Logistics multi-mode combined transportation scheme planning method and system based on optimization algorithm

Abstract

The invention belongs to the technical field of modern aviation logistics, and particularly relates to a logistics multi-mode combined transportation scheme planning method and system based on an optimization algorithm, and the method comprises the steps: collecting transport capacity parameters and waybill data, and carrying out the dimension removal normalization processing; calculating a resource utilization rate difference value according to the loaded cargo data and the rated transport capacity, and calculating a heavy bubble unbalance potential energy index in combination with the remaining time; the orthogonal complementation matching degree of the waybill is calculated based on the heavy bubble unbalance potential energy index and the orthogonal complementation principle; sorting and trial assembly are performed according to the matching degree, and closed-loop feedback is performed after data updating. According to the method, the complementarity waybill of the current aircraft unbalance state can be locked and repaired in real time, the full-load empty-capacity or full-capacity empty-capacity problem is solved, and dual maximization of the flight loading rate is achieved.

Description

A Planning Method and System for Multimodal Transport Schemes Based on Optimization Algorithms Technical Field

[0001] This invention relates to the field of modern air logistics technology. More specifically, this invention relates to a method and system for planning multimodal transport solutions based on optimization algorithms. Background Technology

[0002] In the actual business scenarios of air logistics, air cargo flights, as core capacity resources, have insurmountable rigid dual constraints. Specifically, each aircraft has a clear maximum allowable load limit and a fixed internal total volume limit. Therefore, maximizing both load utilization and volume utilization while ensuring flight safety is the key to improving the economic benefits of air logistics.

[0003] Existing capacity allocation and scheduling operations are typically carried out after air freight waybills arrive at the cargo terminal. Traditional scheduling systems or manual operation modes mainly adopt two strategies: one is based on the principle of first-come, first-served, directly matching loads according to the arrival time of the waybills; the other is based on batch planning of static timetables, that is, using the static knapsack algorithm to perform a one-time calculation before the flight's cut-off time to find the current optimal loading solution.

[0004] However, when faced with massive, heterogeneous, and randomly distributed air waybills, the aforementioned existing technologies reveal significant shortcomings, easily leading to a severe imbalance in the ratio of heavy to light cargo in flight loading. Because the physical density distribution of incoming cargo is extremely discrete, including both high-density heavy cargo such as precision instruments and machinery, and low-density light cargo such as e-commerce parcels and plastic products, the lack of dynamic structural optimization of loading queues often results in two extreme resource waste dilemmas: on the one hand, when heavy cargo arrives in concentrated quantities, the aircraft quickly reaches its load limit, but a large amount of idle space in the cargo hold remains unused, resulting in a full-load, empty-capacity phenomenon; on the other hand, when light cargo arrives in concentrated quantities, the cargo hold volume is filled, but the actual load is far below the rated value, resulting in a full-capacity, empty-load phenomenon.

[0005] In addition, existing methods usually treat weight constraints and volume constraints as two independent threshold conditions for linear judgment, which cannot perform forward screening and complementary matching of waybill flows in the time dimension. The imbalance of transport capacity structure is often only discovered in the later stage of loading, but by then it is difficult to make retrospective adjustments. Summary of the Invention

[0006] To address the technical problem that the existing technology can easily lead to a serious imbalance in the ratio of heavy to light cargo in the aircraft's loading state, the present invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a logistics multimodal transport scheme planning method based on optimization algorithms, comprising: collecting the rated capacity parameters of the target flight, the cumulative data of loaded cargo, and the waybill data in the waybill buffer pool, and performing dimensionless normalization processing on the waybill data; calculating the resource utilization rate difference based on the cumulative data of loaded cargo and the rated capacity parameters, and calculating the re-bubble imbalance potential energy index based on the resource utilization rate difference and the remaining takeoff time; calculating the positive re-bubble imbalance potential energy index and calculating the positive re-bubble matching degree of each waybill in the waybill buffer pool using the positive re-compensation principle; sorting the waybills according to the positive re-compensation matching degree and performing virtual trial loading verification; if the verification passes, locking the waybill and updating the cumulative data of loaded cargo, triggering the recalculation of the re-bubble imbalance potential energy index to execute closed-loop feedback.

[0008] This invention constructs a potential energy model that integrates the difference in resource utilization rate and the remaining takeoff time through dimensionless normalization, and builds a rebubbling imbalance potential energy index to dynamically quantify the urgency of loading imbalance. It calculates the positive interaction complement matching degree by combining the positive interaction complement principle, and locks complementary waybills that can repair the current imbalance state in real time in the waybill buffer pool, and introduces a retention factor to take into account fairness. This closed-loop feedback mechanism solves the problem of full load and empty capacity or full capacity and empty load in multimodal transport, realizes the dual maximization of flight load utilization and volume utilization, and improves the economic benefits of air logistics.

[0009] Preferably, the rated capacity parameters of the target flight include the maximum permissible payload of the aircraft, the maximum available total volume of the aircraft, and the absolute flight cut-off time before the scheduled takeoff of the aircraft; the cumulative data of the loaded cargo includes the cumulative weight and cumulative volume of the loaded cargo; the waybill data includes the actual weight of the waybill, the actual volume of the waybill, and the arrival time.

[0010] Preferably, the dimensionless normalization process for the waybill data includes: dividing the actual weight of the waybill by the maximum permissible payload of the aircraft to obtain the normalized weight of the waybill; and dividing the actual volume of the waybill by the maximum usable total volume of the aircraft to obtain the normalized volume of the waybill.

[0011] Preferably, the difference in resource utilization satisfies the expression: In the formula, This represents the difference in resource utilization. This represents the cumulative weight of the loaded goods. The maximum permissible load for an aircraft; This represents the cumulative volume of the loaded goods. This refers to the maximum usable total volume of the aircraft.

[0012] This invention, by constructing a resource utilization difference, can determine the degree and direction of the current loading state deviating from the perfect balance between load and volume. This difference directly reflects whether the aircraft is in a state of excessive load utilization or excessive volume utilization, providing a direct guiding basis for subsequently determining what type of cargo needs to be replenished.

[0013] Preferably, the heavy bubble imbalance potential energy index satisfies the following expression: In the formula, This is an indicator of the potential energy of the heavy bubble imbalance. This represents the difference in resource utilization. It is a symbolic function; It is a natural exponential function; Sensitivity coefficient; Indicates taking the absolute value; For time factor parameters; This is the absolute cut-off time for flight loading; The current moment; To prevent the value from being zero or a minimum.

[0014] This invention introduces a rebubbling imbalance potential energy index that includes a natural exponential function and a time factor parameter, thereby realizing the dynamic quantification of the correction requirement. As the flight cut-off time approaches or the degree of imbalance intensifies, the potential energy index increases rapidly in a nonlinear manner, forcing the system to enter a strict correction mode. This ensures the load factor while avoiding the risk of takeoff delays caused by excessive waiting for ideal matching waybills through the urgency constraint of the time dimension.

[0015] Preferably, the positive cross-complement matching degree of each waybill in the waybill buffer pool satisfies the expression: In the formula, For waybill Positive interaction complement matching degree; For waybill Normalized volume; For waybill Normalized weight; It is a symbolic function; This is an indicator of the potential energy of the heavy bubble imbalance. Indicates taking the absolute value; For waybill Normalized retention factor; The weighting factor for the loading rate. This is the weighting coefficient for timeliness. ,and .

[0016] This invention utilizes the principle of positive interaction to construct a matching degree, which can accurately filter out waybills from the buffer pool whose physical attributes are complementary to the current imbalance direction. That is, when the aircraft is overweight, bulky cargo is given priority, and vice versa, thereby automatically guiding the system towards balance. At the same time, the expression incorporates a normalized retention factor, which takes into account the fairness of long-waiting cargo while pursuing loading balance, and prevents some waybills from being indefinitely retained due to excessive pursuit of balance.

[0017] Preferably, the normalized retention factor is the quotient obtained by dividing the difference between the current time and the entry time of the waybill by the maximum tolerable waiting time parameter.

[0018] This invention determines the urgency of a waybill's waiting time by calculating the ratio of the difference between the current time and the entry time. As the waiting time increases, the value of this factor increases, thereby improving the overall matching score of the waybill and ensuring that older waybills can be prioritized when the system is relatively balanced, thus achieving a balance between optimizing loading efficiency and ensuring service quality.

[0019] Preferably, the sign function takes the value of 1 when the variable is greater than 0, takes the value of -1 when the variable is less than 0, and takes the value of 0 when the variable is equal to 0.

[0020] Preferably, the virtual trial loading verification includes: determining whether the sum of the cumulative weight of the loaded goods and the actual weight of the waybill is less than or equal to the maximum allowable payload of the aircraft, and whether the sum of the cumulative volume of the loaded goods and the actual volume of the waybill is less than or equal to the maximum usable total volume of the aircraft.

[0021] This invention adds a virtual trial loading verification step before locking the waybill, strictly comparing the cumulative load and volume after adding the new waybill with the aircraft's rated upper limit, ensuring the absolute feasibility of the optimization scheme at the physical level, preventing overloading or overcapacity problems caused by algorithm theoretical calculation errors, boundary conditions or concurrent operations, and effectively ensuring the driving safety of air logistics transportation.

[0022] In a second aspect, the present invention provides a logistics multimodal transport scheme planning system based on optimization algorithms, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned logistics multimodal transport scheme planning method based on optimization algorithms is implemented.

[0023] By adopting the above technical solution, the above-mentioned multimodal transport planning method based on optimization algorithm is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. Terminal equipment can then be made based on the memory and processor for convenient use.

[0024] The beneficial effects of this invention are as follows: By dimensionless normalization, this invention constructs a potential energy model that integrates the difference in resource utilization rate and the remaining takeoff time, and constructs a rebubbling imbalance potential energy index to dynamically quantify the urgency of loading imbalance; combined with the principle of positive interaction complementarity, it calculates the positive interaction complementarity matching degree, locks complementary waybills that can repair the current imbalance state in real time in the waybill buffer pool, and introduces a retention factor to take fairness into account; this closed-loop feedback mechanism solves the problem of full load and empty capacity or full capacity and empty load in multimodal transport, realizes the dual maximization of flight load utilization rate and volume utilization rate, and improves the economic benefits of air logistics. Attached Figure Description

[0025] Figure 1 is a flowchart illustrating the logistics multimodal transport scheme planning method based on optimization algorithm in this invention; Figure 2 is a diagram illustrating the difference in resource utilization between the prior art and this invention; Figure 3 is a diagram illustrating the response curve of the heavy bubble imbalance potential energy index; Figure 4 is a diagram illustrating the comparison of the loading path evolution trajectory between the prior art and this invention; Figure 5 is a diagram illustrating the comparison of the final loading effect between the prior art and this invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] This invention discloses a logistics multimodal transport scheme planning method based on optimization algorithms. Referring to Figure 1, the method includes steps S1-S4: S1: Collect the rated capacity parameters of the target flight, the cumulative data of the loaded cargo, and the waybill data in the waybill buffer pool; perform dimensionless normalization processing on the waybill data, and map the cargo and aircraft status to a dimensionless vector space.

[0029] It should be noted that, since the physical properties of aircraft and cargo include two dimensions—weight and volume—and there is a significant difference in dimensions between the weight unit of ton and the volume unit of cubic meter, direct numerical calculations would lead to confusion in physical terms and calculation errors, thus making it impossible to accurately assess the actual occupancy of transport capacity. Therefore, this invention collects basic data and establishes a mathematical model, and performs dimensionless normalization on the data to eliminate the differences between different physical dimensions, ensuring that subsequent vector calculations are performed within a unified mathematical space.

[0030] Specifically, the rated capacity parameters of the target flight are retrieved through the system interface, including the maximum permissible load of the aircraft. Maximum usable total volume of aircraft And the absolute flight cut-off time before the aircraft's scheduled takeoff. Simultaneously, obtain the current time in real time. Cumulative data on loaded cargo, including the cumulative weight of loaded cargo. and the cumulative volume of loaded cargo .

[0031] Furthermore, a set of waybills that have arrived at the terminal and are ready for dispatch is selected from the waybill flow, and for each waybill in the set... Extract its physical characteristics, including the actual weight of the waybill. Actual volume of waybill and entry time .

[0032] Furthermore, the actual weight and volume of the waybill are dimensionlessly normalized. The specific calculation formula is as follows:

[0033]

[0034] In the formula, For waybill The normalized weight, the larger the value, the higher the proportion of the waybill weight to the total weight of the aircraft; For waybill The actual weight; The maximum permissible load for an aircraft; For waybill The normalized volume, the larger the value, the higher the proportion of the waybill volume to the total volume of the aircraft; For waybill The actual volume; This refers to the maximum usable total volume of the aircraft.

[0035] S2: Calculate the resource utilization difference based on the cumulative data of loaded cargo and the rated transport capacity parameters; calculate the reboiler imbalance potential energy index based on the resource utilization difference and the remaining takeoff time.

[0036] It should be noted that existing simple capacity checks only focus on the size of the remaining space, ignoring the degree and direction of the current loading status deviating from the perfect balance between load and volume. This often leads to resource waste, such as full load with empty capacity or full capacity with empty load. Furthermore, as the takeoff time approaches, the need to correct the balance becomes more urgent. Therefore, this invention constructs a re-bubble imbalance potential energy index, which, combined with the difference in resource utilization and the urgency of time, dynamically quantifies the current system's correction needs.

[0037] Specifically, the calculation formula for the difference in resource utilization rate is as follows:

[0038] In the formula, This is the resource utilization difference, used to characterize the degree of deviation between the current load utilization rate and the volume utilization rate; This represents the cumulative weight of the loaded goods. The maximum permissible load for an aircraft; This represents the cumulative volume of the loaded goods. This refers to the maximum usable total volume of the aircraft.

[0039] Among them, when A value greater than 0 indicates that the load utilization rate is higher than the volume utilization rate, and the aircraft is in a weight-biased state; when When the value is less than 0, it indicates that the volume utilization rate is higher than the load utilization rate, and the aircraft is in a state of partial overflow.

[0040] For example, Figure 2 shows a comparison of the resource utilization difference between the prior art and the present invention. The resource utilization difference in the prior art exhibits a divergent trend; as the loading process progresses, due to the lack of an intervention mechanism, the deviation value increases continuously, ultimately leading to severe resource imbalance. In contrast, the resource utilization difference in the present invention consistently exhibits a high-frequency, small-amplitude oscillation and convergence pattern near the 0 axis. Whenever the resource utilization difference deviates from the 0 axis and enters the unbalanced or overweight zone, it immediately returns to its original state in the next loading step. This confirms the effectiveness of the dynamic closed-loop feedback mechanism of the present invention. Once the resource utilization difference is not equal to 0, the system identifies the current unbalanced or overweight state and uses this deviation signal to force the reverse selection of complementary goods in the next cycle, thereby quickly pulling the imbalance back to the equilibrium point. This real-time correction capability effectively performs forward-looking screening and complementary matching of the waybill flow in the time dimension, solving the problem of the prior art's difficulty in retrospective adjustment.

[0041] Furthermore, based on the difference in resource utilization, the re-bubbling imbalance potential energy index is calculated. The specific calculation formula is as follows:

[0042] In the formula, It is a potential energy index for re-bubbling imbalance. Its absolute value reflects the intensity of the need for correction, and its sign reflects the direction of correction. This represents the difference in resource utilization. This is a sign function, taking the value 1 when the variable is greater than 0, -1 when the variable is less than 0, and 0 when the variable is equal to 0, used to preserve the direction of imbalance; It is a natural exponential function; This is the sensitivity coefficient, used to control the rate at which potential energy increases with deviation; Indicates taking the absolute value; This is a time factor parameter, in minutes; This is the absolute cut-off time for flight loading; The current moment; To prevent the value from being zero or a minimum.

[0043] in, The direction of potential energy is determined, if A positive value indicates that the aircraft is overweight, and the system's potential energy points to the need for additional bulky cargo; if... A negative value indicates that the aircraft is biased towards a bubble, and the system's potential energy points to the need for additional heavy cargo. The term utilizes the properties of the exponential function, such that when the deviation... When the potential energy increases, it grows rapidly and nonlinearly, thus forcing the system into a strict correction mode. The item introduces a time dimension, when the remaining time When the value is large, this value is close to 1, indicating a high system tolerance. When the remaining time is less than a certain threshold, the denominator decreases rapidly, causing this value to surge, forcing the algorithm to exclude goods that exacerbate the imbalance in the final stage.

[0044] in, The larger the value, the more sensitive the system is to balance deviations. In this embodiment, Setting it to 2.5 ensures that the system maintains moderate freedom when the deviation is small and responds quickly when the deviation is large; in other embodiments, implementers can select a value in the range of 2 to 3 according to the actual requirements for balancing accuracy. The value of ; in addition, in this embodiment, Set to 60 minutes. The 10-minute timeframe is based on practical operational experience, which indicates that the hour before takeoff is a critical window for adjusting the loading structure. In other embodiments, the implementers can set the timeframe according to the actual takeoff preparation process. and The value of .

[0045] For example, a schematic diagram of the response curve of the rebubbling imbalance potential energy index is shown in Figure 3. When the difference in resource utilization is close to 0, the rebubbling imbalance potential energy index is extremely small, and the system maintains moderate freedom. However, when the difference changes abruptly, such as when encountering goods with extreme properties in succession, the rebubbling imbalance potential energy index exhibits an exponential explosive growth. This is the core control variable of the present invention, which intuitively demonstrates the corrective driving force of the algorithm.

[0046] S3: Based on the rebubbling imbalance potential energy index, the positive interaction matching degree of each waybill in the waybill buffer pool is calculated using the positive interaction matching principle.

[0047] It should be noted that after calculating the system's imbalance potential energy, specific waybills that can neutralize this potential energy need to be selected from the buffer pool to optimize the transport capacity structure. Furthermore, the physical attributes of the waybills should be complementary to the current imbalance direction; that is, the more severe the imbalance, the higher the weight of complementary matching should be. At the same time, the fairness of long-waiting goods must be taken into account to prevent some waybills from being indefinitely detained due to an excessive pursuit of balance. However, the physical waiting time and dimensionless matching degree cannot be directly calculated. Therefore, this invention introduces a normalized retention factor and uses the principle of positive interactive complementarity to calculate the positive interactive complementarity matching degree of each waybill in the waybill buffer pool.

[0048] Specifically, calculate the waybill The positive cross-complement matching degree is calculated using the following formula:

[0049] In the formula, For waybill The positive interaction complement matching degree, the higher the value, the more suitable the waybill is for the current aircraft's loading requirements or the higher the shipping priority; For waybill Normalized volume; For waybill Normalized weight; This is a sign function; it takes the value 1 when the variable is greater than 0, -1 when the variable is less than 0, and 0 when the variable is equal to 0. This is an indicator of the potential energy of the heavy bubble imbalance. Indicates taking the absolute value; For waybill Normalized retention factor; The weighting factor for the loading rate. This is the weighting coefficient for timeliness. ,and .

[0050] Among them, the normalized retention factor The formula for calculation is:

[0051] In the formula, For waybill Normalized retention factor; The current moment; For waybill Entry time; This is the maximum tolerable waiting time parameter; where, The numerical range of is typically between 0 and 1, representing the relative urgency of the waybill waiting time; in this embodiment, It is set to 1440 minutes, or 24 hours; in other embodiments, implementers may set this parameter based on average turnover efficiency.

[0052] in, The term utilizes the property of exponent sign inversion to achieve automated complementary selection: when At that time, the aircraft is heavier, with an index of 1. This item approximates the bulk weight ratio. A higher bulk weight ratio results in a larger value for this item, thus guiding the system to accept bulky cargo. At that time, the aircraft is biased towards bubble, with an index of -1. When this item is reversed, it is approximately the weight-bubble ratio. The higher the density of the waybill, the larger this value will be, thus guiding the system to absorb heavy cargo. The term serves as a normalized modulus penalty to prevent waybills with huge values ​​from receiving unreasonable priority simply because of their large numerators. As a dynamic gain, the more severe the system imbalance, the more... The larger the value, the greater the score weight of complementary matching, which overwhelms the retention factors, thus establishing the priority principle when correction is urgently needed. The item indicates the waiting time. The increase, Increase the overall matching degree of the waybill. This ensures that older waybills are prioritized during system balancing.

[0053] In this embodiment, Set to 0.7, Setting it to 0.3 prioritizes loading rate and balance while providing appropriate compensation for waiting time. In other embodiments, implementers can select a value between 0.6 and 0.8 based on the actual business requirements regarding loading timeliness and loading rate. Choose a value in the range of 0.2 to 0.4. The value of .

[0054] For example, Figure 4 shows a comparison of the loading path evolution trajectories of the prior art and the present invention. The curve corresponding to the prior art exhibits a significant unidirectional deviation. When cargo density distribution is uneven, the curve rapidly extends towards the load axis, resulting in a low volume utilization rate even when the load utilization rate reaches 100%, leading to a full-load, empty-capacity phenomenon. The curve corresponding to the present invention closely climbs around the ideal equilibrium line, maintaining dynamic synchronization between the load growth rate and the volume consumption rate throughout the loading process, without prematurely reaching the peak. This is thanks to the calculation of the positive interactive matching degree in the present invention: when the aircraft is in a predominantly heavy state, the system automatically prioritizes high-volume waybills (i.e., bulky cargo); when in a predominantly bulky state, it prioritizes high-density waybills (i.e., heavy cargo). This mechanism breaks the randomness of the traditional FIFO algorithm, achieving proactive optimization of transport capacity resources by orthogonally evaluating dimensionless weight and volume, ensuring that the loading path always approaches the ideal state of double full load.

[0055] S4: Sort the waybills according to the positive interaction matching degree and perform virtual trial loading verification; if the verification passes, lock the waybill and update the cumulative data of the loaded goods, trigger the recalculation of the rebubbling imbalance potential energy index, and execute closed-loop feedback.

[0056] It should be noted that since the capacity matching of multimodal transport is a dynamic and continuous process, the imbalance state and remaining capacity of the aircraft change every time a waybill is loaded. If static planning is used, it cannot adapt to the real-time changing loading environment. Therefore, this invention adopts a real-time closed-loop update mechanism, which updates the state and recalculates the potential energy immediately after each waybill is locked, so as to achieve fully dynamic matching optimization.

[0057] Specifically, the waybills in the buffer pool are sorted in descending order based on their positive cross-complement matching degree. The waybill ranked first is selected for virtual trial loading verification, and it is determined whether the following constraints are met after adding this waybill:

[0058]

[0059] In the formula, This represents the cumulative weight of the loaded goods. For waybill The actual weight; The maximum permissible load for an aircraft; This represents the cumulative volume of the loaded goods. For waybill The actual volume; This refers to the maximum usable total volume of the aircraft.

[0060] If the above constraints are met, the waybill is locked and a dispatch instruction is generated, and the cumulative weight of the loaded goods is immediately updated. and cumulative volume The process returns to the steps of calculating the resource utilization difference and the rebubbling imbalance potential energy index. Based on the updated data, a new rebubbling imbalance potential energy index is recalculated. At this time, the positive interaction matching degree of all remaining waybills in the buffer pool will be reset based on the new rebubbling imbalance potential energy index. If the above constraints are not met, the current waybill is skipped and the next waybill is checked.

[0061] This invention, through the aforementioned dynamic closed loop, utilizes the re-bubble imbalance potential energy index and positive interactive matching degree to achieve intelligent scheduling of transportation resources from passive reception to proactive optimization.

[0062] For example, Figure 5 shows a comparison of the final loading effects of the prior art and the present invention. While the prior art achieves a load utilization rate of 99.8%, its volume utilization rate is low, resulting in a significant waste of transport capacity resources. The present invention, however, boasts high load and volume utilization rates, with their values ​​being very close, achieving a high degree of balance. This demonstrates that the present invention successfully solves the technical problem of an imbalanced ratio of heavy and bulky cargo. By optimizing the combination of goods with discrete physical density distributions, the solution successfully avoids the extreme resource waste dilemma of full load / empty capacity or full capacity / empty load. Thus, under the dual rigid constraints of aircraft load limits and volume limits, the loading rate is improved, maximizing the economic benefits of air logistics.

[0063] This invention also discloses a logistics multimodal transport scheme planning system based on optimization algorithms, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the logistics multimodal transport scheme planning method based on optimization algorithms according to this invention is implemented.

[0064] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A multimodal transport planning method based on optimization algorithms, characterized in that, include: The system collects the rated capacity parameters of the target flight, the cumulative data of loaded cargo, and the waybill data in the waybill buffer pool, and performs dimensionless normalization on the waybill data. Based on the cumulative data of loaded cargo and the rated capacity parameters, it calculates the resource utilization difference, and calculates the re-bubble imbalance potential energy index based on the resource utilization difference and the remaining takeoff time. Based on the re-bubble imbalance potential energy index, it calculates the positive re-complement matching degree of each waybill in the waybill buffer pool using the positive re-complement principle. The waybills are sorted according to the positive re-complement matching degree and a virtual trial loading verification is performed. If the verification passes, the waybill is locked, the cumulative data of loaded cargo is updated, and the recalculation of the re-bubble imbalance potential energy index is triggered to execute closed-loop feedback.

2. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 1, characterized in that, The rated capacity parameters of the target flight include the maximum permissible payload of the aircraft, the maximum available total volume of the aircraft, and the absolute flight cut-off time before the scheduled takeoff of the aircraft; the cumulative data of the loaded cargo includes the cumulative weight and cumulative volume of the loaded cargo; the waybill data includes the actual weight of the waybill, the actual volume of the waybill, and the arrival time.

3. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 2, characterized in that, The dimensionless normalization process for the waybill data includes: dividing the actual weight of the waybill by the maximum permissible payload of the aircraft to obtain the normalized weight of the waybill; and dividing the actual volume of the waybill by the maximum available total volume of the aircraft to obtain the normalized volume of the waybill.

4. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 2, characterized in that, The difference in resource utilization rates satisfies the expression: In the formula, This represents the difference in resource utilization. This represents the cumulative weight of the loaded goods. The maximum permissible load for an aircraft; This represents the cumulative volume of the loaded goods. This refers to the maximum usable total volume of the aircraft.

5. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 2, characterized in that, The re-bubbling imbalance potential energy index satisfies the expression: In the formula, This is an indicator of the potential energy of the heavy bubble imbalance. This represents the difference in resource utilization. It is a symbolic function; It is a natural exponential function; Sensitivity coefficient; Indicates taking the absolute value; For time factor parameters; This is the absolute cut-off time for flight loading; The current moment; To prevent the value from being zero or a minimum.

6. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 2, characterized in that, The positive cross-complement matching degree of each waybill in the waybill buffer pool satisfies the expression: In the formula, For waybill Positive interaction complement matching degree; For waybill Normalized volume; For waybill Normalized weight; It is a symbolic function; This is an indicator of the potential energy of the heavy bubble imbalance. Indicates taking the absolute value; For waybill Normalized retention factor; The weighting factor for the loading rate. This is the weighting coefficient for timeliness. ,and 。 7. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 6, characterized in that, The normalized retention factor is the quotient obtained by dividing the difference between the current time and the entry time of the waybill by the maximum tolerable waiting time parameter.

8. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 5 or 6, characterized in that, The symbolic function takes the value 1 when the variable is greater than 0, -1 when the variable is less than 0, and 0 when the variable is equal to 0.

9. The logistics multimodal transport scheme planning method based on optimization algorithm according to claim 2, characterized in that, The virtual trial loading verification includes: determining whether the sum of the cumulative weight of the loaded cargo and the actual weight of the waybill is less than or equal to the maximum allowable payload of the aircraft, and whether the sum of the cumulative volume of the loaded cargo and the actual volume of the waybill is less than or equal to the maximum usable total volume of the aircraft.

10. A logistics multimodal transport scheme planning system based on optimization algorithms, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the logistics multimodal transport scheme planning method based on optimization algorithms according to any one of claims 1-9.

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