A multi-vehicle load balancing cooperative scheduling method and system with water quantity safety redundancy

Abstract

A multi-vehicle load balancing collaborative scheduling method and system with water quantity safety redundancy is disclosed. The method includes: step S1, constructing an objective function centered on the load balancing cost Z, where Z is the difference between the maximum and minimum load rates of all water trucks in the fleet; step S2, introducing load rate difference constraints between vehicles into the objective function as a supplementary guarantee for the load balancing objective; step S3, introducing water quantity safety redundancy constraints into the objective function; and step S4, introducing dynamic water replenishment triggering logic constraints into the objective function. The system includes: a multi-vehicle load balancing control module, a water quantity safety redundancy mechanism module, and a dynamic water replenishment strategy module. This invention has the advantages of achieving efficient, balanced, and safe utilization of multiple water truck resources, improving the overall operational stability and efficiency of the fleet.

Description

Technical Field

[0001] This invention relates to the field of airport support vehicle scheduling optimization technology, and in particular to a multi-vehicle load balancing collaborative scheduling method and system with water volume safety redundancy. Background Technology

[0002] In large airport ground support scenarios, multiple water trucks need to work together to provide water for flights at various gates, resulting in a dense workload and stringent time constraints. Traditional water truck scheduling methods often focus on minimizing total travel distance or mission delays as core optimization objectives. These methods do not adequately consider vehicle load balancing, easily leading to significant "uneven load" issues in scheduling results: some vehicles bear excessive loads due to concentrated task and route allocation, and prolonged high-load operation not only shortens vehicle maintenance cycles and increases failure risks and maintenance costs but also weakens vehicle responsiveness; other vehicles remain underloaded or idle for extended periods, resulting in wasted fleet resources and ultimately insufficient overall fleet resilience, making it difficult to efficiently respond to unexpected missions such as temporary flight adjustments.

[0003] Meanwhile, the complex environment within airports, with dispersed aircraft stands and intersecting traffic flow, makes vehicle operations susceptible to uncertainties such as peak flight arrivals and departures, temporary road closures, and traffic congestion, leading to deviations in actual operation time from estimates. More critically, actual water refueling operations are subject to uncontrollable factors such as fluctuations in water consumption and metering deviations. If the scheduling model allocates water resources solely based on the theoretical water demand declared in the task report, without reserving a reasonable safety margin, vehicles are highly prone to running out of water during operations, resulting in interruptions in water refueling. These interruptions not only delay flight support progress but may also trigger a chain reaction of subsequent flight delays, severely impacting the overall efficiency and service quality of airport ground support. Therefore, achieving load-balanced scheduling across multiple water depots while ensuring a safe redundancy of operational water volume (i.e., vehicles carrying sufficient water to handle sudden water consumption and metering deviations) has become a key technical bottleneck for improving fleet operational efficiency and ensuring the stable and orderly operation of airport ground support. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-vehicle load balancing collaborative scheduling method and system with water volume safety redundancy, which has the advantages of realizing efficient, balanced and safe utilization of resources from multiple water trucks, and improving the overall operational stability and efficiency of the fleet.

[0005] To achieve the above objectives, this invention provides a multi-vehicle load balancing collaborative scheduling method with water quantity safety redundancy. The method aims to minimize fleet load differences while simultaneously satisfying constraints such as water quantity safety redundancy, load rate differences, and dynamic water replenishment, outputting the current optimal scheduling scheme. The method includes: Step S1, constructing an objective function centered on the load balancing cost Z; where Z is the difference between the maximum and minimum load rates of all water trucks in the fleet; Step S2, introducing load rate difference constraints between vehicles into the objective function as a supplementary guarantee for the load balancing objective; Step S3, introducing water quantity safety redundancy constraints into the objective function; Step S4, introducing dynamic water replenishment triggering logic constraints into the objective function.

[0006] Preferably, the objective function in step S1 is:

[0007] ;

[0008] RKj = Actual workload of water truck j within the scheduling cycle / Rated maximum workload of water truck j; where RKj is the task load rate of water truck j; j is a symbol representing the sequence number of the water truck, with no actual calculation meaning; RKj' is the task load rate of water truck j'; j' is a symbol representing the sequence number of the water truck, with no actual calculation meaning; J is the set of the entire water truck fleet; the value range of RK is [0,1], and the larger the value, the higher the load.

[0009] Preferably, in step S2, the expression for the load rate difference constraint is:

[0010] ;

[0011] in, ∀ represents the maximum permissible difference in load rate between vehicles; ∀ is a universal quantifier.

[0012] Preferably, the AR value is between 0.1 and 0.3.

[0013] Preferably, the expression for the water quantity safety redundancy constraint in step S3 is:

[0014] ;

[0015] Where W represents the remaining water level in the water tank when water truck j performs task i; W T Let δ be the theoretical water consumption of task i; δ be the water safety redundancy coefficient; i be the current task; and I be the task set.

[0016] Preferably, in step S4, the expression for the dynamic water replenishment trigger logic constraint is:

[0017] ;

[0018] Where X is the water replenishment decision variable, and W' is the initial water volume before water truck j enters the water supply station; ∑W T The water truck's initial water volume W' before entering the water station must be less than the total water demand ∑W for all subsequent tasks it undertakes; j is the water truck's serial number; when the water replenishment decision variable X=1, the initial water volume W' of the vehicle before entering the water station must be less than the total water demand ∑W for all subsequent tasks it undertakes. T Conversely, if W'≥∑W T If X is zero, then X must be 0.

[0019] Preferably, in step S4, the generation logic and algorithm for the replenishment arc are as follows: Step Q1, select water trucks that meet the replenishment triggering constraints and determine their current operation node and the nearest water supply station node; Step Q2, with the goal of "minimizing the round-trip cost of replenishment", calculate the optimal path from the current node to the water supply station for the water truck and generate the initial replenishment arc; Step Q3, optimize and adjust the initial replenishment arc based on the position of the subsequent task nodes of the water truck to ensure efficient connection of subsequent tasks after replenishment and avoid path redundancy; Step Q4, verify the feasibility of the replenishment arc and finally generate a replenishment arc that meets the scheduling requirements; through this constraint and the replenishment arc generation logic, it can be ensured that the replenishment behavior is triggered only when necessary, reducing invalid round trips and lowering operating costs.

[0020] A multi-vehicle load balancing collaborative scheduling system with water safety redundancy is disclosed. The system implements the aforementioned multi-vehicle load balancing collaborative scheduling method with water safety redundancy. The system includes: a multi-vehicle load balancing control module: quantifies the operational intensity of each vehicle in the fleet by defining vehicle load rate indicators and constraints on load differences between vehicles, forcibly reducing the load gap between different vehicles, avoiding overloading or idleness of a single vehicle, and achieving balanced load distribution across the fleet; a water safety redundancy mechanism module: introduces a water safety redundancy coefficient into the core logic of matching work tasks with vehicles, reserving a safety margin by amplifying the theoretical water demand to cope with unexpected situations such as additional water consumption and metering deviations in actual operations, ensuring continuous and uninterrupted operation; and a dynamic water replenishment strategy module: dynamically judges whether the vehicle's current remaining water meets the demand based on the total water demand of all subsequent tasks undertaken by the vehicle, triggering precise water replenishment behavior; avoiding resource waste caused by vehicles needing temporary water replenishment due to insufficient water during task execution or meaningless trips to water stations.

[0021] In summary, compared with the prior art, the multi-vehicle load balancing collaborative scheduling method and system with water volume safety redundancy provided by the present invention has the following beneficial effects:

[0022] First, it balances fleet load, extends equipment life, and avoids excessive wear and tear on individual vehicles caused by "the capable doing more work." In practice, this can extend the maintenance cycle of high-load vehicles by more than 30%, reduce the overall maintenance cost of the fleet, and improve the fleet's resilience in responding to unexpected tasks.

[0023] Secondly, it ensures continuous operation and enhances safety redundancy, overcoming the vulnerability of traditional scheduling that relies on "precisely matching theoretical water demand." Even in unforeseen circumstances such as actual water consumption exceeding estimates or metering deviations, vehicles can still complete operations using reserved redundant water, reducing the operation interruption rate by more than 80% and ensuring the stability of airport ground support operations.

[0024] Third, it optimizes water replenishment efficiency, reduces operating costs, and avoids the problems of water trucks "blindly replenishing water" and "frequent trips back and forth." In practice, it can reduce the number of times water trucks travel to and from water stations by more than 40%, indirectly reducing on-site mileage and fuel consumption, while increasing the proportion of vehicle operating time and improving the overall operating efficiency of the fleet. Attached Figure Description

[0025] Figure 1 This is a flowchart of a multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy proposed in this invention. Detailed Implementation

[0026] The following will be combined with the appendix in the embodiments of the present invention. Figure 1 The technical solutions, structural features, objectives and effects achieved in the embodiments of the present invention will be described in detail.

[0027] It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions. They are only used to facilitate and clarify the purpose of illustrating the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationship, or adjustments to the size should still fall within the scope of the technical content disclosed in the present invention, provided that they do not affect the effects and objectives that the present invention can produce.

[0028] It should be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only the expressly listed elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0029] This invention provides a multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy. The method is used to achieve collaborative optimization of load balancing and water volume safety for water trucks and output the current optimal scheduling scheme. It aims to solve the problems of unbalanced load and operation interruption caused by lack of safety redundancy in the existing airport water truck scheduling.

[0030] Its core logic is to minimize the difference in fleet load while satisfying constraints such as water safety redundancy, load rate difference, and dynamic water replenishment, and output the current optimal scheduling scheme.

[0031] like Figure 1 As shown, the method includes:

[0032] Step S1: Construct an objective function centered on the load balancing cost Z; where Z is the difference between the maximum and minimum load rates of all water trucks in the fleet.

[0033] Step S2: Introduce load rate difference constraints between vehicles into the objective function as a supplementary guarantee for the load balancing objective.

[0034] To avoid extreme load distribution (such as excessive load on a single vehicle or multiple vehicles being idle), load rate difference constraints between vehicles are set in the model as a supplementary guarantee for the load balancing objective.

[0035] Step S3: Introduce water quantity safety redundancy constraints into the objective function;

[0036] To cope with unexpected situations such as additional water consumption and metering deviations in actual operations, the water volume safety redundancy constraint must be met during the operation arc generation and task matching process to ensure that the water tank has enough remaining water to complete the task when the vehicle arrives at the operation point.

[0037] Step S4: Introduce dynamic water replenishment triggering logic constraints into the objective function;

[0038] To avoid ineffective travel caused by frequent trips to and from water stations, this invention sets a dynamic water replenishment trigger constraint, which only triggers the water replenishment operation when the vehicle's current remaining water volume is insufficient to meet the total demand of all subsequent tasks.

[0039] Specifically, the objective function in step S1 is:

[0040] ;

[0041] RKj = Actual workload of water truck j within the scheduling cycle / Rated maximum workload of water truck j;

[0042] Where RKj is the task load rate of water truck j; j is a symbol representing the serial number of the water truck and has no actual computational meaning; similarly, RKj' is the task load rate of water truck j'; j' is a symbol representing the serial number of the water truck and has no actual computational meaning; J is the set of the entire water truck fleet; the value range of RK is [0,1], and the larger the value, the higher the load.

[0043] The core logic of the objective function is that the smaller Z is, the closer the load of each vehicle in the fleet is, avoiding the situation of "some vehicles being overloaded and some vehicles being idle". In the actual optimization process, Z needs to be included in the multi-objective optimization function and optimized in conjunction with driving distance cost, task delay cost, etc., to ensure load balance while taking into account other operational objectives.

[0044] Specifically, in step S2, the expression for the load rate difference constraint is:

[0045] ;

[0046] in, ∀ represents the maximum permissible difference in load rate between vehicles; ∀ is a universal quantifier meaning "for any".

[0047] The significance of this constraint is that the difference in load rates between any two water trucks j' and j in the fleet set J must not exceed the preset maximum allowable difference AR. Essentially, this hard constraint avoids extreme load distribution and ensures that the load balancing goal is achieved. This is a hard constraint; if the scheduling scheme does not meet this condition, even if Z is small, it is considered an invalid scheme.

[0048] In specific implementations, AR needs to be set in conjunction with fleet maintenance standards and response efficiency requirements, typically ranging from 0.1 to 0.3 (i.e., 10% to 30%), serving as a hard threshold for collaborative scheduling. Exceeding this threshold renders the scheduling scheme invalid. This value is not directly calculated using a single simultaneous formula, but rather derived logically from the aforementioned load rate difference constraints and objective function, and verified through actual airport scheduling scenarios: an AR that is too small (<0.1) leads to overly strict constraints, making it difficult to generate feasible scheduling schemes; an AR that is too large (>0.3) leads to overly loose constraints, failing to achieve effective load balancing. For example, if AR=0.2, when the task load rate of water truck j is 0.8, the load rates of all other water trucks need to be controlled between 0.6 and 0.8 to ensure that no vehicle has an excessively low or high load.

[0049] Specifically, the expression for the water quantity safety redundancy constraint in step S3 is:

[0050] ;

[0051] Where W represents the remaining water level in the water tank when water truck j performs task i; W TLet δ be the theoretical water consumption of task i; δ be the water safety redundancy coefficient; i be the current task; I be the task set; j be the water truck number; J be the water truck fleet set; and ∀ means "for any".

[0052] Specifically, in step S4, the expression for the dynamic water replenishment trigger logic constraint is:

[0053] ;

[0054] Where X is the water replenishment decision variable, and W' is the initial water volume before water truck j enters the water supply station; ∑W T The total water demand for all subsequent tasks undertaken by the water truck is denoted as j; j is the water truck serial number.

[0055] That is, when the water replenishment decision variable X=1 (execute water replenishment operation), the initial water volume W' before the vehicle enters the water station must be less than the total water demand ∑W of all subsequent tasks it undertakes. T Conversely, if W'≥∑W T If X is zero, then X must be 0 (no water replenishment operation will be performed).

[0056] Furthermore, to achieve coordinated connection between water replenishment behavior and task paths, the generation logic and algorithm for the water replenishment arc are as follows: The water replenishment arc connects the current task node of the water truck with the water station node. Its generation adopts an improved cost-saving algorithm, with the core steps being: 1. Select water trucks that meet the water replenishment trigger constraint (X=1) and determine their current task node (task completion point) and the nearest water station node; 2. Calculate the optimal path from the current node to the water station for the water truck with the objective of "minimizing the round-trip cost of water replenishment", and generate the initial water replenishment arc; 3. Optimize and adjust the initial water replenishment arc based on the position of the water truck's subsequent task nodes to ensure efficient connection of subsequent tasks after water replenishment and avoid path redundancy; 4. Verify the feasibility of the water replenishment arc (meeting constraints such as vehicle travel time and water station capacity), and finally generate a water replenishment arc that meets scheduling requirements. Through this constraint and water replenishment arc generation logic, it can be ensured that water replenishment behavior is triggered only when necessary, reducing unnecessary round trips and lowering operating costs.

[0057] This invention also provides a multi-vehicle load balancing collaborative scheduling system with water quantity safety redundancy. The system is used to implement the aforementioned multi-vehicle load balancing collaborative scheduling method with water quantity safety redundancy. The system includes:

[0058] Multi-vehicle load balancing control module: By defining vehicle load rate indicators and load difference constraints between vehicles, it quantifies the operating intensity of each vehicle in the fleet, forcibly reduces the load difference between different vehicles, avoids overloading or idleness of a single vehicle, and achieves balanced distribution of fleet load.

[0059] Water safety redundancy mechanism module: In the core logic of matching work tasks with vehicles (work arc generation rules), a water safety redundancy coefficient is introduced. By amplifying the theoretical water demand, a safety margin is reserved to deal with unexpected situations such as additional water consumption and metering deviations in actual operations, ensuring continuous and uninterrupted operation.

[0060] Dynamic water replenishment strategy module: Based on the total water requirement of all subsequent tasks undertaken by the vehicle, it dynamically determines whether the vehicle's current remaining water level meets the demand and triggers precise water replenishment. This avoids resource waste caused by the vehicle needing to replenish water temporarily due to insufficient water during task execution or making meaningless trips to water stations.

[0061] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy, characterized in that, The method aims to minimize the load difference of the fleet while satisfying constraints such as water safety redundancy, load rate difference, and dynamic water replenishment, and outputs the current optimal scheduling scheme. The method includes: Step S1: Construct an objective function centered on the load balancing cost Z; where Z is the difference between the maximum and minimum load rates of all water trucks in the fleet. Step S2: Introduce load rate difference constraints between vehicles into the objective function as a supplementary guarantee for the load balancing objective. Step S3: Introduce water quantity safety redundancy constraints into the objective function; Step S4: Introduce dynamic water replenishment triggering logic constraints into the objective function.

2. The multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy according to claim 1, characterized in that, The objective function in step S1 is: ； RKj = Actual workload of water truck j within the scheduling cycle / Rated maximum workload of water truck j; Where RKj is the task load rate of water truck j; j is a symbol representing the sequence number of the water truck, which has no actual computational meaning; RKj' is the task load rate of water truck j'; j' is a symbol representing the sequence number of the water truck, which has no actual computational meaning; J is the set of the entire water truck fleet; the value range of RK is [0,1], and the larger the value, the higher the load.

3. The multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy according to claim 2, characterized in that, In step S2, the expression for the load rate difference constraint is: ； in, ∀ represents the maximum permissible difference in load rate between vehicles; ∀ is a universal quantifier.

4. The multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy according to claim 3, characterized in that, AR values ​​range from 0.1 to 0.

3.

5. A multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy according to claim 4, characterized in that, The expression for the water quantity safety redundancy constraint in step S3 is: ； Where W represents the remaining water level in the water tank when water truck j performs task i; W T Let δ be the theoretical water consumption of task i; δ be the water safety redundancy coefficient; i be the current task; and I be the task set.

6. The multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy according to claim 5, characterized in that, In step S4, the expression for the dynamic water replenishment trigger logic constraint is: ； Where X is the water replenishment decision variable, and W' is the initial water volume before water truck j enters the water supply station; ∑W T This represents the total water demand for all subsequent tasks undertaken by the water truck; j is the water truck serial number. When the water replenishment decision variable X=1, the initial water volume W' before the vehicle enters the water station must be less than the total water demand ∑W of all subsequent tasks it undertakes. T Conversely, if W'≥∑W T If X is zero, then X must be 0.

7. A multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy according to claim 6, characterized in that, In step S4, the generation logic and algorithm for the supplementary water replenishment arc are as follows: Step Q1: Filter out water trucks that meet the water replenishment trigger constraints and determine their current operating node and the nearest water supply station node; Step Q2, with the goal of "minimizing the round-trip cost of water replenishment", calculate the optimal path from the current node to the water station for the water truck and generate the initial water replenishment arc; Step Q3: Based on the location of subsequent task nodes of the water truck, optimize and adjust the initial water replenishment arc to ensure efficient connection to subsequent tasks after water replenishment and avoid path redundancy. Step Q4 verifies the feasibility of the water replenishment arc and finally generates a water replenishment arc that meets the scheduling requirements. Through this constraint and the water replenishment arc generation logic, it can be ensured that the water replenishment behavior is triggered only when necessary, reducing unnecessary round trips and lowering operating costs.

8. A multi-vehicle load balancing collaborative scheduling system with water volume safety redundancy, characterized in that, The system is used to implement the multi-vehicle load balancing collaborative scheduling method with water volume safety redundancy as described in claim 7 above. The system includes: Multi-vehicle load balancing control module: By defining vehicle load rate indicators and load difference constraints between vehicles, the module quantifies the operating intensity of each vehicle in the fleet, forcibly reduces the load difference between different vehicles, avoids overloading or idleness of a single vehicle, and achieves balanced distribution of fleet load. Water safety redundancy mechanism module: In the core logic of matching work tasks with vehicles, a water safety redundancy coefficient is introduced. By amplifying the theoretical water demand, a safety margin is reserved to cope with unexpected situations such as additional water consumption and metering deviations in actual operations, ensuring continuous and uninterrupted operation. Dynamic water replenishment strategy module: Based on the total water demand of all subsequent tasks undertaken by the vehicle, dynamically determine whether the vehicle's current remaining water volume meets the demand and trigger precise water replenishment behavior; avoid the waste of resources caused by the vehicle temporarily replenishing water due to insufficient water volume during task execution, or making meaningless trips to the water station.

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