CN110298474B - AIS and GIS-based logistics site selection method for ship spare parts - Google Patents
AIS and GIS-based logistics site selection method for ship spare parts Download PDFInfo
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Abstract
The invention discloses a logistics site selection method of ship spare parts based on AIS and GIS, which comprises the following steps: step 1, AIS and GIS service data are obtained; step 2, analyzing and storing the data, and dividing the area; step 3, according to the distribution density of the ships and the types and the number of the ships, performing demand prediction analysis, determining resource demands at different distances in the area, and determining a candidate area for warehousing site selection; step 4, determining the warehouse logistics service radius, determining the optimal delivery and warehousing address, and selecting the optimal address; and 5, evaluating the selected optimal address. The invention can realize the aims of shortening the distribution period, reasonably configuring the transportation mode, shortening the itineration time, reducing the distribution cost, improving the service quality and the like, ensures the operability of the line optimization result and embodies the advancement of intelligent logistics distribution site selection.
Description
Technical Field
The invention relates to the technical field of logistics in the ship industry, in particular to a logistics site selection method for ship spare parts based on AIS and GIS.
Background
In the current mainstream logistics industry, the site selection of a logistics distribution center occupies an important position in the whole logistics system, and the selection of different storage sites has a great influence on the cost and efficiency of cargo transportation. Due to the characteristics of ship distribution in the shipping industry, the traditional logistics addressing model has certain limitation on cargo transportation of the logistics industry related to shipping, and commonly used methods such as a dynamic addressing model, a cross median model, a gravity center method, a differential method, a graphical method, fuzzy comprehensive evaluation and the like usually carry out careful calculation and comparison according to various transportation functions, time distance parameters and the like to obtain a proper address space.
The AIS is a novel digital navigation aid system and equipment integrating a network technology, a modern communication technology, a computer technology and an electronic information display technology, collects and analyzes more relevant information of a ship except a geographical position on the basis of a Global Positioning System (GPS), and determines the destination, a navigation route, a ship type and basic parameters and data of the ship.
GIS, which combines geography with topography, as well as remote sensing and computer science, has been widely used in various fields, as a computer system for inputting, storing, querying, analyzing and displaying geographic data. After obtaining the location information through the related technology, the user needs to know the geographical environment where the user is located, and query and analyze the environment information, thereby providing information support and service for the user activities. The method has good adaptability aiming at the demand and prediction of spare parts of ships, and can reasonably distribute and predict possible industrial demands.
Disclosure of Invention
The invention aims to solve the technical problem of providing a logistics site selection method of ship spare parts based on AIS and GIS aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a logistics site selection method of ship spare parts based on AIS and GIS, which comprises the following steps:
step 1, AIS and GIS service data are obtained, ship distribution density, ship types and quantity and ship real-time traffic flow are determined, and the data are transmitted to a background;
step 3, performing demand prediction analysis according to the distribution density of ships and the types and the number of the ships, performing regional division according to demand thresholds by combining an autonomous maintenance resource model of the ships, determining resource demands at different distances in the regions, and determining candidate regions for warehousing and site selection according to AIS and GIS service data by combining a logistics transportation mode;
step 4, determining a warehouse logistics service radius, and determining an optimal delivery warehouse address according to delivery timeliness within the service radius; setting delivery requirements, including a delivery distance and time model, and selecting an optimal address by combining a candidate area of a warehousing site selection according to the delivery requirements;
and 5, evaluating the selected optimal address according to the selected optimal address, the distribution distance and the traffic convenience.
Further, the timeliness strategy for delivery within the service radius in step 4 of the present invention is:
the address of the storage logistics center is selected, so that the speed of delivering required spare parts and materials is the fastest, and the number of ships covered by the service radius is maximized.
Further, the method for determining the warehouse logistics service radius in the step 4 of the invention comprises the following steps:
acquiring the demand information of spare parts of a ship;
acquiring road network data, and determining a maximum distributable range in a preset time in an address selection candidate area;
grading the service radius;
establishing a logistics service radiation cloud picture;
determining a distribution demand according to the service radius of the storage center, wherein the distribution demand specifically comprises the following steps: delivery time, delivery mode, and traffic convenience.
Further, the method for selecting the optimal address in step 4 of the present invention comprises:
selecting an optimal address based on the minimum distance of the ship demand, wherein an algorithm target function Z is as follows:
in the formula: w is aiThe demand at the ith point; x is the number ofi，yiCoordinates of the ith demand point; x is the number ofs，ysCoordinates of the pre-selected storage nodes; n is the number of demand points.
Further, the method for establishing the candidate area for warehousing site selection in step 3 of the present invention comprises:
and (3) determining a candidate region of the warehousing node according to the distribution of the ship demands, wherein the corresponding objective function is expressed as:
the end conditions are as follows:
xj∈{0，1}，j∈M
yij≥0，i∈N，j∈M
in the formula: n is the N demand points in the study, N ═ (1, 2, 3 …, N); m is M candidate nodes in the study, where M is (1, 2, …, M); diThe demand of the ith node; cjA spare part level that can be provided for the preselected node; a (j) is a set of demand points that the preselected node can cover; b (i) is b (i) { j | i ∈ a (j) | }, representing a preselected set of nodes covering the demand point; x is the number ofi，yiCoordinates of the ith demand point;
yijthe portion of the demand for node i that is assigned to node j.
The invention provides a logistics site selection system of ship spare parts based on AIS and GIS, which comprises: the system comprises an AIS service module, a GIS space data storage module, a demand prediction module, an addressing module and a resource allocation module; wherein:
the AIS service module is a carrier of a data source and a model, is responsible for collecting, processing and displaying information and calculating ship distribution density;
the GIS space data storage module is used for storing the service data of the AIS service module and communicating the service data with the background data established by the model, so that the visualization of a map and the display of a related ship thermodynamic diagram and a ship traffic flow diagram are realized;
the demand forecasting module is used for determining demand forecasting of the ship autonomous maintenance resources and determining types and actual demands of different types of autonomous maintenance resources;
the location module is used for establishing a location candidate area according to the AIS service data, determining a logistics service radius, determining an optimal distribution warehousing address according to distribution timeliness in the service radius, setting a distribution distance and time model, and selecting the optimal address according to distribution requirements;
and the resource distribution module is used for carrying out data analysis on the distribution demand in the logistics service radius according to the finally selected storage address, and selecting a distribution path and a method with a proper address to minimize the logistics transportation cost.
The invention has the following beneficial effects: according to the logistics site selection method of the ship spare parts based on the AIS and the GIS, the AIS and the GIS are combined with ship navigation big data and ship spare part demand data, the establishment of ship logistics industry storage site selection is facilitated, the existing logistics site selection model is optimized, the storage radiation radius is improved, the cost and the transportation time are reduced, the demand expense of the ship industry on the spare parts is saved, and the development requirement of the current ship industry for autonomous equipment maintenance is met. Meanwhile, the site selection method is not only suitable for the logistics site selection in the ship field, but also provides decision basis for the conventional logistics transportation system.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow diagram of an embodiment of the method of the present invention for site selection of a stream;
FIG. 2 is a hierarchy of modules that the present invention encompasses;
FIG. 3 is an AIS service vessel profile;
FIG. 4 is an AIS service vessel profile;
FIG. 5 is a preselected region profile;
FIG. 6 is a schematic illustration of service data;
fig. 7 is a service data diagram.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1 and fig. 2, the logistics locating system for ship spare parts in AIS and GIS according to the embodiment of the present invention includes five major components, an AIS service module, a GIS space data storage module, a demand prediction module, a locating module, and a resource allocation module. Wherein:
the AIS service module is used as a data source and a carrier of the model, is responsible for collecting, processing and displaying information, and calculating the distribution density of the ship, and is the basis of the whole site selection model, and the distribution diagram of the ship is shown in figures 3 and 4; the GIS space data storage module stores the service data of the AIS system and is communicated with the background data established by the model, so that the visualization of a map and the display of a related ship thermodynamic diagram and a ship traffic flow diagram can be facilitated; the demand forecasting module is used for determining demand forecasting of the ship autonomous maintenance resources and determining types and actual demands of different types of autonomous maintenance resources; the location module is used for establishing a location candidate area according to AIS service data, determining a logistics service radius, determining an optimal distribution warehousing address according to distribution timeliness in the service radius, setting a distribution distance and time model, and selecting an optimal address according to distribution requirements; and the resource distribution module is used for carrying out data analysis on the distribution demand in the logistics service radius according to the finally selected warehousing address, and selecting a distribution path and a method with a proper address, so that the logistics transportation cost is saved, and the resource utilization rate is improved.
The logistics site selection method for the spare parts of the ships in the AIS and the GIS comprises the following steps:
step 1, extracting AIS and GIS service data, determining ship distribution density, ship type and quantity, ship real-time traffic flow, and transmitting data to background data storage module
And 2, summarizing, storing and analyzing the acquired AIS and GIS service data, cleaning the data, acquiring real-time shipping data to obtain the geographic spatial position of the ship, and dividing the area to obtain the distribution condition of the ship in the area, as shown in fig. 6 and 7.
And 3, importing a demand prediction module according to the distribution density of the ships and the types and the number of the ships, performing region division according to a demand threshold value by combining an autonomous maintenance resource model of the ships, determining resource demands at different distances in a region, and determining a location candidate region according to AIS and GIS service data by combining a specific logistics transportation mode, as shown in FIG. 5.
And 4, determining a logistics service radius, wherein in the service radius, according to the delivery timeliness, for example, where to select a logistics storage center, the required spare part materials can be delivered most quickly, and the number of ships which can be covered by the service radius can be maximized. And determining an optimal delivery storage address, setting a delivery distance and time model, and selecting the optimal address according to delivery requirements.
And 5, evaluating the selected warehousing address according to the selected optimal warehousing address by the delivery distance and the traffic convenience, and comprehensively considering the factors to determine a final optimal addressing scheme.
The method for determining the warehouse logistics service radius in the step 4 comprises the following steps:
acquiring the demand information of spare parts of a ship;
acquiring road network data, and determining a maximum distributable range in a preset time in an address selection candidate area;
grading the service radius;
establishing a logistics service radiation cloud picture;
determining a distribution demand according to the service radius of the storage center, wherein the distribution demand specifically comprises the following steps: delivery time, delivery mode, and traffic convenience.
The method for selecting the optimal address in the step 4 comprises the following steps:
selecting an optimal address based on the minimum distance of the ship demand, wherein an algorithm target function Z is as follows:
in the formula: w is aiDemand at point i; x is the number ofi，yiCoordinates of the ith demand point; x is the number ofs，ysCoordinates of the pre-selected storage nodes; n is the number of demand points.
The method for establishing the candidate area of the warehousing site selection in the step 3 comprises the following steps:
and (3) determining a candidate region of the warehousing node according to the distribution of the ship demands, wherein the corresponding objective function is expressed as:
the end conditions are as follows:
xj∈{0，1}，j∈M
yij≥0，i∈N，j∈M
in the formula: n is the N demand points in the study, N ═ (1, 2, 3 …, N); m is M candidate nodes in the study, where M is (1, 2, …, M); diThe demand of the ith node; cjA spare part level that can be provided for the preselected node; a (j) is a set of demand points that can be covered by the preselected node; b (i) is b (i) { j | i ∈ a (j) | }, representing a preselected set of nodes covering the demand point; x is the number ofi，yiCoordinates of the ith demand point;
yijthe portion of the demand for node i that is assigned to node j.
The invention is based on an AIS and a GIS system, combines ship navigation big data and ship spare part requirement data, helps to establish ship logistics industry storage site selection, optimizes the existing logistics site selection model, improves storage radiation radius, reduces cost and transportation time, saves the requirement expense of the ship industry on spare parts, and meets the development requirement of the current ship industry on autonomous equipment maintenance.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (5)
1. A logistics site selection method for ship spare parts based on AIS and GIS is characterized by comprising the following steps:
step 1, AIS and GIS service data are obtained, ship distribution density, ship types and quantity and ship real-time traffic flow are determined, and the data are transmitted to a background;
step 2, summarizing, storing and analyzing the acquired AIS and GIS service data, cleaning the data, acquiring real-time shipping data to obtain a geographical position of the ship, and dividing areas corresponding to the geographical position of the ship to obtain the distribution condition of the ship in each area;
step 3, performing demand prediction analysis according to the distribution density of ships and the types and the number of the ships, performing regional division according to demand thresholds by combining an autonomous maintenance resource model of the ships, determining resource demands at different distances in the regions, and determining candidate regions for warehousing and site selection according to AIS and GIS service data by combining a logistics transportation mode;
step 4, determining a warehouse logistics service radius, and determining an optimal delivery warehouse address according to delivery timeliness within the service radius; setting delivery requirements, including a delivery distance and time model, and selecting an optimal address by combining a candidate area of a warehousing site selection according to the delivery requirements;
step 5, evaluating the selected optimal address according to the selected optimal address, the distribution distance and the traffic convenience;
the method for establishing the candidate area of the warehousing site selection in the step 3 comprises the following steps:
and (3) determining a candidate region of the warehousing node according to the distribution of the ship demands, wherein the corresponding objective function is expressed as:
the constraint conditions are as follows:
xj∈{0，1}，j∈M
yij≥0，i∈N，j∈M
in the formula: n is the N demand points in the study, N ═ (1, 2, 3 …, N); m is M candidate nodes in the study, where M is (1, 2, …, M); diThe demand of the ith node; cjA spare part level that can be provided for the preselected node; a (j) is a set of demand points that can be covered by the preselected node; b (i) is b (i) { j | i ∈ a (j) | }, representing a preselected set of nodes covering the demand point; x is a radical of a fluorine atomi,yiCoordinates of the ith demand point;
yijthe portion of the demand for node i that is assigned to node j.
2. The AIS and GIS-based ship spare part logistics locating method according to claim 1, wherein the timeliness strategy of distribution within the service radius in step 4 is as follows:
the address of the storage logistics center is selected, so that the speed of delivering required spare parts and materials is the fastest, and the number of ships covered by the service radius is maximized.
3. The AIS and GIS-based ship spare part logistics site selection method according to claim 1, wherein the method for determining the warehouse logistics service radius in the step 4 comprises the following steps:
acquiring the demand information of spare parts of a ship;
acquiring road network data, and determining a maximum distributable range in a preset time in an address selection candidate area;
grading the service radius;
establishing a logistics service radiation cloud picture;
determining a distribution demand according to the service radius of the storage center, wherein the distribution demand specifically comprises the following steps: delivery time, delivery mode, and traffic convenience.
4. The AIS and GIS-based ship spare part logistics site selection method according to claim 1, wherein the method for selecting the optimal address in the step 4 comprises the following steps:
selecting an optimal address based on the minimum distance of the ship demand, wherein an algorithm target function Z is as follows:
in the formula: w is aiDemand at point i; x is a radical of a fluorine atomi,yiCoordinates of the ith demand point; x is the number ofs,ysCoordinates of the pre-selected storage nodes; n is the number of demand points.
5. The AIS and GIS-based ship spare part logistics addressing method according to claim 1, wherein the method is implemented by a AIS and GIS-based ship spare part logistics addressing system, which comprises: the system comprises an AIS service module, a GIS spatial data storage module, a demand prediction module, an addressing module and a resource allocation module; wherein:
the AIS service module is a carrier of a data source and a model, is responsible for collecting, processing and displaying information and calculating ship distribution density;
the GIS space data storage module is used for storing the service data of the AIS service module and communicating the service data with the background data established by the model, so that the visualization of a map and the display of a related ship thermodynamic diagram and a ship traffic flow diagram are realized;
the demand forecasting module is used for determining demand forecasting of the ship autonomous maintenance resources and determining types and actual demands of different types of autonomous maintenance resources;
the location module is used for establishing a location candidate area according to the AIS service data, determining a logistics service radius, determining an optimal distribution warehousing address according to distribution timeliness in the service radius, setting a distribution distance and time model, and selecting the optimal address according to distribution requirements;
and the resource distribution module is used for carrying out data analysis on the distribution demand in the logistics service radius according to the finally selected warehousing address, and selecting a distribution path and a method with a proper address to minimize the logistics transportation cost.
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| CN111598359B (en) * | 2020-06-04 | 2023-11-21 | 上海燕汐软件信息科技有限公司 | Logistics station site selection method and system |
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| CN112819409B (en) * | 2021-02-02 | 2022-11-15 | 河北工业职业技术学院 | Maintenance equipment delivery system based on cloud logistics |
| CN112766862A (en) * | 2021-02-04 | 2021-05-07 | 浙江科技学院 | Method for dividing and locating distribution area of petrochemical product supply chain |
| CN113313315B (en) * | 2021-06-11 | 2024-05-28 | 北京中农大农业规划设计院有限公司 | GIS-based agricultural product origin wholesale market planning method and system |
| CN113642799B (en) * | 2021-08-20 | 2024-06-11 | 国网内蒙古东部电力有限公司电力科学研究院 | Method and device for determining straw storage point position, electronic equipment and storage medium |
| CN114219168A (en) * | 2021-12-22 | 2022-03-22 | 中冶赛迪工程技术股份有限公司 | Logistics park automatic site selection system and method based on multiple models and big data |
| CN115719196B (en) * | 2022-11-28 | 2023-12-29 | 山东荣庆物流科技有限公司 | Logistics warehouse site selection analysis and judgment method based on big data |
| CN115809297B (en) * | 2023-01-18 | 2023-05-19 | 亿海蓝（北京）数据技术股份公司 | Method and device for visualizing traffic flow on map and map |
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