Method and electronic device for obtaining target production scheduling plan for a terminal

Abstract

The present invention discloses a method and an electronic device for obtaining a target production schedule for a terminal, and relates to the field of production technology. The method comprises: constructing a berthing and unberthing planning model through production scheduling information, solving the berthing and unberthing planning model for each port arrival sequence to obtain an optimal port arrival sequence, and determining the berthing information of each ship through the optimal port arrival sequence; constructing a transportation task mathematical model based on the production scheduling information, the optimal port arrival sequence, and the berthing information of each ship, and judging the berthing status and ship information through the transportation task mathematical model, dynamically arranging berthing, direct loading, and entry and exit tasks at each discrete moment to obtain the transportation task for each time period; determining the yard information corresponding to the transportation task for each time period through a yard decision model; generating and executing a target production schedule based on the berthing information of each ship, the transportation task for each time period, and its corresponding yard information, thereby improving the efficiency of terminal production scheduling execution.

Description

[0001] This application is a divisional application of the invention patent application with the application date of November 14, 2024, application number 2024116205306, and name “A method and electronic device for obtaining target production scheduling plan for a terminal”. Technical Field

[0002] The present invention relates to the field of production technology, and in particular to a method and an electronic device for obtaining a target production scheduling plan for a dock. Background Art

[0003] With economic development, port production levels are gradually improving, and with them, the complexity of production scheduling has also increased. Currently, port production scheduling plans lack decision-making tools and rely entirely on the experience of central control dispatchers. This approach often leads to low execution efficiency.

[0004] Currently, no effective solution has been proposed to address the problem of low efficiency in terminal production scheduling execution in related technologies. Summary of the Invention

[0005] In order to solve the problem of low execution efficiency of terminal production scheduling in related technologies and improve the efficiency of terminal production scheduling execution, in a first aspect, the present invention proposes a method for obtaining a target production scheduling plan for a terminal, comprising:

[0006] Obtain production scheduling information;

[0007] The objective function and constraint function are set based on the production scheduling information. The berthing and unberthing planning model is constructed based on the objective function and constraint function. The ships that need to dock and the various arrival sequences of each ship are obtained. The berthing and unberthing planning model is solved for each arrival sequence to obtain the optimal arrival sequence. The docking information of each ship is determined based on the optimal arrival sequence.

[0008] A mathematical model for transportation tasks is constructed based on production scheduling information, the optimal port arrival sequence, and each ship's berthing information. This model is then used to determine berth status and ship information, and dynamically schedule berthing, direct loading, and entry / exit tasks at each discrete moment to obtain the transportation tasks for each time period.

[0009] A yard decision model is constructed based on production scheduling information and the transportation tasks in each time period. The yard information corresponding to the transportation tasks in each time period is determined through the yard decision model. The yard information includes the stacking allocation of incoming and outgoing tasks, the use of stackers and reclaimers, and the update of yard status.

[0010] A target production schedule is generated and executed based on the docking information of each ship, the transportation task in each time period, and the corresponding yard information.

[0011] Furthermore, the production scheduling information includes: production scheduling demand task information, objective environment information, terminal static information and terminal dynamic information; the docking information includes: docking location and docking time.

[0012] Furthermore, the construction of the berthing and unberthing planning model is specifically as follows:

[0013] Acquire a first data set, a first parameter group, and a first decision variable group corresponding to a docking and undocking planning model based on the production scheduling information;

[0014] Setting an objective function and a constraint function by using the first data set, the first parameter group, and the first decision variable group, and obtaining a berthing and unberthing planning model by limiting the objective function by using the constraint function;

[0015] Obtain the ships that need to dock and the various port arrival sequences of each ship;

[0016] The berthing and unberthing planning model is solved for each arrival sequence to obtain the optimal arrival sequence, and the berthing information of each ship is determined based on the optimal arrival sequence.

[0017] Furthermore, the objective function is expressed as follows:

[0018] ;

[0019] Where, represents the decision timeframe, express Elements in Indicates time period Whether it meets the direct installation conditions, It means satisfaction; Indicates the latest time when all ships complete berthing operations; Parameter indicating the direct installation coefficient reduction.

[0020] Furthermore, the constraint function includes:

[0021] The first constraint function is used to limit the latest time for all ships to complete berthing operations to be later than the departure time of all ships;

[0022] The second constraint function is used to limit each ship to only the berth it can dock at;

[0023] The third constraint function is used to limit the berthing time of each ship to be at least longer than the sum of the loading and unloading time and other process time; the other process time includes: Estimated operation time, per vessel The process time before loading and unloading is related to the ship time of berthing;

[0024] The fourth constraint function is used to limit the time overlap of ships at the same berth and to allow for berth preparation time;

[0025] The fifth constraint function is used to limit the docking of ships at different berths in sequence. If there is a conflict due to overlapping docking times, the docking times must not overlap;

[0026] The sixth constraint function is used to limit the berthing safety interval time between ships docking at different berths one after another.

[0027] Furthermore, the construction of the mathematical model of the transport task is specifically as follows:

[0028] Setting a second data set, a second parameter set, and a second decision variable set based on production scheduling information, an optimal port arrival sequence, and docking information of each ship;

[0029] A transportation task mathematical model with decision variable calculation logic is constructed through the second data set, the second parameter group and the second decision variable set. By executing the decision variable calculation logic in the transportation task mathematical model, the berth status and ship information are judged, and the berthing, direct loading and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation task for each time period.

[0030] Furthermore, the decision variable calculation logic includes:

[0031] Get the current berth status at each discrete time;

[0032] When a berth is idle, decide whether to arrange a ship to berth in the current period based on the berth preparation time, berthing time range, ship type conflict and safety interval time, and modify the current berth status based on the result of the decision;

[0033] When the berth is busy, the remaining quantity and the quantity to be loaded on the ship are judged to decide whether to make direct loading task decisions and entry and exit task decisions.

[0034] Furthermore, the direct installation task decision is specifically as follows:

[0035] Direct loading conditions are used to identify paired ships that can be directly loaded, and direct loading tasks are arranged for the paired ships. Paired ships refer to the pairing between a first-leg ship and a second-leg ship, ensuring that cargo can be directly transferred from the first-leg ship to the second-leg ship. The first-leg ship refers to a ship that arrives at the terminal from outside to unload cargo; the second-leg ship refers to a ship that leaves the terminal after loading cargo.

[0036] Furthermore, the direct installation conditions are specifically as follows:

[0037] Determine whether there is a matching ship that can be directly loaded among the docked ships;

[0038] Determining whether the type of ore to be loaded and unloaded by the paired ship at this stage is the paired ore type corresponding to the paired ship;

[0039] Determine whether the unloading stage is assembling the ore of the shipowner of the second leg ship;

[0040] It is determined whether the remaining quantity of the directly loaded ore unloaded from the paired ship is greater than the quantity to be loaded.

[0041] Furthermore, the entry and exit task decision is specifically as follows:

[0042] Arrange the task of arranging the entry of ships into the yard and the exit of the yard to the ships.

[0043] Furthermore, the construction of the storage yard decision model is specifically as follows:

[0044] Setting a third data set, a third parameter group, and a third decision variable set according to the production scheduling information and the delivery tasks in each time period;

[0045] Constructing a yard decision model with an allocation model and decision logic through the third data set, the third parameter group, and the third decision variable set;

[0046] The yard information corresponding to the transportation task in each time period is determined by executing the decision logic and allocation model in the yard decision model.

[0047] Furthermore, the decision logic includes the steps of:

[0048] Step 1: Arrange the transportation tasks into entry and exit task sets;

[0049] Step 2: Determine the stacking set that can be used by each task in the entry and exit task sets;

[0050] Step 3: Filter out bucket wheel machines that can perform the task and their working stack sets;

[0051] Calculate the evaluation function impact value for each bucket wheel machine and the task it can work on, and select the optimal stack for each task based on the evaluation function impact value;

[0052] Step 4: Obtain an allocation model by constructing a target allocation function and allocation constraints, and allocate each task to a feasible bucket wheel machine by solving the allocation model;

[0053] Step 5: After the task allocation is completed, at the beginning of each time period, determine whether the current yard status and the task allocation given by the previous allocation model simultaneously meet multiple preset feasibility conditions. If so, proceed to the next step; if not, return to step 2;

[0054] Step 6: Execute all entry and exit tasks for this time period according to the allocation results of the allocation model;

[0055] Step 7: Update the status of the yard based on the execution status of all entry and exit tasks during this time period.

[0056] Furthermore, the allocation constraints include:

[0057] In each discrete time period, each bucket wheel machine can only work on at most one workable task;

[0058] Each task can only be assigned to one bucket wheel machine;

[0059] Different bucket wheel machines cannot work on the same stack while doing their respective tasks in each discrete time period.

[0060] Furthermore, the target allocation function is used to maximize the sum of the impact values of the evaluation functions corresponding to each task.

[0061] Furthermore, the feasibility conditions include:

[0062] Whether all tasks in the current time period have been allocated and completed in the previous allocation model;

[0063] Whether the stack arranged for the incoming task can still accommodate the amount of ore for the incoming task;

[0064] Whether the stack arranged for the exit task still exists at this moment or whether it can meet the output of the ore.

[0065] In a second aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the target production scheduling plan acquisition method for the terminal described above.

[0066] In a third aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent production scheduling method for a terminal as described in the first aspect above.

[0067] In a fourth aspect, an embodiment of the present invention further provides a device for obtaining a target production scheduling plan for a terminal, comprising:

[0068] Acquisition module, used to obtain production scheduling information;

[0069] The berthing and unberthing module is used to set the objective function and constraint function based on the production scheduling information, and to build a berthing and unberthing planning model based on the objective function and constraint function. It obtains the ships that need to dock and the various arrival sequences of each ship, solves the berthing and unberthing planning model for each arrival sequence, obtains the optimal arrival sequence, and determines the docking information of each ship based on the optimal arrival sequence.

[0070] The transport task module is used to build a transport task mathematical model based on production scheduling information, the optimal port arrival sequence, and the berthing information of each ship. Through the transport task mathematical model, it determines the berth status and ship information, and dynamically arranges the berthing, direct loading, and entry and exit tasks at each discrete moment to obtain the transport task for each time period;

[0071] The yard decision module is used to build a yard decision model based on production scheduling information and the transportation tasks in each time period. The yard decision model determines the yard information corresponding to the transportation tasks in each time period. The yard information includes the stacking allocation of incoming and outgoing tasks, the use of stackers and reclaimers, and the update of yard status.

[0072] A generation module is used to generate and execute a target production schedule based on the docking information of each ship, the transportation task in each time period and the corresponding yard information.

[0073] Compared with the related art, the embodiment of the present application provides a method and electronic device for obtaining a target production schedule for a terminal. A berthing and unberthing planning model is constructed through production scheduling information, and the berthing and unberthing planning model is solved for each port arrival sequence to obtain the optimal port arrival sequence, and the berthing information of each ship is determined through the optimal port arrival sequence; a transportation task mathematical model is constructed according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship, and the transportation task mathematical model is used to judge the berthing status and ship information, and dynamically arrange the berthing, direct loading and entry and exit tasks at each discrete moment to obtain the transportation task for each time period; the yard information corresponding to the transportation task for each time period is determined through the yard decision model; a target production schedule is generated and executed according to the berthing information of each ship, the transportation task for each time period and the corresponding yard information, thereby improving the efficiency of terminal production scheduling execution.

[0074] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0076] Figure 1 This is a hardware structure block diagram of a terminal of a method for obtaining a target production scheduling plan for a terminal according to an embodiment of the present invention;

[0077] Figure 2 is a flowchart of a method for obtaining a target production scheduling plan for a terminal according to an embodiment of the present application;

[0078] Figure 3 It is a structural block diagram of a device for obtaining a target production scheduling plan for a terminal according to an embodiment of the present application. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for ordinary technicians in the field related to the contents disclosed in the present application, some changes such as design, manufacturing or production based on the technical contents disclosed in the present application are only conventional technical means and should not be understood as the contents disclosed in the present application being insufficient.

[0080] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0081] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "an," "the," and similar expressions used herein do not denote limitations on quantity and may refer to either the singular or the plural. The terms "comprise," "include," "have," and any variations thereof, used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) is not limited to the listed steps or units but may also include steps or units not listed, or may include other steps or units inherent to the process, method, product, or apparatus. The terms "connected," "connected," "coupled," and similar expressions used herein are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used herein, "plurality" means greater than or equal to two. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. The terms "first", "second", "third" and the like involved in this application are merely used to distinguish similar objects and do not represent a specific ordering of the objects.

[0082] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. Taking running on a terminal as an example, Figure 1 FIG. 1 is a block diagram of the hardware structure of the terminal of the method for obtaining the target production scheduling plan of the terminal according to an embodiment of the present invention. Figure 1 As shown, the terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0083] Memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the method for obtaining a target production schedule for a terminal in an embodiment of the present invention. Processor 102 executes the computer program stored in memory 104 to execute various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located relative to processor 102, and such remote memory may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0084] Transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the terminal's communications provider. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0085] This embodiment provides a method for obtaining a target production schedule for a terminal. Figure 2 Flowchart of the method for obtaining the target production schedule of a terminal according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps:

[0086] Step S201: Obtain production scheduling information.

[0087] In this step, the production scheduling information includes: production scheduling demand task information, objective environment information, terminal static information and terminal dynamic information.

[0088] Among them, production scheduling demand task information, objective environment information, terminal static information, and terminal dynamic information specifically include the following information:

[0089] The production scheduling demand task information mainly includes the first-pass ship task, the second-pass ship task and the mixed ore task.

[0090] A ship mission mainly includes the following fields:

[0091]

[0092] The second-pass ship task mainly includes the following field information:

[0093]

[0094] The mixed ore task mainly includes the following fields:

[0095]

[0096] Objective environmental information mainly includes day and night visibility, tides, and weather, which are key factors affecting production scheduling.

[0097] 1. Sunset and sunrise:

[0098]

[0099] 2. Tidal data:

[0100]

[0101] 3. Weather information:

[0102]

[0103] The static information of the terminal mainly includes the static properties of the resources and equipment within the terminal, as follows:

[0104] 1. Ship loaders, ship unloaders, bucket wheel excavators, stackers, and reclaimers:

[0105]

[0106] 2. Belt conveyor and process:

[0107]

[0108] 3. Berth:

[0109]

[0110] 4. Yard:

[0111]

[0112] The terminal dynamic information mainly includes the internal resources, equipment and attributes of the docked ships at the time when the scheduling algorithm starts, as follows:

[0113] 1. Overall production scheduling target

[0114]

[0115] 2. Ship loaders, ship unloaders, bucket wheel excavators, stackers, and reclaimers:

[0116]

[0117] 3. Belt conveyor and process:

[0118]

[0119] 4. Berths and berthed vessels:

[0120]

[0121] 5. Stacking in the yard:

[0122]

[0123] Step S202: Setting an objective function and a constraint function based on the production scheduling information, and constructing a berthing and unberthing planning model based on the objective function and the constraint function. The berthing and unberthing planning model is then used to obtain the ships that need to dock and the various arrival sequences of the ships. The berthing and unberthing planning model is then solved for each arrival sequence to obtain the optimal arrival sequence. The berthing information of each ship is then determined based on the optimal arrival sequence.

[0124] The construction of the berthing and unberthing planning model is specifically as follows:

[0125] Acquire a first data set, a first parameter group, and a first decision variable group corresponding to a docking and undocking planning model based on the production scheduling information;

[0126] Setting an objective function and a constraint function by using the first data set, the first parameter group, and the first decision variable group, and obtaining a berthing and unberthing planning model by limiting the objective function by using the constraint function;

[0127] Obtain the ships that need to dock and the various port arrival sequences of each ship;

[0128] The berthing and unberthing planning model is solved for each arrival sequence to obtain the optimal arrival sequence, and the berthing information of each ship is determined based on the optimal arrival sequence.

[0129] In this step, the docking information of each ship can be determined based on the order of arrival of loading and unloading ships, ship types, operation time, non-operation process time, procedure time, available berths, pairing of ships that can be directly loaded, tides, weather, sunrise and sunset, berth preparation time, safety combination rules and other information in the scheduling information in step S201.

[0130] In this embodiment, the docking information may include a docking location and a docking time.

[0131] Specifically, step S202 can be executed in a pre-defined docking and undocking module. This pre-defined docking and undocking module requires the ships to be docked and their arrival order. Priority ships can have their arrival order advanced, resulting in multiple arrival orders to be processed. The following docking and undocking planning model is applied to each feasible arrival order, with the optimal order being used as the execution order. The calculation results of the docking and undocking planning model will provide the berths for each ship and the docking order of ships at each berth. The following lists the important sets, parameters, and decision variables required to construct the docking and undocking planning model.

[0132] (1) The first data set (the capital letter is the set name, followed by a lowercase letter is the set element, the same below)

[0133] 1. Decision Time Frame , ;

[0134] 2. One boat trip , ;

[0135] 3. Second-Cheng Ship , ;

[0136] 4. One-way boat type collection , ;

[0137] 5. Collection of Second-City Ship Types , ;

[0138] 6. Each ship Permissible range of berthing time , ;

[0139] 7. Each ship Permissible range of departure time , ;

[0140] 8. Each ship A collection of available berths , ;

[0141] 9. Berth and A combination of ship types that will cause conflicts if they dock one after another and some of their docking times overlap , ;

[0142] 10. Theoretically, direct loading can be carried out on a pair of one-way and two-way vessels , .

[0143] (2) The first parameter group

[0144] 1. Number of ships per trip ;

[0145] 2. Number of second-leg vessels ;

[0146] 3. Boat Ship type ;

[0147] 4. Each ship Estimated duration of the job ;

[0148] 5. Each ship Process time before loading and unloading ;

[0149] 6. Berth Preparation time between ship calls ;

[0150] 7. Berth and Two types of ships docked successively and Required safe berthing interval time ;

[0151] 8. Parameters for direct installation coefficient consumption reduction .

[0152] (3) The first decision variable group

[0153] 1. Boat Berthing time ;

[0154] 2. Boat Departure time ;

[0155] 3. The latest time for all ships to complete berthing operations ;

[0156] 4. Chengchuan Whether to rely on berth ;

[0157] 5. Time period Is it later than the ship? Time to start loading and unloading , satisfy;

[0158] 6. Time period Is it earlier than the ship? End of loading and unloading time , satisfy;

[0159] 7. Time period Is it on a boat? During loading and unloading time , in;

[0160] 8. Time period Is it on a boat? and During loading and unloading time , in;

[0161] 9. Time period Whether it meets the direct installation conditions , satisfy.

[0162] The berthing and unberthing module can directly solve the values of each decision variable in the first decision variable group by constructing a berthing and unberthing planning model. The objective function and some key constraint condition functions of the berthing and unberthing planning model are described below.

[0163] (1) Objective function

[0164] The objective function attempts to strike a balance between "completing a given task in the shortest possible time" and "creating as many direct installation opportunities as possible." The objective function formula is:

[0165] .

[0166] (2) Constraint function

[0167] 1. The first constraint function is used to limit the latest time for all ships to complete berthing operations to be later than the departure time of all ships:

[0168] ;

[0169] 2. The second constraint function is used to limit each ship to the berths it can dock at:

[0170] ;

[0171] 3. The third constraint function is used to limit the berthing time of each ship to be at least longer than the sum of the loading and unloading time and other process time; the other process time includes: Estimated operation time, per vessel The process time before loading and unloading is related to the ship Berthing time; the formula of the third constraint function is:

[0172] ;

[0173] 4. The fourth constraint function is used to limit the time of ships at the same berth to non-overlap and to allow for berth preparation time:

[0174] ;

[0175] 5. The fifth constraint function is used to limit the order in which ships at different berths can dock. If there is a conflict in the docking time, the time must not overlap:

[0176] ;

[0177] 6. The sixth constraint function is used to limit the berthing safety interval time between ships docking at different berths:

[0178] ;

[0179] 7. The seventh constraint function is used to limit The value of the variable:

[0180] ;

[0181] 8. The eighth constraint function is used to limit The value of the variable:

[0182] ;

[0183] 9. The ninth constraint function is used to limit The value of the variable:

[0184] ;

[0185] 10. The tenth constraint function is used to limit The value of the variable:

[0186] ;

[0187] 11. The eleventh constraint function is used to limit The value of the variable:

[0188] ;

[0189] 12. The twelfth constraint function is used to limit the range of variable values: the berthing and unberthing times must meet the following requirements:

[0190] ;

[0191] 13. The thirteenth constraint function is used to limit the variable value range: 0-1 Variable requirements:

[0192] 。

[0193] Step S203: Build a mathematical model for transportation tasks based on production scheduling information, the optimal port arrival sequence, and each ship's berthing information. The mathematical model is used to determine berth status and ship information, and dynamically schedule berthing, direct loading, and entry / exit tasks at each discrete moment to obtain transportation tasks for each time period.

[0194] It should be noted that "assessing ship information" mainly refers to evaluating and judging the current status of the ship and related information to ensure that the transportation task can be arranged reasonably. Specifically, judging ship information includes the following aspects:

[0195] 1. Vessel docking information

[0196] Berth location: the berth where the ship is currently docked;

[0197] Dock time: the vessel’s arrival time and estimated departure time;

[0198] Vessel type: The type of vessel, which affects its docking and loading and unloading requirements.

[0199] 2. Vessel loading and unloading task information

[0200] Mineral types and quantities: the mineral types and quantities that the ship needs to load and unload;

[0201] Shipowner information: each shipper's ore quantity, single mixed pile type, element range allowed when mixing, etc.;

[0202] Remaining quantity / to be loaded quantity: the current remaining quantity of ore or the quantity of ore to be loaded on the ship.

[0203] 3. Operational status of the vessel

[0204] Operation time: the time it takes for a ship to complete the loading and unloading process;

[0205] Process progress: currently completed processes and remaining processes;

[0206] Equipment usage: The status of the ship unloaders, belts and other equipment currently used by the ship.

[0207] 4. Dynamic properties of ships

[0208] Current status: whether the vessel is currently loading or unloading, or in a waiting state;

[0209] Status at the next moment: The expected status of the vessel at the next moment, including whether it needs to leave the berth.

[0210] In other words, "vessel information assessment" primarily involves evaluating and assessing a vessel's docking information, loading and unloading tasks, operational status, and dynamic attributes to ensure the proper scheduling of delivery tasks. This information ensures that berthing, direct loading, and entry and exit tasks are carried out efficiently and orderly at each discrete moment.

[0211] The construction of the mathematical model of the transportation task is specifically as follows:

[0212] Setting a second data set, a second parameter set, and a second decision variable set based on production scheduling information, an optimal port arrival sequence, and docking information of each ship;

[0213] A transportation task mathematical model with decision variable calculation logic is constructed through the second data set, the second parameter group and the second decision variable set. By executing the decision variable calculation logic in the transportation task mathematical model, the berth status and ship information are judged, and the berthing, direct loading and entry and exit tasks at each discrete moment are dynamically arranged to obtain the transportation task for each time period.

[0214] In this step, the transportation task can be determined based on the optimal arrival order obtained in step S202, the ship's berthing information, and the upper limit of the unloaders used by the first-leg ship in the production scheduling information in step S201, the upper limit of the unloaders when directly loading on the second-leg ship, the elemental attributes of each type of ore on the first-leg ship, the single mixed stack types of different shippers, the elemental attribute requirements when mixing, the working efficiency of each major machine, and other information.

[0215] The delivery tasks may include but are not limited to the following:

[0216] 1. How many processes are used for a certain mineral by each shipper on a vessel, and how much weight is transferred to the yard by a single process?

[0217] 2. The amount of weight transferred directly from a first-leg vessel to a second-leg vessel berthed at the same time;

[0218] 3. The weight of a certain mineral from a certain shipper transferred from the storage yard to a certain second-leg vessel;

[0219] 4. How many ship unloaders are used for each entry and direct loading process?

[0220] 5. Which approach belt is used for each approach and direct loading process;

[0221] 6. Which specific process is used for each direct installation process?

[0222] 7. Which ship loader is used for each exit process?

[0223] 8. Berth status at each discrete moment.

[0224] Specifically, step S203 can be executed in a pre-set transport task module. After the docking information of each ship is determined, the transport task module will then determine the transport task within each discrete time period. This part of the decision does not involve the pick-up and drop-off location in the yard, but only determines the time at which the ship is to be picked up. In the transport task module, the weight of a certain mineral from the corresponding ship is transferred to the storage yard, or directly loaded to a secondary vessel berthed at the same time; at the same time, the weight of a certain mineral from the storage yard is transferred to a secondary vessel. Of course, the number of ship unloaders to be scheduled and the belts to be used are also determined here. The following lists the key components, parameters, and decision variables of the transport task mathematical model in the transport task module. The content already provided in the berthing and unberthing module is not repeated here.

[0225] (1) Second data set

[0226] 1. Mineral collection for each ship , .

[0227] (2) Second parameter group

[0228] 1. One boat trip The maximum number of ship unloaders allowed to be used simultaneously ;

[0229] 2. Second-Cheng Ship The maximum number of unloaders allowed to be used simultaneously for direct loading ;

[0230] 3. Each ship Loading and unloading Mineral species , mineral quantity , element attributes ;

[0231] 4. Each ship Loading and unloading The first Name of the consignor , mineral volume , Single mixed stack type , 0 single pile, 1 non-mixed ore, 2 mixed ore;

[0232] 5. Each ship Loading and unloading The first The allowable range of elements when mixing ore for each consignor ;

[0233] 6. Theoretically, direct loading can be paired with one or two vessels Paired mineral species ;

[0234] 7. Theoretically, direct loading can be paired with one or two vessels Name of the second-leg shipper ;

[0235] 8.Total number of ship unloaders ;

[0236] 9. Material reclaiming efficiency of a single ship unloader per unit time period ;

[0237] 10. Bucket wheel excavator single machine reclaiming efficiency per unit time period .

[0238] (3) Second decision variable set

[0239] 1. Time Ship Xiangchuan Direct shipment ;

[0240] 2. Time Ship Goods entering the yard ;

[0241] 3. Time Ship Number of ship unloaders used in the yard ;

[0242] 4. Time yard to ship Goods shipped .

[0243] Calculate the decision variables in the second decision variable set by executing the decision variable calculation logic in the delivery task mathematical model; the decision variable calculation logic includes:

[0244] Get the current berth status at each discrete time;

[0245] When a berth is idle, decide whether to arrange a ship to berth in the current period based on the berth preparation time, berthing time range, ship type conflict and safety interval time, and modify the current berth status based on the result of the decision;

[0246] When the berth is busy, the remaining quantity and the quantity to be loaded on the ship are judged to decide whether to make direct loading task decisions and entry and exit task decisions.

[0247] It should be noted that:

[0248] 1. The remaining quantity refers to the amount of ore that still needs to be completed after the ship has completed part of the loading and unloading tasks. Specifically:

[0249] For unloading berths: the amount of ore not yet unloaded from the vessel;

[0250] For loading berths: the amount of ore on the yard that has not yet been loaded onto vessels.

[0251] 2. The amount to be loaded refers to the amount of ore that the ship needs to load and unload within the current time period. Specifically:

[0252] For unloading berths: the volume of ore that the vessel is about to start unloading;

[0253] For loading berths: The amount of ore on the yard that is about to be loaded onto the vessel.

[0254] Specifically, the decision variable calculation logic includes:

[0255] 1) Input the current berth status at each discrete time, that is, the dynamic attributes listed in the information list.

[0256] 2) Complete the instantaneous decision-making for each discrete moment of free berths and the state transition to the next moment. First, determine whether the berth preparation time has expired. If not, no berthing is scheduled, and the berth remains free at the beginning of the next period. If it is, determine whether the current period falls within the permitted berthing time for the next ship. If not, no berthing is scheduled, and the berth remains free at the next moment. If so, determine whether the next berthing ship's type conflicts with other berthed ships. If so, no berthing is scheduled, and the berth remains free at the next moment. If not, determine whether the next berthing ship's type conflicts with other berthed ships. If so, no berthing is scheduled, and the berth remains free at the next moment. If not, determine whether the next berthing ship's type conflicts with other berthed ships' berthing safety interval. If not, no berthing is scheduled, and the berth remains free at the beginning of the next period. If so, the next ship is scheduled to berth, the current state of the berth is changed, and the next ship's information is recorded. The conflict combination and safety interval are used for unloading berths.

[0257] 3) When the berth is busy, if the ship has not completed the pre-loading and unloading process time, the transportation task will not be arranged, and the remaining process time of one period will be reduced at the beginning of the next moment, and the other attributes will remain unchanged; if the pre-loading and unloading process has been completed, it will be determined whether there is any remaining quantity / quantity to be loaded. If so, the direct loading task decision and status transfer will be completed first, and then the entry and exit task decision and status transfer will be completed; if there is no remaining quantity / quantity to be loaded, it will be determined whether the current period falls within the allowable range of the current ship's departure time. If not, the initial attributes of the next moment will remain unchanged; if it does, the ship will be arranged to leave, and the current state of the berth will be changed to idle, and the initial berth preparation time at the next moment will be increased by one period.

[0258] in:

[0259] The direct installation task decision is specifically as follows:

[0260] Direct loading conditions are used to identify paired ships that can be directly loaded, and direct loading tasks are arranged for the paired ships. Paired ships refer to the pairing between a first-leg ship and a second-leg ship, ensuring that cargo can be directly transferred from the first-leg ship to the second-leg ship. The first-leg ship refers to a ship that arrives at the terminal from outside to unload cargo; the second-leg ship refers to a ship that leaves the terminal after loading cargo.

[0261] The direct installation conditions are specifically as follows:

[0262] Determine whether there is a matching ship that can be directly loaded among the docked ships;

[0263] Determining whether the type of ore to be loaded and unloaded by the paired ship at this stage is the paired ore type corresponding to the paired ship;

[0264] Determine whether the unloading stage is assembling the ore of the shipowner of the second leg ship;

[0265] It is determined whether the remaining quantity of the directly loaded ore unloaded from the paired ship is greater than the quantity to be loaded.

[0266] The entry and exit task decision is specifically as follows:

[0267] Arrange the task of arranging the entry of ships into the yard and the exit of the yard to the ships.

[0268] In detail:

[0269] Complete the direct loading task decision, specifically: identify the judgment rules for direct loading operation (i.e. direct loading conditions): determine whether there is a ship in the set The paired ship in → Determine whether the ore type to be loaded and unloaded by the paired ship at this stage is → Determine whether the unloading stage is loading or not Shipowner's ore → Determine whether the remaining quantity of the direct-loaded ore unloaded from the paired ship is greater than the quantity to be loaded.

[0270] If the above rules are met, then the paired ships that meet the rules The prerequisite for direct installation is met. However, it is necessary to ensure that each paired ship that is ultimately arranged for direct installation They must be different because the second-pass vessel can only use one process at a time. It is also necessary to ensure that the total number of unloaders does not exceed , unloading The total number of ship unloaders used shall not exceed After the above conditions are met, for each paired ship Arrange a direct installation process and unloader, it is possible to determine the direct transfer of each pair within the period Quantitative Minerals. The next moment, Boat Reduction in the remaining amount of minerals , Boat The amount of minerals waiting to be loaded is reduced .

[0271] It needs to be explained that “the final arrangement of direct shipment for each paired ship is must be different" Refers to the identifier of the second-leg vessel. Specifically, this ensures that each pairing of a first-leg vessel and a second-leg vessel is unique during direct loading operations, preventing multiple first-leg vessel pairs from directly loading onto the same second-leg vessel. Direct loading refers to the process whereby cargo is transferred directly from the first-leg vessel to the second-leg vessel without intermediate storage at the yard.

[0272] When all possible direct-fitting ships are completed or there is no direct-fitting ship, the next step is to make an entry and exit task decision. The entry and exit task decision specifically includes:

[0273] For the unloading berth, the approach operation is carried out: the approach process is arranged for the ships with remaining capacity according to the rule of 2 or 3 unloaders with one belt, and 3 are given priority. It is necessary to ensure that the number of approach belts called does not exceed the remaining available after the direct loading decision, and the number of unloaders called does not exceed the remaining available after the direct loading decision. The total number of ship unloaders used shall not exceed If unloading Arranged a total of If a ship unloader is used for the approach, it can be determined that the ship will be transferred to the yard during this period. The order of minerals is as follows: The order in which the goods are unloaded is as follows. The amount of minerals After all are uninstalled, start uninstalling Planting mines. The next moment, The remaining amount of the mineral species being unloaded by the ship is decreasing .

[0274] Carry out exit operation for loading berth: it can be determined that the yard needs to be transferred to the ship within this period The order of minerals is as follows: The order in which the goods are loaded is as follows. The amount of minerals After all are installed, start installing Planting mines. The next moment, The amount of minerals waiting to be loaded on the ship is reduced . Elemental attributes and other mine-related parameters will be allocated to each entry and exit task and serve as input to the yard module.

[0275] Step S204: Build a yard decision model based on the production scheduling information and the transportation tasks for each time period. The yard decision model determines the yard information corresponding to the transportation tasks for each time period. The yard information includes the allocation of stacks for incoming and outgoing tasks, the use of stackers and reclaimers, and the update of yard status.

[0276] The construction of the storage yard decision model is specifically as follows:

[0277] Setting a third data set, a third parameter group, and a third decision variable set according to the production scheduling information and the delivery tasks in each time period;

[0278] Constructing a yard decision model with an allocation model and decision logic through the third data set, the third parameter group, and the third decision variable set;

[0279] The yard information corresponding to the transportation task in each time period is determined by executing the decision logic and allocation model in the yard decision model.

[0280] In this step, the stockpile information corresponding to the transportation task in each time period can be determined based on the transportation task acquired in step S203 and the mixed ore task and stockpile attributes in the production scheduling information in step S201.

[0281] The yard information may include but is not limited to:

[0282] 1. Each incoming transport task will enter which existing stacks or start a new stack at which scale position in which yard;

[0283] 2. The length and direction of the existing stack need to be extended, as well as the length and direction of the new stack;

[0284] 3. From which stack each exit conveying task will be taken;

[0285] 4. Which stacker / reclaimer is used for each entry / exit task?

[0286] 5. At each discrete moment, which three stackers are used to reclaim the finished ore from which two stockpiles and to which stockpile they are stacked, or at which stockpile yard and at which scale position a new stockpile is created;

[0287] 6. Which two stackers are used to retrieve materials from which stack and to which stack at each discrete moment?

[0288] 7. The state of the yard at each discrete moment.

[0289] Specifically, step S204 can be executed in a pre-set yard decision module. After the transportation task decision in the above embodiment is determined, the yard decision will be determined next. The decision includes which stacks the above-mentioned entry transportation tasks will enter, which stacks the exit transportation tasks will be taken from, which bucket wheels will be used, and how to use the remaining bucket wheels for ore mixing operations. The important sets, parameters, and decision variables of the yard decision model in the yard decision module are listed below. The contents already given in the berthing and unberthing modules and the transportation task module described in the above embodiments will not be repeated here.

[0290] (1) Third Data Set

[0291] 1. On-site missions within the timeframe , ;

[0292] 2. Debut missions within the timeframe , ;

[0293] 3. Bucket wheel excavator , ;

[0294] 4. Mixed ore batches , ;

[0295] 5. Stacking in yard , ;

[0296] 6. Finished ore stacking Batch collection , ;

[0297] 7. Yard aisle , ;

[0298] 8. Entry and exit tasks Optional stacking in accordance with the requirements of mineral properties , ;

[0299] 9. Bucket wheel excavator Workable stacking , ;

[0300] 10. Bucket wheel excavator Tasks that can be worked on , ;

[0301] 11. Bucket wheel excavator Work tasks Optional stacking , .

[0302] (2) The third parameter group

[0303] 1. Mission The amount of minerals ;

[0304] 2. Mission Minerals ;

[0305] 3. Mission Mineral density ;

[0306] 4. Mineral Type In the pile Height limit ;

[0307] 5. Mixed ore batches The remaining mixed ore ;

[0308] 6. Mixed ore batches Finished minerals ;

[0309] 7. Mixed ore batches Silicon content of finished ore ;

[0310] 8. Mixed ore batches Raw material minerals 1 ;

[0311] 9. Mixed ore batches Raw material minerals 2 ;

[0312] 10. Stacking Starting position ;

[0313] 11. Stacking End position ;

[0314] 12. Stacking The pile aisle ;

[0315] 13. Stacking The amount of minerals ;

[0316] 14. Stacking Minerals ;

[0317] 15. Stacking Element attributes ;

[0318] 16. Stacking The eigenvector of ;

[0319] 17. Bucket wheel excavator Work tasks Optimal working stacking ;

[0320] 18. Bucket wheel excavator Work tasks The impact value of the evaluation function on the yard .

[0321] (3) The third decision variable set

[0322] 1. Whether to assign tasks Assigned to bucket wheel excavator .

[0323] By executing the decision logic and the allocation model in the yard decision model, the decision variables in the third decision variable set are calculated, and the yard information corresponding to the transportation task in each time period is determined;

[0324] The decision logic includes the steps of:

[0325] Step 1: Arrange the transportation tasks into entry and exit task sets;

[0326] Step 2: Determine the stacking set that can be used by each task in the entry and exit task sets;

[0327] Step 3: Filter out bucket wheel machines that can perform the task and their working stack sets;

[0328] Calculate the evaluation function impact value for each bucket wheel machine and the task it can work on, and select the optimal stack for each task based on the evaluation function impact value;

[0329] Step 4: Obtain an allocation model by constructing a target allocation function and allocation constraints, and allocate each task to a feasible bucket wheel machine by solving the allocation model;

[0330] Step 5: After the task allocation is completed, at the beginning of each time period, determine whether the current yard status and the task allocation given by the previous allocation model simultaneously meet multiple preset feasibility conditions. If so, proceed to the next step; if not, return to step 2;

[0331] Step 6: Execute all entry and exit tasks for this time period according to the allocation results of the allocation model;

[0332] Step 7: Update the status of the yard based on the execution status of all entry and exit tasks during this time period.

[0333] in:

[0334] The allocation constraints include:

[0335] In each discrete time period, each bucket wheel machine can only work on at most one workable task;

[0336] Each task can only be assigned to one bucket wheel machine;

[0337] Different bucket wheel machines cannot work on the same stack while doing their respective tasks in each discrete time period.

[0338] The target allocation function is used to maximize the sum of the impact values of the evaluation functions corresponding to each task.

[0339] The feasibility conditions include:

[0340] Whether all tasks in the current time period have been allocated and completed in the previous allocation model;

[0341] Whether the stack arranged for the incoming task can still accommodate the amount of ore for the incoming task;

[0342] Whether the stack arranged for the exit task still exists at this moment or whether it can meet the output of the ore.

[0343] Regarding the decision logic in the yard decision model, let’s talk about it in detail:

[0344] Step 1: Arrange the decision variables in the transport module into the input set of the yard module and Please note that if multiple processes are arranged for unloading, they also need to be recorded as different tasks and assigned to different bucket wheel machines later.

[0345] Step 2: According to the task The mineral type, element attribute, allowed element attribute range, and the mineral type and element attribute of the existing stack are used to determine whether it is possible to work on the stack and sort out the , the entry mission It can be an empty set, which means that an empty field is required. If it is an empty set, it means that the first ship corresponding to the second ship will unload in the next time or the finished ore will be mixed out in the next time, and the allocation model will not assign this task. Therefore, at least in The allocation model needs to be executed again before the discrete time of the task. The task is assigned to a bucket wheel machine.

[0346] Step 3: If a bucket wheel machine of Contains A subset of , and the bucket wheel machine can complete The task type (in or out) is belong .if belong , for and The intersection of .

[0347] For every bucket wheel excavator and the tasks it can work on calculate , the yard status scoring function is , Used to control the implementation of longer idle gaps: , , is a convex function, which means that when the idle gap becomes longer, the marginal contribution to the scoring function gradually decreases. are adjacent stacks on the same aisle The length of the gap between ), or the gap between the head and tail of the stack closest to the edge. Used to control the dispersion of minerals on the pile: , , which means that the more aisles a mineral of the same type occupies, the better. However, occupying too many aisles will reduce the marginal benefit. of Is a vector that records the types of minerals in each pile The weight and.

[0348] The task Arranged in Each stack in In, according to Calculate the length of the updated stack after this task is completed; if If it is an empty set, stack it from the free gap; thus, calculate the impact on the evaluation function, select the stack with the largest positive impact, and assign its value to .

[0349] Step 4: Construct the following allocation model to assign tasks to bucket-wheel machines and solve it directly. Ensure that each bucket-wheel machine can only work on one workable task within each discrete time period; each task can only be assigned to one bucket-wheel machine; and within each discrete time period, different bucket-wheel machines cannot work on the same stack while performing their respective tasks, ensuring safety limits.

[0350] The formula expression of the target allocation function is: ;

[0351] The formula expression of the allocation constraint is:

[0352] ;

[0353] ;

[0354] .

[0355] Step 5. After the task allocation is complete, at the beginning of each time period, the current stockpile status and the stacking arrangement given in the previous allocation model are evaluated as follows: Are all tasks within this discrete time period already assigned in the previous allocation model? Is the stack assigned to the incoming task still able to accommodate the ore volume of the incoming task? Is the stack assigned to the outgoing task still available or can meet the outgoing ore volume? If all three conditions are met, proceed to the next step. If any of the three conditions are not met, a new round of allocation is required, returning to step 2.

[0356] Step 6: Execute all incoming and outgoing tasks for this time period according to the allocation model. Identify idle belts and bucket wheels within this time period. The belts of interest are: Line ABC for the second incoming section and Line A for the outgoing section. Combined with the number of idle bucket wheels, the first available blending process can be determined. If the first blending process is idle, it will be prioritized. The second available blending process is then determined based on the number of idle bucket wheels. Any unfinished blending batches are scheduled within this discrete time period.

[0357] Step 7: Transfer the stockpile state to the next discrete time period based on the entry and exit tasks and the ore blending tasks. If a new stockpile is not generated, the existing stockpile is directly covered or extended by the specified number of meters. If a new stockpile is generated, a gap for unloading is left or not left as required. Unloading reduces the stockpile length from left to right.

[0358] Step 8: After all the entry and exit arrangements within the decision time range are completed, identify the process number to be used based on the selection of belt conveyor and bucket wheel excavator.

[0359] Step S205: Generate and execute a target production schedule based on the docking information of each ship, the transportation tasks in each time period, and the corresponding yard information.

[0360] Through the above steps S201 to S205, a berthing and unberthing planning model is constructed through the production scheduling information, and the berthing and unberthing planning model is solved for each port arrival sequence to obtain the optimal port arrival sequence, and the berthing information of each ship is determined through the optimal port arrival sequence; a transportation task mathematical model is constructed according to the production scheduling information, the optimal port arrival sequence and the berthing information of each ship, and the transportation task mathematical model is used to judge the berthing status and ship information, and dynamically arrange the berthing, direct loading and entry and exit tasks at each discrete moment to obtain the transportation task for each time period; the yard information corresponding to the transportation task for each time period is determined through the yard decision model; the target production schedule is generated and executed according to the berthing information of each ship, the transportation task for each time period and its corresponding yard information, which solves the problem of low execution efficiency of terminal production schedule in related technologies and improves the execution efficiency of terminal production schedule.

[0361] In some of the embodiments, before determining the docking information of each ship based on the production scheduling information, the production scheduling information may be pre-processed, and the pre-processing is used to process the production scheduling information into a set and parameter form.

[0362] In this embodiment, preprocessing can include multiple aspects: Integrating parameters such as the mid-length and constant into the duration parameters of the remaining ship processes to ensure that the time of all relevant processes is considered when calculating the ship's total berthing time; Integrating the influence of environmental factors such as weather and tides into a set of time periods within which ships can berth and unberth, to ensure the safety and feasibility of ship berthing and unberthing; Integrating berth safety rules into a set of conflict combinations and safe time interval parameters to avoid conflicts in berth use and ensure safe intervals; Integrating the mineral element property requirements of different shippers into a set of selectable stacks for each entry and exit task to meet the mineral element property requirements of different shippers. These preprocessing measures help optimize the scheduling of ship berthing and unberthing tasks and yard tasks, improving overall operational efficiency.

[0363] In some embodiments, determining the docking information of each ship includes: determining the priority of each ship in the production scheduling information according to the production scheduling information; and determining the docking information of each ship according to the priority of each ship.

[0364] In this embodiment, by determining the docking information of each ship by priority, each ship can be better utilized, thereby further improving the reliability of the production scheduling plan.

[0365] In this embodiment, in addition to rule-based prioritization, this can be based on multiple factors, such as the size of the vessel, the urgency of the cargo, the expected waiting time, port contract terms, weather conditions, tide and water depth requirements, etc. For example, large ships or ships carrying perishable cargo may be given higher priority.

[0366] Through the above process, port managers can effectively manage ship calls and maximize port throughput while ensuring safety and customer satisfaction.

[0367] In some of the embodiments, the target production schedule is displayed visually to facilitate user viewing and data monitoring.

[0368] In some embodiments, logs of target production schedule generation and execution are saved and visualized for easy viewing and data monitoring by users. Recording and visualizing logs is crucial when implementing a target production schedule acquisition device, as it not only helps monitor the device's operating status but also provides a basis for subsequent analysis and optimization.

[0369] In some of the embodiments, after the target production schedule is generated and executed, it can also be determined whether emergency data input by the user is received, where the emergency data includes: emergency ship docking data; if it is determined that the emergency data input by the user is received, the target production schedule is adjusted according to the emergency data.

[0370] In this embodiment, a target production schedule (i.e., a predetermined schedule) is first generated and executed. However, due to uncertainties in actual operations, such as sudden weather changes, equipment failures, and urgent order requirements, the equipment needs to be flexible to cope with these emergencies.

[0371] When the device receives "urgent data," such as an unannounced ship suddenly requiring a port call or a ship originally scheduled to dock needing priority for some reason, it triggers a reassessment and adjustment of the current production schedule. This adjustment may involve reallocating resources, reordering operations, optimizing routing, and more to minimize delays and costs while ensuring that new constraints are met.

[0372] In practice, this typically requires a module that can receive and analyze urgent data in real time, as well as an intelligent optimization algorithm that can quickly calculate the optimal production schedule after taking into account new information. This process may involve methods and technologies from fields such as operations research, machine learning, and artificial intelligence.

[0373] For example, for emergency ship call data, the device may need to make adjustments to its plan based on the following factors:

[0374] (1) The size of the vessel and the type of berth required;

[0375] (2) The time and resources required for cargo loading and unloading;

[0376] (3) The impact of other scheduled vessels and operations;

[0377] (4) berth availability and capacity limitations;

[0378] (5) Regulatory and safety requirements.

[0379] In this way, the device can maintain its efficiency and adaptability, allowing it to respond optimally even in the face of unpredictable events.

[0380] This embodiment also provides a device for obtaining a target production schedule for a terminal, which is used to implement the above-mentioned embodiments and preferred implementations, and the details that have been described will not be repeated. As used below, the terms "module," "unit," "subunit," etc. can refer to a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0381] Figure 3 This is a structural block diagram of a target production schedule acquisition device for a terminal according to an embodiment of the present application. Figure 3 As shown, the device includes:

[0382] Acquisition module 31, used to obtain production scheduling information;

[0383] The berthing and unberthing module 32 is coupled to the acquisition module 31 and is used to set the objective function and the constraint function based on the production scheduling information, and to construct a berthing and unberthing planning model based on the objective function and the constraint function, to obtain the ships that need to dock and the multiple arrival sequences of the ships, to solve the berthing and unberthing planning model for each arrival sequence, to obtain the optimal arrival sequence, and to determine the berthing information of each ship based on the optimal arrival sequence;

[0384] The transport task module 33 is coupled to the berthing and unberthing module 32 and is used to construct a transport task mathematical model based on production scheduling information, the optimal port arrival sequence, and the berthing information of each ship. The transport task mathematical model is used to determine the berth status and ship information, and dynamically arrange the berthing, direct loading, and entry and exit tasks at each discrete moment to obtain the transport task for each time period.

[0385] The yard decision module 34 is coupled to the transport task module 33 and is used to build a yard decision model based on the production scheduling information and the transport tasks in each time period. The yard information corresponding to the transport tasks in each time period is determined by the yard decision model. The yard information includes the stack allocation of incoming and outgoing tasks, the use of stackers and reclaimers, and the update of yard status.

[0386] The generation module 35 is coupled to the yard decision module 34 and is used to generate and execute a target production schedule based on the docking information of each ship, the transportation task in each time period and the corresponding yard information.

[0387] In some embodiments, the device further includes: a first display module for visually displaying the target production schedule.

[0388] In some of the embodiments, the device further includes: a second display module for saving and visually displaying the log of generating and executing the target production schedule.

[0389] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0390] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0391] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0392] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0393] S1, obtain production scheduling information;

[0394] S2, using the production scheduling information to set the objective function and constraint function, and constructing a berthing and unberthing planning model based on the objective function and constraint function, obtaining the ships that need to dock and the various arrival sequences of each ship, solving the berthing and unberthing planning model for each arrival sequence, obtaining the optimal arrival sequence, and determining the docking information of each ship based on the optimal arrival sequence;

[0395] S3: Build a mathematical model for transportation tasks based on production scheduling information, the optimal port arrival sequence, and each ship's berthing information. This mathematical model is used to determine berth status and ship information, and dynamically schedule berthing, direct loading, and entry / exit tasks at each discrete moment to obtain the transportation tasks for each time period.

[0396] S4: Build a yard decision model based on the production scheduling information and the transportation tasks in each time period. The yard information corresponding to the transportation tasks in each time period is determined through the yard decision model. The yard information includes the stacking allocation of incoming and outgoing tasks, the use of stackers and reclaimers, and the update of yard status.

[0397] S5, generating and executing a target production schedule based on the docking information of each ship, the transportation task in each time period, and the corresponding yard information.

[0398] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0399] In addition, in conjunction with the target production schedule acquisition method for a terminal in the above-mentioned embodiments, embodiments of the present application may provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, it implements any of the target production schedule acquisition methods for a terminal in the above-mentioned embodiments.

[0400] Those skilled in the art should understand that the various technical features of the above-described embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0401] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for obtaining a target production schedule for a terminal, characterized in that: include: Obtain production scheduling information; The production scheduling information includes: production scheduling demand task information, objective environment information, terminal static information and terminal dynamic information; Obtaining the optimal port arrival sequence through production scheduling information, and determining the docking information of each ship based on the optimal port arrival sequence; the docking information includes: docking location and docking time; A mathematical model for transportation tasks is constructed based on production scheduling information, the optimal port arrival sequence, and each ship's berthing information. This model is then used to determine berth status and ship information, and dynamically schedule berthing, direct loading, and entry / exit tasks at each discrete moment to obtain the transportation tasks for each time period. The construction of the mathematical model of the transportation task is specifically as follows: Setting a second data set, a second parameter set, and a second decision variable set based on production scheduling information, an optimal port arrival sequence, and docking information of each ship; A transport task mathematical model having a decision variable calculation logic is constructed using the second data set, the second parameter group, and the second decision variable set. The berth status and vessel information are determined by executing the decision variable calculation logic in the transport task mathematical model, and berthing, direct loading, and entry and exit tasks are dynamically arranged at each discrete moment to obtain the transport task for each time period. The decision variable calculation logic includes: Get the current berth status at each discrete time; When a berth is idle, decide whether to arrange a ship to berth in the current period based on the berth preparation time, berthing time range, ship type conflict and safety interval time, and modify the current berth status based on the result of the decision; When the berth is busy, the remaining quantity and the quantity to be loaded on the ship are judged to decide whether to proceed with direct loading tasks or entry and exit tasks; The direct installation task decision is specifically as follows: Identify paired vessels capable of direct loading based on direct loading conditions and arrange direct loading tasks for the paired vessels; paired vessels refer to pairings between a first-leg vessel and a second-leg vessel, ensuring that cargo can be directly transferred from the first-leg vessel to the second-leg vessel; the first-leg vessel refers to a vessel arriving at the terminal from outside to unload cargo; the second-leg vessel refers to a vessel leaving the terminal after loading cargo; The direct installation conditions are specifically as follows: Determine whether there is a matching ship that can be directly loaded among the docked ships; Determining whether the type of ore to be loaded and unloaded by the paired ship at this stage is the paired ore type corresponding to the paired ship; Determine whether the unloading stage is assembling the ore of the shipowner of the second leg ship; Determine whether the remaining quantity of the directly loaded ore unloaded from the paired ship is greater than the quantity to be loaded; A yard decision model is constructed based on production scheduling information and the transportation tasks in each time period. The yard information corresponding to the transportation tasks in each time period is determined through the yard decision model. The yard information includes the stacking allocation of incoming and outgoing tasks, the use of stackers and reclaimers, and the update of yard status. A target production schedule is generated and executed based on the docking information of each ship, the transportation task in each time period, and the corresponding yard information.

2. The method for obtaining a target production schedule for a terminal according to claim 1, characterized in that: The entry and exit task decision is specifically as follows: Arrange the task of arranging the entry of ships into the yard and the exit of the yard to the ships.

3. The method for obtaining a target production schedule for a terminal according to claim 2, characterized in that: The construction of the storage yard decision model is specifically as follows: Setting a third data set, a third parameter group, and a third decision variable set according to the production scheduling information and the delivery tasks in each time period; Constructing a yard decision model with an allocation model and decision logic through the third data set, the third parameter group, and the third decision variable set; The yard information corresponding to the transportation task in each time period is determined by executing the decision logic and allocation model in the yard decision model.

4. The method for obtaining a target production schedule for a terminal according to claim 3, characterized in that: The decision logic includes the steps of: Step 1: Arrange the transportation tasks into entry and exit task sets; Step 2: Determine the stacking set that can be used by each task in the entry and exit task sets; Step 3: Filter out bucket wheel machines that can perform the task and their working stack sets; Calculate the evaluation function impact value for each bucket wheel machine and the task it can work on, and select the optimal stack for each task based on the evaluation function impact value; Step 4: Obtain an allocation model by constructing a target allocation function and allocation constraints, and allocate each task to a feasible bucket wheel machine by solving the allocation model; Step 5: After the task allocation is completed, at the beginning of each time period, determine whether the current yard status and the task allocation given by the previous allocation model simultaneously meet multiple preset feasibility conditions. If so, proceed to the next step; if not, return to step 2; Step 6: Execute all entry and exit tasks for this time period according to the allocation results of the allocation model; Step 7: Update the status of the yard based on the execution status of all entry and exit tasks during this time period.

5. The method for obtaining a target production schedule for a terminal according to claim 4, characterized in that: The allocation constraints include: In each discrete time period, each bucket wheel machine can only work on at most one workable task; Each task can only be assigned to one bucket wheel machine; Different bucket wheel machines cannot work on the same stack while doing their respective tasks in each discrete time period.

6. The method for obtaining a target production schedule for a terminal according to claim 5, characterized in that: The target allocation function is used to maximize the sum of the impact values of the evaluation functions corresponding to each task.

7. The method for obtaining a target production schedule for a terminal according to claim 6, characterized in that: The feasibility conditions include: Whether all tasks in the current time period have been allocated and completed in the previous allocation model; Whether the stack arranged for the incoming task can still accommodate the amount of ore for the incoming task; Whether the stack arranged for the exit task still exists at this moment or whether it can meet the output of the ore.

8. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method for obtaining a target production scheduling plan for a terminal according to any one of claims 1 to 7.

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