Seaports participate in hinterland economic development through partnerships with dry ports, and the combined seaport-dry port network serves as the backbone of regional logistics. This paper constructs a location-allocation model for the regional seaport-dry port network optimization problem and develops a greedy algorithm and a genetic algorithm to obtain its solution. This model is applicable to situations under which the geographic distribution of demand is known. A case study involving configuration of dry ports near the west bank of the Taiwan Strait is conducted, and the model is successfully applied.
Rapid development of seaports and intermodal transportation systems under integrated planning has made it necessary for seaports to dynamically assess what constitutes their hinterlands, and the scramble for hinterlands by seaports is heating up. On the other hand, it is increasingly recognized by hinterlands that seaports guide and support regional economic development, and there is a growing need to perform in hinterland locations seaports’ functions except ship loading and unloading. The interactions of these two driving forces have induced rapid development of dry ports as both a means by which seaports vie for hinterland access and a means by which hinterlands stimulate economic growth. Logistics networks, each including a group of seaports and some dry ports, are becoming backbones of regional goods movement. At the end of 2011, there were over 100 dry ports built or being built in China, with the Port of Tianjin leading the development of more than 20 of them. There were also a large number of road and rail transportation hubs which were in many aspects similar to dry ports. The development of dry ports can mitigate problems caused by constraints related to land and others that limit seaports’ growth. Dry ports can also coordinate the operation of the port supply chain and support regional economic development. Consequently, dry ports are changing the dynamics of interaction between seaports and hinterlands. This paper studies the location of dry ports from the perspective of seaport-hinterland interaction and optimizes the configuration of the seaport-dry port system, taking into consideration the relationships between dry ports, seaports, and the regional logistics system.
On the evolution of a port, Bird [
The rapid development of multimodal transportation has driven the movement of containers within inland regions. Since the early 1980s, operators of containerized transport have built sophisticated networks of inland container transport, and major nodes in these networks become the prototype of dry ports. Roso et al. [
Key factors of a seaport group include port location, capacity, origin of shipments, and the cooperative and competitive relationships. If these factors are known, the task of optimizing the regional seaport-dry port system is to determine for the dynamic hinterland the number of dry ports, their locations and capacities, and the relationships among themselves as well as between them and the seaports. The regional configuration of dry ports is constrained by available candidate locations, transport access to these locations, and the shipping demand within the zone of influence of each location. The demand-supply relationship and the choice behavior of agents (seaports, dry ports, shippers, and carriers) need to be reasonably modeled. This research develops a location-allocation model for seaport-dry port system optimization, characterized by a probabilistic choice for shippers’ use of dry ports and a partnership between seaports and dry ports. The research provides both a methodological approach for decision making and new insights on the relationship between seaports and the hinterland.
For a regional seaport-dry port system focusing on exporting freight generated in the hinterland through seaports to the outside, there are two essential types of elements: nodes and links. Nodes include seaports, existing and planned dry ports, and hinterland origins of freight. Links connect the origins of freight to seaports, either directly or through dry-ports. This is shown in Figure
Network flow on a regional seaport-dry port system.
For the abovementioned export-oriented regional seaport-dry port system with a single type of freight, the process of location-allocation is as follows: (1) the government constructs of dry ports and designates for each dry port the seaports it collaborates with; (2) shippers choose to route freight to a seaport, either directly or through a dry port. The government’s action in step (1) must anticipate the choice of shippers in step (2), in order to minimize the overall regional logistics cost. Thus we have a location-allocation model, in which the government determines the number and the locations of dry ports, taking into consideration freight allocation by shippers. The objective is to minimize the regional logistics cost.
With regard to the relationship between dry ports and seaports, two scenarios are considered: (1) a dry port can partner with and send freight to any number of seaports and (2) a dry port can send freight to only one seaport. In both cases, a seaport can receive freight from multiple dry ports.
In addition, the following assumptions are made. The locations of the nodes (freight origins, seaports, and candidate sites for dry ports) are predetermined and known. The transport links between the above nodes are predetermined and known. The freight volume originating from each hinterland origin is known, and freight must be exported through one of the seaports. The overall regional logistics cost includes the annual cost of transport, the amortized cost of setting up the dry ports, the cost of maintaining the transport links between dry ports and seaports, and the cost of maintaining the infrastructure at seaports. The unit transport cost on a link and the cost of setting up a dry port are not dependent on the freight volume, but the cost of maintaining a link or maintaining seaport infrastructure is dependent on the freight volume on the link or through the seaport. Any freight passes through at most one dry port.
The programming model we develop consists of a model of system logistics cost minimization though determining from candidate dry port sites a subset to use, picking the collaborating seaports for each dry port, and allocating freight to different routes.
The model is as follows:
In the objective function,
The decision variables are
Besides transport costs, the system logistics cost contains also handling costs and the costs related to setting up dry ports and their partnerships. As stated in assumption 7, unit handling cost at dry port
The model remains essentially the same, with one additional constraint added to the model:
The objective of regional seaport-dry port system optimization is to determine the quantity, size, and location of the dry ports and the transport links between them and seaports so as to minimize region-wide logistics cost. In the programming model formulated above, the objective function is nonlinear, and the decision variables must take binary values. Thus common optimization techniques do not readily apply. Instead, a greedy algorithm and a genetic algorithm are combined to solve the optimization problem.
A greedy algorithm is adopted to obtain a good feasible solution of the model. The basic idea of the algorithm is to start with the full network (i.e., setting up a dry port at each candidate location and linking each dry port to all seaports in the case of shared dry port or to its nearest seaport in the case of dedicated dry port) and then take out links one at a time to examine if the system logistics cost can be reduced, until no link can be taken out to further reduce the system logistics cost.
When each dry port can be shared by seaports, the steps are the following.
Set up
Allocate the freight volume generated from hinterland origins to routes according to the expression (
Initialize
Let
Start from the link in Sk with the highest unit transport cost; examine the links in Sk one by one to see if any can be removed. If yes, remove that link from Sk and SN.
Examine if the last step removed any link. If yes, go to Step
If Sk is empty, mark dry port
If
Remove all dry ports marked for exclusion from SM.
Examine if from Step
What remains in SM and SN is an initial feasible solution for the programming problem.
With the genetic algorithm to optimize the regional seaport-dry port system, new solution is obtained through random transformation of current solutions. The basic idea is as follows: decide for the decision variables a coding scheme; then generate the initial population, of which individuals shall be corresponding to different dry port-seaport system configurations; use the expression (
The detailed process is shown in Figure
Process of the genetic algorithm.
Set the population size “pop_size,” mutation rate
The initial population
Apply the allocation expression (
Apply the model’s objective function to obtain the fitness of the scheme.
Conduct hybrid and mutation operations with specified
Apply the allocation expression (
Calculate the fitness value of
In accordance with the roulette wheel method and the elite preservation strategy, obtain
If
Two seaports on the coast of Fujian province, Xiamen and Fuzhou, compete but also cooperate with other major Chinese coastal seaports to serve the hinterland regions of the Fujian province. For this study, 13 seaports are considered: Tianjin, Dalian Shanghai, Ningbo, Fuzhou, Xiamen, Qingdao, Guangzhou, Shenzhen, Shantou, Haikou, Zhanjiang, and Nanning, as shown in Table
Seaports and their attractiveness to shippers.
No. | Name of seaport | Attractiveness to shippers |
---|---|---|
1 | Tianjin | 4.5 |
2 | Dalian | 4.4 |
3 | Shanghai | 10.0 |
4 | Ningbo | 4.8 |
5 | Fuzhou | 4.0 |
6 | Xiamen | 4.3 |
7 | Qingdao | 4.4 |
8 | Guangzhou | 4.6 |
9 | Shenzhen | 4.3 |
10 | Shantou | 3.0 |
11 | Haikou | 2.0 |
12 | Zhanjiang | 3.0 |
13 | Nanning | 1.0 |
Hinterland freight origins in Fujian province.
No. | Name of freight origin | Demand volume (tons/year) |
---|---|---|
1 | Fuzhou | 597646 |
2 | Xiamen | 796707 |
3 | Putian | 117423 |
4 | Sanming | 33216 |
5 | Quanzhou | 886176 |
6 | Zhangzhou | 363424 |
7 | Nanping | 52748 |
8 | Ningde | 49893 |
9 | Longyan | 30222 |
Candidate dry ports.
No. | Name of dry port |
---|---|
1 | Nanchang |
2 | Changsha |
3 | Ganzhou |
4 | Nanping |
5 | Sanming |
6 | Longyan |
7 | Jiujiang |
8 | Shangrao |
9 | Meizhou |
10 | Yingtan |
Export volume (in tons/year) between cities in Fujian province and various seaports.
Freight origin | Seaport | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tianjin | Dalian | Shanghai | Ningbo | Fuzhou | Xiamen | Qingdao | Guangzhou | Shenzhen | Shantou | Haikou | Zhanjiang | Nanning | |
Fuzhou | 327 | 124 | 28484 | 1117 | 413254 | 143780 | 1546 | 367 | 7338 | 58 | 11 | 0 | 1240 |
Xiamen | 945 | 186 | 34768 | 2775 | 460 | 744637 | 3681 | 349 | 8242 | 8 | 5 | 2 | 650 |
Putian | 28 | 1 | 4048 | 320 | 46206 | 60446 | 983 | 5 | 5351 | 1 | 0 | 0 | 36 |
Sanming | 177 | 1 | 882 | 100 | 19627 | 10877 | 80 | 0 | 974 | 0 | 0 | 0 | 497 |
Quanzhou | 761 | 141 | 2932 | 1430 | 16900 | 840085 | 1047 | 292 | 18267 | 106 | 2 | 0 | 4213 |
Zhangzhou | 126 | 42 | 3645 | 219 | 268 | 351899 | 619 | 330 | 3793 | 2188 | 2 | 16 | 277 |
Nanping | 8 | 0 | 3387 | 278 | 44351 | 3657 | 74 | 9 | 829 | 1 | 0 | 0 | 153 |
Ningde | 290 | 56 | 242 | 1878 | 36727 | 9574 | 115 | 5 | 759 | 167 | 15 | 1 | 64 |
Longyan | 0 | 0 | 2148 | 7 | 43 | 26453 | 15 | 2 | 1466 | 41 | 0 | 0 | 46 |
Unit transport cost (in Yuan/ton) between cities in Fujian province and alternative dry ports.
Freight origin | Dry port |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|
Nanchang | Changsha | Ganzhou | Nanping | Sanming | Longyan | Jiujiang | Shangrao | Meizhou | Yingtan | |
Fuzhou | 49 | 73 | 50 | 20 | 25 | 32 | 56 | 40 | 44 | 40 |
Xiamen | 60 | 71 | 36 | 33 | 27 | 18 | 69 | 62 | 27 | 50 |
Putian | 55 | 81 | 52 | 31 | 26 | 24 | 64 | 49 | 42 | 49 |
Sanming | 33 | 59 | 32 | 83 | 0 | 26 | 42 | 33 | 26 | 27 |
Quanzhou | 53 | 75 | 41 | 27 | 29 | 24 | 62 | 54 | 35 | 47 |
Zhangzhou | 52 | 66 | 32 | 33 | 27 | 109 | 61 | 62 | 26 | 46 |
Nanping | 35 | 61 | 38 | 0 | 83 | 33 | 44 | 31 | 32 | 29 |
Ningde | 57 | 83 | 60 | 30 | 28 | 38 | 66 | 33 | 54 | 51 |
Longyan | 44 | 58 | 33 | 25 | 26 | 1 | 53 | 47 | 25 | 38 |
Unit transport cost (in Yuan/ton) between cities in Fujian province and various seaports.
Freight origin | Seaport | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tianjin | Dalian | Shanghai | Ningbo | Fuzhou | Xiamen | Qingdao | Guangzhou | Shenzhen | Shantou | Haikou | Zhanjiang | Nanning | |
Fuzhou | 152 | 239 | 66 | 45 | 1 | 30 | 187 | 87 | 67 | 41 | 119 | 78 | 130 |
Xiamen | 160 | 226 | 83 | 65 | 29 | 1 | 138 | 57 | 48 | 22 | 100 | 142 | 104 |
Putian | 160 | 214 | 70 | 53 | 115 | 20 | 126 | 68 | 59 | 33 | 111 | 143 | 70 |
Sanming | 137 | 206 | 66 | 58 | 25 | 27 | 117 | 57 | 55 | 38 | 103 | 130 | 105 |
Quanzhou | 157 | 220 | 76 | 58 | 20 | 92 | 132 | 61 | 53 | 26 | 109 | 147 | 109 |
Zhangzhou | 156 | 220 | 85 | 68 | 33 | 66 | 137 | 54 | 46 | 20 | 98 | 138 | 102 |
Nanping | 139 | 202 | 60 | 52 | 20 | 33 | 113 | 63 | 61 | 44 | 109 | 132 | 111 |
Ningde | 136 | 199 | 55 | 38 | 102 | 28 | 111 | 85 | 73 | 47 | 125 | 112 | 129 |
Longyan | 148 | 217 | 84 | 76 | 32 | 18 | 128 | 49 | 46 | 28 | 96 | 82 | 96 |
Unit transport cost (in Yuan/ton) between alternative dry ports and various seaports.
Dry port | Seaport |
||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tianjin | Dalian | Shanghai | Ningbo | Fuzhou | Xiamen | Qingdao | Guangzhou | Shenzhen | Shantou | Haikou | Zhanjiang | Nanning | |
Nanchang | 109 | 181 | 62 | 53 | 47 | 60 | 124 | 82 | 74 | 76 | 140 | 111 | 104 |
Changsha | 136 | 203 | 94 | 91 | 78 | 96 | 130 | 57 | 68 | 99 | 120 | 91 | 78 |
Ganzhou | 148 | 222 | 93 | 91 | 62 | 44 | 155 | 44 | 41 | 43 | 107 | 69 | 108 |
Nanping | 139 | 202 | 66 | 52 | 20 | 33 | 113 | 63 | 61 | 44 | 109 | 97 | 111 |
Sanming | 156 | 206 | 75 | 67 | 25 | 27 | 118 | 57 | 55 | 38 | 103 | 91 | 105 |
Longyan | 148 | 217 | 84 | 76 | 32 | 18 | 128 | 49 | 46 | 27 | 96 | 82 | 96 |
Jiujiang | 100 | 171 | 55 | 54 | 56 | 69 | 83 | 75 | 75 | 73 | 121 | 108 | 108 |
Shangrao | 120 | 183 | 41 | 33 | 40 | 59 | 96 | 80 | 80 | 66 | 126 | 113 | 113 |
Meizhou | 156 | 225 | 92 | 83 | 44 | 30 | 136 | 32 | 29 | 17 | 78 | 65 | 79 |
Yingtan | 113 | 182 | 49 | 41 | 41 | 54 | 93 | 73 | 73 | 58 | 119 | 106 | 106 |
Export freight volumes between cities in Fujian province and various seaports.
We look at both the case of shared dry ports and the case of dedicated dry ports. The values of other parameters in the objective function (i.e.,
Initial solution and optimization result after 200 iterations.
Dry port | Dry port throughput (shared) |
Dry port throughput (dedicated) |
Dry port throughput (shared) |
Dry port throughput (dedicated) |
---|---|---|---|---|
Nanchang | 0 | 0 | 0 | 0 |
Changsha | 0 | 0 | 0 | 0 |
Ganzhou | 0 | 0 | 0 | 0 |
Nanping | 97818.44531 | 105605.3281 | 412067.5 | 891146.75 |
Sanming | 63477.57422 | 127935.3594 | 31191.81445 | 0 |
Longyan | 387477.7813 | 378427.6563 | 1083868.75 | 878881.5 |
Jiujiang | 0 | 0 | 0 | 0 |
Shangrao | 1015.576355 | 0 | 72.5763855 | 0 |
Meizhou | 10148.79297 | 0.000772247 | 364213.125 | 0 |
Yingtan | 0 | 0 | 0 | 0 |
The iteration process.
Shared
Dedicated
Dry port locations.
Shared
Dedicated
From the above, we can see that development of dry ports dramatically reduces system logistics cost for hinterland origins in Fujian province.
With the increased competition between the regional seaport-dry port networks, optimizing system configuration has attracted attention of many researchers. The regional seaport-dry port system is a complex system. By focusing on the relationship between seaports and dry ports, this paper has developed a location-allocation model for regional seaport-dry port system optimization and has proposed an efficient solution method for the programming problem. This paper provides justifications for developing dry ports at strategic locations and lays a foundation for future research on regional resource integration.
The paper is funded by the National Natural Science Foundation of China (Project no. 51009060), the Research Basis Project of Philosophy and Social Science of Jiangsu Province (09JD017), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (Coastal Development Conservancy).