CN114298419A - Multi-type intermodal transportation planning method and device, electronic equipment and storage medium - Google Patents
Multi-type intermodal transportation planning method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a multi-type intermodal transportation planning method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring the consignment requirement of a consignor; matching a plurality of transportation task hub cities according to the consignment demand; carrying out intensive scheduling on the transportation task hub city to obtain a plurality of inter-hub transportation tasks; extracting carriers serving a plurality of transportation tasks among the hubs according to prestored carrying capacity resource data; and performing multi-mode combined transportation planning according to the transportation tasks among the hubs and the transportation mode of the carrier, and simultaneously outputting the multi-mode combined transportation planning result to the shipper and the carrier respectively. By adopting the technical scheme of the invention, the problem of optimizing the transportation path with different transportation modes under the resource pool scene is solved.
Description
Technical Field
The invention belongs to the technical field of logistics transportation planning, and particularly relates to a multi-type intermodal transportation planning method and device, electronic equipment and a storage medium.
Background
The multimodal transportation is to transport goods from the starting place to the destination by adopting two or more than two transportation modes, is the core of a comprehensive transportation system, plays an important role in the transportation system of China, and has great significance for improving the transportation service level, the competitive power and the social comprehensive benefit.
At the present stage, a multimodal transportation model is mainly researched to model a multimodal transportation problem as a generalized shortest path problem. The traditional shortest path problem can be solved by a label method, namely, the shortest path from one vertex to other vertexes is obtained, and the shortest path problem is solved by traversing the adjacent nodes of the vertex which is closest to the initial point and has no access until the adjacent nodes are expanded to the end point. In the multi-type intermodal transportation problem, the transportation efficiency and the transportation cost of different transportation modes are different, and if only the shortest distance is considered, the requirements of highest transportation efficiency and lowest cost cannot be met. In addition, the carrying capacity of each transportation hub is different, and different transportation modes selected among the transportation hubs form different transportation capacity limits. Therefore, incorporating cost, efficiency, and capacity limitations of different transportation modes into the traditional shortest-path problem will optimize transportation path selection, facilitating more accurate selection of an appropriate path.
The existing research on the multimodal transportation model begins to model a multimodal transportation scheme of a single transportation task by considering factors such as transportation cost, transportation efficiency and transportation capacity limitation, however, the existing research does not consider multimodal transportation planning under the resource pool situation.
Disclosure of Invention
The invention aims to provide a resource pool-based multi-type intermodal transportation planning method and device, electronic equipment and a storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-type intermodal transportation planning method comprises the following steps:
step S1, acquiring the consignment requirement of a consignor;
step S2, matching a plurality of transportation task hub cities according to the consignment demand;
step S3, carrying out intensive scheduling on the transportation task hub city to obtain a plurality of inter-hub transportation tasks;
step S4, extracting a carrier serving a plurality of inter-hub transportation tasks according to prestored carrying capacity resource data;
and step S5, performing multi-mode intermodal transportation planning according to the transportation tasks among the hubs and the transportation mode of the carrier, and simultaneously outputting the multi-mode intermodal transportation planning results to the shipper and the carrier respectively.
Preferably, the consignment demand includes a task origin city, a task arrival city, a transportation task amount, and a transportation distance.
Preferably, in step S3, the integrated clustering is performed on the tasks with the same departure time and arrival time of the same starting point hub and ending point hub in the inter-hub transportation tasks, the transportation tasks which are departed on the same day and arrive on the same day are grouped into one type, and the one type is stored in the same data table, so as to generate the task pool of the multimodal transportation plan.
Preferably, if the task pool of the multi-type intermodal transportation plan is not empty, continuously traversing all transportation tasks from morning to evening according to the departure time and from morning to evening according to the arrival time; and for the tasks with the same departure time and arrival time, obtaining different batches of tasks with the same departure time and the same arrival time based on the principle of whether the tasks belong to the same departure pivot and the same arrival pivot, and continuously traversing the tasks of the different batches to carry out allocation of multimodal transport.
Preferably, in step S5, a Dijkstra algorithm is used to obtain a shortest transportation path planned by multimodal transportation, where the transportation mode includes transportation by road, rail and waterway, and the method specifically includes the following steps:
step S51, constructing a network graph consisting of all logistics hub cities, wherein the network graph consists of N logistics hub city nodes i (i is 1, …, N) and arcs among the nodes, and each arc comprises the transportation distance of three transportation modes and the transportation capacity limit of railway and waterway transportation; all nodes in the initial state are not labeled, namely the label parameter is set to false; the initial node is defined as a starting node of the current task, the initial node is used as the current node, the transportation mode of the initial node is set as road transportation, and the initial node is marked as true;
step S52, selecting a next current node, wherein the next node is generated from the adjacent nodes of the initial node, the node with the shortest distance from the adjacent node of the initial node to the initial node is used as the next current node, and the transportation mode with the smallest transportation distance is selected as the transportation mode from the initial node to the adjacent node;
and step S53, after the current node is selected, selecting the node with the minimum distance cost from the starting node from the unnumbered neighbor nodes of the current node as the next current node.
Preferably, after the shortest transportation path is obtained, calculating a transportation capacity bottleneck section of the shortest transportation path, if the transportation capacity to be transported is below the transportation capacity bottleneck limit, arranging a current task, updating the transportation capacity, removing the current task from a task pool, and continuously traversing the transportation tasks of the next batch; if the volume to be transported exceeds the bottleneck limit of the transport capacity, the transport capacity is arranged according to the bottleneck transport capacity, the transport capacity is updated, the shortest transport path is calculated again according to the remaining volume to be transported, whether the transport capacity exceeds the bottleneck limit is judged, and the steps are repeated in a circulating mode until the current task is completely arranged.
The invention also provides a multi-type intermodal transportation planning device, which comprises:
the acquisition module is used for acquiring the consignment requirement of a consignor;
the matching module is used for matching a plurality of transportation task hub cities according to the consignment demand;
the dispatching module is used for carrying out intensive dispatching on the transportation task hub city to obtain a plurality of inter-hub transportation tasks;
the extraction module is used for extracting a carrier serving a plurality of inter-hub transportation tasks according to prestored carrying capacity resource data;
and the planning module is used for carrying out multi-mode combined transportation planning according to the transportation tasks among the hubs and the transportation mode of the carrier, and simultaneously outputting the multi-mode combined transportation planning result to the shipper and the carrier respectively.
Preferably, the consignment demand includes a task origin city, a task arrival city, a transportation task amount, and a transportation distance.
The present invention also provides an electronic device comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory has stored therein a computer program that, when executed by the processor, causes the processor to execute a weighted multi-modal intermodal transportation planning method.
The present invention also provides a storage medium storing a computer program executable by an electronic device, which when run on the electronic device, causes the electronic device to perform a multimodal transportation planning method.
The technical scheme of the invention solves the problem of optimizing the transportation path with different transportation modes under the resource pool scene. On one hand, in order to minimize the transportation cost, the cost of different transportation modes needs to be considered, so that the judgment on the cost of railways, waterways and highways is added; on the other hand, the capacity limit of various transportation modes needs to be considered, and after the capacity limit is exceeded, the line needs to be adjusted, so that the line with the minimized transportation cost and the transportation mode among various hubs in the line are obtained.
Drawings
FIG. 1 is a structural diagram of the multi-modal intermodal transportation planning method of the present invention;
FIG. 2 is a flow chart of a multimodal transportation planning method of the present invention;
fig. 3 is a structural view of the multimodal transportation planning apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1:
the invention provides a multi-type intermodal transportation planning method, which is characterized in that transportation tasks are converged into a task pool through a third-party platform, the transportation capacity of a plurality of carriers is converged into a transportation capacity pool, and modeling of multi-type intermodal route planning is carried out on the basis of a resource pool. As shown in fig. 1, the present invention includes: the method comprises the steps that a shipper uploads consignment demands (a task starting city, a task arriving city, transportation task amount and transportation distance), a shipper uploads transportation capacity resources (a road shipper, a railway shipper and a waterway shipper), and a third-party platform generates a multi-type intermodal transportation scheme. The consignor uploads the transportation tasks to the platform side, each transportation task corresponds to different starting points and end points cities, for the transportation tasks, due to the fact that the transportation distance is long, transportation is often required to be carried out through the logistics hub city, and transportation among the logistics hub cities is often required to be carried out through multi-mode combined transportation. After receiving a long-distance transportation task, firstly, the platform needs to be matched to obtain logistics hub cities corresponding to the starting point city and the terminal point city of the task, and then transportation and allocation from the starting point to the starting point hub, from the starting point hub to the terminal point hub and from the terminal point hub to the terminal point are carried out. Because the invention emphasizes multi-mode intermodal transportation, the invention only concerns the multi-mode intermodal transportation planning between logistics hub cities corresponding to a starting point and an end point, and the transportation from the starting point city to the corresponding logistics hub and the transportation from the logistics hub corresponding to the end point city belong to short-distance transportation, and the transportation scheduling at this stage is not considered in the invention.
As shown in fig. 2, the present invention provides a resource pool-based multimodal transportation planning method, which includes the following steps:
s1, receiving logistics requirements of shippers by a third-party platform, and gathering remote transportation tasks into a multi-type intermodal task pool;
s2, matching logistics hub cities corresponding to the starting point city and the end point city of each logistics demand in the multi-type intermodal task pool, wherein the matching principle is that the hub city closest to the starting point city and the end point city in the hub city list is selected;
s3, integrating and clustering tasks of the same starting time and the same arrival time of the same starting point hub and the same destination hub, namely gathering transportation tasks which are started on the same day and arrive on the same day into a same category, storing the transportation tasks in the same data table and waiting for unified scheduling, wherein the transportation tasks are consistent in the corresponding hub cities of the starting point and the destination; generating a task pool to be planned by the multi-mode intermodal transportation through three steps S1-S3, traversing tasks in the task pool, and continuously deleting the planned tasks from the task pool until the tasks in the task pool are cleared;
s4, if the task pool to be planned by the multi-mode intermodal transportation is not empty, firstly selecting the tasks to be planned according to the traversing principle of the departure time and the arrival time, namely, continuously traversing all transportation tasks from morning to evening according to the departure time and then from morning to evening according to the arrival time; tasks at the same departure time and arrival time are divided into a plurality of batches of tasks in the third step based on the principle of whether the tasks belong to the same departure pivot and the same arrival pivot, so that different batches of tasks at the same departure time and the same arrival time can be obtained in a summary manner, and the tasks of different batches are continuously traversed to carry out allocation of multimodal transportation in the follow-up process;
and S5, for the current task, firstly, judging whether the time limit of the task can be met by adopting the road transportation (the fastest transportation mode) to complete the whole process. If not, removing the task from the task pool, and handing over to manual processing, and if yes, calculating the shortest path considering various transportation modes;
s6, in the multi-type intermodal problem, each node has multiple transportation modes that can be selected, and in the present invention, three transportation modes are considered, namely road transportation, railway transportation and waterway transportation, where the transportation mode is denoted by k, and k is 0, and 1 and 2 denote road transportation, railway transportation and waterway transportation, respectively. The shortest path calculation method used in the invention is a Dijkstra algorithm, and the method mainly comprises the following steps:
firstly, a network diagram consisting of all logistics hub cities is constructed, wherein the network diagram consists of N logistics hub city nodes i (i is 1.. the., N) and arcs among the nodes, each arc comprises transport distances of three transport modes and transport capacity limits of railway and waterway transport (assuming that the road transport has no transport capacity limit), and the transport distance is dij0，dij1，dij2Indicated by the capacity limit being cij1，cij2And (4) showing. All nodes in the initial state are unnumbered, i.e. the label parameter is set to false. An initial node (init _ node) is defined as a starting node of a current task, the initial node serves as a current node, the transportation mode of the initial node is set as road transportation, and the initial node is marked as true.
Then, the next current node cur _ node is selected, the next node is generated from adjacent nodes (neighbor _ nodes) of the initial node (init _ node), the node with the shortest distance from the adjacent nodes of the initial node to the initial node is used as the next current node, and meanwhile, the transportation mode with the smallest transportation distance is selected as the transportation mode from the initial node to the adjacent nodes.
Next, after the current node is selected, a node with the minimum distance cost to the departure node is selected from the unnumbered (labeled parameter false) neighbor nodes of the current node as a next current node. Regarding the shortest route calculation method, a method of calculating the shortest route from the node adjacent to the current node to the departure node will be described, taking the transportation mode of the current node (i.e., the transportation mode in which the current node is the link to the arrival node) as an example of road transportation.
1) And traversing each neighbor node of the current node. If the shortest path from the current node to the starting node plus the road distance from the current node to the neighbor node is smaller than the current shortest path from the starting node to the neighbor node, updating the shortest path from the neighbor node to the starting node, setting a parent node (parent) of the neighbor node as the current node, and specifying that the transportation mode at the neighbor node adopts road transportation; similar to the above idea, it is continuously determined whether the shortest path is generated by railway transportation and waterway transportation, but since additional conversion cost is required for converting road transportation to railway and waterway transportation, a conversion cost is required to be added when the shortest path distance is calculated for converting railway transportation and waterway transportation;
2) if the shortest path between the current node and the starting node and the road distance between the current node and the neighbor node are greater than the shortest path between the starting node and the neighbor node, directly switching to a transportation scheme for judging whether railway transportation and waterway transportation can generate lower distance cost.
Similarly, if the transportation mode of the current node is railway transportation, when the shortest path of the adjacent node is calculated, whether the shortest path from the starting node to the current node and the total distance of the path from the current node to the adjacent node are smaller than the current shortest path from the starting node to the adjacent node is judged, and if yes, the shortest path from the starting node to the adjacent node is updated. It should be noted that if the transition to road transport or water transport is made at the adjacent node, additional transition cost is added in calculating the shortest path distance. If the transportation mode of the current node is waterway transportation, the method for judging the shortest path is the same, and the detailed description is omitted.
The above process is repeated until all nodes are marked (the marking parameter is true), and the shortest circuit from all nodes to the starting node can be completely solved.
S7, after finding the shortest route considering multiple transportation modes by the shortest route algorithm, it needs to determine whether the route meets the time requirement, i.e. whether the delivery can be completed before the latest delivery time. If the shortest route meets the time requirement, the next step is carried out, if the shortest route does not meet the time requirement, the original transportation mode (railway or waterway transportation) of the shortest route of the off-road transportation needs to be forbidden, the forbidden method is to set the distance of the original transportation mode as a large number, then the shortest route is solved once again, whether the new route meets the time requirement or not is judged, and if the shortest route does not meet the time requirement, the operation is continuously carried out until the shortest route meeting the time requirement is obtained.
S8, after finding the shortest route which takes a plurality of transportation modes into consideration and meets the time requirement through the algorithm, calculating the transportation capacity bottleneck section of the transportation route, namely the transportation capacity of the section with the minimum transportation capacity, if the transportation capacity is below the transportation capacity bottleneck limit, arranging the current task, updating the transportation capacity, removing the current task from the task pool, and continuously traversing the transportation tasks of the next batch;
and S9, if the volume to be transported exceeds the bottleneck limit of the transportation capacity, arranging the transportation capacity according to the bottleneck transportation capacity, updating the transportation capacity, calculating the shortest path again according to the remaining volume to be transported, judging whether the new scheme exceeds the bottleneck limit of the transportation capacity, and repeating the steps until the current task is completely arranged. In the process of calculating the shortest path again, the distance of the original bottleneck road section needs to be set as a large number, so that the algorithm can skip the bottleneck road section to search other paths except the bottleneck road section;
and S10, repeating the steps S4-S9 until the tasks in the task pool are cleared.
The invention carries out matching of logistics hub cities on the transportation tasks of consignors through a third-party platform to generate transportation tasks among hubs; and clustering and integrating the transportation tasks among the hubs to construct a transportation task resource pool among the hubs. And integrating the transportation capacity resources of the carriers of each hub city to construct a carrier transportation capacity resource pool. And generating a multi-type intermodal transportation planning scheme, and outputting a scheduling scheme to the supply and demand parties through an output interface. The third-party platform adopts a multi-mode intermodal transportation planning mode, firstly, the transportation tasks among the hubs are classified according to departure place, destination, departure time and arrival time, and the transportation tasks of the same departure place, destination, departure time and arrival time are planned uniformly; then traversing all the transportation tasks in the same batch of transportation tasks, solving the shortest path between two logistics hubs through a Dijkstra algorithm based on the judgment of railway, waterway and highway costs, continuously checking the transportation capacity bottleneck in the shortest path, and optimizing path selection, thereby solving the optimal route planning and transportation mode.
Assume that the platform receives numerous logistics transportation tasks, including the following key information: supply end city, supply end longitude and latitude, earliest delivery time, latest arrival time, traffic volume, demand end city, and demand end longitude and latitude. Firstly, the platform matches to obtain logistics hub cities corresponding to starting point cities and ending point cities of the logistics tasks, and then the key information of transportation tasks among hubs corresponding to the tasks comprises: the logistics hub city system comprises a starting point logistics hub city, starting point logistics hub city longitude and latitude, earliest departure time, latest arrival time, transportation volume, a destination logistics hub city and destination logistics hub city longitude and latitude. It should be noted that, as shown in fig. 2, transportation from the starting point city to the starting point logistics hub city and from the ending point logistics hub city to the ending point city also requires corresponding transportation time, so the earliest departure time of transportation between hubs needs to be delayed by a certain number of days backward in combination with the transportation time from the starting point city to the starting point hub, and similarly, the latest arrival time of transportation between hubs needs to be advanced by a certain number of days forward in combination with the predicted transportation time from the ending point hub to the ending point city. Table 1 shows an example of the transportation tasks between partial hubs matched by the system, and the tasks shown in the table are all tasks whose departure time and arrival time are the same after the preliminary clustering, note that the city name is replaced by the city number here.
Table 1
According to the method provided by the invention, firstly, all the transportation tasks among the hubs are clustered according to the principle that the departure place, the arrival place, the departure time and the arrival time are consistent, and the transportation tasks among the hubs with the same departure place, the arrival place, the departure time and the arrival time are stored in the same data structure to carry out uniform planning processing. As shown in table 1, task 7 and task 12 can be aggregated into the same batch for uniform processing because the origin hub, the arrival hub, the departure time and the road time are the same, as are task 2 and task 5. Clustering and screening all transportation tasks, converging the transportation tasks into a task pool, traversing one by one from early to late according to the departure time and the arrival time of all the tasks, and firstly judging whether the delivery time can be met if the current task adopts road transportation in the whole process. If the delivery time requirement of the task cannot be met by adopting road transportation in the whole process, the task is put out from the task pool and is manually processed; otherwise, the shortest path of the current task is solved based on the Dijkstra algorithm. And after the shortest path of the current task is obtained, time limitation is considered, whether the obtained shortest path meets the time requirement is judged, if not, the original transportation mode of the section of the off-highway transportation with the shortest transportation distance in the original path is forbidden, the shortest path is solved again, and the steps are repeated continuously until the shortest path meeting the time requirement is found. After finding the shortest path meeting the time requirement, the minimum transport capacity of each section of path in the shortest path needs to be judged, and the minimum transport capacity is compared with the traffic volume of the current task. If the minimum transport capacity can meet the transportation volume of the current task, removing the current task from the task pool, and updating the transport capacity of the corresponding path; otherwise, planning according to the minimum transport capacity, updating the transport capacity to be transported and the transport capacity in the path, forbidding the original minimum transport capacity road section, and solving the shortest path again by the residual transport capacity. And repeating the process until all tasks are scheduled. Through the above process, the algorithm provided by the invention can finally output the results as shown in table 2:
TABLE 2
Example 2:
as shown in fig. 3, the present invention also provides a multi-type intermodal transportation planning apparatus, including:
the acquisition module is used for acquiring the consignment requirement of a consignor;
the matching module is used for matching a plurality of transportation task hub cities according to the consignment demand;
the dispatching module is used for carrying out intensive dispatching on the transportation task hub city to obtain a plurality of inter-hub transportation tasks;
the extraction module is used for extracting a carrier serving a plurality of inter-hub transportation tasks according to prestored carrying capacity resource data;
and the planning module is used for carrying out multi-mode combined transportation planning according to the transportation tasks among the hubs and the transportation mode of the carrier, and simultaneously outputting the multi-mode combined transportation planning result to the shipper and the carrier respectively.
As an implementation manner of this embodiment, the consignment demand includes a task start city, a task arrival city, a transportation task amount, and a transportation distance
Example 3:
the present invention also provides an electronic device comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory has stored therein a computer program that, when executed by the processor, causes the processor to execute a multimodal transportation planning method.
Example 4:
the present invention also provides a storage medium storing a computer program executable by an electronic device, which when run on the electronic device, causes the electronic device to perform a multimodal transportation planning method.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A multi-type intermodal transportation planning method is characterized by comprising the following steps:
step S1, acquiring the consignment requirement of a consignor;
step S2, matching a plurality of transportation task hub cities according to the consignment demand;
step S3, carrying out intensive scheduling on the transportation task hub city to obtain a plurality of inter-hub transportation tasks;
step S4, extracting a carrier serving a plurality of inter-hub transportation tasks according to prestored carrying capacity resource data;
and step S5, performing multi-mode intermodal transportation planning according to the transportation tasks among the hubs and the transportation mode of the carrier, and simultaneously outputting the multi-mode intermodal transportation planning results to the shipper and the carrier respectively.
2. The multimodal transportation planning method of claim 1 wherein said consignment requirements include a task origin city, a task arrival city, a transportation task volume and a transportation distance.
3. The multimodal transportation planning method of claim 2, wherein in step S3, the integrated clustering is performed on the tasks with the same departure time and arrival time of the same start-point hub and end-point hub in the inter-hub transportation tasks, and the transportation tasks with the same departure city and destination city, which depart on the same day and arrive on the same day, are clustered into a group and stored in the same data table, so as to generate the task pool of multimodal transportation planning.
4. The multimodal transportation planning method of claim 3 wherein if the task pool of the multimodal transportation planning is not empty, all transportation tasks are continuously traversed in the order of departure time from morning to evening and arrival time from morning to evening; and for the tasks with the same departure time and arrival time, obtaining different batches of tasks with the same departure time and the same arrival time based on the principle of whether the tasks belong to the same departure pivot and the same arrival pivot, and continuously traversing the tasks of the different batches to carry out allocation of multimodal transport.
5. The multimodal transportation planning method of claim 3, wherein step S5 adopts Dijkstra' S algorithm to obtain the shortest transportation path of multimodal transportation planning, the transportation mode includes road, railway and waterway transportation, and the method specifically comprises the following steps:
step S51, constructing a network graph consisting of all logistics hub cities, wherein the network graph consists of N logistics hub city nodes i (i is 1, …, N) and arcs among the nodes, and each arc comprises the transportation distance of three transportation modes and the transportation capacity limit of railway and waterway transportation; all nodes in the initial state are not labeled, namely the label parameter is set to false; the initial node is defined as a starting node of the current task, the initial node is used as the current node, the transportation mode of the initial node is set as road transportation, and the initial node is marked as true;
step S52, selecting a next current node, wherein the next node is generated from the adjacent nodes of the initial node, the node with the shortest distance from the adjacent node of the initial node to the initial node is used as the next current node, and the transportation mode with the smallest transportation distance is selected as the transportation mode from the initial node to the adjacent node;
and step S53, after the current node is selected, selecting the node with the minimum distance cost from the starting node from the unnumbered neighbor nodes of the current node as the next current node.
6. The multimodal transportation planning method of claim 5, wherein after the shortest transportation path is obtained, a transportation capacity bottleneck section of the shortest transportation path is calculated, if the transportation capacity is below the transportation capacity bottleneck limit, the current task is scheduled, the transportation capacity is updated, the current task is removed from the task pool, and the transportation tasks of the next batch are continuously traversed; if the volume to be transported exceeds the bottleneck limit of the transport capacity, the transport capacity is arranged according to the bottleneck transport capacity, the transport capacity is updated, the shortest transport path is calculated again according to the remaining volume to be transported, whether the transport capacity exceeds the bottleneck limit is judged, and the steps are repeated in a circulating mode until the current task is completely arranged.
7. A multimodal transport planning apparatus comprising:
the acquisition module is used for acquiring the consignment requirement of a consignor;
the matching module is used for matching a plurality of transportation task hub cities according to the consignment demand;
the dispatching module is used for carrying out intensive dispatching on the transportation task hub city to obtain a plurality of inter-hub transportation tasks;
the extraction module is used for extracting a carrier serving a plurality of inter-hub transportation tasks according to prestored carrying capacity resource data;
and the planning module is used for carrying out multi-mode combined transportation planning according to the transportation tasks among the hubs and the transportation mode of the carrier, and simultaneously outputting the multi-mode combined transportation planning result to the shipper and the carrier respectively.
8. The intermodal transportation planning apparatus of claim 7 wherein the consignment requirements include a task origin city, a task arrival city, a volume of transportation tasks and a distance of transportation.
9. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory has stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of the method of any one of claims 1 to 6.
10. A storage medium storing a computer program executable by an electronic device, the program, when executed on the electronic device, causing the electronic device to perform the steps of the method of any one of claims 1 to 6.
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| CN116011915B (en) * | 2023-01-16 | 2023-09-05 | 中国人民解放军军事科学院系统工程研究院 | Multi-mode intermodal scheme generation method and device |
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| CN116542476B (en) * | 2023-05-09 | 2023-11-28 | 重庆赛迪奇智人工智能科技有限公司 | Scheduling method, device, equipment and storage medium of molten iron transport vehicle |
| CN117575451A (en) * | 2024-01-12 | 2024-02-20 | 深圳市今天国际物流技术股份有限公司 | Logistics order control method, device, computer equipment and storage medium |
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