances capacity by establishing an internal warehouse and external material center linkage system. Internal inventory prioritizes response to routine disasters, while external centers serve as a backup supply for major disasters, completely resolving the problem of no material replenishment after the depletion of single internal reserves, thus providing a more comprehensive level of support.

[0024] The timing is aligned with the actual management process, strictly distinguishing between the two stages of pre-disaster static pre-configuration and post-disaster dynamic allocation. It retains the initial supply chain and adds a dynamic adjustment mechanism, fully matching the actual workflow of emergency material planning and scheduling at passenger transport hubs, making the solution highly implementable.

[0025] It comprehensively characterizes uncertainty, constructs a set of discrete scenarios for three major uncertain factors: demand locations, material demand, and emergency access routes, and applies robust constraints based on worst-case scenarios to ensure that the solution remains feasible under various extreme disasters, significantly improving its ability to withstand disturbances.

[0026] Multi-objective collaborative optimization simultaneously optimizes warehousing costs, transportation costs, and material delivery time, balancing operational economics with emergency response timeliness, and avoiding resource waste or delays in rescue efforts caused by a single objective.

[0027] The algorithm is efficient and reliable. It uses a column and constraint generation algorithm to decompose the nested model, avoids the curse of dimensionality, and has a fast iterative convergence speed. It can be adapted to the multi-scenario and large-scale solution scenarios of large passenger transport hubs.

[0028] It is highly versatile, with flexible configuration of model parameters, and does not depend on specific spatial structures. It can be widely used in various integrated passenger transport hubs such as high-speed rail hubs, subway hubs, and multimodal transport hubs. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is the overall flowchart of the two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply according to the present invention. Figure 2 This is a schematic diagram of the dual-source material supply system and two-stage decision-making framework of the present invention; Figure 2 'a' is a schematic diagram of the internal reserve warehouse (internal source) replenishment system and the two-stage decision-making framework. Figure 2 b is a schematic diagram of the supply system and two-stage decision-making framework for designating the city's emergency material reserve center and surrounding area rescue warehouses as external material centers (external sources); Figure 3 This is an iterative flowchart of the column and constraint generation algorithm of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0031] Figure 1 This is the overall flowchart of the two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply according to the present invention. Figure 2 This is a schematic diagram of the dual-source material supply system and two-stage decision-making framework of the present invention; Figure 2 'a' is a schematic diagram of the internal reserve warehouse (internal source) replenishment system and the two-stage decision-making framework. Figure 2 b is a schematic diagram of the supply system and two-stage decision-making framework for designating the city's emergency material reserve center and surrounding area rescue warehouses as external material centers (external sources); Figure 3 This is an iterative flowchart of the column and constraint generation algorithm of this invention. Combined with... Figure 1 , Figure 2 as well as Figure 3 The present invention is described in detail below: Taking a large-scale integrated passenger transport hub combining road and rail transport as the implementation object, this invention presents a two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply sources, comprising the following steps: Step S1: Establishing an internal and external dual-source system, dividing decision-making into two stages, and constructing scenarios to characterize uncertainty; Step S10: Establish an internal and external dual-source system. Designate multiple fixed warehouses within the hub as internal reserve warehouses, with the internal reserve warehouses of the hub as the internal source; designate the urban emergency material reserve center and surrounding area rescue warehouses as external material centers, with the external material centers as the external source, forming a two-level material supply network with internal and external sources. Step S11 involves a two-stage decision-making process, dividing emergency supplies management into a pre-disaster static configuration stage and a post-disaster dynamic allocation stage. The pre-disaster static configuration stage involves determining the maximum storage capacity and regular inventory levels of each internal warehouse, as well as the initial supply chain from the warehouse to potential demand points in waiting areas, platforms, and equipment areas, based on historical risk data from the hub, before a disaster occurs. The dynamic allocation stage involves identifying disaster-stricken locations, material demand, and damage to transportation routes in real time after an emergency occurs; prioritizing the allocation of internal inventory for rescue efforts; if inventory is insufficient to cover demand, initiating cross-regional replenishment from external material centers and adjusting transportation routes in real time based on the status of transportation routes. Step S12, constructing a scenario to depict uncertainty; The uncertainties and disruptive factors identified are: shifts in demand locations, fluctuations in the amount of materials needed, and changes in the accessibility of emergency channels. By combining historical risk data, including but not limited to fire, flooding, and equipment failure, a discrete disaster scenario set is constructed as the basis for model validation and optimization.

[0032] Step S2, Standardization definition of model elements: Define the model set, phased decision variables, and model operating parameters in a unified manner; In step S2, the model set includes an internal reserve warehouse set, an external material center set, a discrete disaster scenario set, a pre-disaster potential demand point set, a post-disaster actual demand point set, and a two-stage supply and demand chain set.

[0033] In step S2, the phased decision variables are first-stage static decision variables and second-stage dynamic decision variables; wherein, The first-stage static decision variables include the internal reserve point material storage quantity, the initial supply chain 0-1 variable, and the pre-positioned material allocation quantity. The dynamic decision variables in the second stage include the activation of external materials (0-1 variable), the transportation volume of external materials, the post-disaster internal material allocation (0-1 variable), and the post-disaster internal material transportation volume.

[0034] The definitions of sets are shown in Table 1.

[0035] Table 1. Set Definition

[0036] The decision variables in the emergency supplies allocation phase mainly revolve around pre-positioned storage of supplies, emergency access planning, and initial supply deployment. Specific decision variables are shown in Table 2 below.

[0037] Table 2 Definitions of Decision Variables in the First Stage

[0038] The decision variables for the dynamic response phase of emergency supplies are used to represent the allocation and transportation decisions of various supplies during the post-disaster emergency response process. The specific variable symbols are shown in Table 3.

[0039] Table 3 Definitions of Decision Variables in the Second Stage

[0040] The model parameters are defined as shown in Table 4.

[0041] Table 4 Parameter Definitions

[0042] The parameter configuration input includes operating parameters such as unit transportation time, distance between nodes, unit price of materials, warehouse management cost, external material freight, maximum warehouse capacity, large constant, and time threshold.

[0043] Step S3: Build a two-stage robust optimization model: Construct a two-stage multi-objective objective function, including a static configuration objective function in the first stage and a dynamic allocation objective function in the second stage, and configure full-dimensional constraints on inventory, supply and demand, transportation, variable attributes, and internal and external supply linkage. In step S3, the objective function of the first stage static configuration stage aims to minimize the internal material storage cost and the comprehensive cost of material pre-positioning; the objective function of the second stage dynamic allocation stage aims to minimize the material delivery time, internal transportation cost, and external supply transportation cost; the overall optimization objective is to minimize the comprehensive cost and optimize the material delivery time under the worst-case disaster scenario. As shown in equations (1) to (3).

[0044] (1) (2) (3) In step S3, the constraints include warehouse capacity constraints, sufficient material supply constraints, two-stage supply relationship inheritance constraints, link and transportation volume linkage constraints, 0-1 variable constraints, non-negative material flow constraints, internal inventory call-up limit constraints, and internal and external replenishment linkage constraints.

[0045] The following constraints are configured sequentially: warehouse capacity constraint, material supply constraint, two-stage supply relationship inheritance constraint, link and transportation volume linkage constraint, 0-1 variable constraint, material non-negativity constraint, internal inventory call upper limit constraint, and internal and external replenishment linkage constraint, to ensure that the model logic conforms to the actual business rules. The specific constraints are shown in equations (4) to (22) below.

[0046] (4)

[0047] Constraint (4) means that node f is allowed to establish a supply relationship with demand point s only if node f is a pre-determined emergency reserve point.

[0048] (5)

[0049] Constraint (5) means that for the known demand points in the first stage, all demand points need to be supplied by at least one warehouse.

[0050] (6)

[0051] Constraint (6) indicates whether a road connection from f to s is established, and is a 0-1 variable.

[0052] (7)

[0053] Constraint (7) means that the warehouse storage volume should be greater than or equal to the warehouse's planned outsourcing volume of materials.

[0054] (8)

[0055] Constraint (8) indicates that the amount of materials planned to be dispatched from the warehouse should be greater than a certain proportion of the warehouse's demand.

[0056] (9)

[0057] Constraint (9) means that the warehouse storage capacity should be less than or equal to the warehouse capacity.

[0058] (10)

[0059] Constraint (10) means that when the route is not used for transportation, the transportation volume on the route must be 0.

[0060] (11)

[0061] Constraint (11) means that the quantity of materials stored in the warehouse is 0 when the storage point is not activated.

[0062] (12) (13) Constraints (12) and (13) indicate that the variables should be greater than or equal to 0 when they conform to reality. This indicates the amount of supplies stored at the reserve points during the first phase. This indicates the amount of materials supplied from the reserve point to the material demand point in the first stage. Both of these variables should be greater than or equal to 0.

[0063] The constraints in the second phase correspond to the situation in the emergency response phase, as shown in the following formula: (14) Among them, constraint (14) represents the requirement satisfaction constraint, for the set of actual requirement points in the second stage. For each demand point s in the scenario, the sum of the supply of materials from internal storage points and the supplementary transportation volume from external warehouses must not be less than the emergency material demand of that point after the scenario is revealed. This is to ensure that emergency needs are met.

[0064] (15)

[0065] Constraint (15) represents the internal storage point inventory constraint: for any internal warehouse f, the total amount of emergency supplies it issues to each demand point in the second phase cannot exceed the amount of supplies already stored in the first phase. This constraint reflects the connection between the material allocation phase and the dynamic response phase, meaning that the internal allocation in the second phase is based on the inventory level established in the first phase.

[0066] (16)

[0067] Constraint (16) represents the inheritance constraint of the supply relationship. This applies to the set of potential demand points that simultaneously appear in the first stage. Set of actual needs in the second phase The second phase continues the internal supply relationships established in the first phase to reflect the continuity of the material allocation plan in dynamic response.

[0068] (17)

[0069] Constraint (17) represents the internal transport correlation constraint. The corresponding internal transport volume is only allowed when a supply relationship is established between the internal warehouse f and the demand point s in the second stage. Greater than 0; if no supply relationship is established, the transport volume on this route should be 0.

[0070] (18) (19) Constraints (18) and (19) represent the decision variables for external supply relations in the second stage, respectively. Decision variables related to internal supply All are 0-1 variables.

[0071] (20)

[0072] Constraint (20) represents an external transport association constraint. The corresponding external transport volume is only permitted when an external supply relationship is established between the external warehouse w and the demand point s. Greater than 0.

[0073] (twenty one) (twenty two) Constraints (21) and (22) represent the non-negativity constraints on the internal and external transport volumes in the second stage, respectively.

[0074] Step S4, iterative solution of the model: The nested extreme value structure of the robust model is decomposed by column and constraint generation algorithm. The worst disaster scenario is screened by iteratively selecting the main problem and sub-problems. After convergence, the optimal allocation of materials and the internal and external linkage coordination scheme are output.

[0075] In step S4, the execution steps of the column and constraint generation algorithm are as follows: Step S41: Initialize the algorithm's convergence accuracy, disaster scenario parameters, and iteration upper and lower bounds, and construct the initial master problem containing only basic constraints; Step S42: Solve the main problem to obtain static configuration schemes such as internal warehouse inventory and initial supply chain, and update the lower bound. Step S43: Traverse all discrete disaster scenarios, solve sub-problems group by group, select the worst perturbation scenario with the highest comprehensive cost, and update the upper bound of the iteration. Step S44: Generate the cutting plane and new constraints based on the worst-case scenario, and feed the constraints and variables back to the main problem to complete the model update; For the worst-case scenario, the optimal cutting plane and feasible cut are generated, and the new constraints and variables are fed back to the main problem to complete the model update; Step S45: Repeat the iterative steps from S41 to S45. Stop iterating when the difference between the upper and lower bounds is less than the preset convergence accuracy. Determine that the model has converged and output the globally optimal configuration allocation scheme.

[0076] The main problem corresponds to the pre-disaster static configuration stage, which is used to determine long-term fixed decisions such as internal warehouse inventory and initial supply chain; the sub-problems correspond to the post-disaster dynamic allocation stage, which are used to verify the feasibility and economy of internal and external material allocation schemes under different disaster scenarios.

[0077] Device and storage media applications: This invention also provides a robust optimization device for a two-stage robust optimization method for emergency supplies in passenger transport hubs that enables dual-source replenishment from both internal and external sources, comprising: The system and scenario construction module is used to build an internal and external dual-source material support system, divide a two-stage decision-making architecture, identify uncertain factors, and construct a set of discrete disaster scenarios. The model definition module is used to uniformly define the model set, phased decision variables, and model running parameters; The robust modeling module is used to construct a two-stage multi-objective objective function, configure full-dimensional constraints, and complete the construction of a robust optimization model. The iterative solution module is used to run column and constraint generation algorithms, decompose the main problem and sub-problems to complete iterative solutions, and output the optimal resource allocation and internal and external linkage coordination scheme.

[0078] The system and scenario construction module, model definition module, robust modeling module, and iterative solution module work together to automatically complete the entire process from scenario construction and modeling to solution. By writing the computer program of this method into a computer-readable storage medium and integrating it into the passenger transport hub emergency management platform, it can automate the calculation and output of emergency material allocation and internal / external coordination plans, meeting the needs of both routine planning and emergency dispatch for sudden events.

[0079] This invention presents a logically rigorous and flexibly configurable technical solution. It utilizes a two-stage robust optimization model built upon an internal + external dual-source supply system, employing a mature column and constraint generation algorithm for solution completion. This solution eliminates the need for complex spatial topologies and is adaptable to various transportation hubs, including high-speed rail, subways, long-distance bus stations, and integrated transportation hubs. The solution can be directly embedded into passenger hub emergency management systems for routine emergency material inventory planning, supply chain deployment, and internal material scheduling and external material coordination during emergencies. This invention effectively solves the problems of traditional material management models, such as single-source supply, weak risk resistance, and difficult solution processing. It offers lower operating costs, stronger emergency response capabilities, and possesses high engineering application value and promising prospects for industrialization.

[0080] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0081] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause an electronic device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0083] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply sources, characterized in that, Includes the following steps: Step S1: Establishing an internal and external dual-source system, dividing decision-making into two stages, and constructing scenarios to characterize uncertainty; Step S10: Establish an internal and external dual-source system. Designate multiple fixed warehouses within the hub as internal reserve warehouses, with the internal reserve warehouses of the hub as the internal source; designate the urban emergency material reserve center and surrounding area rescue warehouses as external material centers, with the external material centers as the external source, forming a two-level material supply network with internal and external sources. Step S11 involves a two-stage decision-making process, dividing emergency supplies management into a pre-disaster static configuration stage and a post-disaster dynamic allocation stage. The pre-disaster static configuration stage involves determining the maximum storage capacity and regular inventory levels of each internal warehouse, as well as the initial supply chain from the warehouse to potential demand points in waiting areas, platforms, and equipment areas, based on historical risk data from the hub, before a disaster occurs. The dynamic allocation stage involves identifying disaster-stricken locations, material demand, and damage to transportation routes in real time after an emergency occurs; prioritizing the allocation of internal inventory for rescue efforts; if inventory is insufficient to cover demand, initiating cross-regional replenishment from external material centers and adjusting transportation routes in real time based on the status of transportation routes. Step S12, constructing a scenario to depict uncertainty; The uncertainties and disruptive factors identified are: shifts in demand locations, fluctuations in the amount of materials needed, and changes in the accessibility of emergency channels. By combining historical risk data, including but not limited to fire, flooding, and equipment failure, a discrete disaster scenario set is constructed as the basis for model validation and optimization. Step S2, Standardization definition of model elements: Define the model set, phased decision variables, and model operating parameters in a unified manner; Step S3: Build a two-stage robust optimization model: Construct a two-stage multi-objective objective function, including the first-stage static configuration objective function and the second-stage dynamic allocation objective function, and configure full-dimensional constraints on inventory, supply and demand, transportation, variable attributes, and internal and external supply linkage. Step S4, iterative solution of the model: The nested extreme value structure of the robust model is decomposed by column and constraint generation algorithm. The worst disaster scenario is screened through the main problem and sub-problems. After convergence, the optimal allocation of materials and the internal and external linkage coordination scheme are output.

2. The two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply as described in claim 1, characterized in that, In step S2, the model set includes an internal reserve warehouse set, an external material center set, a discrete disaster scenario set, a pre-disaster potential demand point set, a post-disaster actual demand point set, and a two-stage supply and demand chain set.

3. The two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply as described in claim 1, characterized in that, In step S2, the phased decision variables are first-stage static decision variables and second-stage dynamic decision variables; wherein, The first-stage static decision variables include the internal reserve point material storage quantity, the initial supply chain 0-1 variables, and the pre-set material allocation quantity; The dynamic decision variables in the second stage include the activation of external materials (0-1 variable), the transportation volume of external materials, the post-disaster internal material allocation (0-1 variable), and the post-disaster internal material transportation volume.

4. The two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply as described in claim 1, characterized in that, In step S3, the objective function of the first stage static configuration stage aims to minimize the internal material storage cost and the comprehensive cost of material pre-positioning; the objective function of the second stage dynamic allocation stage aims to minimize the material delivery time, internal transportation cost, and external supply transportation cost; the overall optimization objective is to minimize the comprehensive cost and optimize the material delivery time under the worst disaster scenario.

5. The two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply as described in claim 1, characterized in that, In step S3, the constraints include warehouse capacity constraints, sufficient material supply constraints, two-stage supply relationship inheritance constraints, link and transportation volume linkage constraints, 0-1 variable constraints, non-negative material flow constraints, internal inventory call-up limit constraints, and internal and external replenishment linkage constraints.

6. The two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply as described in claim 1, characterized in that, In step S4, the execution steps of the column and constraint generation algorithm are as follows: Step S41: Initialize the algorithm's convergence accuracy, disaster scenario parameters, and iteration upper and lower bounds, and construct the initial master problem containing only basic constraints; Step S42: Solve the main problem to obtain the static resource allocation scheme, and update the lower bound of the iteration. Step S43: Iterate through all discrete disaster scenarios to solve subproblems, select the worst-case perturbation scenario with the highest comprehensive cost, and update the upper bound of the iteration. Step S44: Generate the cutting plane and new constraints based on the worst-case scenario, and feed the constraints and variables back to the main problem to complete the model update; Step S45: Repeat the iterative steps from S41 to S45. Stop iterating when the difference between the upper and lower bounds is less than the preset convergence accuracy, and output the globally optimal configuration allocation scheme.

7. The two-stage robust optimization method for emergency supplies in passenger transport hubs with dual internal and external supply as described in claim 6, characterized in that, The main problem corresponds to the pre-disaster static configuration stage, which is used to determine long-term fixed decisions such as internal warehouse inventory and initial supply chain; the sub-problems correspond to the post-disaster dynamic allocation stage, which are used to verify the feasibility and economy of internal and external material allocation schemes under different disaster scenarios.

8. A robust optimization device for implementing the two-stage robust optimization method for dual-source (internal and external) emergency supplies in passenger transport hubs as described in any one of claims 1-7, characterized in that, include: The system and scenario construction module is used to build an internal and external dual-source material support system, divide a two-stage decision-making architecture, identify uncertain factors, and construct a set of discrete disaster scenarios. The model definition module is used to uniformly define the model set, phased decision variables, and model running parameters; The robust modeling module is used to construct a two-stage multi-objective objective function, configure full-dimensional constraints, and complete the construction of a robust optimization model. The iterative solution module is used to run column and constraint generation algorithms, decompose the main problem and sub-problems to complete iterative solutions, and output the optimal resource allocation and internal and external linkage coordination scheme.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method described in claims 1 to 8.

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