CN105404941A - Intelligent optimization method and system for logistics transportation mode and path - Google Patents
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Abstract
The present invention provides an intelligent optimization method and system for a logistics transportation mode and path. The optimization method mainly comprises the steps of: preparing node data and edge data, and acquiring an optimal transportation mode and path through calculation. The method of acquiring the optimal transportation mode and path comprises: representing the node data and the edge data by using a weighted directed graph; adding a new node to a graph G to acquire a graph G' and calculating the graph G'; processing G' by using a Bellman-Ford algorithm, and forming a minimum distance between the new node and each node; updating a weight value of the edge data; performing a Dijkstra algorithm on all node data in the graph G, to calculate a minimum distance from other node data; and optimizing a cost preference and time preference transportation mode and path. According to the intelligent optimization method for the logistics transportation mode and path, a problem is resolved that an optimal transportation mode and path is intelligently calculated and selected with given limitations such as goods, a starting address and a destination, from different transportation modes and transportation paths, and according to cost preference and time preference.
Description
Technical field
The present invention relates to intelligent optimization method, refer to a kind of logistics transportation mode and path intelligent optimization method especially.
Background technology
Two large core contents of freight transportation: quick, accurately, the delivery service of saving; Safe and reliable freight transportation and clearing.At present, shipping present situation is also in extensive developing stage, and the sky of return car and boat is sailed transport power and can't be fully used, and logistics cost is high on the one hand, sky sails transport power on the other hand wastes serious.Existing goods is joined goods station and is also rested on original driver and look for goods and goods to look for the stage of car; The transport power how reduced in transportation is wasted and is reduced the cost of transportation, improve the efficiency of shipping, just need to carry out systematic analysis to the traveling road conditions of car and boat, according to the preferential sorting charge of the needs of freight transportation minimum route or the shortest route of event, the invention solves given goods, starting point and destination, from different means of transportation and transportation route, according to the restriction element such as price priority, time priority, calculate and select best means of transportation and the problem of transportation route intelligently.
So far to Johnson algorithm, bellman-ford algorithm and the most application of dijkstra's algorithm are also in simple research algorithm itself, they do not combine by prior art, jointly be applied to logistics transportation industry thus the technical matters of solution means of transportation and path optimization, therefore, the present invention's innovation above-mentioned polyalgorithm is conducted in-depth research, and based on mass data analog simulation and field test checking, successfully propose the path intelligent optimization method based on above-mentioned algorithm and system, and the problem of the means of transportation efficiently solved when car and boat run and transportation route.
Summary of the invention
The invention provides a kind of logistics transportation mode and path intelligent optimization method, specific as follows:
Comprise the optimization analyzing means of transportation and path from time priority and the preferential two kinds of conditions of expense, optimization method step is as follows:
(1) node data are prepared
Node data refer to the set of railway freight station, highway freight station and port and pier, node data are as shipping starting point and shipping destination, the starting point of car and boat master in the transport of transport power and destination are determined, then set shipping starting point as X, shipping destination is Y.
(2) limit data are prepared
1) limit data refer to all possible path between any two nodes be communicated with by a certain means of transportation, and number of path is N, comprise the time from spending required for the specific means of transportation of two node X to Y or Y to X and expense cost.
2) possible following path is got rid of according to real world applications scene
The adaptation issues of goods and means of transportation, some goods is because size, safety factor only can use specific mode to transport;
The restriction of means of transport, comprises car, ship, the quantity of train and capacity problem;
The depot storage capacity of shipping station, port and pier, the restriction of handling capacity;
Transprovincially with the official inspection in city and time consuming problem;
The Scheduling factors of means of transport, in special time period, the height problem of means of transport;
Handling cost problem between several modes, is transformed into the expense of the time that another kind of mode needs from a kind of means of transportation.
3) the transportation route M that limited bar is possible is finally obtained;
(3) the best means of transportation of acquisition and path is calculated
Step 1: node data and limit data Weighted Directed Graph are represented, is expressed as G=(V, L);
V represents the set of node data, and namely the set of described railway freight station, highway freight station and port and pier, is expressed as V={V by algebraic expression
1, V
2, V
3, V
1, V
2, V
3represent the node data acquisition of railway freight station, highway freight station and port and pier respectively, wherein V
1={ v
11, v
12... v
1i... v
1r, wherein 1≤i≤r, r≤M, v
irepresent i-th node data, same represented highway freight station and the node data of port and pier of railway freight station in digraph.
L represents limit data, and the path namely between any two nodes be communicated with by a certain means of transportation, algebraic expression is expressed as L={v
i, v
j, wherein 1≤i, j≤M, v
i, v
jrepresent node data v
i, v
jbetween path.
The weights ω of described Weighted Directed Graph considers from the time travelled between any two node data needed for per unit length, expense, urgency level, and in actual motion continuous training study.
Step 2: calculating chart G adds the figure G'=(V', L') after new node, the weights between the new node X added to all former nodes are zero, namely
v'=V ∪ (X), L'=L ∪ (X, u): u ∈ V}, and to all u ∈ V, ω (X, u)=0; Wherein X be newly add logistics railway freight station, shipping starting point in highway freight station or port and pier, certain node u represents from shipping starting point to shipping destination.
Step 3: described shipping starting point is expressed as dis (X, u) or dis (X, v) to the distance described in other between node data u or v for X, and limit data are L (u, v), and the weights of its correspondence are ω (u, v);
If perform circulation dis (X, u)+ω (u, v) <dis (X, v), then upgrade dis (X, v)=dis (X, u)+ω (u, v), if dis (X, v) does not upgrade or part point data is unreachable, then end loop, obtain the bee-line set R (dis) of described shipping starting point X to other node datas, and described shipping starting point X is left in array h (x) to each bee-lines of other node data; Wherein x ∈ R (dis) i.e. x is in described shipping starting point X to the bee-line set R (dis) of other node datas.
Step 4: to the weights ω (u, v) of all limits data, be updated to ω
,(u, v)=ω (u, v)+h (u)-h (v), wherein u, certain node v represents from shipping starting point to shipping destination, ω (u, v) represents u, the weights of the limit data that v is corresponding, the i.e. possibility in the path of process between two logistics railway freight stations, highway freight station or port and piers.
Step 5: described all node data are expressed as V={S, U}, S={u}, U={ other node data except u }; Wherein S is the set of the node data having obtained optimal path, and U is that all the other do not determine the set of the node data of optimal path, and ω ' (u)=0, v ∈ U.
Step 6: choose weights ω ' (u from U, k), the condition that described weights meet is the weights that the weights of u to k are less than other node data, and described node data k is added in S, with the node data that k is new, in amendment U, the weights of each node data, arrange dis (k, v)=min [dis (k, v)+w
,(k, v)]; Wherein k is the qualified node data in new selected logistics railway freight station, highway freight station or port and pier, v represents car and boat certain node data not in the logistics railway freight station of process, highway freight station or port and pier, ω ' (k, v) represents the weights from node data k to v.
Step 7, the transportation route preferential according to step 6 optimization expense:
Cost
min(k, v)=min [(dis (k, v) * up), ((dis (k, v)+ω (k, v)) * up)], repeat step 6 until all node data are included in S, thus the preferential optimal path cost of cost calculating from described shipping starting point to other node data
min(X, v); Wherein up represents that certain shipping car and boat type is transporting the distance of 1KM and the average cost under other same transportation environments.
Transportation route according to step 6 optimization time priority:
Time
min(k, v)=min [(dis (k, v)/vel), ((dis (k, v)+ω (k, v))/vel)], repeat step 6 until all node data are included in S, thus the time priority optimal path time calculating from described shipping starting point to other node data
min(X, v); Wherein vel represents that certain shipping car and boat model is possessing the average velocity under other same transportation environments.
Above-mentioned logistics railway freight station, highway freight station or port and pier mainly comprise and join goods point, join goods station, car and boat master, Logistic Park, the owner of cargo, logistics company, forwarder, port and pier.
Preferential, optimization method can also optimize railway freight station, highway freight station and port and pier V first respectively
1, V
2, V
3means of transportation and path, then carry out Data Integration.
First the present invention arranges node data and limit data, namely arrange logistics railway freight station, highway freight station or port and pier etc. and join goods station point, then calculate with Johnson algorithm and obtain best means of transportation and path, mainly comprise: described node data and limit data Weighted Directed Graph are represented; Calculating chart G adds the figure after new node; Use bellman-ford algorithm process G', and form the minor increment of new node to each node; Upgrade the weights of limit data; To the bee-line of all node data run dijkstra's algorithm calculating and other node data in figure G; The means of transportation of the preferential and time priority of optimization expense and path.This method solve given goods, starting point and destination, from different means of transportation and transportation route, according to the restriction element such as price priority, time priority, calculate and select best means of transportation and the problem of transportation route intelligently.
Accompanying drawing explanation
Fig. 1 is intelligent optimization method process flow diagram of the present invention.
Fig. 2 is means of transportation of the present invention and path intelligent optimization algorithm process flow diagram.
Embodiment
For making the technical problem to be solved in the present invention, technical scheme and advantage clearly, be described in detail below in conjunction with the accompanying drawings and the specific embodiments.
Embodiment 1
According to Fig. 1, the invention provides a kind of logistics transportation mode and path intelligent optimization method, according to Johnson algorithm, this algorithm fusion bellman-ford algorithm and dijkstra's algorithm, and dijkstra's algorithm itself also uses priority array, Performance Ratio is better, reaches O (V
2lgV+VL) time complexity is the fastest in without negative power loop diagram, more representative, the specifically main optimization analyzing means of transportation and path from time priority and the preferential two kinds of conditions of expense, and it is characterized in that, optimization method step is as follows:
(1) node data are prepared
Node data refer to the set of railway freight station, highway freight station and port and pier, node data are as shipping starting point and shipping destination, the starting point of car and boat master in the transport of transport power and destination are determined, then set described shipping starting point as X, shipping destination is Y.
(2) limit data are prepared
1) limit data refer to all possible path between any two nodes be communicated with by a certain means of transportation, and number of path is N, comprise the time from spending required for the specific means of transportation of two node X to Y or Y to X and expense cost.
2) possible following path is got rid of according to real world applications scene
The adaptation issues of goods and means of transportation, some goods is because size, safety factor only can use specific mode to transport;
The restriction of means of transport, comprises car, ship, the quantity of train and capacity problem;
The depot storage capacity of shipping station, port and pier, the restriction of handling capacity;
Transprovincially with the official inspection in city and time consuming problem;
The Scheduling factors of means of transport, in special time period, the height problem of means of transport;
Handling cost problem between several modes, is transformed into the expense of the time that another kind of mode needs from a kind of means of transportation.
3) the transportation route M that limited bar is possible is finally obtained;
(3) the best means of transportation of acquisition and path is calculated
Step 1: as shown in Figure 2, represents node data and limit data Weighted Directed Graph, is expressed as G=(V, L);
V represents the set of node data, i.e. the set of railway freight station, highway freight station and port and pier, can classify to be expressed as V={V by algebraic expression
1, V
2, V
3, V
1, V
2, V
3represent the node data acquisition of railway freight station, highway freight station and port and pier respectively, wherein V
1={ v
11, v
12... v
1i... v
1r, wherein 1≤i≤r, r≤M, v
irepresent i-th node data of railway freight station in digraph, same represented highway freight station and the node data of port and pier;
L represents limit data, and the path namely between any two nodes be communicated with by a certain means of transportation, algebraic expression is expressed as L={v
i, v
j, wherein 1≤i, j≤M, v
i, v
jrepresent node data v
i, v
jbetween road length.
The weights ω of described Weighted Directed Graph considers from the time travelled between any two node data needed for per unit length, expense, urgency level, and in reality operation continuous training study.
Step 2: calculating chart G adds the figure G' after new node, between the new node data added to all former node data, distance is 0, forms new limit collection L' simultaneously, mainly comprises:
Calculating chart G adds the figure G'=(V', L') after new node, and the weights between the new node X added to all former nodes are zero, namely
v'=V ∪ (X), L'=L ∪ (X, u): u ∈ V}, and to all u ∈ V, ω (X, u)=0; Wherein X be newly add logistics railway freight station, shipping starting point in highway freight station or port and pier, certain node u represents from shipping starting point to shipping destination.
Step 3: use bellman-ford algorithm process G', and form the minor increment of new node to each node, mainly comprise:
Shipping starting point X is expressed as dis (X, u) or dis (X, v) to the distance described in other between node data u or v, and limit data are L (u, v), and the weights of its correspondence are ω (u, v);
If perform circulation dis (X, u)+ω (u, v) <dis (X, v), then upgrade dis (X, v)=dis (X, u)+ω (u, v), if dis (X, v) does not upgrade or part point data is unreachable, then end loop, obtain the bee-line set R (dis) of described shipping starting point X to other node datas, and described shipping starting point X is left in array h (x) to each bee-lines of other node data; Wherein x ∈ R (dis) i.e. x is in described shipping starting point X to the bee-line set R (dis) of other node datas.
Step 4: the weights upgrading limit data
To the weights ω (u of all limits data, v), be updated to ω ' (u, v)=ω (u, v)+h (u)-h (v), wherein u, certain node v represents from shipping starting point to shipping destination, ω (u, v) represents u, the weights of the limit data that v is corresponding, the i.e. quantized value of the possibility in the path of process between two logistics railway freight stations, highway freight station or port and piers.
Step 5: to the bee-line of all node data run dijkstra's algorithm calculating and other node data in figure G, mainly comprise:
1) described all node data are expressed as V={S, U}, S={u}, U={ other node data except u }; Wherein S is the set of the node data having obtained optimal path, and U is that all the other do not determine the set of the node data of optimal path, and ω ' (u)=0, v ∈ U.
2) from U, choose weights ω ' (u, k), the condition that described weights meet is the weights that the weights of u to k are less than other node data, and described node data k is added in S, and be new node data with k, the weights of each node data in amendment U, dis (k is set, v)=min [dis (k, v)+w'(k, v)];
Wherein k is the qualified node data in new selected logistics railway freight station, highway freight station or port and pier, v represents car and boat certain node data not in the logistics railway freight station of process, highway freight station or port and pier, ω ' (k, v) represents the weights from node data k to v.
Step 6: means of transportation and the path of optimizing the preferential and time priority of expense, mainly comprise:
According to 2) the preferential transportation route of optimization expense:
Cost
min(k, v)=min [(dis (k, v) * up), ((dis (k, v)+ω (k, v)) * up)], repeat step 6 until all node data are included in S, thus the preferential optimal path cost of cost calculating from described shipping starting point to other node data
min(X, v); Wherein up represents that certain shipping car and boat type is transporting the distance of 1KM and the average cost under other same transportation environments.
According to 2) transportation route of optimization time priority:
Time
min(k, v)=min [(dis (k, v)/vel), ((dis (k, v)+ω (k, v))/vel)], repeat step 6 until all node data are included in S, thus the time priority optimal path time calculating from described shipping starting point to other node data
min(X, v); Wherein vel represents that certain shipping car and boat model is possessing the average velocity under other same transportation environments.
The means of transportation of prioritizing selection when can learn that car and boat transport when time priority and the preferential transportation route of expense are determined.
The logistics railway freight station mentioned in above-mentioned, highway freight station or port and pier mainly comprise and join goods point, join goods station, car and boat master, Logistic Park, the owner of cargo, logistics company, forwarder, port and pier.
Embodiment 2
The present invention can also adopt and optimize railway freight station, highway freight station and port and pier V respectively
1, V
2, V
3means of transportation and path, then carry out Data Integration, namely respectively the V in embodiment 1 replaced with V respectively
1, V
2, V
3reclassify optimization, and Johnson algorithm is reused to the result of Classified optimization carry out global optimization.
Embodiment 3
Present invention also offers the system of a kind of logistics transportation mode and path intelligent optimization, it comprises: prepare node data processing module, it is for preparing node data, described node data refer to the set of railway freight station, highway freight station and port and pier, the starting point of car and boat master in the transport of transport power and destination are determined, then set described shipping starting point as X, shipping destination is Y;
Limit data preparation module is for preparing limit data, described limit data refer to all possible path between any two nodes be communicated with by a certain means of transportation, number of path is N, comprises the time from spending required for the specific means of transportation of two node X to Y or Y to X and expense cost;
Best means of transportation and path acquisition module adopt Johnson algorithm will deposit node data and limit data in digraph G as node and calculate optimum means of transportation and path, and this system is mainly used in the method step realizing embodiment 1,2.
The above is the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from principle of the present invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (9)
1. a method for logistics transportation mode and path intelligent optimization, comprise the optimization analyzing means of transportation and path from time priority and the preferential two kinds of conditions of expense, it is characterized in that, optimization method step is as follows:
(1) node data are prepared
Node data refer to the set of railway freight station, highway freight station and port and pier, described node data are as shipping starting point and shipping destination, the starting point of car and boat master in the transport of transport power and destination are determined, then set described shipping starting point as X, shipping destination is Y;
(2) limit data are prepared
1) limit data refer to all possible path between any two nodes be communicated with by a certain means of transportation, and number of path is N, comprise the time from spending required for the specific means of transportation of two node X to Y or Y to X and expense cost;
2) possible following path is got rid of according to real world applications scene:
The adaptation issues of goods and means of transportation, some goods is because size, safety factor only can use specific mode to transport;
The restriction of means of transport, comprises car, ship, the quantity of train and capacity problem;
The depot storage capacity of shipping station, port and pier, the restriction of handling capacity;
The official inspection in trans-regional (province and city) and time consuming problem;
The Scheduling factors of means of transport, in special time period, the height problem of means of transport;
Handling cost problem between several modes, is transformed into the expense of the time that another kind of mode needs from a kind of means of transportation; Obtain the transportation route that limited bar is possible;
(3) the best means of transportation of acquisition and path is calculated
Step 1: described node data and limit data Weighted Directed Graph are represented, is expressed as G=(V, L); The weights ω of described Weighted Directed Graph considers from the time travelled between any two node data needed for per unit length, expense, urgency level, and in actual motion continuous training study;
Step 2: calculating chart G adds the figure G' after new node, between the new node data added to all former node data, distance is 0, forms new limit collection L' simultaneously;
Step 3: use bellman-ford algorithm process G', and form the minor increment of new node to each node;
Step 4: the weights upgrading limit data, to the weights ω (u, v) of all limits data, are updated to ω ' (u, v)=ω (u, v)+h (u)-h (v);
Step 5: to the bee-line of all node data run dijkstra's algorithm calculating and other node data in figure G;
Step 6: means of transportation and the path of optimizing the preferential and time priority of expense.
2. the method for intelligent optimization according to claim 1, it is characterized in that, described logistics railway freight station, highway freight station or port and pier mainly comprise and join goods point, join goods station, car and boat master, Logistic Park, the owner of cargo, logistics company, forwarder, port and pier.
3. the method for intelligent optimization according to claim 1, is characterized in that, according to the result of the optimal path between each node data obtained.
4. the method for intelligent optimization according to claim 1, is characterized in that, can also optimize railway freight station, the means of transportation of highway freight station and port and pier and path first respectively, then carry out Data Integration.
5. the method for intelligent optimization according to claim 1, is characterized in that, described step 5 comprises further:
(1) described all node data are expressed as V={S, U}, S={u}, U={ other node data except u }; Wherein S is the set of the node data having obtained optimal path, and U is that all the other do not determine the set of the node data of optimal path, and ω ' (u)=0, v ∈ U.
(2) from U, choose weights ω ' (u, k), the condition that described weights meet is the weights that the weights of u to k are less than other node data, and described node data k is added in S, and be new node data with k, the weights of each node data in amendment U, dis (k is set, v)=min [dis (k, v)+w'(k, v)];
Wherein k is the qualified node data in new selected logistics railway freight station, highway freight station or port and pier, v represents car and boat certain node data not in the logistics railway freight station of process, highway freight station or port and pier, ω ' (k, v) represents the weights from node data k to v.
6. the method for intelligent optimization according to claim 5, is characterized in that, described step 6 comprises further:
The transportation route preferential according to (2) step optimization expense in described step 5: cost
min(k, v)=min [(dis (k, v) * up), ((dis (k, v)+ω (k, v)) * up)], repeat step 6 until all node data are included in S, thus the preferential optimal path cost of cost calculating from described shipping starting point to other node data
min(X, v); Wherein up represents that certain shipping car and boat type is transporting the distance of 1KM and the average cost under other same transportation environments.
Transportation route according to (2) step optimization time priority in step 5:
Time
min(k, v)=min [(dis (k, v)/vel), ((dis (k, v)+ω (k, v))/vel)], repeat step 6 until all node data are included in S, thus the time priority optimal path time calculating from described shipping starting point to other node data
min(X, v); Wherein vel represents that certain shipping car and boat model is possessing the average velocity under other same transportation environments.
7. a system for logistics transportation mode and path intelligent optimization, is characterized in that, mainly comprises node data preparation module, limit data preparation module and best means of transportation and path acquisition module;
Described node data preparation module is for preparing node data, described node data refer to the set of railway freight station, highway freight station and port and pier, the starting point of car and boat master in the transport of transport power and destination are determined, then set described shipping starting point as X, shipping destination is Y;
Limit data preparation module is for preparing limit data, described limit data refer to all possible path between any two nodes be communicated with by a certain means of transportation, number of path is N, comprises the time from spending required for the specific means of transportation of two node X to Y or Y to X and expense cost;
Best means of transportation and path calculation module adopt Johnson algorithm will deposit node data and limit data in digraph G as node and calculate optimum means of transportation and path.
8. a system for logistics transportation mode and path intelligent optimization, is characterized in that, described limit data preparation module also needs to get rid of possible following path:
The adaptation issues of goods and means of transportation, some goods is because size, safety factor only can use specific mode to transport;
The restriction of means of transport, comprises car, ship, the quantity of train and capacity problem;
The depot storage capacity of shipping station, port and pier, the restriction of handling capacity;
The official inspection in trans-regional (province and city) and time consuming problem;
The Scheduling factors of means of transport, in special time period, the height problem of means of transport;
Handling cost problem between several modes, is transformed into the expense of the time that another kind of mode needs from a kind of means of transportation; Obtain the transportation route that limited bar is possible.
9. a system for logistics transportation mode and path intelligent optimization, is characterized in that, the step realizing described best means of transportation and road computing module also comprises:
Step 1: described node data and limit data Weighted Directed Graph are represented, is expressed as G=(V, L);
The weights ω of described Weighted Directed Graph considers from the time travelled between any two node data needed for per unit length, expense, urgency level, and in reality operation continuous training study;
Step 2: calculating chart G adds the figure G' after new node, between the new node data added to all former node data, distance is 0, forms new limit collection L' simultaneously;
Step 3: use bellman-ford algorithm process G', and form the minor increment of new node to each node;
Step 4: the weights upgrading limit data, to the weights ω (u, v) of all limits data, are updated to ω ' (u, v)=ω (u, v)+h (u)-h (v);
Step 5: to the bee-line of all node data run dijkstra's algorithm calculating and other node data in figure G;
Step 6: means of transportation and the path of optimizing the preferential and time priority of expense.
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| CN105956814A (en) * | 2016-06-30 | 2016-09-21 | 石莉 | Water transport logistics time estimation method and system |
| CN106169128A (en) * | 2016-07-06 | 2016-11-30 | 石莉 | Water transport logistics route generates method and system |
| CN106228324A (en) * | 2016-07-18 | 2016-12-14 | 石莉 | Water transport logistics website method and system |
| CN107133339A (en) * | 2017-05-17 | 2017-09-05 | 北京趣拿软件科技有限公司 | Circuit query method and apparatus and storage medium, processor |
| CN107341633A (en) * | 2017-06-26 | 2017-11-10 | 海航创新科技研究有限公司 | Regulate and control the method and apparatus of logistics route |
| WO2018000329A1 (en) * | 2016-06-30 | 2018-01-04 | 石莉 | Method and system for time estimation for water transport logistics |
| WO2018000328A1 (en) * | 2016-06-30 | 2018-01-04 | 石莉 | Method and system for time estimation for vehicle transport logistics |
| WO2018000326A1 (en) * | 2016-06-30 | 2018-01-04 | 石莉 | Method and system for time estimation for air transport logistics |
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| CN109801023A (en) * | 2019-02-22 | 2019-05-24 | 北京航空航天大学 | A kind of multimode traffic through transport method and device under multi-constraint condition |
| CN109829565A (en) * | 2018-12-25 | 2019-05-31 | 益萃网络科技（中国）有限公司 | Optimum choice method, apparatus, computer equipment and the storage medium of logistics route |
| CN110400113A (en) * | 2019-08-06 | 2019-11-01 | 三江学院 | Part real-time scheduling method and system are pulled in a kind of logistics |
| CN112017062A (en) * | 2020-07-15 | 2020-12-01 | 北京淇瑀信息科技有限公司 | Resource limit distribution method and device based on guest group subdivision and electronic equipment |
| CN113393044A (en) * | 2021-06-22 | 2021-09-14 | 中远海运科技(北京)有限公司 | Logistics resource emergency optimization system based on big data integration |
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| CN113610453A (en) * | 2021-06-30 | 2021-11-05 | 宁波诺丁汉大学 | Multi-transportation-mode combined container transportation path selection method |
| CN113869591A (en) * | 2021-09-30 | 2021-12-31 | 浙江创邻科技有限公司 | Logistics management system and method based on graph technology |
| CN114418496A (en) * | 2022-01-19 | 2022-04-29 | 中冶赛迪工程技术股份有限公司 | Visual representation method and system for material flow of steel mill, electronic equipment and medium |
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| CN105956814A (en) * | 2016-06-30 | 2016-09-21 | 石莉 | Water transport logistics time estimation method and system |
| WO2018000329A1 (en) * | 2016-06-30 | 2018-01-04 | 石莉 | Method and system for time estimation for water transport logistics |
| WO2018000328A1 (en) * | 2016-06-30 | 2018-01-04 | 石莉 | Method and system for time estimation for vehicle transport logistics |
| WO2018000326A1 (en) * | 2016-06-30 | 2018-01-04 | 石莉 | Method and system for time estimation for air transport logistics |
| CN106169128A (en) * | 2016-07-06 | 2016-11-30 | 石莉 | Water transport logistics route generates method and system |
| CN106228324A (en) * | 2016-07-18 | 2016-12-14 | 石莉 | Water transport logistics website method and system |
| CN107133339A (en) * | 2017-05-17 | 2017-09-05 | 北京趣拿软件科技有限公司 | Circuit query method and apparatus and storage medium, processor |
| CN107341633A (en) * | 2017-06-26 | 2017-11-10 | 海航创新科技研究有限公司 | Regulate and control the method and apparatus of logistics route |
| CN108805484A (en) * | 2018-04-26 | 2018-11-13 | 中铁第四勘察设计院集团有限公司 | Railway cold chain process engineering system based on technology of Internet of things and control method |
| CN109829565A (en) * | 2018-12-25 | 2019-05-31 | 益萃网络科技（中国）有限公司 | Optimum choice method, apparatus, computer equipment and the storage medium of logistics route |
| CN109801023A (en) * | 2019-02-22 | 2019-05-24 | 北京航空航天大学 | A kind of multimode traffic through transport method and device under multi-constraint condition |
| US11441915B2 (en) | 2019-06-18 | 2022-09-13 | M. A. Mortenson Company | Circuits for electricity-generating units |
| CN110400113A (en) * | 2019-08-06 | 2019-11-01 | 三江学院 | Part real-time scheduling method and system are pulled in a kind of logistics |
| CN110400113B (en) * | 2019-08-06 | 2022-01-11 | 三江学院 | Real-time scheduling method and system for logistics package |
| CN112017062A (en) * | 2020-07-15 | 2020-12-01 | 北京淇瑀信息科技有限公司 | Resource limit distribution method and device based on guest group subdivision and electronic equipment |
| CN112017062B (en) * | 2020-07-15 | 2024-06-07 | 北京淇瑀信息科技有限公司 | Resource quota distribution method and device based on guest group subdivision and electronic equipment |
| CN113393044A (en) * | 2021-06-22 | 2021-09-14 | 中远海运科技(北京)有限公司 | Logistics resource emergency optimization system based on big data integration |
| CN113610453A (en) * | 2021-06-30 | 2021-11-05 | 宁波诺丁汉大学 | Multi-transportation-mode combined container transportation path selection method |
| CN113610453B (en) * | 2021-06-30 | 2024-07-02 | 宁波诺丁汉大学 | Container transportation path selection method combining multiple transportation modes |
| CN113467522A (en) * | 2021-08-01 | 2021-10-01 | 陈军 | Method and system for unmanned aerial vehicle to approach unmanned aerial vehicle airport |
| CN113869591A (en) * | 2021-09-30 | 2021-12-31 | 浙江创邻科技有限公司 | Logistics management system and method based on graph technology |
| CN114418496A (en) * | 2022-01-19 | 2022-04-29 | 中冶赛迪工程技术股份有限公司 | Visual representation method and system for material flow of steel mill, electronic equipment and medium |
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