CN108109477B

CN108109477B - Logistics planning teaching system with path planning and intelligent address selection functions

Info

Publication number: CN108109477B
Application number: CN201711365388.5A
Authority: CN
Inventors: 赵磊; 王阳; 黄一挥
Original assignee: Tsinghua University
Current assignee: Tsinghua University
Priority date: 2017-12-18
Filing date: 2017-12-18
Publication date: 2020-01-17
Anticipated expiration: 2037-12-18
Also published as: CN108109477A

Abstract

The invention discloses a logistics planning teaching system of path planning and intelligent site selection, which supports a user to automatically plan a path and a system to automatically plan a path and supports the user to automatically select a site and the system to automatically select a site, compares different models of an accurate algorithm of an automatically planned path of the system and different models of the accurate algorithm of the automatically planned path of the system by comparing the automatically planned path of the user with the automatically planned path of the system and comparing the automatically selected site of the user with the automatically selected site of the system, compares the accurate algorithm of the automatically planned path of the system with an approximate algorithm and compares the accurate algorithm of the automatically selected site of the system with the approximate algorithm, compares the results of problem accurate algorithms of different objective functions with the same input information, can help students fully know the problem complexity which is rapidly increased along with the increase of the scale of the problem, and fully know the huge benefits brought by the model and the algorithm adopted by the system, the significant influence of different objective functions on the optimization result is fully understood.

Description

Logistics planning teaching system with path planning and intelligent address selection functions

Technical Field

The invention relates to the technical field of logistics, in particular to a logistics planning teaching system for path planning and intelligent site selection.

Background

The path planning and the intelligent site selection are important practices in the logistics field, are widely applied to the actual operation of the last kilometer of the electric enterprise industry, the site selection of factory warehouses and the like, have non-negligible influence on the aspects of reducing the cost of the enterprise, improving the efficiency, improving the customer satisfaction degree and the like, and are always valued by the industry. However, in combination with the variable reality, the path planning and intelligent addressing problem has high mathematical complexity, and the optimal solution of the system cannot be achieved by only experience intuition. Therefore, how to let more people in the field of logistics realize the limitations of experience intuition and fully understand a series of processes of path planning and modeling and solving of an intelligent address selection problem becomes a very important practical problem.

The teaching process represented by the traditional display, blackboard writing, dictation and the like has limitation on the teaching effect. For the students, the traditional teaching mode has no active participation of the students, the students can only passively accept related concepts, models and algorithms of path planning and intelligent address selection, all knowledge is difficult to master in limited time, the variety problem difference can be lack of full understanding, the problem scale can not be intuitively understood about the influence of the problem complexity, the difference between experience and intuition and the system optimization can not be quantitatively understood, the influence of different models and different algorithms on the solution result can not be quantitatively understood, and the difference between the solution results caused by different optimization targets can not be quantitatively understood. For teachers, the traditional teaching mode is adopted, and teachers cannot know knowledge mastered by students and cannot visually, accurately and quantitatively show differences among different problem varieties, different problem scales, different mathematical models, different algorithms and different optimization targets in path planning and intelligent addressing problems. How to solve the limitations of the traditional teaching mode of path planning and intelligent address selection becomes a technical problem to be solved urgently.

Disclosure of Invention

The object of the present invention is to solve at least to some extent one of the above mentioned technical problems.

Therefore, the first objective of the present invention is to provide a logistics planning teaching system for path planning and intelligent site selection, which supports the user to automatically plan the path and the system to automatically plan the path and supports the user to automatically select the site and the system to automatically select the site, by comparing the user's automatically planned path with the system's automatically planned path and comparing the user's automatically planned path with the system's automatically planned path's automatically planned automatically accurate path's accurately model with the system's automatically selected site's accurately model, comparing the system's automatically planned path's accurately algorithm with the approximate algorithm and comparing the system's automatically selected site's accurately algorithm with the approximate algorithm, comparing the problem accurate algorithm results of different objective functions with the same input information, and can help students fully understand the problem complexity which increases rapidly with the increase of the problem scale, the huge benefits brought by the model and algorithm adopted by the system are fully known, and the important influence of different objective functions on the optimization result is fully known. Meanwhile, the method can generate visual feeling on the relation between the factors such as problem scale, algorithm selection and the like and the solution running time. Meanwhile, the system requires the students to keep participation in class, is beneficial to teachers to know the class state of the students in time, and can give lessons with more pertinence.

In order to achieve the above object, a logistics planning teaching system for path planning and intelligent address selection according to an embodiment of the first aspect of the present invention includes: a path planning teaching platform and an intelligent address selection teaching platform.

The path planning teaching platform is used for receiving path planning information imported by a user, performing user autonomous path planning and system automatic path planning according to the path planning information, comparing a planning result obtained by the user autonomous path planning with a planning result obtained by the system automatic path planning, and respectively displaying the path planning information, the planning result obtained by the user autonomous path planning, the planning result obtained by the system automatic path planning and a path planning comparison result;

the intelligent site selection teaching platform is used for receiving site selection information imported by a user, performing user autonomous site selection and system automatic site selection according to the site selection information, comparing a site selection result obtained by the user autonomous site selection with a site selection result obtained by the system automatic site selection, and respectively displaying the site selection information, the site selection result obtained by the user autonomous site selection, the site selection result obtained by the system automatic site selection and the site selection comparison result.

The system comprises a game setting module, a first user autonomous path planning module, a first system automatic path planning module, a first path display module and a first information description module;

the first game setting module is used for receiving a data generation mode selected by a user and importing first path planning information according to the data generation mode, wherein the first path planning information comprises map data, customer demand data and fleet data;

the first user autonomous path planning module is used for receiving the first path planning information of the first game setting module, receiving a path autonomously planned by a user according to the site position information, the client position information and the road information in the map data measurement, measuring the distance between each site according to the site position information in the map data measurement, and generating a user autonomous path planning result according to the autonomously planned path and the distance between each site;

the first system automatic path planning module is used for receiving the first path planning information of the first game setting module, automatically planning a path according to the first path planning information and a system solving mode selected by a user, and generating a system automatic path planning result;

the first path display module is used for respectively receiving the first path planning information of the first game setting module, the user autonomous path planning result of the first user autonomous path planning module and the system automatic path planning result of the first system automatic path planning module, and respectively displaying the first path planning information, the user autonomous path planning result and the system automatic path planning result;

the first information description module is used for receiving the user autonomous path planning result of the first user autonomous path planning module and displaying the user autonomous path planning result; and comparing the user autonomous path planning result of the first user autonomous path planning module with the system automatic path planning result of the first system automatic path planning module to obtain and display a path planning comparison result, wherein the path planning comparison result comprises a comparison result of the path length.

The system as described above, the user-selected system solution includes an approximate solution and an exact solution;

when the system solution selected by the user is an approximate solution, the first system automatic path planning module is specifically configured to receive the first path planning information of the first game setting module, automatically plan a path according to the first path planning information and a first preset algorithm, and generate a first system automatic path planning result;

and when the system solving mode selected by the user is the accurate solving mode, the first system automatic path planning module is specifically used for receiving the first path planning information of the first game setting module, automatically planning a path according to the first path planning information and a first preset model, and generating a second system automatic path planning result.

The system as described above, the first path display module is further configured to receive a first system automatic path planning result and a second system automatic path planning result output by the first system automatic path planning module, and respectively display the first system automatic path planning result and the second system automatic path planning result;

the first information description module is further configured to compare the first system automatic planning path result with the second system automatic planning path result, obtain and display a comparison result of the system automatic planning path, where the comparison result of the system automatic planning path includes a comparison result of time spent by the system automatic planning path and a comparison result of path lengths corresponding to different system solution modes.

The system comprises a path planning teaching platform and a path planning teaching platform, wherein the path planning teaching platform comprises a variant problem expansion system, and the variant problem expansion system comprises a second game setting module, a second user autonomous path planning module, a second system automatic path planning module, a second path display module and a second information description module;

the second game setting module is used for receiving a data generation mode selected by a user and importing second path planning information according to the data generation mode, wherein the second path planning information comprises map data, customer demand data and fleet data;

the second user autonomous path planning module is used for receiving the second path planning information of the second game setting module, receiving a path autonomously planned by a user according to the site position information, the client position information and the road information in the map data measurement, measuring the distance between each site according to the site position information in the map data measurement, and generating a user autonomous path planning result according to the autonomously planned path and the distance between each site;

the second system automatic path planning module is used for receiving the second path planning information of the second game setting module, automatically planning a path according to the second path planning information and a system solving mode selected by a user, and generating a system automatic path planning result;

the second path display module is configured to receive and display the second path planning information of the second game setting module, the user autonomous path planning result of the second user autonomous path planning module, and the system autonomous path planning result of the second system autonomous path planning module, respectively;

the second information description module is used for receiving the user autonomous planned path result of the second user autonomous path planning module and displaying the current vehicle, the remaining capacity, the running distance and the current time in the user autonomous planned path result; and comparing the user autonomous path planning result of the second user autonomous path planning module with the system automatic path planning result of the second system automatic path planning module to obtain and display a path planning comparison result, wherein the path planning comparison result comprises a comparison result of a variety problem optimization target value.

when the system solution selected by the user is an approximate solution, the second system automatic path planning module is specifically configured to receive the second path planning information of the second game setting module, automatically plan a path according to the second path planning information and a second preset algorithm, and generate a third system automatic path planning result;

and when the system solving mode selected by the user is the accurate solving mode, the second system automatic path planning module is specifically used for receiving the second path planning information of the second game setting module, automatically planning a path according to the second path planning information and a second preset model, and generating a fourth system automatic path planning result.

The system as described above, the second path display module is further configured to receive a third system automatic path planning result and a fourth system automatic path planning result output by the second system automatic path planning module, and display the third system automatic path planning result and the fourth system automatic path planning result respectively;

the second information description module is further configured to compare the third system automatic planning path result with the fourth system automatic planning path result, obtain and display a comparison result of the system automatic planning path, where the comparison result of the system automatic planning path includes a comparison result of time spent in different system solution modes and a comparison result of a corresponding variant problem optimization target value.

The system as described above, the path planning teaching platform includes a variation problem comparison system, and the variation problem comparison system includes a third game setting module, a third system automatic path planning module, a third path display module, and a third information description module;

the third game setting module is used for receiving a data generation mode selected by a user and importing third path planning information according to the data generation mode, wherein the third path planning information comprises map data, customer demand data and fleet data;

the third system automatic path planning module is used for receiving the third path planning information of the third game setting module, respectively automatically planning paths according to the third path planning information and path planning algorithms of different optimization targets, and generating system automatic path planning results of the different optimization targets;

the third path display module is configured to receive the third path planning information of the third game setting module and the system automatic planning path result of different optimization targets of the third system automatic path planning module, and perform visual display respectively;

the third information description module is configured to receive system automatic path planning results of different optimization targets of the third system automatic path planning module, and compare and display optimization target values corresponding to system automatic planning paths of different optimization targets of the third system automatic path planning module.

The system comprises the intelligent site selection teaching platform and a site selection problem expansion system, wherein the site selection problem expansion system comprises a fourth game setting module, a first user autonomous site selection module, a first system automatic site selection module, a first site selection display module and a fourth information description module;

the fourth game setting module is used for receiving a data generation mode selected by a user and importing first addressing information according to the data generation mode, wherein the first addressing information comprises map data, customer demand data and warehouse data;

the first user independent address selection module is used for receiving the first address selection information of the fourth game setting module, receiving a warehouse selected by a user according to the first address selection information, and generating a user independent address selection result according to the warehouse selected by the user;

the first system automatic address selection module is used for receiving the first address selection information of the fourth game setting module, automatically selecting a warehouse according to the first address selection information and a system solving mode selected by a user, and generating a system automatic address selection result according to the warehouse selected by the system;

the first warehouse display module is used for respectively receiving the first address selection information of a fourth game setting module, the user autonomous address selection result of the first user autonomous address selection module and the system automatic address selection result of the first system automatic address selection module, and respectively displaying;

the fourth information description module is used for receiving the user autonomous addressing result of the first user autonomous addressing module and the system automatic addressing result of the first system automatic addressing module, comparing the user autonomous addressing result with the system automatic addressing result, and acquiring and displaying a comparison result.

when the system solving mode selected by the user is an approximate solving mode, the first system automatic address selecting module is specifically used for receiving the first address selecting information of the fourth game setting module, automatically selecting a warehouse according to the first address selecting information and a third preset algorithm, and generating a first system automatic address selecting result according to the warehouse selected by the system;

and when the system solving mode selected by the user is the accurate solving mode, the first system automatic addressing module is specifically used for receiving the first addressing information of the fourth game setting module, automatically selecting a warehouse according to the first addressing information and a third preset model, and generating a second system automatic addressing result according to the warehouse selected by the system.

The system as described above, the first warehouse display module is further configured to receive a first system automatic addressing result and a second system automatic addressing result output by the first system automatic addressing module, and respectively display the first system automatic addressing result and the second system automatic addressing result;

the fourth information description module is further configured to compare the first system automatic address selection result with the second system automatic address selection result, obtain a comparison result of the system automatic address selection result, and display the comparison result.

The system comprises the intelligent site selection teaching platform and a site selection problem expansion system, wherein the site selection problem expansion system comprises a fifth game setting module, a second system automatic site selection module, a second warehouse display module and a fifth information description module;

the fifth game setting module is used for receiving a data generation mode selected by a user and importing second addressing information according to the data generation mode, wherein the second addressing information comprises map data, customer demand data and fleet data;

the second system automatic site selection module is used for receiving the second site selection information of the fifth game setting module and respectively and automatically selecting the warehouse according to the second site selection information and site selection models of different optimization targets; respectively generating system automatic site selection results of different optimization targets according to the warehouse data of the warehouse corresponding to the different optimization targets and the client position information in the second site selection information;

the second warehouse display module is used for respectively receiving the second site selection information of a fifth game setting module and system automatic site selection results of different optimization targets of the second system automatic site selection module, and respectively displaying the second site selection information and the system automatic site selection results;

and the fifth information description module is used for receiving the system automatic addressing results of different optimization targets of the second system automatic addressing module, and comparing and displaying the optimization target values corresponding to the system automatic addressing results of different optimization targets of the second system automatic addressing module.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which,

fig. 1 is a block diagram of a logistics planning teaching system for path planning and intelligent address selection according to an embodiment of the present invention;

fig. 2 is a block diagram of a logistics planning teaching system for path planning and intelligent address selection according to another embodiment of the present invention;

FIG. 3 is a schematic of an exemplary conventional vehicle routing problem system;

FIG. 4 is a schematic illustration of an exemplary variant problem propagation system;

FIG. 5 is a schematic of an exemplary variant problem comparison system;

FIG. 6 is a schematic illustration of an exemplary addressing problem propagation system;

FIG. 7 is a schematic diagram of an exemplary addressing problem comparison system.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

The following describes a logistics planning teaching system for path planning and intelligent address selection according to an embodiment of the invention with reference to the accompanying drawings.

Fig. 1 is a block diagram of a logistics planning teaching system for path planning and intelligent address selection according to an embodiment of the present invention. As shown in fig. 1, the logistics planning teaching system for path planning and intelligent address selection provided in this embodiment may include: a path planning teaching platform and an intelligent address selection teaching platform.

The path planning teaching platform is used for receiving path planning information imported by a user, performing user autonomous path planning and system automatic path planning according to the path planning information, comparing a planning result obtained by the user autonomous path planning with a planning result obtained by the system automatic path planning, and respectively displaying the path planning information, the planning result obtained by the user autonomous path planning, the planning result obtained by the system automatic path planning and a path planning comparison result.

In this embodiment, the path planning teaching platform can support teaching of various vehicle path variation problems, support a user to autonomously plan a path and automatically plan a path by a system, and is helpful for students to intuitively experience decision flow and decision effect of a vehicle path problem in a classroom. Meanwhile, the students can compare the vehicle path problems of different types conveniently.

In the embodiment, the intelligent site selection teaching platform supports teaching of various intelligent site selection variant problems, supports independent site selection of a user and automatic site selection of a system, and can help students understand decision flow and decision effect of intelligent site selection. Especially for students with poor bases, the teaching method can lead the students to experience basic problem abstract modes of intelligent address selection and factors mainly concerned to influence decision making more easily.

The logistics planning teaching system for path planning and intelligent site selection provided by the embodiment supports a user to automatically plan a path and a system to automatically plan a path and supports the user to automatically select a site and the system to automatically select a site, and by comparing the user automatically planned path with the system automatically planned path and comparing the user automatically selected site with the system automatically selected site, students can be helped to understand huge benefits brought by an algorithm adopted by the system, the advantages of algorithm solution are realized, and meanwhile, the relationship between problem scale and algorithm operation time can be visually experienced. Meanwhile, the system requires the students to keep participation in class, is beneficial to teachers to know the class state of the students in time, and can give lessons with more pertinence.

Fig. 2 is a block diagram of a logistics planning teaching system for path planning and intelligent address selection according to an embodiment of the present invention. As shown in fig. 2, the logistics planning teaching system for path planning and intelligent address selection provided in this embodiment may include: a path planning teaching platform and an intelligent address selection teaching platform.

The path planning teaching platform may include at least one of a conventional vehicle path problem system, a variant problem expanding system, and a variant problem comparing system, but is not limited thereto.

Specifically, the conventional vehicle path problem system comprises a first game setting module, a first user autonomous path planning module, a first system automatic path planning module, a first path display module and a first information description module.

Further, the system solution selected by the user comprises an approximate solution and an accurate solution;

Further, the first path display module is further configured to receive a first system automatic path planning result and a second system automatic path planning result output by the first system automatic path planning module, and respectively display the first system automatic path planning result and the second system automatic path planning result;

How to teach using the conventional vehicle path problem system is explained below by specific examples. FIG. 3 is a schematic of an exemplary conventional vehicle routing problem system.

After the user logs in the conventional vehicle path problem teaching system, first, the user performs a game setting step. The game setting link is mainly used for generating a data source required by a planned path, and the data generation mode can be various and can accept the selection of a user, such as random generation, real-time self-making (real-time clicking of a right visual window to generate data), actual import (for example, importing a map in Beijing City to perform on-site verification), and the like. First path planning information such as map data (site position information, customer position information, road information and the like), customer demand data, fleet data (vehicle quantity, vehicle capacity and the like) is generated according to a data generation mode selected by a user, and the generated first path planning information can be displayed on a visual interface in real time for the user to view.

Secondly, entering a user autonomous path planning step. A user can view first path planning information on a visual interface, interactive operations such as distance measurement, path planning, single step cancellation, path resetting, path hiding and the like are performed on the visual interface according to the viewed first path planning information, and if a manual operation path is not feasible due to vehicle capacity limitation, the path cannot be clicked and generated. The user-autonomous planned path result obtained by the first user-autonomous path planning module at least includes a path autonomously planned by the user, distances between stations on the path, and the like.

And thirdly, entering a step of automatically planning the path by the system. The system automatically plans the path, which is automatically completed by the system, and particularly, the traditional vehicle path problem teaching system can automatically call a corresponding algorithm or model and automatically plan the path according to the first path planning information and the corresponding algorithm or model.

In this embodiment, the system may also select different algorithms or models to automatically plan the path. For example, the system provides system solutions such as exact solution and approximate solution for the user to select. The exact solution may be understood as automatically planning a path by using preset models, for example, for the exact solution, the first preset model selected by the system includes a double subscript vehicle flow model and a double subscript double cargo flow model, but is not limited thereto, and when one of the models is selected, the selected model is solved to plan the path. The approximate solution manner may be understood as automatically planning a path by using algorithms, for example, for the approximate solution manner, the first preset algorithm selectable by the system includes heuristic algorithms such as a saving algorithm, a scanning algorithm and the like, and meta-heuristic algorithms such as an adaptive large-scale domain search algorithm and the like, but is not limited thereto, and when one of the algorithms is selected, the path is planned by using the selected algorithm to operate on planning information of the first path.

And entering a path display link. In the link, a visual interface is provided, first path planning information such as map data, customer demand data and fleet data, a path planned by a user independently and a path planned automatically by a system are displayed, and interactive operation such as path hiding/displaying can be carried out at any time. It should be noted that the path planned autonomously by the user and the path planned automatically by the system are displayed in different colors on the visualization interface. For example, the color of the path planned autonomously by the user is blue, and the color of the path planned automatically by the system is green.

And finally, entering an information description link. In the link, a visual interface is provided to show the current situation of the user self-planned path (such as the current vehicle, the residual capacity, the driving distance and the current time of the user self-planned path) so as to remind the learning user of reasonably utilizing the current resource; and displaying the comparison of the user autonomous planned path result and the system automatic planned path result and the comparison of the system automatic planned paths in different solving modes so as to help the learning user to reasonably evaluate the quality of the current path. For example, the path length of the user-autonomous planned path and the path length of the system-automatic planned path may be compared, the solution time taken by the system-automatic planned path obtained by the exact solution and the solution time taken by the system-automatic planned path obtained by the approximate solution may be compared, and the path length of the system-automatic planned path obtained by the exact solution and the path length of the system-automatic planned path obtained by the approximate solution may be compared, but the present invention is not limited thereto.

The traditional vehicle path problem system can help a user to know the mastering degree of the user on path planning, know the problem complexity which is rapidly increased along with the increase of the problem scale, know the huge benefits brought by the model and the algorithm adopted by the system, know the important influence of different objective functions on the optimization result, and simultaneously can generate visual feeling on the relationship between the problem scale, the algorithm selection and other factors and the solution operation time. In addition, after the system automatically plans the path, the user can still adjust the self-automatically planned path to compare and analyze the self-automatically planned path with the automatically planned path of the system more deeply and carefully.

The variety problem expanding system comprises a second game setting module, a second user autonomous path planning module, a second system automatic path planning module, a second path display module and a second information description module.

Further, the second path display module is further configured to receive a third system automatic path planning result and a fourth system automatic path planning result output by the second system automatic path planning module, and respectively display the third system automatic path planning result and the fourth system automatic path planning result;

The following describes how to teach using the variant problem propagation system by way of specific examples. The embodiment specifically takes a variation problem development system of a time window vehicle path problem teaching system as an example. FIG. 4 is a schematic diagram of an exemplary variant problem propagation system.

After the user logs in the variant problem expansion system, first, the user proceeds with the game setting step. The game setting link is mainly used for generating a data source required by a planned path, and the data generation mode can be various and can accept the selection of a user, such as random generation, real-time self-making (real-time clicking of a right visual window to generate data), actual import (for example, importing a map in Beijing City to perform on-site verification), and the like. And generating second path planning information such as map data (site position information, customer position information, road information and the like), customer demand data, fleet data (vehicle quantity, vehicle capacity and the like) and the like according to the data generation mode selected by the user, wherein the generated second path planning information can be displayed on a visual interface in real time for the user to view.

Secondly, entering a user autonomous path planning step. The user can check the second path planning information on a visual interface, interactive operations such as distance measurement, path planning, single step cancellation, path resetting, path hiding and the like are performed on the visual interface according to the checked second path planning information, and if a manual operation path is not feasible due to vehicle capacity limitation, the path cannot be clicked and generated. The user-autonomous planned path result obtained by the second user-autonomous path planning module at least includes a path autonomously planned by the user, distances between stations on the path, and the like.

And thirdly, entering a step of automatically planning the path by the system. The system automatically plans the path by the system, specifically, the variant problem expanding system automatically calls a corresponding algorithm or model, and automatically plans the path according to the second path planning information and the corresponding algorithm or model.

In this embodiment, the system may also select different algorithms or models to automatically plan the path. For example, the system provides system solutions such as exact solution and approximate solution for the user to select. The exact solution may be understood as automatic planning of the path by using a selection model, for example, for the exact solution, the second preset model selectable by the system includes basic models such as a three-index vehicle flow model, a set partitioning model and the like, but is not limited thereto, and when one of the models is selected, the path is planned by using the selected model. The approximate solution manner may be understood as automatically planning a path by using a selected algorithm, for example, for the approximate solution manner, the second preset algorithm selectable by the system includes heuristics such as an insert algorithm, meta-heuristics such as an adaptive large-scale domain search algorithm, but is not limited thereto, and when one of the algorithms is selected, the path is planned by using the selected algorithm to operate on planning information of the second path.

And entering a path display link. In the link, a visual interface is provided, second path planning information such as map data, customer demand data and fleet data, a path planned by a user independently and a path planned automatically by a system are displayed, and interactive operation such as path hiding/displaying can be carried out at any time. It should be noted that the path planned autonomously by the user and the path planned automatically by the system are displayed in different colors on the visualization interface.

And finally, entering an information description link. In the link, a visual interface is provided to show the current situation of the user self-planned path (such as the current vehicle, the residual capacity, the driving distance and the current time of the user self-planned path) so as to remind the learning user of reasonably utilizing the current resource; and displaying the comparison of the user autonomous planned path result and the system automatic planned path result and the comparison of the system automatic planned paths in different solving modes so as to help the learning user to reasonably evaluate the quality of the current path. For example, the path length of the user-autonomous planned path and the path length of the system-automatic planned path may be compared, the solution time taken by the system-automatic planned path obtained by the exact solution and the solution time taken by the system-automatic planned path obtained by the approximate solution may be compared, and the path length of the system-automatic planned path obtained by the exact solution and the path length of the system-automatic planned path obtained by the approximate solution may be compared, but the present invention is not limited thereto. It should be noted that the target value for the optimization of the variant problem may be the path length, but is not limited thereto.

The variety problem expanding system can help users to know the mastering degree of the users to the path planning, can also know the huge benefit brought by the algorithm through learning the comparison result, can realize the advantages of the algorithm solution, and can generate visual feeling to the relation between the problem scale and the algorithm operation time. In addition, after the system automatically plans the path, the user can still adjust the self-automatically planned path to compare and analyze the self-automatically planned path with the automatically planned path of the system more deeply and carefully.

Specifically, the variant problem comparison system comprises a third game setting module, a third system automatic path planning module, a third path display module and a third information description module.

The following describes how to teach using the variant problem comparison system by way of specific examples. The present embodiment specifically takes a variant of the problem comparison teaching system for distance/fuel consumption minimization vehicle path problem as an example. For the distance minimization vehicle path problem teaching system, the optimization goal is to make the planned path have the minimum driving distance; for the fuel consumption minimization vehicle path problem teaching system, the optimization goal is to enable the planned path to have the minimum fuel consumption. FIG. 5 is a schematic diagram of an exemplary variant problem comparison system.

After the user logs into the variant problem comparison system, first, the user proceeds with the game setup step. The game setting link is mainly used for generating a data source required by a planned path, and the data generation mode can be various and can accept the selection of a user, such as random generation, real-time self-making (real-time clicking of a right visual window to generate data), actual import (for example, importing a map in Beijing City to perform on-site verification), and the like. And generating third path planning information such as map data (site position information, customer position information, road information and the like), customer demand data, fleet data (vehicle quantity, vehicle capacity and the like) and the like according to the data generation mode selected by the user, wherein the generated third path planning information can be displayed on a visual interface in real time for the user to view.

It should be noted that the data sources of the path planning algorithms of the different optimization objectives are kept consistent and displayed in the respective visualization interfaces in time. Taking the distance/oil consumption minimization vehicle path problem comparison teaching system as an example, the distance minimization vehicle path problem comparison teaching system is consistent with the data source of the oil consumption minimization vehicle path problem comparison teaching system. The optimization target of the distance minimization vehicle path problem is distance minimization, and the optimization target of the fuel consumption minimization vehicle path problem is fuel consumption minimization.

Secondly, the method enters a step of the system planning the path autonomously. The system automatically plans the path, and specifically, the variant problem comparison system automatically calls a corresponding path planning algorithm and automatically plans the path according to the third path planning information and the corresponding path planning algorithm. Taking the distance/oil consumption minimization vehicle path problem comparison teaching system as an example, the third system automatic path planning module calls a distance minimization optimization algorithm, and plans a path of an optimization target, namely the distance, according to the third path planning information and the distance minimization optimization algorithm; meanwhile, the third system automatic path planning module calls an oil consumption minimization optimization algorithm and plans a path of an oil consumption optimization target according to the third path planning information and the oil consumption minimization optimization algorithm.

And entering a path display link. In the link, a visual interface is provided, third path planning information such as map data, customer demand data and fleet data and paths automatically planned by systems with different optimization targets are displayed, and interactive operation such as path hiding/displaying can be carried out at any time. It is noted that the paths automatically planned by the system for different optimization objectives are presented in different colors on the visualization interface.

And finally, entering an information description link. In the link, a visual interface is provided, and the comparison result of the automatic planning path of the system for visually displaying different optimization targets is provided, so that a user is helped to deeply and carefully think and analyze the difference of the vehicle path problems of the different optimization targets. For example, in the information description link, the user can visually see the difference between the distance-to-oil consumption minimized vehicle path problem and the distance-to-oil consumption minimized vehicle path problem, and see the distance comparison result and the oil consumption comparison result of the distance-to-oil consumption minimized vehicle path problem on the visual interface corresponding to the third information description module.

The intelligent site selection teaching platform includes at least one of a site selection problem expansion system and a site selection problem comparison system, but is not limited thereto.

Specifically, the address selection problem expansion system comprises a fourth game setting module, a first user independent address selection module, a first system automatic address selection module, a first warehouse display module and a fourth information description module.

Further, the first warehouse display module is further configured to receive a first system automatic addressing result and a second system automatic addressing result output by the first system automatic addressing module, and respectively display the first system automatic addressing result and the second system automatic addressing result;

The following explains how to use the address selection problem expansion system for teaching through specific examples. This embodiment specifically takes an addressing problem expansion system, which is a P-median problem teaching system, as an example for explanation. FIG. 6 is a schematic diagram of an exemplary addressing problem propagation system.

After the user logs in the address selection problem expansion system, firstly, the user performs a game setting step. The game setting link is mainly used for generating a data source required by a planned path, and the data generation mode can be various and can accept the selection of a user, such as random generation, real-time self-making (real-time clicking of a right visual window to generate data), actual import (for example, importing a map in Beijing City to perform on-site verification), and the like. First addressing information such as map data (customer position information and the like), customer demand data and warehouse data (warehouse positions, required warehouse quantity information and the like) is generated according to a data generation mode selected by a user, and the generated first addressing information can be displayed on a visual interface in real time for the user to view.

Secondly, entering a user autonomous address selection step. The user can check the first addressing information on a visual interface, interactive operations such as clicking the position of the selectable warehouse by clicking the position of the selectable warehouse, single step withdrawing, resetting addressing, hiding/displaying addressing and the like are performed on the visual interface according to the checked first addressing information to select the warehouse, and if the number of manually operated addressing warehouses is larger than the required number, the warehouse cannot be clicked and selected. It is noted that the customer covered by the click-selected warehouse is connected to the warehouse with a thin line, such as blue, wherein the maximum distance between the customer and the warehouse is highlighted with a thin red color, for example. The user autonomous addressing result obtained by the first user autonomous addressing module includes at least a maximum distance between the warehouse selected by the user and the warehouse selected by the corresponding user, and the like.

And thirdly, entering the step of automatically selecting the address of the system. The system automatic site selection is automatically completed by the system, specifically, the site selection problem expansion system can automatically call a corresponding algorithm or model, and site selection of the warehouse is automatically performed according to the first site selection information and the corresponding algorithm or model.

In this embodiment, the system may also select a different algorithm or model for automatic addressing. For example, the system provides system solutions such as exact solution and approximate solution for the user to select. The accurate solution method may be understood as automatically planning a path by using a model, for example, for the accurate solution method, a third preset model selectable by the system includes, but is not limited to, a P-median problem classical model, and the like, and the selected model is used to automatically realize the site selection of the warehouse. The approximate solution manner may be understood as automatically implementing address selection of the warehouse by using an algorithm, for example, for the approximate solution manner, a third preset algorithm that may be selected by the system includes, but is not limited to, clustering and other algorithms, and the address selection of the warehouse is automatically implemented by using the operation of the selected algorithm on the first address selection information.

And thirdly, entering a warehouse display step. In the link, a visual interface is provided, first addressing information such as map data, customer demand data and warehouse data, a warehouse which is automatically addressed by a user and a warehouse which is automatically addressed by a system are displayed, and interactive operation such as hiding/displaying of the warehouse can be carried out at any time. It should be noted that the warehouse that is automatically addressed by the user and the warehouse that is automatically addressed by the system are presented in different colors on the visual interface.

And finally, entering an information description link. In the link, a visual interface is provided to show the comparison of the user independent address selection result and the system automatic address selection result and the comparison of the system automatic address selection results in different solving modes so as to help the learning user to reasonably evaluate the quality of the current address selection. For example, the average weighted distance of the user's autonomous site selection and the average weighted distance automatically selected by the system may be compared; the maximum distance of the user self-addressing and the maximum distance of the system self-addressing can be compared; the average weighted distance of the automatic system selection obtained by the precise solution method and the average weighted distance of the automatic system selection obtained by the approximate solution method may also be compared, and the maximum distance of the automatic system site selection obtained by the precise solution method and the maximum distance of the automatic system site selection obtained by the approximate solution method may also be compared, but not limited thereto.

Specifically, the address problem comparison system comprises a fifth game setting module, a second system automatic address selection module, a second warehouse display module and a fifth information description module.

The following describes how to teach using the address problem comparison system by using specific examples. This embodiment specifically takes an addressing problem comparison system, which is a P median/P center problem comparison teaching system, as an example for explanation. For the P-median problem teaching system, the optimization goal is to make the selected warehouse have the minimum average weighted distance; for the P-center problem teaching system, the optimization objective is to minimize the maximum distance of the selected warehouse. FIG. 7 is a schematic diagram of an exemplary addressing problem comparison system.

After the user logs in the address selection problem comparison system, firstly, the user performs a game setting step. The game setting link is mainly used for generating a data source required by a planned path, and the data generation mode can be various and can accept the selection of a user, such as random generation, real-time self-making (real-time clicking of a right visual window to generate data), actual import (for example, importing a map in Beijing City to perform on-site verification), and the like. And generating second addressing information such as map data (customer position information and the like), customer demand data, warehouse data (warehouse positions, required warehouse quantity information and the like) and the like according to the data generation mode selected by the user, wherein the generated first addressing information can be displayed on a visual interface in real time for the user to view. It should be noted that the data sources of the addressing optimization algorithms of different optimization targets are kept consistent and displayed in respective visual interfaces in time. Taking the P-median/P-center problem comparison teaching system as an example, the data sources of the P-median problem teaching system and the P-center problem teaching system are consistent.

And thirdly, entering the step of automatically selecting the address of the system. The system automatic site selection is automatically completed by the system, specifically, the site selection problem expansion system can automatically call a corresponding site selection optimization algorithm, and site selection of the warehouse is automatically performed according to the second site selection information and the corresponding algorithm or model. Taking the P-median/P-center problem comparison teaching system as an example, the second system automatic addressing module calls an addressing optimization algorithm of the minimum average weighted distance corresponding to the P-median system, and selects a warehouse of an optimization target of the minimum average weighted distance according to the second addressing information and the minimum average weighted distance algorithm; meanwhile, the second system automatic site selection module calls a site selection optimization algorithm of the minimum maximum distance corresponding to the P center system, and selects a warehouse of an optimization target of the minimum maximum distance according to the second site selection information and the minimum maximum distance algorithm.

And thirdly, entering a warehouse display step. In the link, a visual interface is provided, second addressing information such as map data, customer demand data and warehouse data and warehouses with different optimization targets selected by the system automatically are displayed, and interactive operation such as road warehouse hiding/displaying can be carried out at any time. It is noted that the warehouse automatically addressed by the system of different optimization objectives is presented on the visualization interface in different colors.

And finally, entering the step of information description. In the link, a visual interface is provided to visually display the comparison result of the warehouse automatically addressed by the system with the optimization target, so as to help the user to carry out deeper and more detailed thinking and analysis on the differentiation of the warehouse addressing problems of different optimization targets. For example, on the visual interface corresponding to the information description, the user can visually check the difference between the P-median problem and the P-center problem, and check the comparison result of the average weighted distance and the comparison result of the maximum distance between the two problems.

The logistics planning teaching system for path planning and intelligent site selection provided by the embodiment takes two major classical problems of path planning and intelligent site selection as entry points, considers multiple practical variation problems of the two problems, and provides sufficient human-computer interaction and result visual comparison. The system supports the import of an example with a field map as a background, is helpful for assisting students to understand the actual background of a problem by utilizing the familiar environment of the students and establishes a connection with an abstract mathematical model; the method can provide examples of all problems in real time in a classroom, and students can try to independently make site selection decisions or path planning decisions and record the site selection decisions or the path planning decisions so that the students can know all specific intelligent site selection and path planning decision situations more intuitively. Compared with the traditional intelligent address selection and path planning teaching method, the knowledge mastering condition of the students can be known in real time, the students can be helped to realize the huge practical significance of the model algorithm, and the learning enthusiasm of the students is stimulated.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims

1. A logistics planning teaching system of path planning and intelligent site selection is characterized by comprising: a path planning teaching platform and an intelligent address selection teaching platform;

the intelligent site selection teaching platform is used for receiving site selection information imported by a user, performing user autonomous site selection and system automatic site selection according to the site selection information, comparing a site selection result obtained by the user autonomous site selection with a site selection result obtained by the system automatic site selection, and respectively displaying the site selection information, the site selection result obtained by the user autonomous site selection, the site selection result obtained by the system automatic site selection and the site selection comparison result;

the path planning teaching platform comprises a traditional vehicle path problem system, wherein the traditional vehicle path problem system comprises a first game setting module, a first user autonomous path planning module, a first system automatic path planning module, a first path display module and a first information description module;

the first information description module is used for receiving the user autonomous path planning result of the first user autonomous path planning module and displaying the user autonomous path planning result; comparing the user autonomous path planning result of the first user autonomous path planning module with the system automatic path planning result of the first system automatic path planning module to obtain and display a path planning comparison result, wherein the path planning comparison result comprises a comparison result of the path length;

the intelligent site selection teaching platform comprises a site selection problem expansion system, wherein the site selection problem expansion system comprises a fourth game setting module, a first user independent site selection module, a first system automatic site selection module, a first warehouse display module and a fourth information description module;

2. The system of claim 1, wherein the user-selected system solution comprises an approximate solution and an exact solution;

3. The system of claim 2, wherein the first path showing module is further configured to receive and show a first system automatic path planning result and a second system automatic path planning result output by the first system automatic path planning module;

4. The system of claim 1, wherein the path planning tutorial platform comprises a variant problem expansion system comprising a second game setup module, a second user-independent path planning module, a second system automatic path planning module, a second path presentation module, a second information specification module;

5. The system of claim 4, wherein the user-selected system solution includes an approximate solution and an exact solution;

6. The system of claim 5, wherein the second path display module is further configured to receive and display a third system automatic path planning result and a fourth system automatic path planning result output by the second system automatic path planning module;

7. The system of claim 1, wherein the path planning tutorial platform comprises a variant problem comparison system comprising a third game setup module, a third system auto path planning module, a third path demonstration module, a third information description module;

8. The system of claim 1, wherein the user-selected system solution comprises an approximate solution and an exact solution;

9. The system of claim 8, wherein the first warehouse display module is further configured to receive and display a first system automatic addressing result and a second system automatic addressing result output by the first system automatic addressing module;

10. The system of claim 1, wherein the intelligent site selection teaching platform comprises a site selection problem comparison system, and the site selection problem expansion system comprises a fifth game setting module, a second system automatic site selection module, a second warehouse display module and a fifth information description module;