CN107220737B - Port container liner branch network optimization method under Hub-spoke mode - Google Patents

Port container liner branch network optimization method under Hub-spoke mode Download PDF

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CN107220737B
CN107220737B CN201710623582.2A CN201710623582A CN107220737B CN 107220737 B CN107220737 B CN 107220737B CN 201710623582 A CN201710623582 A CN 201710623582A CN 107220737 B CN107220737 B CN 107220737B
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蒋柳鹏
杨义林
季婕
蒋鹤
王娇娇
陈丽玲
丁之仪
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Hohai University HHU
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Abstract

The invention discloses a port container liner branch network optimization method in Hub-spoke mode, which comprises the following steps: step one, establishing model description; step two, establishing a model hypothesis; step three, establishing model parameters and symbols; and step four, establishing a mathematical model. Under the constraint conditions of multi-ship type, ship capacity, time of a liner, bidirectional flow and port water depth, a container Hub-spoke branch line transportation model of a Hub port-feeding port is constructed by taking the lowest transportation cost as guidance, and the optimal configuration of ship transportation capacity is realized. Compared with the similar models, the model provided by the invention is closer to the actual container transportation and can realize the optimal transportation configuration of a container transportation network.

Description

Port container liner branch network optimization method under Hub-spoke mode
Technical Field
The invention relates to the technical field of waterway transportation, in particular to a method for optimizing a port container liner branch network in a Hub-spoke mode.
Background
Ocean shipping of containers is used as a necessary means for integration of international trade and global economy, has the advantages of large transport capacity and low cost, and plays a key role in the development of international economy.
Container ocean shipping requires a high demand for ports and berths, and has both sufficient sources of goods and sufficient depth conditions, and small ports (including coastal small ports, inland river ports and land waterless ports) often do not have the freight organization and berthing capability for opening ocean routes, and ocean shipping is carried out after containers are gathered to a large harbor in the form of large port feeding. At present, container ocean transport routes mainly comprise pendulum type, multi-port hanging type and Hub-spoke type, wherein the Hub-spoke mode has more cost and time advantages.
At present, most researches on the problem of a port container branch bidirectional transportation scheduling optimization model in a Hub-spoke mode construct the port container branch transportation scheduling optimization model in the Hub-spoke mode on the basis of considering factors such as empty box allocation, ship line speed, ship configuration, ship time, ship capacity and inventory capacity. However, under the condition of considering the bidirectional flow of the container for water supply and water discharge, the port water depth limit and the like, the research results of how to carry out transportation optimization and ship scheduling are less, and particularly, the model considering the bidirectional flow and the port water depth limit simultaneously is a model.
Therefore, it is a problem to be solved urgently to construct a Hub-spoke branch line transportation model of a container of a Hub port-feeding port under the constraint conditions of multi-ship type, ship capacity, ship time, bidirectional flow and port water depth.
Disclosure of Invention
In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is to provide a method for optimizing a network of a container liner branch of a port in Hub-spoke mode, so as to solve the defects in the prior art.
In order to achieve the above-mentioned objects,
a port container liner network optimization method under Hub-spoke mode comprises the following steps:
step one, establishing model description;
step two, establishing a model hypothesis;
step three, establishing model parameters and symbols;
and step four, establishing a mathematical model.
In the first step, the model building description is specifically as follows:
in the setting of a hub-spoke mode ocean route of a container, a hub port v of the ocean route on one side of a hub-spoke network is constructed0Hub harbor v0There are n ports fed as { v i1,2, …, n, the period of the flight line is T, and the shipping network is defined as a network formed by the hub port and the hub portThe directed privileged hub-spoke network formed by the feeding port G ═ (V, E, W), V ═ ViI ═ 0,1, …, n } denotes ports in the network, and E ═ E { (E) }ij) (i, j) represents viAnd vjI, j ∈ (1,2, …, n), i ≠ j, W ═ dijRepresenting a flight path eijThe right of (1).
Step two, establishing a model hypothesis specifically comprises the following steps:
(2-1) transporting the containers from the feeding port to the terminal port by differentiated ship transportation and ocean-going transportation by large ships at the terminal port, assuming that the amount of containers produced for the feeding and discharging of the containers at the feeding port is known;
(2-2) the loading and unloading service of the containers at each feeding port receives only one ship transportation service, and the ship loading capacity cannot exceed the rated loading capacity;
(2-3) from the feeding port to the hub port, the loading capacity, the transportation speed and the cost of different transportation modes are different, and the hub port needs to complete the transportation task within a time window;
(2-4) the vessel departs from the terminal port and all container logistics services of the feeding port are completed and finally returns to the terminal port.
Step three, establishing model parameters and symbols specifically comprises the following steps:
(3-1) establishing ship parameters and signs
(3-1a) in the hub-spoke network, the container transportation means are ships, and are divided into inland river ships and ocean-going ships, and the definition is
Figure BDA0001362265480000021
Is a ship set;
Figure BDA0001362265480000022
total K vessels, mkFor the number of vessels of the kth type,
Figure BDA0001362265480000023
the total number of ships;
(3-1B) defining a set of ship attributes Bk，Bk＝(sk,ck,dk,uk,pk,fk,ek,λk) Where K is 1,2, …, K, BkSet of attributes, s, representing the kth type of vesselkFor depreciation of the vessel, ckRepresenting the maximum capacity of the vessel, dkIndicating the required depth of water, u, of the vesselkRepresenting the cost per unit distance travelled by the vessel, pkRepresenting the cost per unit time for berthing and departing of the vessel, fkIndicating the starting cost of the ship, ekThe unit time cost of the ship when the ship is parked is represented; lambda [ alpha ]kIs a variable of 0,1, lambda when the kth vessel belongs to a coastal transport vessel k1, otherwise λk＝0；
(3-2) establishing Port parameters and symbols
(3-2a) the amount of containers fed and discharged to and from the known feeding port i is
Figure BDA0001362265480000031
And
Figure BDA0001362265480000032
Figure BDA0001362265480000033
the amount of containers when a first vessel of the k-type vessels travels away from port i to port j;
Figure BDA0001362265480000034
the amount of containers when a first vessel of the k-type vessels is driven down away from the feeding port i to the feeding port j;
(3-2b) definition of hiBerthing the ship at the depth of water for the feeding port i; definition of dijThe sailing distance from the feeding port i to the feeding port j; definition of
Figure BDA0001362265480000035
The service time for serving the first ship in the k-type ships to the port i in the ascending process;
Figure BDA0001362265480000036
in a k-type vesselThe service time for serving the feeding port i in the descending process of the first ship;
Figure BDA0001362265480000037
the voyage time for the first vessel of the k-type vessel to travel from the feeding port i to the feeding port j;
Figure BDA0001362265480000038
docking and departure times to serve the first of the k-type vessels to port i;
(3-2c) defining control variables:
Figure BDA0001362265480000039
the feeding port i finishes the ascending distribution by the first ship in the k types of ships, then
Figure BDA00013622654800000310
Otherwise
Figure BDA00013622654800000311
The feeding port i completes the downward distribution by the first ship in the k types of ships, then
Figure BDA00013622654800000312
Otherwise
Figure BDA00013622654800000313
The first of the k-type vessels is steered from the feeding port i to the feeding port j, then xijkl1, otherwise xijkl＝0。
In the fourth step, the establishment of the mathematical model specifically comprises the following steps:
determining a path set of a ship to carry out transportation service on the water feeding container and the water discharging container of n feeding ports, wherein an objective function of a Hub-spoke branch transportation model of the container port is as follows:
Figure BDA00013622654800000314
the constraint St.
Figure BDA00013622654800000315
Figure BDA0001362265480000041
Figure BDA0001362265480000042
Figure BDA0001362265480000043
Figure BDA0001362265480000044
Figure BDA0001362265480000045
Figure BDA0001362265480000046
The formula (1) is an objective function, the objective requires that the distribution cost of all ships is minimum, μ P is a penalty term, μ is a stage function, and μ is 0 when the total time spent by each ship is less than the course period, otherwise μ is 1; p is a positive number (the value range of P is 1000000-10000000);
the constraint of equation (2) means that all the containers at the feeding port are serviced by the ship and only one ship is serviced;
equation (3) is a constraint condition, which means that all the downstream containers at the feeding port are serviced by the ship and are serviced by only one ship;
the formula (4) is a constraint condition and represents the flow of the ship to a closed loop;
equation (5) is a constraint condition, which indicates that the loading box amount of each ship does not exceed the maximum capacity;
formula (6) is a constraint condition, which indicates that all ships are going to depart from the hub port and return to the hub port;
the formula (7) is a constraint condition, which indicates that the water depth of the feeding port meets the requirement of a ship for berthing;
the formula (8) is a constraint condition, the service completion time of all ships is less than the cycle of the air route, and the loading and unloading of the container cannot be completed simultaneously.
The invention has the beneficial effects that:
according to the invention, through the constructed container Hub-spoke branch transport model of the Hub port-feeding port, the model is solved by using an algorithm, and the optimal transport configuration of the container Hub-spoke transport network can be obtained.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
Fig. 1 is a spur network optimization iteration diagram of the present invention.
Detailed Description
As shown in fig. 1, a method for optimizing a network of a container liner branch in a port under Hub-spoke mode includes the following steps:
step one, establishing model description;
step two, establishing a model hypothesis;
step three, establishing model parameters and symbols;
and step four, establishing a mathematical model.
In this embodiment, in the first step, the establishing of the model description specifically includes:
in a certain container hub-spoke mode ocean route, constructing a hub port v of the ocean route on one side of the hub-spoke network0Hub harbor v0There are n ports fed as { v i1,2, …, n, the period of the route is T, the shipping network is defined as a weighted hub-spoke network consisting of hub ports and feeding ports, G (V, E, W), V (V)iI ═ 0,1, …, n } denotes ports in the network, and E ═ E { (E) }ij) (i, j) represents viAnd vjCourse between i, j∈(1,2,…,n)，i≠j，W＝dijRepresenting a flight path eijThe term "right of way" in the present invention refers to the distance of the routes between ports.
In this embodiment, in the second step, the assumption of establishing the model specifically includes:
(2-1) transporting the containers from the feeding port to the terminal port by differentiated ship transportation and ocean-going transportation by large ships at the terminal port, assuming that the amount of containers produced for the feeding and discharging of the containers at the feeding port is known;
(2-2) the loading and unloading service of the containers at each feeding port receives only one ship transportation service, and the ship loading capacity cannot exceed the rated loading capacity;
(2-3) from the feeding port to the hub port, the loading capacity, the transportation speed and the cost of different transportation modes are different, and the hub port needs to complete the transportation task within a time window;
(2-4) the vessel departs from the terminal port and all container logistics services of the feeding port are completed and finally returns to the terminal port.
In this embodiment, in the third step, the establishing of the model parameters and symbols specifically includes:
(3-1) establishing ship parameters and signs
(3-1a) in the hub-spoke network, the main tools for container transportation are ships, which are divided into inland river ships and ocean-going ships, and are defined as
Figure BDA0001362265480000061
Is a ship set;
Figure BDA0001362265480000062
total K vessels, mkFor the number of vessels of the kth type,
Figure BDA0001362265480000063
is the total number of vessels.
(3-1B) defining a set of ship attributes Bk，Bk＝(sk,ck,dk,uk,pk,fk,ek,λk) Wherein k is 1,2, …,K，BkSet of attributes, s, representing the kth type of vesselkFor depreciation of the vessel, ckRepresenting the maximum capacity of the vessel, dkIndicating the required depth of water, u, of the vesselkRepresenting the cost per unit distance travelled by the vessel, pkRepresenting the cost per unit time for berthing and departing of the vessel, fkIndicating the starting cost of the ship, ekRepresenting the cost per unit time for the vessel when docked. Lambda [ alpha ]kIs a variable of 0,1, lambda when the kth vessel belongs to a coastal transport vessel k1, otherwise λk＝0。
(3-2) establishing Port parameters and symbols
(3-2a) the amount of containers fed and discharged to and from the known feeding port i is
Figure BDA0001362265480000064
And
Figure BDA0001362265480000065
Figure BDA0001362265480000066
the amount of containers when a first vessel of the k-type vessels travels away from port i to port j;
Figure BDA0001362265480000067
the amount of containers when the first vessel of the k-type vessels is sailed down away from the feeding port i towards the feeding port j.
(3-2b) definition of hiBerthing the ship at the depth of water for the feeding port i; definition of dijThe sailing distance from the feeding port i to the feeding port j; definition of
Figure BDA0001362265480000068
The service time for serving the first ship in the k-type ships to the port i in the ascending process;
Figure BDA0001362265480000069
the service time for serving the first ship in the k-type ships to the port i in the descending process;
Figure BDA00013622654800000610
the voyage time for the first vessel of the k-type vessel to travel from the feeding port i to the feeding port j;
Figure BDA00013622654800000611
docking and departure times to serve the first of the k-type vessels to port i;
(3-2c) defining control variables:
Figure BDA00013622654800000612
the feeding port i finishes the ascending distribution by the first ship in the k types of ships, then
Figure BDA00013622654800000613
Otherwise
Figure BDA00013622654800000614
The feeding port i completes the downward distribution by the first ship in the k types of ships, then
Figure BDA00013622654800000615
Otherwise
Figure BDA00013622654800000616
The first of the k-type vessels is steered from the feeding port i to the feeding port j, then xijkl1, otherwise xijkl＝0。
In this embodiment, the step four, establishing the mathematical model specifically includes:
determining a path set of a ship to carry out transportation service on the water feeding container and the water discharging container of n feeding ports, wherein an objective function of a Hub-spoke branch transportation model of the container port is as follows:
Figure BDA00013622654800000617
Figure BDA0001362265480000071
the constraint St.
Figure BDA0001362265480000072
Figure BDA0001362265480000073
Figure BDA0001362265480000074
Figure BDA0001362265480000075
Figure BDA0001362265480000076
Figure BDA0001362265480000077
Figure BDA0001362265480000078
The formula (1) is an objective function, the objective requires that the distribution cost of all ships is minimum, μ P is a penalty term, μ is a stage function, and μ is 0 when the total time spent by each ship is less than the course period, otherwise μ is 1; p is a positive number (the value range of P is 1000000-10000000);
the constraint of equation (2) means that all the containers at the feeding port are serviced by the ship and only one ship is serviced;
equation (3) is a constraint condition, which means that all the downstream containers at the feeding port are serviced by the ship and are serviced by only one ship;
the formula (4) is a constraint condition and represents the flow of the ship to a closed loop;
equation (5) is a constraint condition, which indicates that the loading box amount of each ship does not exceed the maximum capacity;
formula (6) is a constraint condition, which indicates that all ships are going to depart from the hub port and return to the hub port;
the formula (7) is a constraint condition, which indicates that the water depth of the feeding port meets the requirement of a ship for berthing;
and (4) the formula (8) is a constraint condition, T is a course cycle, and represents that the service completion time of all ships is less than the course cycle, and the loading and unloading of the containers cannot be completed simultaneously.
Example 1
The method for optimizing the Hub-spoke network of the port container under the Hub-spoke mode is applied to a Hub-spoke transport network from Shanghai to Meixi, and is implemented as follows:
selecting Shanghai harbor as hub harbor, Yangtze river along the route of Changjiang river, Zhenjiang, Yangzhou, Nanjing and Changzhou as feeding and Heishang harbor, issuing one ocean route each week by Shanghai harbor, in order to meet the import and export requirements of the Yangtze river along the route of Changjiang river, all ships are issued from Shanghai, serving the feeding harbor according to the requirements of time window, unloading the imported containers to the corresponding harbor, loading the export containers, and returning all the ships to the Shanghai harbor within the set time.
The distances between hub harbor, the seaport and the feeding harbor are shown in table 1.
TABLE 1 distance between Shanghai and Feihong (Unit: sea)
Figure BDA0001362265480000081
The water depth required for each feeding port waterway is shown in table 2.
TABLE 2 Water depth of each feeding port and waterway (unit: meter)
Figure BDA0001362265480000082
The amount of inlet and outlet tanks in each feeding port in one week is shown in Table 3.
TABLE 3 case volume (Unit: TEU) for each feeding port
Figure BDA0001362265480000083
The container ship's loading and unloading costs at each feeding port are obtained from the amount of the imported and exported containers, as shown in table 4. Parking handling costs include container port costs and container handling package costs.
TABLE 4 Loading and unloading charges (Unit: Yuan) for each feeding port
Figure BDA0001362265480000091
According to the container amount of the inlet and the outlet of the feeding port, the ship type is selected from 350TEU, 500TEU, 800TEU and 1000TEU, and the basic parameters of the four ship types are shown in the table 5.
Table 5 represents the basic parameters of the ship type
Figure BDA0001362265480000092
The cost for the four ship types is shown in table 6. Wherein the initial cost is calculated according to the depreciation cost of the container ship, the initial cost is collected according to 5% of the ship value, and the depreciation period is 15 years. The sailing cost is obtained according to the prices of heavy oil and light oil consumed by sailing the ship and the wages of crews. The berthing and departure cost is obtained according to the net ton of the ship, and port service cost, ship tonnage, pilotage cost, berthing cost, mooring cost and tugboat cost are obtained.
Table 6 represents the cost of the ship model voyage
Figure BDA0001362265480000093
(data source: research on team wheel network of branch line centralized packing case in uncertain environment & Liujian autumn)
According to the data, the model is solved by using a multi-agent genetic algorithm, the model requires that the container ship is attached to a feeding port to meet the draught requirement, and the whole shipping network meets the time window requirement (the time window is limited to 1 week). The correlation coefficients are shown in Table 7.
TABLE 7 correlation coefficient of multi-agent genetic algorithm
Figure BDA0001362265480000101
Under the win8 operating system, 4G of memory and 4G of CPU are hardware platforms of Intel (R) core (TM) i5-4210U, matlab2017a platform programming is adopted, and a multi-agent genetic algorithm and a genetic algorithm are respectively used for optimizing iteration, and the process is shown in figure 1.
The running time of the multi-agent genetic algorithm is 12.38 seconds, the algorithm converges to an extreme point 2336787 about 25 times, and the algorithm is stable. The genetic algorithm runs for 26.848 seconds and the algorithm converges to extreme point 2336787 on the order of 75. As known from the optimization process, the multi-agent genetic algorithm has obvious advantages compared with the genetic algorithm, and the convergence speed and the convergence time are obviously improved.
And (5) counting the multiple optimization results to obtain the optimal branch transport network shown in the table.
TABLE 8 optimal Branch transport network
Figure BDA0001362265480000102
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (1)

1. A port container liner network optimization method under Hub-spoke mode is characterized by comprising the following steps:
step one, establishing model description;
step two, establishing a model hypothesis;
step three, establishing model parameters and symbols;
step four, establishing a mathematical model;
in the first step, the model building description is specifically as follows:
in the setting of a hub-spoke mode ocean route of a container, a hub port v of the ocean route on one side of a hub-spoke network is constructed0Hub harbor v0There are n ports fed as { vi1,2, …, n, the period of the route is T, the shipping network is defined as a directional and authorized hub-spoke network consisting of hub ports and feeding ports, G (V, E, W), V (V { [ V ] }iI ═ 0,1, …, n } denotes ports in the network, and E ═ E { (E) }ij) (i, j) represents viAnd vjI, j ∈ (1,2, …, n), i ≠ j, W ═ dijRepresenting a flight path eijThe right of (1) refers to the distance of routes between ports;
step two, establishing a model hypothesis specifically comprises the following steps:
(2-1) transporting the containers from the feeding port to the terminal port by differentiated ship transportation and ocean-going transportation by large ships at the terminal port, assuming that the amount of containers produced for the feeding and discharging of the containers at the feeding port is known;
(2-2) the loading and unloading service of the containers at each feeding port receives only one ship transportation service, and the ship loading capacity cannot exceed the rated loading capacity;
(2-3) from the feeding port to the hub port, the loading capacity, the transportation speed and the cost of different transportation modes are different, and the hub port needs to complete the transportation task within a time window;
(2-4) the ship starts from the hub port, all container logistics services of the feeding port are completed, and finally the ship returns to the hub port;
step three, establishing model parameters and symbols specifically comprises the following steps:
(3-1) establishing ship parameters and signs
(3-1a) in the hub-spoke network, the container transportation means are ships, and are divided into inland river ships and ocean-going ships, and the definition is
Figure FDA0002602534920000011
Is a ship set;
Figure FDA0002602534920000012
total K vessels, mkFor the number of vessels of the kth type,
Figure FDA0002602534920000021
the total number of ships;
(3-1B) defining a set of ship attributes Bk，Bk＝(sk,ck,dk,uk,pk,fk,ek,λk) Where K is 1,2, …, K, BkSet of attributes, s, representing the kth type of vesselkFor depreciation of the vessel, ckRepresenting the maximum capacity of the vessel, dkIndicating the required depth of water, u, of the vesselkRepresenting the cost per unit distance travelled by the vessel, pkRepresenting the cost per unit time for berthing and departing of the vessel, fkIndicating the starting cost of the ship, ekThe unit time cost of the ship when the ship is parked is represented; lambda [ alpha ]kIs a variable of 0,1, lambda when the kth vessel belongs to a coastal transport vesselk1, otherwise λk＝0；
(3-2) establishing Port parameters and symbols
(3-2a) the amount of containers fed and discharged to and from the known feeding port i is
Figure FDA0002602534920000027
And
Figure FDA0002602534920000028
Figure FDA0002602534920000029
the amount of containers when a first vessel of the k-type vessels travels away from port i to port j;
Figure FDA00026025349200000210
the amount of containers when a first vessel of the k-type vessels is driven down away from the feeding port i to the feeding port j;
(3-2b) definition of hiBerthing the ship at the depth of water for the feeding port i; definition of dijThe sailing distance from the feeding port i to the feeding port j; definition of
Figure FDA00026025349200000211
The service time for serving the first ship in the k-type ships to the port i in the ascending process;
Figure FDA00026025349200000212
the service time for serving the first ship in the k-type ships to the port i in the descending process;
Figure FDA00026025349200000213
the voyage time for the first vessel of the k-type vessel to travel from the feeding port i to the feeding port j;
Figure FDA00026025349200000214
docking and departure times to serve the first of the k-type vessels to port i;
(3-2c) defining control variables:
Figure FDA0002602534920000022
the feeding port i finishes the ascending distribution by the first ship in the k types of ships, then
Figure FDA00026025349200000215
Otherwise
Figure FDA0002602534920000023
Figure FDA0002602534920000024
The feeding port i completes the downward distribution by the first ship in the k types of ships, then
Figure FDA00026025349200000216
Otherwise
Figure FDA00026025349200000217
Figure FDA0002602534920000025
The first of the k-type vessels is steered from the feeding port i to the feeding port j, then xijkl1, otherwise xijkl＝0；
In the fourth step, the establishment of the mathematical model specifically comprises the following steps:
determining a path set of a ship to carry out transportation service on the water feeding container and the water discharging container of n feeding ports, wherein an objective function of a Hub-spoke branch transportation model of the container port is as follows:
Figure FDA0002602534920000026
the constraint St.
Figure FDA0002602534920000031
Figure FDA0002602534920000032
Figure FDA0002602534920000033
Figure FDA0002602534920000034
Figure FDA0002602534920000035
Figure FDA0002602534920000036
Figure FDA0002602534920000037
The formula (1) is an objective function, the objective requires that the distribution cost of all ships is minimum, μ P is a penalty term, μ is a stage function, and μ is 0 when the total time spent by each ship is less than the course period, otherwise μ is 1; p is a positive number;
the constraint of equation (2) means that all the containers at the feeding port are serviced by the ship and only one ship is serviced;
equation (3) is a constraint condition, which means that all the downstream containers at the feeding port are serviced by the ship and are serviced by only one ship;
the formula (4) is a constraint condition and represents the flow of the ship to a closed loop;
equation (5) is a constraint condition, which indicates that the loading box amount of each ship does not exceed the maximum capacity;
formula (6) is a constraint condition, which indicates that all ships are going to depart from the hub port and return to the hub port;
the formula (7) is a constraint condition, which indicates that the water depth of the feeding port meets the requirement of a ship for berthing;
the formula (8) is a constraint condition, T is a course cycle, and represents that the service completion time of all ships is less than the course cycle, and the loading and unloading of the containers cannot be completed simultaneously;
the value range of P is 1000000-10000000.
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