A yard slot allocation method and system for load balancing and minimizing container rehandling

Abstract

This invention relates to the field of container yard scheduling technology, providing a method and system for load balancing and minimizing container handling in container yard allocation. The method involves acquiring the estimated arrival time, estimated pickup time, and yard status information of containers to be allocated; filtering available container locations based on feasibility conditions; calculating the loading load for container entry, pickup and handling, and estimated handling costs by combining arrival time, pickup time, operating distance from the container yard's transit area to the container location, and layer number; and determining spatial and temporal load imbalances. With the goal of minimizing overall load imbalance and total estimated handling costs, an optimization model for container location allocation is established and solved, incorporating constraints such as location allocation and unit-time operating capacity, to obtain target container areas and target container locations. This approach reduces long-term load imbalance and periodic congestion in container areas, lowers future handling workload, and improves yard operational stability.

Description

Technical Field

[0001] This application relates to the field of container scheduling technology in container yards, specifically to a container yard allocation method and system that balances load and minimizes container turnover. Background Technology

[0002] As global trade continues to expand and port container throughput continues to grow, the operational pressure on port yards is also increasing. Container yards serve as a crucial component of port landside operations, handling functions such as container distribution, temporary storage, and loading / unloading coordination. Operations within the yard typically include container loading, storage, retrieval, and necessary container handling. The rationality of container allocation directly impacts subsequent operational efficiency, equipment utilization, and yard turnover capacity.

[0003] In actual operations, after containers enter the yard, their storage locations need to be determined based on factors such as the current state of the yard, the estimated pickup time, the remaining capacity of the container area, and the distribution of operating equipment. If the container allocation is unreasonable, it can easily lead to over-concentration of operations in certain areas, causing yard cranes, trucks, and other equipment to operate in the same area within a short period, resulting in waiting times, congestion, or decreased operational efficiency. Furthermore, if a container awaiting pickup is covered by other containers, the obstructing container must be moved first, resulting in a container repositioning operation. Repositioning not only increases the number of operations but also occupies equipment resources, prolongs operation time, and increases energy consumption and management costs. Currently, many container allocation methods still rely heavily on manual experience or fixed rules. Therefore, when facing large-scale, multi-batch, and dynamic container entry and exit tasks, existing container allocation methods still suffer from uneven load distribution and poor overall scheduling efficiency due to numerous container repositioning operations. Summary of the Invention

[0004] This application aims to solve the technical problem of poor overall scheduling efficiency in the prior art, and provides a yard container allocation method and system that improves overall scheduling efficiency by load balancing and minimizing container turnover.

[0005] To solve the above problems, this application is implemented as follows:

[0006] Firstly, this application provides a yard container allocation method that balances load and minimizes container turnover, including the following steps: S1. Obtain task information and initial yard status information of containers to be allocated during the planning period, and divide the planning period into multiple time units; wherein, the task information includes: the estimated arrival time and estimated pickup time of each container to be allocated, and the initial yard status information includes: the transfer area capacity of each container area, the unit time operation capacity, the operation distance from the transfer area to each bay, the initial occupancy status of each container bay, and the maximum allowable stacking height; S2. Based on the initial state information of the yard, construct a set of candidate container locations for each container to be allocated according to container area, bay, column and layer, and filter the set of candidate container locations based on preset container location feasibility conditions to obtain a set of allocable container locations corresponding to each container to be allocated. S3. Based on the available container slots corresponding to each container to be allocated, and combining the time unit to which the estimated arrival time of the container to be allocated belongs, the time unit to which the estimated container pickup time belongs, the operating distance from the candidate container area transfer area to the candidate container slot, and the layer number of the candidate container slot, calculate the container entry operation load, container pickup and handling operation load, and estimated handling cost. Based on the container entry operation load and the container pickup and handling operation load, determine the estimated operation load of each container area in each time unit. Then, based on the estimated total operation load of each container area in the planning period, determine the spatial load imbalance between each container area. Based on the fluctuation of the estimated operation load of each container area in different time units, determine the temporal load imbalance. S4. Taking the set of allocable container slots corresponding to each container to be allocated as the allocation range, with the optimization objectives being the minimum comprehensive load imbalance determined by the spatial load imbalance and the temporal load imbalance, and the minimum total estimated turnover cost of all containers to be allocated, and with the constraints being that each container to be allocated is assigned only one target container slot, each container slot is assigned at most one container based on the initial occupancy status of each container slot, and each container area meets the transit area capacity limit and unit time operation capacity limit in each time unit, a container slot allocation optimization model is established, and the target container area and the target container slots in the target container area for each container to be allocated are solved, and the container slot allocation results are output. S5. Based on the container allocation results, stack each container to be allocated to the corresponding target container location.

[0007] Secondly, this application provides a yard container allocation system for load balancing and minimizing container turnover, including a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and the program or instructions, when executed by the processor, implement the steps of the above-described method.

[0008] Compared with the prior art, this application has the following beneficial effects: By calculating the estimated arrival and pickup times of containers to be allocated during the planning period, and combining this with the transshipment area capacity, unit-time operational capacity, operational distance from the transshipment area to each bay, initial occupancy status of each bay, and maximum allowable stacking height, the model calculates the container loading and unloading workload, the unloading and handling workload, and the estimated handling cost. It also transforms the operational pressure in different container areas and time units into quantifiable indicators that can be used for optimization. In this way, the container allocation optimization model, when selecting target container areas and bays, can avoid some container areas bearing excessively high total operational loads in a long period, reduce congestion caused by concentrated operations in certain time units, and reduce unloading operations caused by unreasonable stacking heights and unloading sequences. The final container allocation result can balance current container loading efficiency and future unloading efficiency, thereby improving the overall operational stability of the yard. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart provided in one embodiment of this application; Figure 2 This is a schematic diagram of the system provided in one embodiment of this application. Detailed Implementation

[0011] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] The terms "comprising" and "having," and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus. Furthermore, the use of "and / or" in this application indicates at least one of the connected objects, such as A and / or B and / or C, representing seven possibilities: including A alone, B alone, C alone, and the presence of both A and B, both B and C, both A and C, and the presence of A, B, and C.

[0013] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0014] The following describes a yard container allocation method and system that provides load balancing and minimizes container turnover, as provided in this application.

[0015] Please see Figure 1 One embodiment of the present invention provides a yard container allocation method for load balancing and minimizing container turnover, comprising the following steps: S1. Obtain task information for containers to be allocated during the planning period and initial status information of the yard, and divide the planning period into multiple time units; among them, task information includes the estimated arrival time and estimated pickup time of each container to be allocated, and initial status information of the yard includes the transfer area capacity of each container area, the unit time operation capacity, the operation distance from the transfer area to each bay, the initial occupancy status of each container bay, and the maximum allowable stacking height. In addition, the initial status information of the yard also includes the location of the stacked containers and the stacking attribute information, which includes at least one of weight class, size factor and estimated container pick-up time; S2. Based on the initial state information of the yard, construct a set of candidate container locations for each container to be allocated according to the container area, bay, column and layer, and filter the candidate container locations based on the preset container location feasibility conditions to obtain the set of allocable container locations corresponding to each container to be allocated. S3. Based on the available container slots corresponding to each container to be allocated, and considering the time unit to which the estimated arrival time of the container belongs, the time unit to which the estimated pickup time belongs, the operating distance from the candidate container area's transfer area to the candidate container slot, and the layer number of the candidate container slot, calculate the container entry operation load, container pickup and handling operation load, and estimated handling cost. Based on the container entry operation load and the container pickup and handling operation load, determine the estimated operation load of each container area in each time unit; then sum the estimated operation loads of the same container area in each time unit within the planning period to obtain the estimated total operation load of the container area during the planning period. Based on the estimated total operation load of each container area during the planning period, determine the spatial load imbalance between container areas; and based on the fluctuation of the estimated operation load of each container area in different time units, determine the temporal load imbalance. S4. Taking the set of allocable container slots corresponding to each container to be allocated as the allocation range, with the optimization objectives being the minimum comprehensive load imbalance determined by the spatial load imbalance and the temporal load imbalance, and the minimum total estimated turnover cost of all containers to be allocated, and with the constraints being that each container to be allocated is allocated only one target container slot, each container slot is allocated at most one container based on the initial occupancy status of each container slot, and each container area meets the transit area capacity limit and unit time operation capacity limit in each time unit, a container slot allocation optimization model is established, and the target container area and target container slots within the target container area for each container to be allocated are solved. S5. Output the container allocation results and stack each container to be allocated to the corresponding target container location according to the container allocation results.

[0016] In this embodiment, container yard allocation is not simply selecting a location from currently empty spaces. Instead, it considers the operational pressure during container entry, the risk of container handling during future retrieval, and the differences in operational capacity between container areas. Based on the estimated arrival and retrieval times of containers to be allocated within the planning period, container entry and future retrieval operations can be mapped to multiple time units. Furthermore, by combining the transfer area capacity, unit-time operational capacity, operational distance from the transfer area to each bay, the initial occupancy status of each container location, and the maximum allowable stacking height, it is possible to determine whether different candidate container locations have the practical allocation conditions and further estimate the impact of different location selections on subsequent operations. Candidate container locations no longer participate in allocation merely as spatial positions but also carry information such as the candidate container area, candidate bay, and the layer number of the candidate container location. This allows for the calculation of container entry operational load, container handling and retrieval operational load, and the estimated handling cost, transforming the operational pressure in different container areas and time units into quantitative indicators that can be used for optimization solutions. In this way, the container allocation optimization model, when selecting target container areas and locations, can avoid some container areas bearing excessively high total operational loads in space for extended periods, reduce congestion caused by concentrated operations within certain time units, and minimize container turning operations due to unreasonable stacking levels and container retrieval sequences. The final container allocation result can balance current container loading efficiency and future container retrieval efficiency, thereby improving the overall operational stability of the yard.

[0017] For ease of explanation, in this application, "container area" refers to the operational area in a container yard used for container stacking; "bay" refers to the stacking location within a container area along its length; "column" refers to the stacking location within a bay along its width; and "layer" refers to the vertical stacking level of a container space, with layer numbers increasing from the bottom layer upwards, the bottom layer being layer 1. A container space can be determined by the container area, bay, column, and layer. "Planning period" refers to the time frame for optimizing container space allocation, which is divided into multiple time units. "Operating load" represents a quantitative value characterizing the operational pressure of a container area within a corresponding time unit, not the container weight. "Estimated operating load" represents the combined operational pressure of a container area within a time unit, calculated from the container entry and container handling operating loads. The estimated total operating load for the planning period represents the sum of the estimated operating loads for each time unit within the same container area during the planning period.

[0018] In this application, the estimated layer-based container turning risk represents the potential turning risk estimated based on the layer number of a candidate layer after the container to be allocated is placed there. The higher the layer number, the greater the likelihood that the container will be located above other containers and obstruct subsequent container retrieval. The estimated turning cost represents the future turning operation cost estimated based on the expected retrieval time, the stacking location of other containers in the same column, the layer number of the candidate layer, and the layer height penalty coefficient after the container to be allocated is placed in a candidate container location. The estimated turning cost characterizes the impact of the candidate container location on subsequent retrieval operations and does not represent the actual number of turning operations. The total estimated turning cost represents the sum of the estimated turning costs for each container after all containers to be allocated have been assigned to their corresponding target locations.

[0019] Spatial load imbalance indicates the degree of difference in the total estimated workload borne by different container areas during the planning period. Temporal load imbalance indicates the degree of concentration or fluctuation in the estimated workload of container areas within different time units. Overall load imbalance is a comprehensive evaluation value determined based on spatial and temporal load imbalance, used to evaluate whether the workload distribution of the container allocation scheme is balanced between container areas and between time units.

[0020] Specifically, in step S1, the planning period can be determined based on port yard operation plans, vessel arrival plans, truck entry plans, or container pick-up reservation information. After the planning period is divided into multiple time units, the estimated arrival time and estimated pick-up time of each container to be allocated can be assigned to the corresponding time unit. Subsequent calculations can identify the container handling pressure and container pick-up and handling pressure that a certain container area may experience within a certain time unit. The initial state information of the yard is used to describe the operational capacity and space occupancy of the yard at the beginning of the planning period. Among them, the transshipment area capacity and unit time operation capacity reflect the capacity limit of the container area in the time dimension, the operation distance from the transshipment area to each bay reflects the movement consumption of different bays, and the initial occupancy status and maximum allowable stacking height of each container bay reflect the availability of candidate container bays in the spatial dimension.

[0021] In step S2, the three-dimensional space of the yard can be traversed according to container area, bay, column, and layer to form a set of candidate container positions for each container to be allocated. After the candidate container position set is formed, it is screened based on preset container position feasibility conditions. Specifically, the preset container position feasibility conditions may include conditions such as the container position not being occupied, the layer number of the candidate container position not exceeding the maximum allowable stacking layer height, the candidate container position meeting stacking support requirements, the candidate container position meeting weight balance requirements, and the candidate container position meeting size adaptation requirements. After screening, candidate container positions that obviously do not meet the actual stacking conditions are excluded, and the set of allocable container positions corresponding to each container to be allocated is closer to the actual executable range of the yard, reducing the invalid search of the container position allocation optimization model.

[0022] In step S3, for each allocable container slot corresponding to each container to be allocated, the loading load for container entry is calculated based on the time unit to which the expected arrival time belongs, and the loading load for container retrieval and handling is calculated based on the time unit to which the expected retrieval time belongs. This is combined with the operating distance from the candidate container area's transfer zone to the candidate bay and the layer number of the candidate container slot. The greater the operating distance from the candidate container area's transfer zone to the candidate bay, the higher the movement cost during container entry is usually; the higher the layer number of the candidate container slot, the greater the possibility of being affected by containers above during subsequent retrieval. After summing the loading load for container entry and the loading load for container retrieval and handling, the estimated operating load for each container area in each time unit can be obtained. The estimated total operating load for each container area during the planning period is used to evaluate the spatial load imbalance between different container areas, and the fluctuation of the estimated operating load for each container area in different time units is used to evaluate the temporal load imbalance. This facilitates the comparison of multiple candidate schemes in the subsequent container slot allocation optimization model.

[0023] In step S4, the container allocation optimization model uses the set of allocable container slots corresponding to each container to be allocated as the allocation range. Since the set of allocable container slots has excluded candidate slots that do not meet the preset feasibility conditions, the optimization model can focus on solving the actually executable allocation scheme. Minimizing the load imbalance is used to constrain the differences in operational pressure between container areas and between time units, and minimizing the total estimated turnover cost of all containers to be allocated is used to constrain the turnover risk during future container pickup. Each container to be allocated is assigned only one target container slot, which can avoid the same container being repeatedly arranged; each container slot is assigned at most one container, which can avoid different containers being assigned to the same physical container slot; each container area meets the transit area capacity limit and unit time operation capacity limit in each time unit, which can avoid the allocation scheme exceeding the actual operational carrying capacity of the container area. By solving the container allocation optimization model, the target container area and the target container slots within the target container area for each container to be allocated can be obtained.

[0024] In step S5, the container allocation results can be output to the yard management system, the yard crane dispatching system, or the operation terminal. Based on the allocation results, each container to be allocated is stacked in its corresponding target location. Since the target location is obtained after comprehensively considering load imbalance and the total estimated handling cost of all containers to be allocated, this result can reduce problems such as localized container area overload, concentrated operations within a time unit, and excessive subsequent container handling times.

[0025] In one embodiment, in step S3 The formula for calculating the load of the container loading operation satisfies:

[0026] The formula for calculating the load of the box lifting and turning operation satisfies:

[0027] The risk estimate for container rummaging at different levels meets the following criteria:

[0028] The formula for calculating the estimated workload satisfies:

[0029] In the formula, Indicates box area In time unit The load of the box loading operation inside, Indicates container The estimated arrival time, Indicates box area From the transit zone center to its internal bay Working distance, This represents the unit movement cost coefficient. This represents the cost coefficient for grasping and releasing operations; Indicates box area In time unit The load of lifting and turning boxes inside the container. Indicates container The estimated pickup time, Indicates container The estimated risk of container overturning at different levels; This represents the cost coefficient for the box-turning operation; Indicates box area In time unit The estimated workload within, This represents the discount factor for future assignments. Indicates container Have you been assigned to a container area? Beta ,List ,layer Decision variables.

[0030] Specifically, the container handling load is calculated by aggregating containers awaiting allocation whose expected arrival times belong to the same time unit, and also taking into account the operational distance from the transshipment area center to the bay. Therefore, containers allocated to bays further away will result in a higher container handling load. The container retrieval and handling load is calculated by aggregating containers awaiting allocation whose expected retrieval times belong to the same time unit, and using a layer-by-layer handling risk estimate to reflect the impact of the candidate bay's layer number on subsequent retrieval operations. The estimated operational load is coordinated between the container handling and container retrieval / handling loads using a discount factor, ensuring that current and future operational costs are reflected in the same load evaluation system.

[0031] In one embodiment, in step S3, the formula for calculating the estimated cost of turning over boxes satisfies:

[0032] In the formula, Indicates container Placed in position Subsequently, because the container below in the same column preceded the container... The container was picked up and made The estimated cost of retrieving the container that becomes an obstacle; This represents a collection of containers to be allocated. Indicates container Compared to containers The probability of being picked up first (e.g., when the container is picked up first). The expected pickup time for containers is earlier than that for other containers. hour, Take 1; that evening at 10:00, Take 0). Indicates container Assigned to position The indicator parameters, This represents the floor height penalty coefficient.

[0033] Specifically, the estimated container turnover cost is used to characterize the expected turnover cost that occurs when a container to be allocated is placed in a candidate container position and obstructs the container that was picked up earlier in the same row.

[0034] In one embodiment, in step S4, the optimization objective of minimizing the total estimated turnover cost of all containers to be allocated satisfies:

[0035] In the formula, This indicates the total estimated cost of rummaging through the boxes. Indicates container Have you been assigned to a container area? Beta ,List ,layer Decision variables.

[0036] Specifically, the total estimated container handling cost is obtained by summing the estimated handling costs of each container at different candidate locations multiplied by the corresponding decision variables. When a location is selected as the target container location, the corresponding decision variable takes a valid value, and the estimated handling cost of that location is included in the total estimated handling cost; when a location is not selected as the target container location, the estimated handling cost of that location is not included in the objective function. This is achieved by minimizing... The container allocation optimization model can prioritize target container locations that have a smaller impact on future container lifting operations, thereby reducing the amount of subsequent container handling operations, while meeting various constraints.

[0037] In one embodiment, in step S4, the optimization objective of minimizing load imbalance satisfies:

[0038] in, This represents the overall load imbalance target. Indicates the degree of spatial load imbalance. Indicates the degree of time load imbalance. and Represents the weighting coefficient; and Greater than The sum of the two is 1.

[0039] The spatial load imbalance is used to characterize the maximum deviation between the estimated total workload of each container area and the average estimated total workload of the container area during the planning period, and satisfies:

[0040] The time load imbalance Used to characterize the degree to which the estimated workload of the container area exceeds a preset load threshold within each time unit; where, Indicates the area with the container Another bin index, used to distinguish between bins, is used to traverse the bin set. Each box area in the middle.

[0041] In the formula, Indicates box area Each time unit within the planning period The estimated total workload, Represents the set of bin regions The number of intermediate compartments.

[0042] Specifically, when the estimated total workload of a container depot is significantly higher than the average estimated total workload of the depot, the spatial load imbalance increases, and the model will tend to reduce the number of containers that depot continues to receive for allocation. Temporal load imbalance affects whether the estimated workload of container depots is concentrated within each time unit. When the estimated workload of container depots exceeds a preset load threshold in some time units, the temporal load imbalance increases, and the model will tend to choose an allocation scheme that has a smaller impact on the operational pressure of that time unit. By setting weighting coefficients, the influence of spatial load balancing and temporal load balancing on the optimization objective can be adjusted according to the actual management needs of the yard.

[0043] In one embodiment, when linearizing the spatial load imbalance, an auxiliary variable is introduced. and auxiliary variables As spatial load imbalance The linearized representation of the auxiliary variable makes satisfy:

[0044] In the formula, Indicates box area Each time unit within the planning period The estimated total workload, Represents the set of bin regions The number of intermediate compartments This represents the maximum load difference across all container areas. Spatial load imbalance involves calculating both the maximum deviation and the absolute value; directly incorporating this into the container allocation optimization model increases the solution complexity. Therefore, an auxiliary variable is introduced. Then, the deviations of the estimated total workload for each container area from being higher than the average estimated total workload for that container area, and the deviations of the estimated total workload for each container area from being lower than the average estimated total workload for that container area, can be constrained separately. This is because both the positive and negative deviations for each container area are limited by auxiliary variables. Minimizing this auxiliary variable within a certain range can reduce the spatial load imbalance. This linearization process makes the container allocation optimization model easier to solve using integer programming or mixed integer programming methods, and also facilitates the rapid formation of target container areas and target container positions in actual yard operations.

[0045] In one embodiment, solving the container allocation optimization model in step S4 includes: A multi-objective processing approach is adopted to coordinate the minimization of the load imbalance and the minimization of the total estimated container turnover cost, transforming the container allocation optimization model into an evaluation objective for comparing the merits of different container allocation schemes; wherein, the multi-objective processing approach is one of the weighted sum method, ε-constraint method, and Pareto front generation method. The container allocation optimization model is solved using a hybrid integer programming solver, a heuristic algorithm, or a metaheuristic algorithm to obtain a container allocation scheme that satisfies the constraints as a feasible solution. Calculate the load imbalance degree and total estimated box-turning cost for each feasible solution, and determine the evaluation results of each feasible solution according to the multi-objective processing method; Based on the evaluation results of each feasible solution, a target container allocation scheme is selected from multiple feasible solutions; The container area in the target container space allocation scheme is determined as the target container area for each container to be allocated, and the bay, column and layer in the target container space allocation scheme are determined as the target container space for each container to be allocated within the corresponding target container area.

[0046] Specifically, the container allocation optimization model simultaneously includes two optimization objectives: minimizing load imbalance and minimizing the total estimated container handling cost. These two objectives do not necessarily achieve optimal results simultaneously in all situations. For example, distributing containers to be allocated across multiple container areas helps reduce load imbalance, but some dispersed container locations may increase subsequent container handling operations; concentrating containers to be allocated in locations with lower handling risk may increase the operational load in some container areas or time units. Therefore, a multi-objective approach is needed to coordinate the two optimization objectives during the solution process.

[0047] When using the weighted sum method, weights can be assigned to load imbalance and total estimated container turnover cost based on yard management requirements, transforming the two optimization objectives into a single comprehensive objective for solution. When using the ε-constraint method, one optimization objective can be used as the primary objective, while the other is transformed into a constraint condition to control the trade-offs between load balancing and container turnover reduction in feasible solutions. When using the Pareto front generation method, multiple feasible solutions with different trade-offs between load imbalance and total estimated container turnover cost can be obtained, and the target container allocation scheme can then be determined based on actual operational needs.

[0048] During the model solution phase, a mixed-integer programming solver, heuristic algorithm, or metaheuristic algorithm can be selected based on the number of containers to be allocated, the number of candidate container locations, and the real-time requirements of the yard. For smaller-scale scenarios or those with high optimality requirements, a mixed-integer programming solver can be used. For scenarios with a large number of containers to be allocated, a wide range of candidate locations, or those requiring rapid response, heuristic or metaheuristic algorithms can be used to obtain a container location allocation scheme that satisfies the constraints. By first obtaining a feasible solution and then evaluating it based on the load imbalance and the total estimated turnover cost, it is possible to avoid selecting container locations based on a single objective, thus making the final target container location allocation scheme more in line with the comprehensive needs of actual yard operations.

[0049] In one embodiment, the task information also includes the weight class of each container to be assigned. Batch grouping and size factor ; In the formula, Indicates container Weight class, Indicates container Batch grouping Indicates container Size factor.

[0050] Specifically, weight class Batch grouping and size factor Used to further describe the stacking attributes of containers to be allocated. Weight class This can be used to determine whether the weight balance requirement is met between the container to be allocated and the containers already stacked below the candidate container slot, preventing heavier containers from being allocated on top of lighter containers and affecting stacking stability. Batch grouping It can be used to identify containers in the same or related batches, providing a basis for candidate container location selection or model constraints when centralized storage, zoned storage, or group management according to work plans are required. (Size factor) It can be used to characterize the size attributes of containers to be allocated, so that the container allocation process can determine whether the candidate container positions are compatible with the size requirements of the containers to be allocated.

[0051] In one embodiment, the feasibility conditions for the preset container location in step S2 include at least the following conditions: The candidate container slots are not occupied; The layer number of the candidate container location is less than or equal to the maximum allowable stacking layer height; The candidate container slot is either the bottom container slot or the corresponding position below the candidate container slot is already occupied, in order to meet the continuous stacking requirements; When containers are already stacked below the candidate container slot, the weight class of the containers below should not be less than the weight class of the container to be allocated, so as to meet the weight balance requirements. The bay, column, or container size corresponding to the candidate container slots are matched with the size factor of the container to be allocated to meet the size adaptation requirements. Only candidate container locations that simultaneously meet the above conditions will be identified as allocable container locations.

[0052] Specifically, the preset container location feasibility conditions are used to exclude unsuitable locations from the candidate container location set before establishing the container location allocation optimization model. If the candidate container location is unoccupied, different containers cannot be assigned to the same location; if the layer number of the candidate container location is less than or equal to the maximum allowable stacking height, the allocation result can be guaranteed to not exceed the layer height range allowed by the yard equipment and stacking rules; if the corresponding position below the candidate container location is occupied, the containers can be placed in a continuous stacking manner, avoiding suspended stacking.

[0053] Furthermore, weight balance requirements and size compatibility requirements further limit the availability of candidate container slots. When containers are already stacked below a candidate slot, comparing the weight class of the containers below with the weight class of the container to be assigned can prevent containers with larger weight classes from being placed on top of containers with smaller weight classes, thereby improving stacking stability. By determining whether the bay, column, or container slot specifications corresponding to the candidate slot match the size coefficient of the container to be assigned, it is possible to avoid assigning mismatched containers to the wrong slot, reducing subsequent repositioning and repeated handling.

[0054] In one specific embodiment, the constraints for constructing the container allocation optimization model specifically include: A container to be allocated must be allocated one and only one container slot, satisfying the following conditions:

[0055] In the formula, Represents the set of bin regions. Indicates box area The set of beta positions within, Indicates the position The set of columns within, Indicates the maximum allowed heap height. Indicates container Have you been assigned to a container area? Beta ,List ,layer Decision variables, This represents a collection of containers to be allocated.

[0056] The container area ownership variable and the container location allocation variable satisfy the coupling constraint, which is:

[0057]

[0058] In the formula, Indicates container Have you been assigned to a container area? Decision variables.

[0059] The two sets of constraints mentioned above together ensure that the variables and Consistency. If and only if the container I was assigned to the container area A specific location.

[0060] Each container slot can accommodate a maximum of one container, satisfying the following:

[0061] In the formula, This indicates the initial occupancy status of the container space. Indicates the inclusion of beta. container area Perform a traversal.

[0062] The heap height continuity constraint satisfies:

[0063] In the formula, Indicates from the bottom layer to the layer number. Layer index; The weight balance constraint is satisfied as follows:

[0064] In the formula, Indicates a pre-defined large positive number. Indicates the floor number Another layer number index to distinguish them; Indicates container Weight class, The constraint of stacking containers in the same group together is satisfied (taking the same ship as an example):

[0065] In the formula, Indicates box area Internal reserves are reserved for the corresponding ship number or batch grouping. The set of beta positions, Indicates container The corresponding centralized stacking constraint slack variables.

[0066] The transit area capacity constraint is satisfied:

[0067] In the formula, It is a container The size factor, Indicates box area The maximum capacity of the transit area.

[0068] The container area operation capacity constraints are satisfied:

[0069] In the formula, Indicates box area The theoretical maximum working capacity per unit time. Represents a set of time units.

[0070] Domain constraints are satisfied and used to define the mathematical properties of decision variables.

[0071]

[0072] like Figure 2 As shown, this application also provides a yard container allocation system 400 with load balancing and minimal container turnover, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described yard container allocation method embodiment with load balancing and minimal container turnover, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0073] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described embodiments of the yard container allocation method for load balancing and minimizing container turnover, and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0074] The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (Read-Only Memory). Only memory (ROM), random access memory (RAM), magnetic disks or optical disks, etc.

[0075] 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 apparatus 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 apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0076] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A yard container allocation method for load balancing and minimizing container turnover, characterized in that, Including the following steps: S1. Obtain task information and initial yard status information of containers to be allocated during the planning period, and divide the planning period into multiple time units; wherein, the task information includes: the estimated arrival time and estimated pickup time of each container to be allocated, and the initial yard status information includes: the transfer area capacity of each container area, the unit time operation capacity, the operation distance from the transfer area to each bay, the initial occupancy status of each container bay, and the maximum allowable stacking height; S2. Based on the initial state information of the yard, construct a set of candidate container locations for each container to be allocated according to container area, bay, column and layer, and filter the set of candidate container locations based on preset container location feasibility conditions to obtain a set of allocable container locations corresponding to each container to be allocated. S3. Based on the available container slots corresponding to each container to be allocated, and combining the time unit to which the estimated arrival time of the container to be allocated belongs, the time unit to which the estimated container pickup time belongs, the operating distance from the candidate container area transfer area to the candidate container slot, and the layer number of the candidate container slot, calculate the container entry operation load, container pickup and handling operation load, and estimated handling cost. Based on the container entry operation load and the container pickup and handling operation load, determine the estimated operation load of each container area in each time unit. Then, based on the estimated total operation load of each container area in the planning period, determine the spatial load imbalance between each container area. Based on the fluctuation of the estimated operation load of each container area in different time units, determine the temporal load imbalance. S4. Taking the set of allocable container slots corresponding to each container to be allocated as the allocation range, with the optimization objectives being the minimum comprehensive load imbalance determined by the spatial load imbalance and the temporal load imbalance, and the minimum total estimated turnover cost of all containers to be allocated, and with the constraints being that each container to be allocated is assigned only one target container slot, each container slot is assigned at most one container based on the initial occupancy status of each container slot, and each container area meets the transit area capacity limit and unit time operation capacity limit in each time unit, a container slot allocation optimization model is established, and the target container area and the target container slots in the target container area for each container to be allocated are solved, and the container slot allocation results are output. S5. Based on the container allocation results, stack each container to be allocated to the corresponding target container location.

2. The method according to claim 1, characterized in that, In S3, The formula for calculating the load of the box loading operation satisfies: The formula for calculating the load of the box lifting and turning operation satisfies: Risk estimate of rummaging through boxes at different levels The formula for calculating the estimated workload satisfies: In the formula, Indicates box area In time unit The load of the box loading operation inside, Indicates container The estimated arrival time, Indicates box area From the transit zone center to its internal bay Working distance, This represents the unit movement cost coefficient. This represents the cost coefficient for grasping and releasing operations; Indicates box area In time unit The load of lifting and turning boxes inside the container. Indicates container The estimated pickup time, Indicates container Placed in the The estimated risk of container overturning at the next level is due to the presence of containers picked up earlier in the lower level, which could create obstructions. This represents the cost coefficient for the box-turning operation; Indicates box area In time unit The estimated workload within, This represents the discount factor for future assignments. Indicates container Have you been assigned to a container area? Beta ,List ,layer Decision variables.

3. The method according to claim 1, characterized in that, In S3, the formula for calculating the estimated cost of turning over boxes satisfies: In the formula, Indicates container Placed in position The estimated cost of rummaging through the box, This represents a collection of containers to be allocated. Indicates container Compared to containers The probability of being taken away first. Indicates container Assigned to position Indication parameters, This represents the floor height penalty coefficient.

4. The method according to claim 3, characterized in that, In step S4, the optimization objective of minimizing the total estimated turnover cost of all containers to be allocated satisfies: In the formula, This indicates the total estimated cost of rummaging through the boxes. Indicates container Have you been assigned to a container area? Beta ,List ,layer Decision variables.

5. The method according to claim 1, characterized in that, In step S4, the comprehensive load imbalance target determined by the spatial load imbalance and the temporal load imbalance satisfies: in, This represents the overall load imbalance target. Indicates the degree of spatial load imbalance. Indicates the degree of time load imbalance. and Indicates the weighting coefficient; The spatial load imbalance is used to characterize the maximum deviation between the estimated total workload of each container area and the average estimated total workload of the container area during the planning period, and satisfies: The time load imbalance Used to characterize the extent to which the estimated workload of the box area exceeds the preset load threshold within each time unit; In the formula, Indicates box area Each time unit within the planning period The estimated total workload, Represents the set of bin regions The number of intermediate compartments.

6. The method according to claim 5, characterized in that, When linearizing the spatial load imbalance, an auxiliary variable is introduced. and the auxiliary variables As the spatial load imbalance The linearized representation of the auxiliary variable makes the auxiliary variable satisfy: In the formula, Indicates box area Each time unit within the planning period The estimated total workload, Represents the set of bin regions The number of intermediate compartments, This indicates the maximum load difference across all box sections.

7. The method according to claim 5, characterized in that, In step S4, solving the container allocation optimization model includes: The container allocation optimization model is solved using a hybrid integer programming solver, a heuristic algorithm, or a metaheuristic algorithm to obtain a container allocation scheme that satisfies the constraints as a feasible solution. Calculate the load imbalance degree and total estimated box-turning cost for each feasible solution, and determine the evaluation results of each feasible solution according to the multi-objective processing method; Based on the evaluation results of each feasible solution, a target container allocation scheme is selected from multiple feasible solutions; The container area in the target container space allocation scheme is determined as the target container area for each container to be allocated, and the bay, column and layer in the target container space allocation scheme are determined as the target container space for each container to be allocated within the corresponding target container area.

8. The method according to claim 1, characterized in that, The task information also includes the weight class of each container to be assigned. Batch grouping and size factor ; In the formula, Indicates container Weight class, Indicates container Batch grouping Indicates container Size factor.

9. The method according to claim 8, characterized in that, The feasibility conditions for the preset container location in S2 include at least the following conditions: The candidate container slots are not occupied; The layer number of the candidate container location is less than or equal to the maximum allowable stacking layer height; The candidate container slot is either the bottom-level container slot or the corresponding position below the candidate container slot is already occupied, in order to meet the continuous stacking requirements; When containers are already stacked below the candidate container slot, the weight class of the containers below should not be less than the weight class of the container to be allocated, so as to meet the weight balance requirements. The bay, column, or container size corresponding to the candidate container slots are matched with the size factor of the container to be allocated to meet the size adaptation requirements. Only candidate container positions that simultaneously meet all of the above conditions are determined as allocable container positions.

10. A yard container allocation system for load balancing and minimizing container turnover, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions executable on the processor, the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 9.

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