Storage yard distribution template design method and device and storage medium

Abstract

The invention relates to a storage yard distribution template design method and device and a storage medium. According to the method, a preset first-stage model is utilized to distribute positions for pre-obtained import and export container batches, and a current container sequence is obtained; pre-packaging the box sequence to obtain a plurality of pre-packaged box areas, and distributing corresponding sub-box area positions for the plurality of pre-packaged box areas by using a preset second-stage model to complete storage yard distribution and template design; wherein the first-stage model and the second-stage model form a two-stage mixed integer programming model, the first-stage model is optimized by taking storage yard space resources and equipment resources as constraints and taking space utilization rate maximization as a target, and the second-stage model is optimized by taking inner container truck total operation distance minimization as a target. Compared with the prior art, the container truck has the advantages that the overall utilization rate of the storage yard space is increased, and the total operation distance of the container truck is shortened.

Description

Technical Field

[0001] The invention relates to the technical field of container terminal yard storage template design, and in particular to a yard allocation template design method, device and storage medium. Background Art

[0002] As an efficient and energy-saving international logistics transportation mode, sea-rail transport has become the focus of container multimodal transport development. The bottleneck of port operations has shifted from the terminal side to the yard side. Yard allocation template design plays a vital role in container yard management. The existing sea-rail transport container terminal yard faces the dilemma of low space utilization and waste of equipment resources. Existing yard management technology mainly focuses on two aspects: one is to optimize the allocation of yard storage space, and the other is to optimize the deployment of equipment such as quay cranes and yard cranes.

[0003] At present, the design of container terminal yard allocation templates is mostly optimized for the total running distance of container trucks or automated guided vehicles and the utilization rate of yard space, and the storage capacity and operating efficiency of the yard are improved through single or coordinated scheduling of space and equipment resources. In the existing research on the design of yard allocation templates for sea-rail transport container terminals, the specific slot allocation is mainly for a single import container or export container, or for export containers and transshipment containers. There are few considerations for the joint allocation of specific slots for import and export containers. The specific slots of import and export containers directly affect the total running distance of container trucks, thereby affecting the overall operating efficiency of the terminal yard.

[0004] Therefore, how to consider the joint allocation of import and export containers to specific slots when designing the container terminal yard allocation template, and then optimize the yard allocation template design to further improve the overall terminal operation performance has become a problem that needs to be solved in this field. Summary of the invention

[0005] The purpose of the present invention is to overcome the defect of underutilization of yard space and equipment resources in the above-mentioned existing yard allocation template design and to provide a yard allocation template design method, device and storage medium. Based on the cluster concept and taking into account the two-way flow of import and export containers, under the dual-circulation transportation strategy of container trucks, storage templates are designed for import and export containers in the sea-rail intermodal shared yard, so as to further improve the overall utilization rate of the yard space and shorten the total running distance of the container trucks.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] According to a first aspect of the present invention, a method for designing a yard allocation template in a shared yard handover mode of a sea-rail intermodal container terminal is provided, comprising the following steps: S101, using a preset first-stage model to allocate bays for pre-acquired import and export container batches to obtain a current box sequence; S201, pre-packing the box sequence to obtain a plurality of pre-packed box areas, and using a preset second-stage model to allocate corresponding sub-box area positions for the plurality of pre-packed box areas to complete yard allocation and template design; wherein: the first-stage model and the second-stage model constitute a two-stage mixed integer programming model, the first-stage model is optimized with the yard space resources and equipment resources as constraints and with the goal of maximizing space utilization, and the second-stage model is optimized with the goal of minimizing the total running distance of internal container trucks.

[0008] As a preferred technical solution, during the optimization process, an adaptive best fit algorithm is used to solve the first stage model, and a Hungarian algorithm is used to solve the second stage model.

[0009] As a preferred technical solution, an adaptive best fit algorithm is used to solve the first stage model, specifically including: S111, initializing the two-dimensional packing problem; S112, defining the container batches and the yard size; S113, clustering to form clusters according to whether the ships they belong to are consistent; S114, calculating the fitness function of the current cluster and the sub-box area; S115, iterative update; S116, judging whether all container batches have been placed: if not, returning to S114; if yes, outputting the pre-packed box sequence and space utilization.

[0010] As a preferred technical solution, the Hungarian algorithm is used to solve the second-stage model, specifically including: S211, initializing the transport distance matrix from the container truck to each pre-selected berth of the ship; S212, calculating the total running distance of the container truck; S213, performing a row reduction operation; S214, performing a column reduction operation; S215, marking the current zero element; S216, judging whether there is only one zero element in each row and column: if not, then readjusting the rows and columns, finding the augmented path, updating the current transport distance matrix, and returning to S215; if yes, then outputting the optimal allocation plan and the total running distance.

[0011] As a preferred technical solution, the objective function of the first-stage model is:

[0012]

[0013] In the formula, I represents the set of clusters, with i as the index; L represents the set of ships, with l as the index; D represents the set of export containers, with d as the index; J represents the set of import containers, with j as the index; V ild Indicates container batch Bld Whether it is assigned to cluster i; x ld Indicates container batch B ld The number of bays required for the accumulated containers in the ld Indicates export container batch B ld The time spent in the yard; V ilj Indicates container batch B lj Whether it is assigned to cluster i; x lj Indicates container batch B lj The number of bays required for the accumulated containers in the lj Indicates import container batch B ld The dwelling time in the yard; r represents the total number of bays in a single sub-box area k; K represents the set of sub-box areas in the yard, indexed by k; T represents the set of planning time periods, indexed by t; the constraints of the first-stage model include: the first constraint, which is used to ensure that the boundaries of adjacent clusters do not overlap; the second constraint, which is used to ensure that the bay height of each cluster in time period t is determined by the total number of containers in the container batch assigned to it; the third and fourth constraints, which are used to limit each container batch to be assigned to at most one cluster; the fifth and sixth constraints, which are used to limit only import container batches and export container batches that arrive in the same time period and belong to the same ship to be assigned to the same cluster i; the seventh constraint, which is used to limit a ship to be assigned to only one cluster; the eighth constraint, which is used to ensure that a yard crane handles all clusters in a sub-box area in each time period t; and the ninth constraint, which is used to ensure that in the same time period t, the distance between different clusters sharing a yard crane in a sub-box area does not exceed the maximum service distance of the yard crane.

[0014] As a preferred technical solution, the objective function of the second stage model is:

[0015]

[0016] In the formula, Z il Indicates whether cluster i is reserved for ship l, h lk represents the distance from the berth corresponding to ship l to the sub-box area k, P kl Indicates whether sub-box area k is reserved for ship l, R ilt the total number of containers accumulated by cluster i reserved for ship l within time period t; the constraints of the second-stage model include at least: the tenth constraint and the eleventh constraint, which are used to ensure that each pre-packed box is only assigned to one sub-box area, and one sub-box area is also only assigned to one pre-packed box; and the twelfth constraint, which is used to limit the total number of containers accumulated by cluster i reserved for ship l within time period t.

[0017] As a preferred technical solution, the constraints of the second stage model also include decision variable constraints:

[0018]

[0019] In the formula, represents the starting position of cluster i in period t; It represents the ending position of cluster i in time period t.

[0020] As a preferred technical solution, the method is implemented based on a double-circulation loading and unloading strategy.

[0021] According to a second aspect of the present invention, there is provided a device for designing a yard allocation template in a shared yard handover mode of a sea-rail intermodal container terminal, comprising a memory, a processor, and a program stored in the memory, wherein the method described is implemented when the processor executes the program.

[0022] According to a third aspect of the present invention, there is provided a storage medium having a program stored thereon, wherein the program implements the method described above when executed.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The yard design method provided by the present invention reserves storage space for the same batch of containers of the ship by establishing a two-stage mixed integer programming model, and specifically allocates the yard space from the bay level. The first stage of the model considers the constraints of yard space resources and equipment resources, takes maximizing space utilization as the goal, and solves the adaptive best fit heuristic algorithm to determine the number of sub-box areas to be used; the second stage takes minimizing the total running distance of internal container trucks as the goal, and solves the Hungarian algorithm to determine the specific allocation of sub-box areas. The present invention can consider the joint allocation of import and export containers to specific container positions when designing the yard allocation template of the container terminal, and then optimize the yard allocation template design, while improving the overall utilization of the yard space, shortening the total running distance of the container trucks, and further improving the overall operation performance of the terminal;

[0025] 2. The present invention is based on a double-cycle loading and unloading strategy, which can further shorten the total running distance of container trucks and improve the overall operating performance of the terminal;

[0026] 3. The present invention refines the yard sharing to the bay area. Import and export containers can be stored in the same container area. Under the dual-loop transportation mode, the yard is allocated based on the clustering method. Compared with the yard allocation method under the single-loop transportation mode, the results confirm the superiority of the yard allocation and template design method proposed based on clustering. Under the dual-loop transportation strategy, the space utilization rate is higher, which can be increased by about 10%-80%, and the total running distance is also shorter, saving about 50% of the distance. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a process for providing a method in Example 1 of the present invention;

[0028] Figure 2 This is a schematic diagram of the inner container truck operation process in a shared yard handover mode provided in Example 1 of the present invention;

[0029] Figure 3 A schematic diagram of a cluster provided in Example 1 of the present invention;

[0030] Figure 4 A schematic diagram of sub-box area allocation provided in Embodiment 1 of the present invention;

[0031] Figure 5 A two-stage mixed integer programming model solving process provided in Embodiment 1 of the present invention;

[0032] Figure 6 This is a schematic diagram of container batch positions marked by horizontal lines in Example 1 of the present invention;

[0033] Figure 7 This is the distribution pattern of container arrival quantity under different yard sizes provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0034] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0035] Embodiment 1:

[0036] like Figure 1 As shown, this embodiment provides a method for designing a yard allocation template in a shared yard handover mode of a sea-rail intermodal container terminal, comprising the following steps: Step S101, using a preset first-stage model to allocate bays for pre-acquired import and export container batches to obtain the current box sequence; Step S201, pre-packing the box sequence to obtain multiple pre-packed container areas, and using a preset second-stage model to allocate corresponding sub-box area positions for multiple pre-packed container areas to complete yard space allocation and template design; wherein: the first-stage model and the second-stage model constitute a two-stage mixed integer programming model, the first-stage model is optimized with yard space resources and equipment resources as constraints and with the goal of maximizing space utilization, and the second-stage model is optimized with the goal of minimizing the total running distance of internal container trucks. The specific design and implementation process of the aforementioned method is as follows:

[0037] 1. Problem Description

[0038] The shared yard handover mode is more universal than the direct vehicle-ship mode due to its particularity; compared with the separate yard handover mode, the container transshipment process can be appropriately simplified and the efficiency of sea-rail transport connection can be improved.

[0039] like Figure 2 As shown in the figure, it is a schematic diagram of the basic operation process under the sea-rail transport shared yard handover mode, that is, the yard is set up in the middle of the sea-side terminal operation area and the land-side railway operation area. The railway operation area does not need to set up a separate yard. The import and export containers in the sea-side area and the land-side area share one yard, which can make better use of the yard space and equipment resources. At the same time, due to the complexity of the common storage of import and export container cargo flows, the problem of yard space allocation is also born.

[0040] As the main means of transport for import and export containers in the yard, when loading and unloading containers in the yard, container trucks can be divided into two loading and unloading strategies: single-cycle and double-cycle, according to the order of operation and the empty or heavy state during the operation. It can be clearly seen from the figure that the blue arrows and green arrows represent the transportation route of the container truck under the single-cycle loading and unloading strategy. The container truck travels between the berth and the sub-box area of ​​the yard. First, it loads an imported container from the berth to the shared yard for unloading. After unloading, it runs empty to the temporary storage area of ​​the terminal. The red arrows and yellow arrows represent the transportation route of the container truck under the double-cycle loading and unloading strategy. The container truck first loads an imported container to the shared yard, then runs to the sub-box area where the export container is located, and carries an export container to the corresponding berth, thus completing a heavy-load and return process. It can be seen that nearly half of the trips of the container truck under the single-cycle loading and unloading strategy are empty, and the double-cycle loading and unloading strategy can effectively shorten the total running distance of the container truck. The calculation of the total distance of the double-circulation operation mainly consists of three parts, including the distance from the preferred berth to the import container storage location, the distance between the import container and the export container, and the distance from the export container to the preferred berth. In this embodiment, batches of import and export containers can be mixed and stored in the same container area, so the running distance between batches of import and export containers can be ignored.

[0041] On this basis, combined with the actual operational resource constraints of the yard, this embodiment optimizes the design of the storage template of the sea-rail intermodal shared yard based on the double-cycle loading and unloading strategy. According to the size of the target unit considered, the yard space allocation can generally be divided into five levels: yard section, yard box area, yard sub-box area, yard bay and yard single slot. The minimum unit allocated in this embodiment is set as the yard bay.

[0042] In the first stage, the specific allocation of yard bays is carried out based on the arrival attributes of the ships. It is assumed that all ships have been allocated berths, and the arrival time, departure time, ship to which they belong, number of loaded containers and other information of the import and export container batches are known. In order to facilitate model design, it is assumed that the loading and unloading activities of each import and export container batch are evenly distributed in the corresponding time in port. Container batches are divided into import batches J and export batches D. Within the total planning time T, there are several import batches and export batches arriving at different time periods t. Each batch corresponds to a unloading or loading ship l. The container batches with the same ship l are clustered to form new container batches, which are recorded as B and D. ld and B lj .like Figure 3 As shown in the figure, each batch can be regarded as a small rectangular object, the length of the rectangle is the time the container batch is in port, and the width is the number of bays it occupies cumulatively. Container batches that arrive at the same time and belong to the same ship can be placed adjacent to each other in the same sub-box area, that is, B in the same period t ld With B lj Clustered together to form cluster i. Each sub-box area is regarded as a large rectangular box, with a length of the total planning time T and a width of the total number of bays in the sub-box area. Therefore, the original problem can be transformed into a two-dimensional packing problem for solution, that is, while considering the occupancy of time and space, use the minimum number of boxes to load all several container batches to improve the utilization rate of yard space. Note that in the single-loop transportation mode, import batches and export batches are stored in separate areas, with import batches stored close to the sea side and export batches stored close to the land side. In the dual-loop transportation mode, unlike the storage method in existing research, import and export batches can be mixed and stored in the same sub-box area, further saving the occupancy of yard space.

[0043] The second stage, such as Figure 4 As shown in the figure, each box allocated in the first stage is pre-packed to form several pre-packed box areas, and then specific sub-box area locations are assigned to these pre-packed boxes under the goal of minimizing the total running distance of the internal container truck. During the assignment process, the location, ship, quantity and other attributes of the small rectangular items in each box remain unchanged. Considering the actual loading and unloading operation requirements, mixed stacking will have a certain impact on the work of the yard crane. In order to avoid potential conflicts between different clusters during stacking, the yard crane needs to set service capacity constraints and service distance constraints according to actual operations.

[0044] In summary, the main feature of the yard space allocation template design method is to form container batches with precise information. At the bay level, import and export container batches can share space for storage, and coordinately optimize the yard space allocation from the two aspects of space utilization and transportation distance, so as to further improve the overall operational performance of the terminal.

[0045] 2. Model Construction

[0046] 1. Model Assumptions

[0047] (1) Only export containers and import containers are considered. In the single-loop transport mode, the import and export containers are located in different container areas. In the dual-loop transport mode, the import and export containers can be mixed and stored.

[0048] (2) The arrival and departure times of the ships, the practices at the port, and the number of import and export containers loaded and unloaded are all known, including the unloading cycle, loading cycle, number of containers handled, etc.

[0049] (3) The loading and unloading activities of each ship are evenly distributed during the corresponding time in port.

[0050] (4) Each container truck handles one TEU of container in one loading and unloading trip. TEU is a standard unit for calculating the number of containers and transportation volume, which can be understood as a "standard container".

[0051] (5) A dual field bridge configuration is adopted, and the two dedicated field bridges cannot cross-operate in a sub-box area.

[0052] (6) Each ship has been assigned a corresponding preferred berth, and the distance from each berth to each berth is known.

[0053] (7) A container batch is assigned to at most one cluster, while a cluster can accommodate multiple container batches, and each cluster can only be assigned one storage location.

[0054] (II) Parameter design and meaning

[0055] (1) Collection settings

[0056] K: the set of sub-box areas in the yard, with k as the index, K = {k|1≤k≤K}, K is the total number of sub-box areas in the yard;

[0057] I: the set of clusters, with i as the index, I = {i|1≤i≤I}, I is the total number of clusters in the yard;

[0058] D: the set of export containers, indexed by d, D = {i|1≤d≤D}, where D is the total number of export container batches during the planning period;

[0059] J: the set of imported containers, indexed by j, J = {i|1≤j≤J}, where J is the total number of imported container batches during the planning period;

[0060] L: the set of ships, indexed by l, L = {l|1≤l≤L}, where L is the total number of ships in the planning period;

[0061] Ik : The set of clusters i in sub-box k, I k = {I k |1≤I k ≤r}, r is the total number of bays in a single sub-box area;

[0062] T: the set of planning time periods, indexed by t, T = {l|1≤t≤T}, T is the total number of planning time periods;

[0063] (2) Parameter settings

[0064] A d : Arrival time of export container d;

[0065] A j : Arrival time of import container j;

[0066] Ψ dt : Whether the export container d is loaded or unloaded during the period t, if yes, it is 1, otherwise, it is 0;

[0067] Whether the import container j carries out loading and unloading activities in period t, if yes, it is 1, otherwise, it is 0;

[0068] b ld : Whether the export container d belongs to the ship l, it is 1, otherwise it is 0, and the set of these container batches is recorded as B ld ;

[0069] b lj : Whether the imported container d belongs to the ship l, it is 1, otherwise it is 0, and the set of these container batches is recorded as B lj ;

[0070] e ld :Export container batch B ld Duration of stay in the yard;

[0071] e lj :Import container batch B ld Duration of stay in the yard;

[0072] x ld :Container batch B ld The number of bays required for the accumulated containers;

[0073] x lj :Container batch B lj The number of bays required for the accumulated containers;

[0074] y ld :Container batch B ld Total number of containers accumulated in;

[0075] ylj :Container batch B ld Total number of containers accumulated in;

[0076] h lk : The distance from the preferred berth corresponding to ship l to sub-box area k;

[0077] λ it : Whether cluster i performs loading and unloading activities during period t, if yes, it is 1, otherwise, it is 0;

[0078] c: the maximum number of containers that a single crane can serve within a unit period t;

[0079] w: the maximum number of containers that a single bay can accommodate, calculated in TEU;

[0080] g: the maximum distance between different clusters sharing the same field bridge in the same period t, calculated by the number of bays;

[0081] θ: the ratio of the number of import batch containers to the number of export batch containers in cluster i;

[0082] r: the total number of bays in a single sub-box k;

[0083] M: A very large positive number.

[0084] (3) Decision variable setting

[0085] V ild :Container batch B ld Whether it is assigned to cluster i, if yes, it is 1, otherwise it is 0;

[0086] V ilj :Container batch B lj Whether it is assigned to cluster i, if yes, it is 1, otherwise it is 0;

[0087] Z il : Whether cluster i is reserved for ship l, if yes, it is 1, otherwise, it is 0;

[0088] P kl : Whether the sub-box area k is reserved for ship l, if yes, it is 1, otherwise it is 0;

[0089] The starting position of cluster i in period t;

[0090] The ending bay position of cluster i in period t;

[0091] R ilt : The total number of containers accumulated in cluster i for ship l during period t.

[0092] (III) Model construction

[0093] First stage model:

[0094] Based on the clustering concept, specific bays are allocated to import and export container batches to facilitate further allocation in the second stage.

[0095] The objective function of the first stage model is:

[0096]

[0097] The objective function (1) aims to maximize the utilization of the space in these container yards within the planning time.

[0098] The constraints of the first-stage model include:

[0099]

[0100] The above constraints are explained as follows:

[0101] Constraint (2) is the first constraint, which ensures that the boundaries of adjacent clusters do not overlap; Constraint (3) is the second constraint, which indicates that the height of each cluster in the time shift period t is determined by the total number of containers in the container batches assigned to it, and the cluster space should be large enough to accommodate all containers in these container batches; Constraints (4) and (5) are the third and fourth constraints, which indicate that each container batch can only be assigned to one cluster at most; Constraints (6) and (7) are the fifth and sixth constraints, which indicate that only import container batches and export container batches with the same ships can be assigned to the same cluster i; Constraint (8) is the seventh constraint, which indicates that a ship can only be assigned to one cluster; Constraint (9) is the eighth constraint, which ensures that a yard bridge can meet the working capacity of processing all clusters in a block in each time period t; Constraint (10) is the ninth constraint, which ensures that in the same time period t, the distance between different clusters sharing a yard bridge in a sub-box area cannot exceed the maximum service distance of the yard bridge.

[0102] Second stage model:

[0103] The several container areas pre-packed in the first stage are secondary allocated to determine the specific assigned sub-container area locations, and ultimately determine the specific locations of each container batch in the yard.

[0104] The objective function of the second stage model is:

[0105]

[0106] The objective function (11) aims to minimize the total running distance of the container truck.

[0107] The constraints of the first-stage model include:

[0108]

[0109] The above constraints are explained as follows:

[0110] Constraints (12) and (13) are the tenth and eleventh constraints, indicating that each pre-packed box can only be assigned to one sub-box area, and a sub-box area can only be assigned to one pre-packed box; constraint (14) is the twelfth constraint, which represents the calculation formula for the total number of containers accumulated in cluster i reserved for ship l within time period t; constraints (15) to (20) represent the constraint ranges of other parameters, all of which are decision variable constraints.

[0111] 3. Algorithm Design and Model Solution

[0112] According to the two-stage mixed integer programming model constructed above, it is divided into a two-dimensional packing problem and an assignment problem for two-stage solution. Due to the excessive constraints of the actual operation of the terminal, this embodiment adopts an adaptive best fit algorithm to solve the first-stage model and a Hungarian algorithm to solve the second-stage model. The two solving algorithms are combined to solve the two-stage mixed integer programming model proposed in this embodiment. Figure 5 One of the processes is shown, which specifically includes:

[0113] Step S111, initializing a two-dimensional packing problem;

[0114] Step S112, defining container batches and yard size;

[0115] Step S113, clustering to form a cluster according to whether the ships belong to the same group;

[0116] Step S114, calculating the fitness function of the current cluster and sub-box area;

[0117] Step S115, iterative update;

[0118] Step S116, determining whether all container batches have been placed:

[0119] If not, return to step S114; if yes, output the pre-packed box sequence and space utilization U.

[0120] Step S211, initializing the transport distance matrix from the container truck to each pre-selected berth of the ship;

[0121] Step S212, calculating the total running distance of the container truck;

[0122] Step S213, performing a row reduction operation;

[0123] Step S214, performing a column reduction operation;

[0124] Step S215, marking the current zero element;

[0125] Step S216, determine whether there is only one zero element in each row and column:

[0126] If not, readjust the rows and columns, find the augmented path, update the current transport distance matrix, and return to step S215; if yes, output the optimal allocation plan and the total running distance F.

[0127] The principle of the above solution process is as follows:

[0128] The yard space allocation problem of automated container terminals is a dynamic process that requires consideration of ever-changing space. In addition, due to the mixed storage of import and export containers in the shared yard, the complex flow of containers, and the complex allocation of resources such as container trucks and yard cranes, the traditional best fit algorithm for solving the two-dimensional packing problem cannot take into account the allocation of resources such as yard cranes, nor can it simultaneously take into account the multi-objective requirements of the shortest truck transportation distance. In view of this, the adaptive best fit algorithm can be improved based on the idea of ​​the best fit algorithm, and combined with other algorithms to solve the yard allocation template design problem that adapts to the characteristics of the terminal shared yard.

[0129] In the first stage, an adaptive best fit algorithm is used. In order to ensure that the yard space resources are used as little as possible, the container batch with the highest fitness value is selected until no feasible container batch can be placed. The location of the container batch can be represented by a horizontal line, which is consistent with the representation in the heuristic solution of the two-dimensional packing problem. Each container batch is regarded as a regular rectangle with a length of e. ld or e lj , width is x ld or x lj , the length represents the container batch stay time, and the width represents the total number of bays occupied by the containers. Combined with the marking method in the lowest level algorithm, such as Figure 6 As shown, according to container batch B 12 Arrival information of container batch B 12 The coordinates of the lower left corner are marked (A1,b). The horizontal axis corresponds to its arrival time, and the vertical axis is a preset initial starting position. 12 (A1,b+x 12 ,A1+e 12 ,b) to represent container batch B 12The specific location of the container yard. Due to the actual operation limitations of the container yard, the yard's space occupancy and time occupancy are considered at the same time. The yard is regarded as a rectangular box, which is also a regular rectangle. The length corresponds to the planned time set, and the width corresponds to the bay set. Therefore, the horizontal axis a represents the arrival time of the batch, and the vertical axis b represents the starting bay assigned to the rectangle. It is a known and certain information, that is, the small rectangle cannot move in the horizontal direction. According to the constraints of the field bridge service capacity and the yard space resources in the model, the algorithm dynamically selects the placement position and continuously adjusts the shape and size of the remaining space by evaluating the fitness value of each container batch, and finally achieves the best fitting effect, thereby maximizing the utilization of the two-dimensional packing space and outputting the final optimal spatial layout position.

[0130] The second stage determines the specific sub-box area location for each container batch. First, the packing results obtained in the first stage are pre-packed to fix the bay space allocation results of the first stage, that is, each used box is regarded as a pre-packed container area, and the bay location of the determined container batch in the pre-packed container area does not change during the next allocation. The number of bays occupied by each ship l in each pre-packed sub-box area can be represented by a matrix. The distance from each sub-box area to the corresponding preferred berth of each ship l is known and can also be represented by a matrix, and the objective function is set to the shortest total running distance of the container truck, which meets the requirements of the Hungarian algorithm for solving the assignment problem. Its essence is to use graph theory methods to solve the assignment problem. Based on the concepts of bipartite graph and augmenting path, the augmenting path is found through depth-first search or breadth-first search, and the container truck transportation task is matched with the sub-container area through alternating matching and alternating marking. Each time the augmenting path is searched, the algorithm will try to select the unassigned sub-container area with the shortest transportation distance, and then adjust it according to the current matching situation to finally obtain the final position of each pre-packed container area, and output the specific space allocation of each container batch, as well as the final yard space utilization and the total distance of the container truck operation.

[0131] 4. Implementation and Comparison

[0132] Next, the effectiveness of this method is verified by comparing experimental results.

[0133] Based on four different container arrival quantity distribution patterns and three different yard sizes, comparative experiments were conducted, and all examples were compared and analyzed under the single-loop and double-loop transportation modes. All examples were solved using Python 3.8. The specific settings of the four container arrival quantity distribution patterns are as follows: Figure 7 As shown in the figure, distribution mode 1 and distribution mode 2 are uniform distribution, distribution mode 3 and distribution mode 4 are uneven distribution, and the number of concentrated distributions is also different. The specific settings of the three yard scale examples are as follows:

[0134] like Figure 7 As shown in part (a) of the example, the small-scale yard is set up with 2 berths, 6 sub-container areas, each with 10 bays, and 7 import and export container batches. Figure 7 As shown in part (b) of the figure, the medium-scale example yard is set up with 4 berths, 12 sub-container areas, each with 16 berths, and 12 import and export container batches. Figure 7 As shown in part (c) of FIG. 5 , the large-scale example yard is set up with 6 berths, 18 sub-container areas, each container area has 18 berths, and there are 17 import and export container batches.

[0135] The distribution pattern of the small-scale example is represented by S1~S4, the distribution pattern of the medium-scale example is represented by M1~M4, and the distribution pattern of the large-scale example is represented by L1~L4. The results of all the examples under three different scales are shown in Table 1. It can be clearly seen that compared with the single-cycle transportation mode, the allocation scheme under the double-cycle transportation mode has more advantages in terms of overall space utilization of the yard and transportation distance. In terms of space utilization, there is a significant increase in the three scale examples, and the increase fluctuates between 10%-80%, with large fluctuations; in terms of transportation distance, the fluctuation is relatively stable, and basically about 50% of the distance can be saved. At the same time, it can be seen that the distribution pattern of the number of container arrivals also has a great influence on the yard allocation. In general, the space optimization effect under the uniform distribution mode is stronger than the uneven distribution mode, and it is particularly obvious under medium and large scales. However, in terms of transportation distance, some data of the uneven distribution perform better, but the gap between the two is small.

[0136] Table 1 Comparative experimental results under different yard scales

[0137]

[0138] Note: Δ1=(U 双循环 -U 单循环 ) / U 单循环 *100%;Δ2=(F 单循环 -F 双循环 ) / F 单循环 *100%.

[0139] In summary, this method aims at double-loop transportation, considers bidirectional container flow, proposes a yard allocation template design method based on cluster thinking, establishes a two-stage mixed integer programming model and uses the adaptive best fit algorithm and Hungarian algorithm to solve it, and obtains the yard space allocation and template design results, which has at least the following advantages:

[0140] (1) Different from the traditional shared yard template design, this method refines the yard sharing to the bay, and import and export containers can be stored in the same container area. Under the dual-loop transportation mode, the yard is allocated based on the clustering method. Compared with the yard allocation method under the single-loop transportation mode, the results confirm the superiority of the yard allocation template design method proposed based on clustering, which has higher space utilization and shorter total running distance.

[0141] (2) The optimization effect of the yard space allocation scheme is calculated under different yard sizes and different container volume distribution patterns. It is proved that the model design provided by this method is universal and has advantages in allocating internal truck yard space under the dual-circulation operation mode, which can provide a strong reference for the optimization of port yard space configuration.

[0142] Embodiment 2:

[0143] This embodiment provides a yard allocation template design device in a shared yard handover mode of a sea-rail intermodal container terminal, including a memory, a processor, and a program stored in the memory. When the processor executes the program, one or more steps of the method in Example 1 are implemented.

[0144] Optionally, the device includes a first-stage model solving module and a second-stage model solving module. The first-stage model solving module is used to use the preset first-stage model to allocate bays for the pre-acquired import and export container batches to obtain the current box sequence; the second-stage model solving module pre-packs the box sequence to obtain multiple pre-packed container areas, and uses the preset second-stage model to allocate corresponding sub-box area positions for the multiple pre-packed container areas to complete the yard allocation and template design. The first-stage model and the second-stage model constitute a two-stage mixed integer programming model. The first-stage model is optimized with the yard space resources and equipment resources as constraints and the goal of maximizing space utilization, and the second-stage model is optimized with the goal of minimizing the total running distance of the internal container truck.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of each module can refer to the corresponding process in the aforementioned embodiment 1, and will not be repeated here.

[0146] Furthermore, this embodiment also provides a storage medium on which a program is stored, and when the program is executed, one or more steps of the method in embodiment 1 are implemented. Storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. According to the definition in the present invention, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carriers.

[0147] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal, characterized in that: The following steps are involved: S101, using a preset first-stage model to assign bays to the pre-acquired import and export container batches to obtain a current container sequence; S201, pre-packing the box sequence to obtain multiple pre-packed box areas, and using a preset second-stage model to allocate corresponding sub-box area positions to the multiple pre-packed box areas, completing yard allocation and template design; Among them: the first-stage model and the second-stage model constitute a two-stage mixed integer programming model. The first-stage model is optimized with the yard space resources and equipment resources as constraints and the goal of maximizing space utilization, and the second-stage model is optimized with the goal of minimizing the total running distance of the internal container trucks.

2. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to claim 1 is characterized in that: During the optimization process, the first-stage model is solved using an adaptive best-fit algorithm, and the second-stage model is solved using the Hungarian algorithm.

3. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to claim 2 is characterized in that: Solving the first stage model using an adaptive best fit algorithm specifically includes: S111, Initialize the two-dimensional packing problem; S112, define container batches and yard size; S113, clustering to form clusters according to whether the ships they belong to are consistent; S114, calculating the fitness function of the current cluster and sub-box area; S115, iterative update; S116, determining whether all container batches have been placed: If not, return to S114; if yes, output the pre-packed box sequence and space utilization.

4. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to claim 3 is characterized in that: The Hungarian algorithm is used to solve the second stage model, which includes: S211, initializing the transport distance matrix from container trucks to each pre-selected berth of each ship; S212, calculating the total running distance of the container truck; S213, performing a line reduction operation; S214, performing a column reduction operation; S215, mark the current zero element; S216, determine whether there is only one zero element in each row and column: If not, readjust the rows and columns, find the augmented path, update the current transportation distance matrix, and return to S215; if yes, output the optimal allocation plan and the total running distance.

5. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to claim 1, characterized in that: The objective function of the first-stage model is: In the formula, I represents the set of clusters, with i as the index; L represents the set of ships, with l as the index; D represents the set of export containers, with d as the index; J represents the set of import containers, with j as the index; V ild Indicates container batch B ld Whether it is assigned to cluster i; x ld Indicates container batch B ld The number of bays required for the accumulated containers in the ld Indicates export container batch B ld Duration of stay in the yard; V ilj Indicates container batch B lj Whether it is assigned to cluster i; x lj Indicates container batch B lj The number of bays required for the accumulated containers in the lj Indicates import container batch B ld The residence time in the yard; r represents the total number of bays in a single sub-box area k; K represents the set of sub-box areas in the yard, with k as the index; T represents the set of planning time periods, with t as the index; The constraints of the first-stage model include: a first constraint, which is used to ensure that the boundaries of adjacent clusters do not overlap; a second constraint, which is used to ensure that the bay height of each cluster in time period t is determined by the total number of containers in the container batch assigned to it; a third constraint and a fourth constraint, which are used to limit each container batch to be assigned to at most one cluster; a fifth constraint and a sixth constraint, which are used to limit only import container batches and export container batches that arrive in the same time period and belong to the same ship to be assigned to the same cluster i; a seventh constraint, which is used to limit a ship to be assigned to only one cluster; an eighth constraint, which is used to ensure that a yard bridge handles all clusters in a sub-container area in each time period t; and a ninth constraint, which is used to ensure that within the same time period t, the distance between different clusters sharing a yard bridge in a sub-container area does not exceed the maximum service distance of the yard bridge.

6. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to claim 5, characterized in that: The objective function of the second stage model is: In the formula, Z il Indicates whether cluster i is reserved for ship l, h lk represents the distance from the berth corresponding to ship l to the sub-box area k, P kl Indicates whether sub-box area k is reserved for ship l, R ilt The total number of containers accumulated in cluster i for vessel l during period t; The constraints of the second-stage model include at least: a tenth constraint and an eleventh constraint, which are used to ensure that each pre-packaged box is assigned only one sub-box area, and a sub-box area is also assigned only to one pre-packaged box; and the twelfth constraint, which is used to limit the total number of containers accumulated by cluster i for ship l in period t.

7. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to claim 6, characterized in that: The constraints of the second stage model also include decision variable constraints: In the formula, represents the starting position of cluster i in period t; It represents the ending position of cluster i in time period t.

8. The method for designing a yard allocation template in a shared yard handover mode of a sea-rail transport container terminal according to any one of claims 1 to 7, characterized in that: The method is implemented based on a double-circulation loading and unloading strategy.

9. A device for designing a yard allocation template in a shared yard handover mode of a sea-rail intermodal container terminal, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

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