A ship replenishment route cooperative planning method and device

Abstract

The application discloses a ship replenishment route cooperative planning method and device, and the method comprises the following steps: collecting a total set of basic information, a set of supply and demand parameters, a set of ship capacity parameters and a set of route and environment parameters; defining a set of decision variables; performing ship replenishment route cooperative planning processing on the total set of basic information, the set of supply and demand parameters, the set of ship capacity parameters, the set of route and environment parameters and the set of decision variables to obtain a set of ship replenishment route information. The scheme can take into account the key material priority guarantee, the replenishment time limit and control, the overall satisfaction improvement and the scheme execution feasibility.

Description

Technical Field

[0001] This invention relates to the fields of maritime transport support and route optimization as well as industrial data processing, and specifically to a method and apparatus for collaborative planning of ship replenishment routes. Background Technology

[0002] Maritime replenishment route planning is one of the key issues in maritime transport support, emergency material delivery, and support for complex missions. In mission environments with multiple warehouses, multiple objectives, and multiple materials, the replenishment system usually needs to coordinate and allocate the differentiated needs of different objectives under limited ship resources, while taking into account requirements such as replenishment timeliness, material satisfaction, priority support for critical missions, and efficiency in the utilization of transportation resources.

[0003] Existing methods primarily focus on route optimization, transportation allocation, or emergency logistics scheduling, achieving some progress in areas such as transportation distance, transportation costs, and service time. However, when the application scenario expands to maritime replenishment missions involving multiple vessels, multiple voyages, multiple supplies, dynamic sea conditions, and restricted ports, the following problems still commonly exist: First, there is insufficient characterization of the differences in urgency among different targets and supplies, making it difficult to highlight the priority replenishment needs of critical supplies and key support targets; second, there is insufficient consideration of the joint constraints between vessel adaptability, port water depth, and sea conditions, resulting in weak executability of the planning results; third, there is insufficient dynamic response capability to changes in demand, inventory, and environmental disturbances during mission execution, lacking rolling updates and replanning mechanisms.

[0004] Therefore, it is necessary to propose a collaborative planning scheme for ship replenishment routes that can comprehensively consider replenishment priorities, port water depth conditions, sea conditions, multi-ship multi-voyage coordination, and dynamic rolling optimization. Summary of the Invention

[0005] This invention mainly addresses the problems of insufficient priority expression, inadequate consideration of joint constraints, and weak dynamic adaptability in existing ship replenishment route planning. This invention discloses a collaborative planning method and apparatus for ship replenishment routes.

[0006] In a first aspect, this invention discloses a method for collaborative planning of ship replenishment routes, comprising: S1 collects the basic information set, the supply and demand parameter set, the ship capacity parameter set, and the route and environment parameter set; S2, Define the set of decision variables; S3. Perform collaborative planning of ship replenishment routes on the basic information set, supply and demand parameter set, ship capacity parameter set, route and environment parameter set, and decision variable set to obtain ship replenishment route information set.

[0007] The overall set of basic information includes: a set of storage locations. Set of supply targets Ship assembly Collection of material types The set of all executable candidate voyages, the set of all nodes, and the set of critical supplies for all vessels. The set of storage points includes several storage points; the set of supply targets includes several supply targets; the set of ships includes several ships; the set of material types includes several types of materials; the set of executable candidate voyages for ship k is represented as... ; , The set of all nodes; The set of supply and demand parameters includes: a subset of warehouse inventory data, a subset of target demand data, a subset of target existing inventory data, a subset of target material urgency weights, a subset of material importance weights, a subset of target material supply disruption risk indicators, a subset of target material execution feasibility correction factors, a subset of target material remaining sustainable time under current consumption levels, and a subset of target material replenishment time requirements. Elements of the subset of warehouse inventory data , indicating storage point Available supplies Quantity; elements of the target requirement data subset , indicating the target For supplies The demand; elements of the target existing inventory data subset. , indicating the target Current supplies Quantity; The elements of the subset of material urgency weights for the target. , indicating the target For supplies The urgency weight of the supplies; elements of the subset of supplies importance weights. , indicating supplies The importance weight of the materials; the elements of the subset of indicators of the risk of supply disruption to the materials of the target. , indicating the target For supplies The risk indicator of supply disruption; the elements of the subset of the feasibility modification factors for the execution of the target on materials. , indicating the target For supplies The feasibility correction factor for implementation The target element is the subset of the remaining sustainable time of resources at the current consumption level. , indicating the target For supplies The remaining sustainable time at the current consumption level; elements of the subset of the time requirement for the replenishment of resources to the target. , representing the target For supplies The latest expected delivery time; The set of ship capability parameters includes: the maximum deadweight of each ship, the maximum cargo capacity of each ship, the cargo transport compatibility of each ship, the speed of each ship, and the maximum safe sea state class for each ship. Indicates a ship Maximum load capacity, Indicates a ship Maximum cabin capacity Indicates a ship For supplies Transport compatibility markings Indicates a ship speed, Indicates a ship The maximum sea state class for safe navigation; The set of route and environmental parameters includes: the distance between all two nodes, the sailing time of the vessel in the segment, the permissible water depth of the berths in the ports where all nodes are located, the sea state level information of all segments, and the service time of the nodes to the vessels. Represents a node To node segment The range of the flight, Indicates a ship In the flight segment The sailing time on the ship , Represents a node The permitted water depth of the berths in the port is Indicates time Next segment Sea state rating, Represents a node For ships Service hours; The set of decision variables includes whether the ship passes through a voyage segment, the quantity of goods loaded at the storage point, the quantity of goods unloaded to the target, the time when the ship arrives at the node in each voyage, the remaining load of the ship after completing loading and unloading at the node in each voyage, the unmet demand for goods from the target, and the start and end times of each voyage. Indicates a ship In the During the voyage, is it from the node? sail to the node A value of 1 indicates yes, and a value of 0 indicates no; Indicates a ship In the During the voyage at the storage point Loaded supplies quantity; Indicates a ship In the During the voyage towards the target Unloaded supplies quantity; Indicates a ship In the Nodes reached during the voyage The moment; Indicates a ship In the During the voyage at the node The remaining load after loading and unloading; Indicate target For supplies Unmet needs; and Representing ships In the The start and end times of the voyage This indicates the effective delivery time when target d reaches the preset minimum guarantee threshold for material m.

[0008] The process of performing collaborative planning of ship replenishment routes on the basic information set, supply and demand parameter set, ship capacity parameter set, route and environmental parameter set, and decision variable set yields a ship replenishment route information set, including: S31, the basic information set, supply and demand parameter set, ship capability parameter set, and route and environment parameter set are preprocessed to obtain a preprocessed information set; the preprocessed information set includes the preprocessed basic information set, supply and demand parameter set, ship capability parameter set, and route and environment parameter set. S32, Based on the preprocessed information set and the decision variable set, a route collaborative planning optimization model is constructed; S33, Solve the route coordination planning optimization model to obtain the solution information of the decision variable set; S34, determine the solution information of the decision variable set, which is the ship resupply route information set.

[0009] The route coordination planning optimization model, constructed based on the preprocessed information set and the decision variable set, includes: S321, Based on the preprocessed information set, perform priority calculation to obtain priority information; S322, Based on the priority information, the preprocessed information set, and the decision variable set, the objective function is constructed; S323, Based on the preprocessed information set and the decision variable set, a constraint function set is constructed; S324. Using the objective function and the set of constraint functions, a route collaborative planning optimization model is constructed.

[0010] The expression for calculating the priority is: , , , , in, For the goal For supplies The current shortage ratio indicator This is a preset measure to prevent extremely small positive numbers with a denominator of zero. Indicate target For supplies The feasibility correction factor for implementation For the goal For supplies Basic priority, For the goal For supplies The overall supply priority, Indicate target For supplies Supply disruption risk indicators , , and The preset weighting factor, Indicate target For supplies The urgency weight of supplies Indicates supplies The importance weight of materials, Indicate target For supplies The demand, Indicate target Current supplies Quantity, Indicate target For supplies The remaining sustainable time at the current consumption level; the priority information includes the overall replenishment priority of each target for each type of material.

[0011] The expression for the objective function is: , , , , , in, This indicates that the supply is sufficient to meet the needs. This indicates losses due to supply delays. This indicates that the gap loss was not met. Indicates the cost of transportation. , , and These are the preset non-negative weighting coefficients. Describe the objective function. The preset bias factor, Indicate target For supplies The urgency weight of supplies Indicates a ship In the During the voyage towards the target Unloaded supplies quantity, This represents the effective delivery time of target d when material m reaches the preset minimum guarantee threshold. Represented as target For supplies The latest expected delivery time, Indicate target For supplies Unmet needs, Indicates a ship In the During the voyage, is it from the node? sail to the node A value of 1 indicates yes, and a value of 0 indicates no. Indicates a ship In the flight segment The sailing time on the ship.

[0012] The set of constraint functions includes: warehousing supply constraint function, demand satisfaction balance constraint function, loading and unloading balance constraint function, voyage continuity constraint function, time recursion constraint function, and minimum guarantee constraint function for critical materials. The warehousing supply constraint, which characterizes the fact that the total loading volume cannot exceed the inventory supply at the warehousing point, is expressed as follows: , in, Indicates a ship In the During the voyage at the storage point Loaded supplies quantity, Indicates the storage point Available supplies quantity; The demand satisfies a balance constraint, which means that the sum of the received quantity and the unmet quantity equals the demand quantity. Its expression is: ; The loading and unloading balance constraint is used to indicate that the total unloading volume of any ship, any voyage, and any cargo shall not exceed the total loading volume, and its expression is: ; The voyage continuity constraint indicates that the start time of a subsequent voyage must not be earlier than the end time of the previous voyage. , in, Indicates a ship In the The start time of the voyage Indicates a ship In the End time of the voyage; The time recursion constraint is used to indicate that when a certain flight segment is selected, the arrival time of the node satisfies the cumulative relationship between the flight time and the node service time, and its expression is: ; in, and Representing ships In the Nodes reached during the voyage and nodes At that moment, Represents a node For ships Service hours, Indicates a ship In the flight segment The sailing time on the ship This is a preset time constraint factor; The minimum guarantee constraint for critical materials is used to indicate that the satisfaction rate of critical materials is not lower than a preset threshold, and its expression is: , in, For the preset target The satisfaction rate threshold of key material m, This is the preset bias factor.

[0013] A second aspect of the present invention discloses a ship replenishment route collaborative planning device, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the ship replenishment route collaborative planning method.

[0014] In a third aspect of this invention, a computer-readable storage medium is disclosed, wherein the computer-readable storage medium stores computer instructions, which, when invoked by a computer, are used to execute the aforementioned ship replenishment route collaborative planning method.

[0015] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the aforementioned ship replenishment route collaborative planning method.

[0016] The beneficial effects of this invention are as follows: Compared with existing technologies, the present invention has at least the following beneficial effects: First, it can prioritize the protection of key materials and key objectives; second, it can improve the feasibility of the plan while meeting the practical constraints such as port water depth, ship draft and sea state; third, it can enhance the dynamic response capability to changes in demand, inventory changes and environmental disturbances through a rolling optimization mechanism; and fourth, it can improve the efficiency of transportation capacity utilization and the overall supply satisfaction. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of the hierarchical closed-loop solution method of the present invention. Detailed Implementation

[0018] To better understand the content of this invention, an embodiment is provided here.

[0019] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of the hierarchical closed-loop solution method of the present invention.

[0020] Example 1: In a first aspect, this invention discloses a method for collaborative planning of ship replenishment routes, comprising: S1 collects the basic information set, the supply and demand parameter set, the ship capacity parameter set, and the route and environment parameter set; S2, Define the set of decision variables; S3. Perform collaborative planning of ship replenishment routes on the basic information set, supply and demand parameter set, ship capacity parameter set, route and environment parameter set, and decision variable set to obtain ship replenishment route information set.

[0021] The overall set of basic information includes: a set of storage locations. Set of supply targets Ship assembly Collection of material types The set of all executable candidate voyages, the set of all nodes, and the set of critical supplies for all vessels. The set of storage points includes several storage points; the set of supply targets includes several supply targets; the set of ships includes several ships; the set of material types includes several types of materials; the set of executable candidate voyages for ship k is represented as... ; , The set of all nodes; the set of key materials is a subset of the set of material types, including several categories of key materials; the supply target is referred to as the target; the set of all nodes includes several nodes; the node is a target or a storage point; The set of supply and demand parameters includes: a subset of warehouse inventory data, a subset of target demand data, a subset of target existing inventory data, a subset of target material urgency weights, a subset of material importance weights, a subset of target material supply disruption risk indicators, a subset of target material execution feasibility correction factors, a subset of target material remaining sustainable time under current consumption levels, and a subset of target material replenishment time requirements. Elements of the subset of warehouse inventory data , indicating storage point Available supplies Quantity; elements of the target requirement data subset , indicating the target For supplies The demand; elements of the target existing inventory data subset. , indicating the target Current supplies Quantity; The elements of the subset of material urgency weights for the target. , indicating the target For supplies The urgency weight of the supplies; elements of the subset of supplies importance weights. , indicating supplies The importance weight of the materials; the elements of the subset of indicators of the risk of supply disruption to the materials of the target. , indicating the target For supplies The risk indicator of supply disruption; the elements of the subset of the feasibility modification factors for the execution of the target on materials. , indicating the target For supplies The feasibility correction factor for implementation The target element is the subset of the remaining sustainable time of resources at the current consumption level. , indicating the target For supplies The remaining sustainable time at the current consumption level; elements of the subset of the time requirement for the replenishment of resources to the target. , representing the target For supplies The latest expected delivery time.

[0022] The materials , representing the m-th type of material, target This indicates the target with sequence number d, the storage point. Indicates the sequence number is The storage point.

[0023] It can be determined based on one or more of the following factors: number of candidate storage points, number of executable vessels, time to nearest feasible arrival, percentage of feasible routes, and degree of current constraint satisfaction.

[0024] The set of ship capability parameters includes: the maximum deadweight of each ship, the maximum cargo capacity of each ship, the cargo transport compatibility of each ship, the speed of each ship, and the maximum safe sea state class for each ship. Indicates a ship Maximum load capacity, Indicates a ship Maximum cabin capacity Indicates a ship For supplies Transport compatibility markings Indicates a ship speed, Indicates a ship The maximum sea state class for safe navigation.

[0025] The set of route and environmental parameters includes: the distance between all two nodes, the sailing time of the vessel in the segment, the permissible water depth of the berths in the ports where all nodes are located, the sea state level information of all segments, and the service time of the nodes to the vessels. Represents a node To node segment The range of the flight, Indicates a ship In the flight segment The sailing time on the ship , Represents a node The permitted water depth of the berths in the port is Indicates time Next segment Sea state rating, Represents a node For ships Service hours.

[0026] The set of decision variables includes whether the ship passes through a voyage segment, the quantity of goods loaded at the storage point, the quantity of goods unloaded to the target, the time when the ship arrives at the node in each voyage, the remaining load of the ship after completing loading and unloading at the node in each voyage, the unmet demand for goods from the target, and the start and end times of each voyage. Indicates a ship In the During the voyage, is it from the node? sail to the node A value of 1 indicates yes, and a value of 0 indicates no; Indicates a ship In the During the voyage at the storage point Loaded supplies quantity; Indicates a ship In the During the voyage towards the target Unloaded supplies quantity; Indicates a ship In the Nodes reached during the voyage The moment; Indicates a ship In the During the voyage at the node The remaining load after loading and unloading; Indicate target For supplies Unmet needs; and Representing ships In the The start and end times of the voyage. This indicates the effective delivery time when target d reaches the preset minimum guarantee threshold for material m.

[0027] The process of performing collaborative planning of ship replenishment routes on the basic information set, supply and demand parameter set, ship capacity parameter set, route and environmental parameter set, and decision variable set yields a ship replenishment route information set, including: S31, the basic information set, supply and demand parameter set, ship capability parameter set, and route and environment parameter set are preprocessed to obtain a preprocessed information set; the preprocessed information set includes the preprocessed basic information set, supply and demand parameter set, ship capability parameter set, and route and environment parameter set. S32, Based on the preprocessed information set and the decision variable set, a route collaborative planning optimization model is constructed; S33, Solve the route coordination planning optimization model to obtain the solution information of the decision variable set; S34, determine the solution information of the decision variable set, which is the ship resupply route information set; The preprocessing includes uniform encoding, dimension normalization, and time reference alignment of the aforementioned data.

[0028] The route coordination planning optimization model, constructed based on the preprocessed information set and the decision variable set, includes: S321, Based on the preprocessed information set, perform priority calculation to obtain priority information; S322, Based on the priority information, the preprocessed information set, and the decision variable set, the objective function is constructed; S323, Based on the preprocessed information set and the decision variable set, a constraint function set is constructed; S324. Using the objective function and the set of constraint functions, a route collaborative planning optimization model is constructed.

[0029] The expression for calculating the priority is: , , , , in, For the goal For supplies The current shortage ratio indicator This is a preset measure to prevent extremely small positive numbers with a denominator of zero. For the goal For supplies Basic priority, For the goal For supplies The overall supply priority, Indicate target For supplies Supply disruption risk indicators , , and The weighting factor is preset; the priority information includes the comprehensive supply priority of each target for each type of material.

[0030] The expression for the objective function is: , , , , , in, This indicates that the supply is sufficient to meet the needs. This indicates losses due to supply delays. This indicates that the gap loss was not met. Indicates the cost of transportation. , , and These are the preset non-negative weighting coefficients. Describe the objective function. This is the preset bias factor.

[0031] The set of constraint functions includes: warehousing supply constraint function, demand satisfaction balance constraint function, loading and unloading balance constraint function, voyage continuity constraint function, time recursion constraint function, and minimum guarantee constraint function for critical materials. The warehousing supply constraint, which characterizes the fact that the total loading volume cannot exceed the inventory supply at the warehousing point, is expressed as follows: ; The demand satisfies a balance constraint, which means that the sum of the received quantity and the unmet quantity equals the demand quantity. Its expression is: ; The loading and unloading balance constraint is used to indicate that the total unloading volume of any ship, any voyage, and any cargo shall not exceed the total loading volume, and its expression is: ; The voyage continuity constraint indicates that the start time of a subsequent voyage must not be earlier than the end time of the previous voyage. ; The time recursion constraint is used to indicate that when a certain flight segment is selected, the arrival time of the node satisfies the cumulative relationship between the flight time and the node service time, and its expression is: ; The minimum guarantee constraint for critical materials is used to indicate that the satisfaction rate of critical materials is not lower than a preset threshold, and its expression is: , in, For the preset target The satisfaction rate threshold of key material m, As a preset time constraint factor, This is the preset bias factor.

[0032] The solution information for the set of decision variables is obtained by solving the route coordination planning optimization model. This can be done first for any ship. In each voyage, considering the candidate voyage segment, the following factors are comprehensively verified: material compatibility, ship load capacity, cargo hold capacity, ship draft constraints under load conditions, port depth constraints at key points, and sea state seaworthiness constraints within the corresponding time window. When these conditions are met, the corresponding voyage segment is deemed feasible, and feasibility determination variables are constructed. These feasibility determination variables are then used to assess... The constraint is applied, and its expression is as follows: , in, The variables are used for feasibility determination. Based on the above constraints, by pre-eliminating unreachable, unloadable, or non-compliant flight segments, a set of feasible flight segments for engineering execution is obtained, which is used as the initial solution, thereby reducing the solution scale and improving the feasibility of subsequent optimization results. Based on the initial solution, operations research or optimization algorithms are used to solve the route collaborative planning optimization model to obtain the solution information of the decision variable set.

[0033] Example 2: The following description, in conjunction with symbol definitions, objective functions, constraint relationships, and solution procedures, further illustrates a preferred embodiment of the present invention. This embodiment is intended to explain the present invention and not to limit its scope of protection.

[0034] 1.1 Problem Modeling and Basic Assumptions Consider a scenario where multiple storage points supply supplies to multiple targets via maritime transport. The system contains various types of supplies, with different targets having varying demands, urgency levels, and timeliness requirements for different supplies. Simultaneously, there are multiple vessels capable of performing multiple voyages, differing in carrying capacity, types of supplies they can carry, speed, and seaworthiness. Furthermore, practical factors such as port depth constraints, vessel draft constraints, and sea state limitations must be comprehensively considered during the supply process.

[0035] In this implementation, a single vessel performs only one voyage at any given time; a single voyage is allowed to start from a storage point and, under the condition of satisfying path accessibility and capacity constraints, visit other storage points for reloading, and then visit one or more target nodes for unloading and resupply; the same target's demand for the same material is allowed to be fulfilled in batches by multiple vessels in multiple voyages; completed voyages remain frozen during the rolling replanning process.

[0036] 1.2 Sets, Parameters, and Decision Variables The set is defined as follows: For the collection of storage points, For the set of targets to be supplied, For the assembly of ships, To categorize the types of supplies, For ships A set of executable candidate voyages. This refers to the set of all nodes. The set of key resources. Supply and demand parameters include: Warehouse inventory data Target demand data Target existing inventory data , Indicates storage point Available supplies quantity, Indicate target For supplies The demand, Indicate target Current supplies Quantity. Target For supplies Emergency weight of supplies Supplies Importance weight of materials , For the goal For supplies Supply disruption risk indicators For the goal For supplies The feasibility correction factor for implementation.

[0037] It can be determined based on one or more of the following factors: number of candidate storage points, number of executable vessels, time to nearest feasible arrival, percentage of feasible routes, and degree of current constraint satisfaction. Indicate target For supplies Remaining sustainable time at the current consumption level For the goal For supplies Time to meet demand Ship capability parameters include: Indicates a ship Maximum load capacity, Indicates a ship Maximum cabin capacity Indicates a ship For supplies Transport compatibility markings Indicates a ship speed, Indicates a ship The maximum sea state class for safe navigation.

[0038] Route and environmental parameters include: Represents a node To node segment The range of the flight, Indicates a ship In the flight segment The sailing time on the ship Represents a node The permitted water depth at the port or berth is Indicates time Next segment Sea state rating, This indicates the node service time.

[0039] The main decision variables include: Indicates a ship In the During the voyage, is it from the node? sail to the node ; Indicates at the storage point Loaded supplies quantity; Indicates to the target Unloaded supplies quantity; Indicates arrival at node The moment; Indicates at node The remaining load after loading and unloading; Indicate target For supplies Unmet needs.

[0040] 1.3 Target-Material Level Supply Priority Modeling To reflect the differences in the necessity, urgency, and current feasibility of resupply among different targets and materials, a target-material level integrated resupply priority function is constructed.

[0041] in, This is an indicator of the current shortage ratio. To prevent extremely small positive numbers with a denominator of zero. Based on priority, For overall supply priority, Indicators of supply disruption risk In engineering implementation, priority can be given according to comprehensive supply requirements. A priority sequence for replenishment tasks is formed from high to low, and this sequence is used to guide the initial allocation of subsequent ships, voyages, and targets.

[0042] 1.4 Construction of Feasible Flight Segment Network Considering the combined constraints of vessel capacity, port depth, and sea state conditions, a network of feasible voyage segments is constructed for each vessel and voyage. For each candidate voyage i→j, if vessel k satisfies the constraints of cargo suitability, draft under load conditions, port depth at node j, and sea state suitability within the corresponding time window on voyage r, then the voyage segment is marked as feasible.

[0043] And constrain the segment selection variables to satisfy: in, The ship's draft is used as a feasibility determination variable. It can be represented by a load-draft correspondence table, a piecewise linear function, or a pre-calibrated relationship. By pre-eliminating infeasible sections, the solution size can be significantly reduced and the solution stability improved.

[0044] 1.5 Collaborative Optimization Model This implementation constructs a multi-objective mixed integer optimization model, focusing on the benefits of supply fulfillment, losses due to supply delays, unmet needs, and transportation costs.

[0045] in, , , , It is a non-negative weighting coefficient, which can be set according to the requirements of timeliness, economy and satisfaction based on the task scenario.

[0046] 1.6 Key Constraints (1) Warehouse supply constraints: The total loading volume cannot exceed the warehouse inventory supply.

[0047] (2) Demand fulfillment balance constraint: The sum of the delivered quantity and the unmet quantity equals the demand quantity.

[0048] (3) Loading and unloading balance constraint: The total unloading volume of any vessel, any voyage, and any material shall not exceed the total loading volume.

[0049] (4) Voyage continuity constraint: The start time of the next voyage shall not be earlier than the end time of the previous voyage.

[0050] (5) Time recursion constraint: When selecting segment i→j, the node arrival time satisfies the cumulative relationship between flight time and node service time.

[0051] (6) Minimum guarantee constraint for critical materials: The satisfaction rate of critical materials shall not be lower than the preset threshold.

[0052] in, For the preset target The threshold for the satisfaction rate of key material m; The time constraint factor is preset; when a more refined expression is needed, node flow balance constraints, sub-loop elimination constraints, load recursion constraints, access and supply consistency constraints, and port water depth constraints can also be introduced.

[0053] 1.7 Phased Solution Process In engineering implementation, a hierarchical closed-loop solution framework of "priority assessment - network construction - initial allocation - joint optimization - feasibility verification - rolling correction" can be adopted: First, the supply priority of each target-material combination is calculated; second, an reachable route network is constructed based on ship capacity, port water depth, and sea state conditions; third, an initial allocation scheme of ship-voyage-target is generated based on priority and reachability; then, the access order of each ship, the connection of multiple voyages, the loading capacity, and the delivery time are jointly optimized; then, the feasibility of the optimization results is verified and feedback is provided for correction when necessary; when sea state, inventory, or demand changes, only unexecuted voyages and uncompleted tasks are updated and replanned on a rolling basis.

[0054] In all embodiments of the present invention, the variables involved in all computational expressions or mathematical functions have been dimensionlessized before computation.

[0055] In all embodiments of the present invention, the values ​​of the independent variables in the input of all computational expressions or mathematical functions meet the reasonable requirements of the input range of the computational expressions or mathematical functions, and can ensure that the computational expressions or mathematical functions can be calculated smoothly without violating physical laws or mathematical rules.

[0056] A second aspect of the present invention discloses a ship replenishment route collaborative planning device, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the ship replenishment route collaborative planning method.

[0057] In a third aspect of this invention, a computer-readable storage medium is disclosed, wherein the computer-readable storage medium stores computer instructions, which, when invoked by a computer, are used to execute the aforementioned ship replenishment route collaborative planning method.

[0058] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the aforementioned ship replenishment route collaborative planning method.

[0059] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for collaborative planning of a replenishment route of a ship, characterized in that, include: S1 collects the basic information set, the supply and demand parameter set, the ship capacity parameter set, and the route and environment parameter set; S2, Define the set of decision variables; S3. Perform collaborative planning of ship replenishment routes on the basic information set, supply and demand parameter set, ship capacity parameter set, route and environment parameter set, and decision variable set to obtain ship replenishment route information set.

2. The ship replenishment route collaborative planning method as described in claim 1, characterized in that, The basic information total set includes: a warehouse point set , a replenishment target set , a ship set , a material type set , a set of executable candidate voyages of all ships, a set of all nodes and a set of key materials ; the warehouse point set includes several warehouse points; the replenishment target set includes several replenishment targets; the ship set includes several ships; the material type set includes several types of materials; The set of supply and demand parameters includes: a subset of warehouse inventory data, a subset of target demand data, a subset of target existing inventory data, a subset of target material urgency weights, a subset of material importance weights, a subset of target material supply disruption risk indicators, a subset of target material execution feasibility correction factors, a subset of target material remaining sustainable time under current consumption levels, and a subset of target material replenishment time requirements. The set of ship capability parameters includes: the maximum deadweight of each ship, the maximum cargo capacity of each ship, the cargo transport compatibility of each ship, the speed of each ship, and the maximum safe sea state class for each ship. The set of route and environmental parameters includes: the distance between all two nodes, the sailing time of the vessel in the segment, the permissible water depth of the berths in the ports where all nodes are located, the sea state level information of all segments, and the service time of the nodes to the vessels. The set of decision variables includes whether the ship passes through a voyage segment, the quantity of goods loaded at the storage point, the quantity of goods unloaded to the target, the time when the ship arrives at the node in each voyage, the remaining load of the ship after completing loading and unloading at the node in each voyage, the unmet demand for goods from the target, and the start and end times of each voyage.

3. The ship replenishment route cooperative planning method according to claim 1, characterized by, The process of performing collaborative planning of ship replenishment routes on the basic information set, supply and demand parameter set, ship capacity parameter set, route and environmental parameter set, and decision variable set yields a ship replenishment route information set, including: S31, the basic information set, supply and demand parameter set, ship capability parameter set, and route and environment parameter set are preprocessed to obtain a preprocessed information set; the preprocessed information set includes the preprocessed basic information set, supply and demand parameter set, ship capability parameter set, and route and environment parameter set. S32, Based on the preprocessed information set and the decision variable set, a route collaborative planning optimization model is constructed; S33, Solve the route coordination planning optimization model to obtain the solution information of the decision variable set; S34, determine the solution information of the decision variable set, which is the ship resupply route information set.

4. The ship replenishment route cooperative planning method according to claim 3, characterized by, The route coordination planning optimization model, constructed based on the preprocessed information set and the decision variable set, includes: S321, Based on the preprocessed information set, perform priority calculation to obtain priority information; S322, Based on the priority information, the preprocessed information set, and the decision variable set, the objective function is constructed; S323, Based on the preprocessed information set and the decision variable set, a constraint function set is constructed; S324. Using the objective function and the set of constraint functions, a route collaborative planning optimization model is constructed.

5. The ship replenishment route collaborative planning method as described in claim 4, characterized in that, The expression for calculating the priority is: ， ， ， ， in, For the goal For supplies The current shortage ratio indicator This is a preset measure to prevent extremely small positive numbers with a denominator of zero. Indicate target For supplies The feasibility correction factor for implementation For the goal For supplies Basic priority, For the goal For supplies The overall supply priority, Indicate target For supplies Supply disruption risk indicators , , and The preset weighting factor, Indicate target For supplies The urgency weight of supplies Indicates supplies The importance weight of materials, Indicate target For supplies The demand, Indicate target Current supplies Quantity, Indicate target For supplies The remaining sustainable time at the current consumption level; the priority information includes the overall replenishment priority of each target for each type of material.

6. The ship replenishment route collaborative planning method as described in claim 5, characterized in that, The expression for the objective function is: ， ， ， ， ， in, This indicates that the supply is sufficient to meet the needs. This indicates losses due to supply delays. This indicates that the gap loss was not met. Indicates the cost of transportation. , , and These are the preset non-negative weighting coefficients. Describe the objective function. The preset bias factor, Indicate target For supplies The urgency weight of supplies Indicates a ship In the During the voyage towards the target Unloaded supplies quantity, This represents the effective delivery time of target d when material m reaches the preset minimum guarantee threshold. Represented as target For supplies The latest expected delivery time, Indicate target For supplies Unmet needs, Indicates a ship In the During the voyage, is it from the node? sail to the node A value of 1 indicates yes, and a value of 0 indicates no. Indicates a ship In the flight segment The sailing time on the ship.

7. The ship replenishment route collaborative planning method as described in claim 4, characterized in that, The set of constraint functions includes: warehousing supply constraint function, demand satisfaction balance constraint function, loading and unloading balance constraint function, voyage continuity constraint function, time recursion constraint function, and minimum guarantee constraint function for critical materials. The warehousing supply constraint, which characterizes the fact that the total loading volume cannot exceed the inventory supply at the warehousing point, is expressed as follows: ， in, Indicates a ship In the During the voyage at the storage point Loaded supplies quantity, Indicates the storage point Available supplies quantity; The demand satisfies a balance constraint, which means that the sum of the received quantity and the unmet quantity equals the demand quantity. Its expression is: ； The loading and unloading balance constraint is used to indicate that the total unloading volume of any ship, any voyage, and any cargo shall not exceed the total loading volume, and its expression is: ； The voyage continuity constraint indicates that the start time of a subsequent voyage must not be earlier than the end time of the previous voyage. ， in, Indicates a ship In the The start time of the voyage Indicates a ship In the End time of the voyage; The time recursion constraint is used to indicate that when a certain flight segment is selected, the arrival time of the node satisfies the cumulative relationship between the flight time and the node service time, and its expression is: ； in, and Representing ships In the Nodes reached during the voyage and nodes At that moment, Represents a node For ships Service hours, Indicates a ship In the flight segment The sailing time on the ship This is a preset time constraint factor; The minimum guarantee constraint for critical materials is used to indicate that the satisfaction rate of critical materials is not lower than a preset threshold, and its expression is: ， in, For the preset target The satisfaction rate threshold of key material m, This is the preset bias factor.

8. A ship replenishment route collaborative planning device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the ship replenishment route collaborative planning method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked by a computer, are used to execute the ship replenishment route collaborative planning method as described in any one of claims 1 to 7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the ship replenishment route collaborative planning method as described in any one of claims 1 to 7.

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