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Planning connections between underground logistics system and container ports
Journal Pre-proofs Planning connections between underground logistics system and container ports Yiqun Fan, Chengji Liang, Xiaoyuan Hu, Ye Li PII: DOI: Reference:
S0360-8352(19)30668-0 https://doi.org/10.1016/j.cie.2019.106199 CAIE 106199
To appear in:
Computers & Industrial Engineering
Please cite this article as: Fan, Y., Liang, C., Hu, X., Li, Y., Planning connections between underground logistics system and container ports, Computers & Industrial Engineering (2019), doi: https://doi.org/10.1016/j.cie. 2019.106199
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© 2019 Published by Elsevier Ltd.
Planning connections between underground logistics system and container ports YIQUN FAN
Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. 901, Shanghai Zhongshan North Second District, Yangpu District, Shanghai 200082 [email protected] CHENGJI LIANG
Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 201306 [email protected] XIAOYUAN HU
Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 201306 [email protected] YE LI
Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. 901, Shanghai Zhongshan North Second District, Yangpu District, Shanghai 200082 [email protected]
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Highlights 1) Proposed to establish a port-convergence-station to connect the underground logistics system(ULS) with ports. 2) Considered the uncertainties of the carrying capacity of an ULS. 3) Proposed a robust optimization model to find the appropriate location of the port-convergence-station. 4) Verified the effectiveness of establishing the ULS and port-convergence-station in the case by VISSIM simulation.
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Abstract - In order to alleviate the traffic congestion inside the port cities caused by container transportation, the underground logistics system (ULS) is considered in this paper to solve the container transportation problem. By establishing port-convergence-station, the connection between the ULS and the port is realized. Specifically, the paper describes the internal layout and floor space of the port-convergence-station. Meanwhile, a robust optimization model is established to deal with the uncertainties of the carrying capacity of an ULS. Moreover, a case study based on Waigaoqiao Port Area to Logistics Park in Jiading is conducted to intuitively solve the robust optimization model to obtain the appropriate location of the port-convergence-station. And the effectiveness of establishing ULS and port-convergence-stations in the case is verified by simulation to illustrate the congestion problem in the container port area. Keywords: underground logistics system; port-convergence-station; station planning; robust optimization; container ports
1. Introduction With the rapid development of ports, container throughput has been increasing and the existing transportation system around port areas have been unable to meet the demand for containers in port areas. Therefore, road traffic congestion around the port is becoming more and more serious, and the contradiction between the port and the city is deepening (Zhao, 2009). In addition, container truck transport has caused great pollution to the environment, which cannot be ignored. The “plan of eastern corridor” has been put forward in Los Angeles to mitigate urban traffic congestion and environmental pollution. Rotterdam Port has proposed a 20% reduction in the traffic during peak periods (Li, 2013), and New York Port is gradually transformed into a virtual port by keeping only a part of the administrative function but keeping the real port away from the city centre. ULS, also known as the underground freight system, refers to a new concept of transportation and supply system that realizes the transportation of solid goods through large-diameter underground pipelines, tunnels and other transportation channels by means of carrying tools such as automatic guided vehicles (AGVs) and amphibious trucks (Liu, 2013). On the one hand, emerging ULS can reduce the pressure on urban traffic and the urban traffic accident rate. On the other hand, it can optimize the structure of urban economy and improve the urban ecological environment. Meanwhile, ULS is fast, low cost and automatic. Moreover, the advantages of high precision can be used to reduce traffic congestion in the port area and environmental pollution, thus improving the accessibility and stability of container transport (Fan and Qian, 2011). In general, the idea of using ULS to solve the problem of congestion in cities eventually grabbed the attention of researchers. The number of trucks in the city can be significantly reduced by the means of combining public subway services with conventional freight vehicles to transport goods efficiently from suburban to downtown areas (Jun, 2012). There is a way of using underground pipelines to transport solids and powders, effectively preventing traffic congestion and accidents (Ewa, 2016). Yan (2016) studied the node location of ULS through the system layout design method. Huang (2006) analysed the distribution centre’s operation processes, function settings and internal relations. To solve the traffic congestion problems of ports, establishing a port-convergence-station to connect underground logistics channels (ULCs) with the port is necessary. Hence, the locations of portconvergence-stations are very important. Yang (2007) considered the restrictions of the practical applications, calculated alternative locations, and used discrete model to solve the best point of a distribution centre based on the calculation of the location of a centre by using the centre-of-gravity method. Huang (2011) applied a particle swarm optimization algorithm to a logistics distribution centre with the objective of achieving the lowest logistics costs and established a corresponding mathematical model solved by CPLEX. Zhou (2011) explored the new characteristics of current logistics distribution centres, then established a multi-period model of logistics and distribution centres with stochastic constraint planning. Besides, Zhou analysed the model and verified by practical examples with the objective of the lowest total logistic costs during the operation period. Guo, Xie and Chen (2012) pointed out the severe traffic situation in China’s megacities, and the necessity of developing underground logistics in Beijing was analysed. Meanwhile, they proposed the application field of underground logistics, and believed that traffic pressure, severe urban space resources, energy and environmental capacity constraints are the motivation and research trend for the development of underground container transportation. Based on the actual situation of Xianlin district, Nanjing city, the problems of logistics node selection, design of underground channel network and optimization of 3
logistics network are solved by Feng (2018) using relevant data and mathematical model. Qu and Xiong (2018) took advantages of investigation and numerical regression analysis, SLP and other methods to discuss and forecast the site selection, layout, construction technology and other issues in the feasibility study of urban ULS in Wuhan area. However, the port-convergence-station in an ULS is a new research field. This paper studies the queuing in the port-convergence-station in order to avoid congestion. Lee, Wong, and Li (2015) developed a real-time estimation approach for lane-based queue lengths. Based on detector information at isolated signalized junctions, the number of queued vehicles in each lane is determined. Giorno, Nobile, and Pirozzi (2018) considered the Markovian single-server queueing model with Poisson arrivals and state dependent service rates, characterized by a logarithmic steady-state distribution. In a new design for a bi-directional AGV system, in which two AGVs can exchange their loads, their scheduled transportation tasks, and even their vehicle numbers when they move in opposite directions. Hsueh (2010) proposed an off-line mathematical model and carried out a series of simulant experiments. Furthermore, Hsueh confirmed that the new system performs efficiently and robustly. From previous studies, the existing ULS is mainly for pipeline or capsule transportation, aiming at short-distance, small items distribution. It is a good idea to consider the ULS based on container transportation to solve the congestion in port cities. At present, there is a lack of research on the connection between ULS and container ports, such as port-convergence-stations. This paper studies the connection of ULS and port areas with port-convergence-station, designs different schemes for portconvergence-stations. The queuing theory is used to establish a calculation model of the congestion degree of the underground guided vehicle (UGV) access underground buffer zone. Then the number of the underground buffer areas of a station and the area of the port-convergence-station can be calculated. Considering the uncertainties of the carrying capacity of an ULS, a robust optimal location model is established. The planning position of the port-convergence-station is determined. At last, the planning of three schemes is simulated to validate the effectiveness of the ULS to solve the contradiction between ports and cities. The remainder of this paper is organized as follows. In Section 2, the considered problem is described in detail. Section 3 proposes a robust optimization model for the location of port-convergencestations. Section 4 gives the internal layout of port-convergence-station. In section 5, a case study is conducted to evaluate the performance of the proposed methods. Section 6 concludes this paper and indicates future research directions.
2. Problem Description The task of receiving and dispatching containers is mainly accomplished through container transportation. The rapid development of ports will lead to the rapid growth of container throughput, which will lead to the congestion and cost increase of container transportation. This study considers to transport containers through ULS to alleviate traffic congestion. Unlike the conventional ground logistics system, the transportation part of the ULS is deep underground and needs the connection between underground and ground. The ULS is mainly composed of two parts, including terminal subsystem and transportation subsystem, which constitute the underground logistics network. The terminal subsystem is responsible for the connection between the ground subsystem and the transportation subsystem. There are many types of terminal subsystem, such as highway freight station, railway freight station and other transportation hubs, as shown in Fig. 1.
Fig. 1. The composition of ULS.
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The ULS is operated in bi-directional transportation. The operating process of ULS can be understood in conjunction with Fig. 2, 3 and 4. The process of establishing a port-convergence-station can be described as follows. (1)An outbound container is carried by an UGV through the ULC to reach an underground logistics buffer area. (2)Then the loaded UGV travels to the area directly below the shaft with a fixed yard crane above. (3)The fixed yard crane picks up the container and places it onto a waiting non-loaded container truck, which carries it to the yard area. (Considering that the priority of UGV is higher than that of container truck, it is assumed that the number of container trucks is sufficient and there is no case of UGV waiting for container truck.) (4)After that, the fixed yard crane picks up an inbound container from a loaded container truck and places it onto a non-loaded UGV. (Because the task of UGV is known, when UGV is unloaded, there must be loaded container truck waiting for operation on the ground.) (5)The loaded UGV runs into the ULC. Port 2
Port 1
Port 4
Port 5
Port 6
Exit
Entrance unload
Exit Entrance
load ULS
Jiading Logistics Park
Entrance Exit
unload
load
Fig. 2. Connection layout of port and ULS. Fixed yard crane Shaft
Yard Ground
Yard crane
1
Shaft
Vertical channel
2
Shaft Fixed yard crane
Node A
Container truck
ULC
station
ULC
Fig.2(b). The profile of handling area for loading and unloading containers
Fig.2(a). The layout of port-convergence-station
Fig. 3. Diagram of port-convergence-station.
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Above ground
Under ground
UGV
Shaft UGV
Fixed yard crane
UGV
ULC
UGV
Channel in the buffer area
Handling area
Fig. 4. Container transfer diagram. The problem of the convergence of the ULS and the port need to be considered when containers are transported through the system. According to the layout and operation flow of the container terminal, establishing a port-convergence-station between the ULS and the port is the most appropriate choice. However, the scheme of the port area docking with the port directly requires an ULC to be set up in the various terminals. The cost is enormous and coordination between the ULS’s and port’s planning and scheduling is not easy to achieve. So, this paper discusses the best scheme for the establishment of a port-convergence-station to achieve a convergence of the ULS and the port.
3. Robust Optimization Model The port-convergence-station is an important terminal of the ULS, and its position determines whether the establishment of the system can effectively reduce traffic congestion in the surrounding roads of the port area. Regret model, which is one of the robust optimization model, is used to solve the location problem of port-convergence-stations (Liu, Yang and Yang, 2013). In the regret model of this paper, the difference between the objective function value of the feasible solution and the optimal objective value of the scenario is used to measure the regret value of the scenario. This paper takes into account the uncertainty of the number of the containers carried by the ULS from the port area. Here different numbers of containers are treated as different scenarios, then a robust optimization location model is established and Matlab is used as a platform to call the YALMIP toolbox to solve the model (Wang and He, 2009). The location problem considered in this robust optimization model is based on Shanghai city. The overall structure of ULS in this paper is shown in Fig. 5. Node A is the node of ULC, where ULC can be diverted to different port-convergence-stations.
Fig. 5. Overall structure of ULS in this paper.
3.1 Assumptions (1) The daily throughput of Node A is equal to the daily throughput of the port-convergence-station. 6
(2) The container is Twenty-feet Equivalent Unit (TEU). (3) The construction cost of each port-convergence-station is known and same for every station. (4) To simplify the calculation, this section considers uni-directional demand. (5) All scenarios have the same probability of occurrence. 3.2 Notation Sets set of the port-convergence-stations, indexed by j set of the ports in Waigaoqiao, indexed by k set of the scenarios, indexed by q Parameters probability of occurrence of scenario q q
J K Q
c m
construction cost per km of the ULC (unit: CNY/km) transportation cost per km from Node A to each port-convergence-station (unit: CNY/km)
b jk
transportation cost per km from port-convergence-station j to port k (unit: CNY/km)
vj
daily management cost per TEU for container at port-convergence-station j (unit: CNY/TEU/day) fixed construction cost of port-convergence-station j (unit: CNY)
fj y mj
number of containers that Node A can carry (unit: TEU) maximum number of containers that port-convergence-station j can carry (unit: TEU)
d kq
number of containers that port k needs to transport under scenario q (unit: TEU)
Pj
distance between Node A and port-convergence-station j (unit: km)
Pjk n a
distance between port-convergence-station j and port k (unit: km)
sj
binary, equals one if a port-convergence-station is established at j, and zero otherwise
maximum number of port-convergence-station daily coefficient regret limited coefficient Decision variables integer, represents the number of containers through jth port-convergence-station from Node x qjk A to kth port under scenario q
3.3Model construction In a given scenario, the model’s parameters are deterministic, so the problem of choosing a location for the port-convergence-station is a deterministic optimization problem. The model’s minimum target *
value is recorded as Fq .
min F q Fq
(1)
qQ
Fq
c
j J
Pj f
x
j J k K q Q
q jk
j
s
b
jk
j
Pj k m Pj a v j
Subject to:
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(2)
x jJ kK
x
kK
q jk
x jJ
q jk
q jk
y, q Q
(3)
m j s j , j J , q Q
(4)
d kq , k K , q Q
(5)
s jJ
1, sj 0, Fq Fq* Fq*
j
n
x
kK
q jk
(6)
0
x qjk 0
(7)
, q Q
(8)
kK
s j 0,1, j J
(9)
x N , j J , k K , q Q
(10)
q jk
Equation (1) is the objective function, which means minimizing the total cost of all scenarios. Equation (2) is the total costs of a given scenario, including the construction cost of the ULC and portconvergence-station, transportation cost of the ULC and port-convergence-station to each port area, and the daily management cost of the port-convergence-station. Constraint (3) indicates that the total delivery capacity of the container at the port-convergence-station does not exceed the number of containers carried by Node A. Constraint (4) guarantees that the number of containers moving through the convergence station cannot exceed the station’s maximum capacity. Constraint (5) means that the number of containers transported to each port should satisfy the port’s demand. Constraint (6) represents that port-convergence-station should not exceed the maximum number. Constraint (7) states that the jth port-convergence-station is selected or not. Constraint (8) ensures the difference between the value of the objective function of the feasible solution and the optimal target value within a given range in a given scenario. Constraints (9) - (10) define variables.
4. The Internal Layout of Port-convergence-station The establishment of the port-convergence-station requires land of a certain size. The area will be composed mainly of three parts: the area of the vertical channel used for loading and unloading containers, the gate area used for checking container trucks travelling in and out of the station, and the yard area. This section discusses the layout of port-convergence-station, as shown in Fig. 3. Some equipment performance assumptions are based on reference to Zhenhua Heavy Industries Co., Ltd. (ZPMC). The assumptions in this section are made as follows. (1) All UGVs have the same capacity and shape and transport two TEUs at a time. (2) The efficiency of the fixed yard crane at the handling area is consistent. (3) Special types of containers are not considered, such as those carrying dangerous or refrigerated goods. 4.1Handling area for loading and unloading containers In the ULC, a container is transported by an UGV and loaded/unloaded by a fixed yard crane in the handling area, as shown in Fig. 4. An UGV can transport two TEUs. Each shaft with a fixed yard crane can carry four TEUs while handling two UGVs. The congestion degree of the UGVs in the handling area is determined by the number of channels in the buffer area and the number of containers carried by the ULS. This section describes the use of queuing theory to establish a model for calculating 8
the congestion degree when the UGVs are being loaded/unloaded in the handling area and the number of shafts to be set up when the system carries a different number of containers for reducing the congestion degree. According to the number of shafts and the size of the area required for the container trucks to turn, the size of the handling area for loading and unloading containers can be calculated. The average waiting time of an UGV can be calculated by the M/M/C queuing model (Zhao, 2013).
The acceptable average waiting time of an UGV in the buffer area is no more than 0.1 minute. The minimum numbers of buffer and shafts can be obtained. The relevant notations are defined as follows. number of shafts n turning radius of a container truck e width of a container truck Wv length of a shaft
Lc Wc
width of a shaft The handling area can be calculated by:
H1 n2e Lc 4Wv Wc
(11)
4.2 Yard area This section discusses the calculation of the yard area based on the throughput of the portconvergence-station. The relevant notations are defined as follows. the container throughput per day Qh
tdc
the average stacking time of containers at the yard
K BK
the unbalanced coefficient of the containers stacking at the yard
N1
number of container stacks
As h
use ratio of the yard capacity
area required per unit of container The yard capacity, location required by the containers at the yard, and yard area are calculated by Equations (12) – (14), respectively.
E y Qh tdc K BK Ns
Ey
(12) (13)
N1 As
H 2 Ns h
(14)
4.3 Total area of the port-convergence-station If the handling area for loading and unloading containers is equal to that of the gate area used for checking the container trucks traveling in and out of the station, then the total area of the portconvergence-station is calculated by follows. (15) H 2 H1 H 2
5. Case Analysis This paper takes Waigaoqiao Port Area of Shanghai Port as a case study. An ULC connects Logistics Park in Jiading and Waigaoqiao Port Area. Assuming that Node A in Fig. 6 is an ULC node, the channel can be branched at that node. This paper discusses only the transportation of containers between Node A and Waigaoqiao Port Area. Containers are transported between Node A and the portconvergence-station through the ULC, then transported by container trucks through the portconvergence-station and Waigaoqiao Port Area. The location of each terminal in Waigaoqiao Port Area and that of Node A is shown in Fig. 6. Port 5 and 6 share the same gate, so a merger can be considered. 9
When an outbound container arrives through the ULC at the vertical channel place of the handling area of the port-convergence-station, the unloaded spreader of a fixed yard crane descends to lift the container from the UGV and loads it onto an empty container truck, which will carry it to the yard, where the unloaded spreader of another fixed yard crane lifts it from the container truck and loads it onto an UGV through the vertical channel. Then, the container is transported through the ULC to the Logistics Park in Jiading.
Fig. 6. The location of Node A and each port of Waigaoqiao. Due to the particularity of Waigaoqiao Port Area, there are multiple discrete terminals. In this paper, three kinds of schemes for planning port-convergence-stations are proposed. In Fig. 7, 8 and 9, the red solid line indicates the route of containers transported by UGV through the ULC and the green dotted line indicates transport by container trucks while the blue dotted line indicates transport by container trucks through a viaduct. Scheme 1: Establishment of one port-convergence-station for container distribution. Containers are transported through the ULC to the station, then carried to each terminal by container trucks, as shown in Fig. 7. Scheme 2: Establishment of two port-convergence-stations. Some containers are transported through the ULC to Station 1, and then transported by container trucks to Port 1 and 2 while other containers are transported through the ULC to Station 2, then carried to Ports 4-6 by container trucks, as shown in Fig. 8. Scheme 3: Establishment of one port-convergence-station and a viaduct. Containers are transported through the ULC to the station near Port 1 and 2. Then, any containers destined for Port 46 are carried through the viaduct of the container trucks special passage, as shown in Fig. 9.
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Fig. 7. Scheme 1: one port-convergence-station.
Fig. 8. Scheme 2: two port-convergence-stations.
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Fig. 9. Scheme 3: one port-convergence-station and a viaduct.
5.1 Select alternative locations According to the above formulation, the calculated total area of the port-convergence-station is 19,8060 m2. The values of the relevant parameters are shown in Table 1. According to the obtained area of the station, some alternative locations near Waigaoqiao Port Area are selected, as shown in Fig. 10. Table 1 Related values of parameters used for solving the port-convergence-station’s area Qh K BK N1 Wv Lc Wc t dc As Parameter h e Value 24,000 1 1.05 4 0.95 27 15 3 13 6 Notes: Some parameters are from the Operations Research Course (Hu and Guo, 2007) and the Computer & Industrial Engineering (Gao ea al, 2019).
Fig. 10.
Alternative locations of the port-convergence-station.
5.2 Solving for the location of the port-convergence-station The optimal position of the port-convergence-station in Scheme 1 is selected by the robust optimization model. We can observe in Table 2 that the data on the port’s throughput in each scenario. The distances from the ULC Node A to each alternate position are in Table 3. While the distances from each alternate position to each port are showed in Table 4. The transportation cost of each container carried by container truck is about 10 CNY/km, while carried by the ULC is about 5 CNY/km. The construction cost of the ULC is 100 million CNY/km. The daily management cost of each container at the station is 0.2 CNY/TEU/day. The fixed cost of establishing a port-convergence-station is 500 million CNY. This case takes “20×365” to represent the daily coefficient of 20 years of operation. The above costs data are obtained through research and the Computer & Industrial Engineering (Gao et al, 2019). Table 2 Data on each port’s throughput in each scenario (unit: TEU) Port 1
Port 2
Port 4
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Port 5&6
Probability of Each Scenario
scenario 1 372 674 504 scenario 2 743 1347 1008 scenario 3 1115 2021 1512 scenario 4 1487 2695 2016 scenario 5 1859 3369 2519 scenario 6 2230 4042 3023 scenario 7 2602 4716 3527 scenario 8 2974 5390 4031 scenario 9 3346 6063 4535 scenario 10 3717 6737 5039 Notes: The data are from the websites of ports in Waigaoqiao.
722 1443 2165 2887 3608 4330 5052 5773 6495 7217
0.09 0.19 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
https://www.spict.com/dotnetweb/docc/cn_home.aspx; https://www.sipgzct.com/; http://www.sect.com.cn/hdwbs/webpages/index.jsp; https://www.smct.com.cn/wbs/webpages/index.jsp.
Table 3 Distance from Node A to each port-convergence-station (unit: km) Station 1-1 1-2 1-3 1-4 1-5 1-6 Distance 5.7 8.6 7.0 11.9 13.6 15.2
2-1 7
2-2 15.1
3-1 7.4
Note: Station 1-1 represents alternative position 1 of the port-convergence-station in Scheme 1. Station 2-1 represents the port-convergence-station near Ports 1 and 2 in Scheme 2. Station 3-1 represents the portconvergence-station in Scheme 3. All the distances are measured on a map.
Table 4 Distance between each port-convergence-station and each port (unit: km) Station 1-1 1-2 1-3 1-4 1-5 1-6 Port 1 3.4 6.1 4.4 6.7 7.1 10.2 2 2.0 6.8 5.1 7.4 7.8 10.9 4 9.6 7.3 4.7 2.9 3.1 2.1 5&6 11.1 8.7 6.0 4.3 3.1 0.0 Notes: All the distances are measured on a map.
2-1
2-2
3-1
1.1 0.5 -
1.4 0.6
0.6 0.7 7.8 9.1
The YALMIP toolbox is used and called in Matlab 2014a to solve the model. Finally, we obtain the target function value of 8.33 billion CNY, and location 3 among the alternative locations is selected to establish the port-convergence-station of Scheme I. It is un necessary to use the above-mentioned robust optimization model, due to the positions of the port-convergence-station in Schemes II and III are known. The total cost structures of both schemes are equal to that of Scheme I and can be solved by Equation (7). In Fig. 11, there are three programs corresponding to the different total cost when the daily coefficient takes different values. As can be seen, due to the high fixed construction cost of the ULS, the scheme with a short ULC has lower cost during short operating times. As time goes by, the daily management cost, rather than the fixed construction cost, plays a decisive role in the total cost of the ULS. Thus, the total cost of Scheme I is equal to that of Scheme III while the total cost of the latter is less than the former.
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Fig. 11. Total costs of different schemes during different durations of operation. 5.3 Simulation Considering the modelling of urban traffic and public transports based on time intervals and driving behaviour, TV-VISSIM is used as a simulation software in this study. Through the simulation the case of Waigaoqiao Port Area, the effectiveness of the solution can be judged by observing the queuing time of each intersection. Fig. 12 is the road network created by TV-VISSIM. The specific details are shown in Fig. 13.
Fig. 12. Road network of Waigaoqiao Port Area.
Fig. 13. Specific details of the road network.
We set the capacity of the ULS in the three schemes above is 20% of the throughput of Waigaoqiao Port Area. By simulating the current situation and the three schemes, the average queuing length of the container trucks at the traffic lights is obtained, as shown in Fig. 14. In other words, the three schemes can alleviate the traffic congestion in the port area to varying degrees, however, the average queuing length of the three schemes is not less than that of the current situation. For example, queue counter 1702 represents the average length of the queue of vehicles from Gangjian Road left to Port 5. The main traffic congestion of the section is caused by the container trucks from the west side of the Waihuan highway, west side of Gangcheng Road and east side of the outer-ring expressway to Port 5. For scheme I and scheme II, although the ground traffic flow is reduced by 20% after the ULS is built, the number of vehicles driven out by port-convergence-station are added additionally. Therefore, the traffic flow and average queue length change little compared with the current situation. As for scheme III, even if this congestion would be reduced by 20%, all the containers carried by the port-convergence-station require transportation from Gangjian Road left to Port 5. Hence, the traffic
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flow would not actually be reduced but increased, making the average queuing length of the container trucks even longer. It is worth to note that, in general, Scheme II is more effective in mitigating traffic congestion in the Waigaoqiao Port Area.
Fig. 14. Average queuing length of container trucks on different roads and in different schemes.
6. Conclusion This paper studies a new way to alleviate traffic congestion caused by container trucks in port cities. The ULS is proposed as a new transportation system, meanwhile, the port-convergence-station within this system is designed. This paper mainly considers the problem of the convergence of the ULS and ports, discusses the construction of port-convergence-stations. Aiming at improving transportation efficiency around port area, a robust optimization model is established to minimize the total cost of all scenarios for the optimal location. Moreover, numerical experiments based on the particularity of Shanghai Waigaoqiao Port Area is conducted to intuitively illustrate that the ULS can effectively alleviate traffic congestion, and three connection schemes between the ULS and the ports are designed. Besides, we obtained the target function value of 8.33 billion CNY, and select Location 3 among the alternative locations to establish the port-convergence-station of Scheme I by experiment. Finally, the effectiveness of the three schemes to reduce congestion is verified by simulation. Consequently, the ULS and the port-convergence-station are a new way to solve the problem of congestion in the port city. However, the study has its own limitation. The ULS is a complex integrated service system, which involves multi-disciplinary contents, such as urban planning, civil engineering, risk, environmental factors and so on. This paper only considers the factors related to the warehousing and distribution of logistics, so multi-disciplinary contents can be future research directions. On the other hand, in order to obtain more accurate and reasonable location plan through the model, data should also be determined according to the real situation. References
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Acknowledgements This work was financially supported by National Natural Science Foundation of China (No. 71471110 and No.71631007), Science and Technology Commission capacity construction project of Shanghai (No.16040501500) and Science and technology innovation action plan social development project of Shanghai (No.16DZ1201402).
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S0360-8352(19)30668-0 https://doi.org/10.1016/j.cie.2019.106199 CAIE 106199
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Please cite this article as: Fan, Y., Liang, C., Hu, X., Li, Y., Planning connections between underground logistics system and container ports, Computers & Industrial Engineering (2019), doi: https://doi.org/10.1016/j.cie. 2019.106199
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Planning connections between underground logistics system and container ports YIQUN FAN
Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. 901, Shanghai Zhongshan North Second District, Yangpu District, Shanghai 200082 [email protected] CHENGJI LIANG
Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 201306 [email protected] XIAOYUAN HU
Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai 201306 [email protected] YE LI
Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. 901, Shanghai Zhongshan North Second District, Yangpu District, Shanghai 200082 [email protected]
1
Highlights 1) Proposed to establish a port-convergence-station to connect the underground logistics system(ULS) with ports. 2) Considered the uncertainties of the carrying capacity of an ULS. 3) Proposed a robust optimization model to find the appropriate location of the port-convergence-station. 4) Verified the effectiveness of establishing the ULS and port-convergence-station in the case by VISSIM simulation.
2
Abstract - In order to alleviate the traffic congestion inside the port cities caused by container transportation, the underground logistics system (ULS) is considered in this paper to solve the container transportation problem. By establishing port-convergence-station, the connection between the ULS and the port is realized. Specifically, the paper describes the internal layout and floor space of the port-convergence-station. Meanwhile, a robust optimization model is established to deal with the uncertainties of the carrying capacity of an ULS. Moreover, a case study based on Waigaoqiao Port Area to Logistics Park in Jiading is conducted to intuitively solve the robust optimization model to obtain the appropriate location of the port-convergence-station. And the effectiveness of establishing ULS and port-convergence-stations in the case is verified by simulation to illustrate the congestion problem in the container port area. Keywords: underground logistics system; port-convergence-station; station planning; robust optimization; container ports
1. Introduction With the rapid development of ports, container throughput has been increasing and the existing transportation system around port areas have been unable to meet the demand for containers in port areas. Therefore, road traffic congestion around the port is becoming more and more serious, and the contradiction between the port and the city is deepening (Zhao, 2009). In addition, container truck transport has caused great pollution to the environment, which cannot be ignored. The “plan of eastern corridor” has been put forward in Los Angeles to mitigate urban traffic congestion and environmental pollution. Rotterdam Port has proposed a 20% reduction in the traffic during peak periods (Li, 2013), and New York Port is gradually transformed into a virtual port by keeping only a part of the administrative function but keeping the real port away from the city centre. ULS, also known as the underground freight system, refers to a new concept of transportation and supply system that realizes the transportation of solid goods through large-diameter underground pipelines, tunnels and other transportation channels by means of carrying tools such as automatic guided vehicles (AGVs) and amphibious trucks (Liu, 2013). On the one hand, emerging ULS can reduce the pressure on urban traffic and the urban traffic accident rate. On the other hand, it can optimize the structure of urban economy and improve the urban ecological environment. Meanwhile, ULS is fast, low cost and automatic. Moreover, the advantages of high precision can be used to reduce traffic congestion in the port area and environmental pollution, thus improving the accessibility and stability of container transport (Fan and Qian, 2011). In general, the idea of using ULS to solve the problem of congestion in cities eventually grabbed the attention of researchers. The number of trucks in the city can be significantly reduced by the means of combining public subway services with conventional freight vehicles to transport goods efficiently from suburban to downtown areas (Jun, 2012). There is a way of using underground pipelines to transport solids and powders, effectively preventing traffic congestion and accidents (Ewa, 2016). Yan (2016) studied the node location of ULS through the system layout design method. Huang (2006) analysed the distribution centre’s operation processes, function settings and internal relations. To solve the traffic congestion problems of ports, establishing a port-convergence-station to connect underground logistics channels (ULCs) with the port is necessary. Hence, the locations of portconvergence-stations are very important. Yang (2007) considered the restrictions of the practical applications, calculated alternative locations, and used discrete model to solve the best point of a distribution centre based on the calculation of the location of a centre by using the centre-of-gravity method. Huang (2011) applied a particle swarm optimization algorithm to a logistics distribution centre with the objective of achieving the lowest logistics costs and established a corresponding mathematical model solved by CPLEX. Zhou (2011) explored the new characteristics of current logistics distribution centres, then established a multi-period model of logistics and distribution centres with stochastic constraint planning. Besides, Zhou analysed the model and verified by practical examples with the objective of the lowest total logistic costs during the operation period. Guo, Xie and Chen (2012) pointed out the severe traffic situation in China’s megacities, and the necessity of developing underground logistics in Beijing was analysed. Meanwhile, they proposed the application field of underground logistics, and believed that traffic pressure, severe urban space resources, energy and environmental capacity constraints are the motivation and research trend for the development of underground container transportation. Based on the actual situation of Xianlin district, Nanjing city, the problems of logistics node selection, design of underground channel network and optimization of 3
logistics network are solved by Feng (2018) using relevant data and mathematical model. Qu and Xiong (2018) took advantages of investigation and numerical regression analysis, SLP and other methods to discuss and forecast the site selection, layout, construction technology and other issues in the feasibility study of urban ULS in Wuhan area. However, the port-convergence-station in an ULS is a new research field. This paper studies the queuing in the port-convergence-station in order to avoid congestion. Lee, Wong, and Li (2015) developed a real-time estimation approach for lane-based queue lengths. Based on detector information at isolated signalized junctions, the number of queued vehicles in each lane is determined. Giorno, Nobile, and Pirozzi (2018) considered the Markovian single-server queueing model with Poisson arrivals and state dependent service rates, characterized by a logarithmic steady-state distribution. In a new design for a bi-directional AGV system, in which two AGVs can exchange their loads, their scheduled transportation tasks, and even their vehicle numbers when they move in opposite directions. Hsueh (2010) proposed an off-line mathematical model and carried out a series of simulant experiments. Furthermore, Hsueh confirmed that the new system performs efficiently and robustly. From previous studies, the existing ULS is mainly for pipeline or capsule transportation, aiming at short-distance, small items distribution. It is a good idea to consider the ULS based on container transportation to solve the congestion in port cities. At present, there is a lack of research on the connection between ULS and container ports, such as port-convergence-stations. This paper studies the connection of ULS and port areas with port-convergence-station, designs different schemes for portconvergence-stations. The queuing theory is used to establish a calculation model of the congestion degree of the underground guided vehicle (UGV) access underground buffer zone. Then the number of the underground buffer areas of a station and the area of the port-convergence-station can be calculated. Considering the uncertainties of the carrying capacity of an ULS, a robust optimal location model is established. The planning position of the port-convergence-station is determined. At last, the planning of three schemes is simulated to validate the effectiveness of the ULS to solve the contradiction between ports and cities. The remainder of this paper is organized as follows. In Section 2, the considered problem is described in detail. Section 3 proposes a robust optimization model for the location of port-convergencestations. Section 4 gives the internal layout of port-convergence-station. In section 5, a case study is conducted to evaluate the performance of the proposed methods. Section 6 concludes this paper and indicates future research directions.
2. Problem Description The task of receiving and dispatching containers is mainly accomplished through container transportation. The rapid development of ports will lead to the rapid growth of container throughput, which will lead to the congestion and cost increase of container transportation. This study considers to transport containers through ULS to alleviate traffic congestion. Unlike the conventional ground logistics system, the transportation part of the ULS is deep underground and needs the connection between underground and ground. The ULS is mainly composed of two parts, including terminal subsystem and transportation subsystem, which constitute the underground logistics network. The terminal subsystem is responsible for the connection between the ground subsystem and the transportation subsystem. There are many types of terminal subsystem, such as highway freight station, railway freight station and other transportation hubs, as shown in Fig. 1.
Fig. 1. The composition of ULS.
4
The ULS is operated in bi-directional transportation. The operating process of ULS can be understood in conjunction with Fig. 2, 3 and 4. The process of establishing a port-convergence-station can be described as follows. (1)An outbound container is carried by an UGV through the ULC to reach an underground logistics buffer area. (2)Then the loaded UGV travels to the area directly below the shaft with a fixed yard crane above. (3)The fixed yard crane picks up the container and places it onto a waiting non-loaded container truck, which carries it to the yard area. (Considering that the priority of UGV is higher than that of container truck, it is assumed that the number of container trucks is sufficient and there is no case of UGV waiting for container truck.) (4)After that, the fixed yard crane picks up an inbound container from a loaded container truck and places it onto a non-loaded UGV. (Because the task of UGV is known, when UGV is unloaded, there must be loaded container truck waiting for operation on the ground.) (5)The loaded UGV runs into the ULC. Port 2
Port 1
Port 4
Port 5
Port 6
Exit
Entrance unload
Exit Entrance
load ULS
Jiading Logistics Park
Entrance Exit
unload
load
Fig. 2. Connection layout of port and ULS. Fixed yard crane Shaft
Yard Ground
Yard crane
1
Shaft
Vertical channel
2
Shaft Fixed yard crane
Node A
Container truck
ULC
station
ULC
Fig.2(b). The profile of handling area for loading and unloading containers
Fig.2(a). The layout of port-convergence-station
Fig. 3. Diagram of port-convergence-station.
5
Above ground
Under ground
UGV
Shaft UGV
Fixed yard crane
UGV
ULC
UGV
Channel in the buffer area
Handling area
Fig. 4. Container transfer diagram. The problem of the convergence of the ULS and the port need to be considered when containers are transported through the system. According to the layout and operation flow of the container terminal, establishing a port-convergence-station between the ULS and the port is the most appropriate choice. However, the scheme of the port area docking with the port directly requires an ULC to be set up in the various terminals. The cost is enormous and coordination between the ULS’s and port’s planning and scheduling is not easy to achieve. So, this paper discusses the best scheme for the establishment of a port-convergence-station to achieve a convergence of the ULS and the port.
3. Robust Optimization Model The port-convergence-station is an important terminal of the ULS, and its position determines whether the establishment of the system can effectively reduce traffic congestion in the surrounding roads of the port area. Regret model, which is one of the robust optimization model, is used to solve the location problem of port-convergence-stations (Liu, Yang and Yang, 2013). In the regret model of this paper, the difference between the objective function value of the feasible solution and the optimal objective value of the scenario is used to measure the regret value of the scenario. This paper takes into account the uncertainty of the number of the containers carried by the ULS from the port area. Here different numbers of containers are treated as different scenarios, then a robust optimization location model is established and Matlab is used as a platform to call the YALMIP toolbox to solve the model (Wang and He, 2009). The location problem considered in this robust optimization model is based on Shanghai city. The overall structure of ULS in this paper is shown in Fig. 5. Node A is the node of ULC, where ULC can be diverted to different port-convergence-stations.
Fig. 5. Overall structure of ULS in this paper.
3.1 Assumptions (1) The daily throughput of Node A is equal to the daily throughput of the port-convergence-station. 6
(2) The container is Twenty-feet Equivalent Unit (TEU). (3) The construction cost of each port-convergence-station is known and same for every station. (4) To simplify the calculation, this section considers uni-directional demand. (5) All scenarios have the same probability of occurrence. 3.2 Notation Sets set of the port-convergence-stations, indexed by j set of the ports in Waigaoqiao, indexed by k set of the scenarios, indexed by q Parameters probability of occurrence of scenario q q
J K Q
c m
construction cost per km of the ULC (unit: CNY/km) transportation cost per km from Node A to each port-convergence-station (unit: CNY/km)
b jk
transportation cost per km from port-convergence-station j to port k (unit: CNY/km)
vj
daily management cost per TEU for container at port-convergence-station j (unit: CNY/TEU/day) fixed construction cost of port-convergence-station j (unit: CNY)
fj y mj
number of containers that Node A can carry (unit: TEU) maximum number of containers that port-convergence-station j can carry (unit: TEU)
d kq
number of containers that port k needs to transport under scenario q (unit: TEU)
Pj
distance between Node A and port-convergence-station j (unit: km)
Pjk n a
distance between port-convergence-station j and port k (unit: km)
sj
binary, equals one if a port-convergence-station is established at j, and zero otherwise
maximum number of port-convergence-station daily coefficient regret limited coefficient Decision variables integer, represents the number of containers through jth port-convergence-station from Node x qjk A to kth port under scenario q
3.3Model construction In a given scenario, the model’s parameters are deterministic, so the problem of choosing a location for the port-convergence-station is a deterministic optimization problem. The model’s minimum target *
value is recorded as Fq .
min F q Fq
(1)
qQ
Fq
c
j J
Pj f
x
j J k K q Q
q jk
j
s
b
jk
j
Pj k m Pj a v j
Subject to:
7
(2)
x jJ kK
x
kK
q jk
x jJ
q jk
q jk
y, q Q
(3)
m j s j , j J , q Q
(4)
d kq , k K , q Q
(5)
s jJ
1, sj 0, Fq Fq* Fq*
j
n
x
kK
q jk
(6)
0
x qjk 0
(7)
, q Q
(8)
kK
s j 0,1, j J
(9)
x N , j J , k K , q Q
(10)
q jk
Equation (1) is the objective function, which means minimizing the total cost of all scenarios. Equation (2) is the total costs of a given scenario, including the construction cost of the ULC and portconvergence-station, transportation cost of the ULC and port-convergence-station to each port area, and the daily management cost of the port-convergence-station. Constraint (3) indicates that the total delivery capacity of the container at the port-convergence-station does not exceed the number of containers carried by Node A. Constraint (4) guarantees that the number of containers moving through the convergence station cannot exceed the station’s maximum capacity. Constraint (5) means that the number of containers transported to each port should satisfy the port’s demand. Constraint (6) represents that port-convergence-station should not exceed the maximum number. Constraint (7) states that the jth port-convergence-station is selected or not. Constraint (8) ensures the difference between the value of the objective function of the feasible solution and the optimal target value within a given range in a given scenario. Constraints (9) - (10) define variables.
4. The Internal Layout of Port-convergence-station The establishment of the port-convergence-station requires land of a certain size. The area will be composed mainly of three parts: the area of the vertical channel used for loading and unloading containers, the gate area used for checking container trucks travelling in and out of the station, and the yard area. This section discusses the layout of port-convergence-station, as shown in Fig. 3. Some equipment performance assumptions are based on reference to Zhenhua Heavy Industries Co., Ltd. (ZPMC). The assumptions in this section are made as follows. (1) All UGVs have the same capacity and shape and transport two TEUs at a time. (2) The efficiency of the fixed yard crane at the handling area is consistent. (3) Special types of containers are not considered, such as those carrying dangerous or refrigerated goods. 4.1Handling area for loading and unloading containers In the ULC, a container is transported by an UGV and loaded/unloaded by a fixed yard crane in the handling area, as shown in Fig. 4. An UGV can transport two TEUs. Each shaft with a fixed yard crane can carry four TEUs while handling two UGVs. The congestion degree of the UGVs in the handling area is determined by the number of channels in the buffer area and the number of containers carried by the ULS. This section describes the use of queuing theory to establish a model for calculating 8
the congestion degree when the UGVs are being loaded/unloaded in the handling area and the number of shafts to be set up when the system carries a different number of containers for reducing the congestion degree. According to the number of shafts and the size of the area required for the container trucks to turn, the size of the handling area for loading and unloading containers can be calculated. The average waiting time of an UGV can be calculated by the M/M/C queuing model (Zhao, 2013).
The acceptable average waiting time of an UGV in the buffer area is no more than 0.1 minute. The minimum numbers of buffer and shafts can be obtained. The relevant notations are defined as follows. number of shafts n turning radius of a container truck e width of a container truck Wv length of a shaft
Lc Wc
width of a shaft The handling area can be calculated by:
H1 n2e Lc 4Wv Wc
(11)
4.2 Yard area This section discusses the calculation of the yard area based on the throughput of the portconvergence-station. The relevant notations are defined as follows. the container throughput per day Qh
tdc
the average stacking time of containers at the yard
K BK
the unbalanced coefficient of the containers stacking at the yard
N1
number of container stacks
As h
use ratio of the yard capacity
area required per unit of container The yard capacity, location required by the containers at the yard, and yard area are calculated by Equations (12) – (14), respectively.
E y Qh tdc K BK Ns
Ey
(12) (13)
N1 As
H 2 Ns h
(14)
4.3 Total area of the port-convergence-station If the handling area for loading and unloading containers is equal to that of the gate area used for checking the container trucks traveling in and out of the station, then the total area of the portconvergence-station is calculated by follows. (15) H 2 H1 H 2
5. Case Analysis This paper takes Waigaoqiao Port Area of Shanghai Port as a case study. An ULC connects Logistics Park in Jiading and Waigaoqiao Port Area. Assuming that Node A in Fig. 6 is an ULC node, the channel can be branched at that node. This paper discusses only the transportation of containers between Node A and Waigaoqiao Port Area. Containers are transported between Node A and the portconvergence-station through the ULC, then transported by container trucks through the portconvergence-station and Waigaoqiao Port Area. The location of each terminal in Waigaoqiao Port Area and that of Node A is shown in Fig. 6. Port 5 and 6 share the same gate, so a merger can be considered. 9
When an outbound container arrives through the ULC at the vertical channel place of the handling area of the port-convergence-station, the unloaded spreader of a fixed yard crane descends to lift the container from the UGV and loads it onto an empty container truck, which will carry it to the yard, where the unloaded spreader of another fixed yard crane lifts it from the container truck and loads it onto an UGV through the vertical channel. Then, the container is transported through the ULC to the Logistics Park in Jiading.
Fig. 6. The location of Node A and each port of Waigaoqiao. Due to the particularity of Waigaoqiao Port Area, there are multiple discrete terminals. In this paper, three kinds of schemes for planning port-convergence-stations are proposed. In Fig. 7, 8 and 9, the red solid line indicates the route of containers transported by UGV through the ULC and the green dotted line indicates transport by container trucks while the blue dotted line indicates transport by container trucks through a viaduct. Scheme 1: Establishment of one port-convergence-station for container distribution. Containers are transported through the ULC to the station, then carried to each terminal by container trucks, as shown in Fig. 7. Scheme 2: Establishment of two port-convergence-stations. Some containers are transported through the ULC to Station 1, and then transported by container trucks to Port 1 and 2 while other containers are transported through the ULC to Station 2, then carried to Ports 4-6 by container trucks, as shown in Fig. 8. Scheme 3: Establishment of one port-convergence-station and a viaduct. Containers are transported through the ULC to the station near Port 1 and 2. Then, any containers destined for Port 46 are carried through the viaduct of the container trucks special passage, as shown in Fig. 9.
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Fig. 7. Scheme 1: one port-convergence-station.
Fig. 8. Scheme 2: two port-convergence-stations.
11
Fig. 9. Scheme 3: one port-convergence-station and a viaduct.
5.1 Select alternative locations According to the above formulation, the calculated total area of the port-convergence-station is 19,8060 m2. The values of the relevant parameters are shown in Table 1. According to the obtained area of the station, some alternative locations near Waigaoqiao Port Area are selected, as shown in Fig. 10. Table 1 Related values of parameters used for solving the port-convergence-station’s area Qh K BK N1 Wv Lc Wc t dc As Parameter h e Value 24,000 1 1.05 4 0.95 27 15 3 13 6 Notes: Some parameters are from the Operations Research Course (Hu and Guo, 2007) and the Computer & Industrial Engineering (Gao ea al, 2019).
Fig. 10.
Alternative locations of the port-convergence-station.
5.2 Solving for the location of the port-convergence-station The optimal position of the port-convergence-station in Scheme 1 is selected by the robust optimization model. We can observe in Table 2 that the data on the port’s throughput in each scenario. The distances from the ULC Node A to each alternate position are in Table 3. While the distances from each alternate position to each port are showed in Table 4. The transportation cost of each container carried by container truck is about 10 CNY/km, while carried by the ULC is about 5 CNY/km. The construction cost of the ULC is 100 million CNY/km. The daily management cost of each container at the station is 0.2 CNY/TEU/day. The fixed cost of establishing a port-convergence-station is 500 million CNY. This case takes “20×365” to represent the daily coefficient of 20 years of operation. The above costs data are obtained through research and the Computer & Industrial Engineering (Gao et al, 2019). Table 2 Data on each port’s throughput in each scenario (unit: TEU) Port 1
Port 2
Port 4
12
Port 5&6
Probability of Each Scenario
scenario 1 372 674 504 scenario 2 743 1347 1008 scenario 3 1115 2021 1512 scenario 4 1487 2695 2016 scenario 5 1859 3369 2519 scenario 6 2230 4042 3023 scenario 7 2602 4716 3527 scenario 8 2974 5390 4031 scenario 9 3346 6063 4535 scenario 10 3717 6737 5039 Notes: The data are from the websites of ports in Waigaoqiao.
722 1443 2165 2887 3608 4330 5052 5773 6495 7217
0.09 0.19 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
https://www.spict.com/dotnetweb/docc/cn_home.aspx; https://www.sipgzct.com/; http://www.sect.com.cn/hdwbs/webpages/index.jsp; https://www.smct.com.cn/wbs/webpages/index.jsp.
Table 3 Distance from Node A to each port-convergence-station (unit: km) Station 1-1 1-2 1-3 1-4 1-5 1-6 Distance 5.7 8.6 7.0 11.9 13.6 15.2
2-1 7
2-2 15.1
3-1 7.4
Note: Station 1-1 represents alternative position 1 of the port-convergence-station in Scheme 1. Station 2-1 represents the port-convergence-station near Ports 1 and 2 in Scheme 2. Station 3-1 represents the portconvergence-station in Scheme 3. All the distances are measured on a map.
Table 4 Distance between each port-convergence-station and each port (unit: km) Station 1-1 1-2 1-3 1-4 1-5 1-6 Port 1 3.4 6.1 4.4 6.7 7.1 10.2 2 2.0 6.8 5.1 7.4 7.8 10.9 4 9.6 7.3 4.7 2.9 3.1 2.1 5&6 11.1 8.7 6.0 4.3 3.1 0.0 Notes: All the distances are measured on a map.
2-1
2-2
3-1
1.1 0.5 -
1.4 0.6
0.6 0.7 7.8 9.1
The YALMIP toolbox is used and called in Matlab 2014a to solve the model. Finally, we obtain the target function value of 8.33 billion CNY, and location 3 among the alternative locations is selected to establish the port-convergence-station of Scheme I. It is un necessary to use the above-mentioned robust optimization model, due to the positions of the port-convergence-station in Schemes II and III are known. The total cost structures of both schemes are equal to that of Scheme I and can be solved by Equation (7). In Fig. 11, there are three programs corresponding to the different total cost when the daily coefficient takes different values. As can be seen, due to the high fixed construction cost of the ULS, the scheme with a short ULC has lower cost during short operating times. As time goes by, the daily management cost, rather than the fixed construction cost, plays a decisive role in the total cost of the ULS. Thus, the total cost of Scheme I is equal to that of Scheme III while the total cost of the latter is less than the former.
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Fig. 11. Total costs of different schemes during different durations of operation. 5.3 Simulation Considering the modelling of urban traffic and public transports based on time intervals and driving behaviour, TV-VISSIM is used as a simulation software in this study. Through the simulation the case of Waigaoqiao Port Area, the effectiveness of the solution can be judged by observing the queuing time of each intersection. Fig. 12 is the road network created by TV-VISSIM. The specific details are shown in Fig. 13.
Fig. 12. Road network of Waigaoqiao Port Area.
Fig. 13. Specific details of the road network.
We set the capacity of the ULS in the three schemes above is 20% of the throughput of Waigaoqiao Port Area. By simulating the current situation and the three schemes, the average queuing length of the container trucks at the traffic lights is obtained, as shown in Fig. 14. In other words, the three schemes can alleviate the traffic congestion in the port area to varying degrees, however, the average queuing length of the three schemes is not less than that of the current situation. For example, queue counter 1702 represents the average length of the queue of vehicles from Gangjian Road left to Port 5. The main traffic congestion of the section is caused by the container trucks from the west side of the Waihuan highway, west side of Gangcheng Road and east side of the outer-ring expressway to Port 5. For scheme I and scheme II, although the ground traffic flow is reduced by 20% after the ULS is built, the number of vehicles driven out by port-convergence-station are added additionally. Therefore, the traffic flow and average queue length change little compared with the current situation. As for scheme III, even if this congestion would be reduced by 20%, all the containers carried by the port-convergence-station require transportation from Gangjian Road left to Port 5. Hence, the traffic
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flow would not actually be reduced but increased, making the average queuing length of the container trucks even longer. It is worth to note that, in general, Scheme II is more effective in mitigating traffic congestion in the Waigaoqiao Port Area.
Fig. 14. Average queuing length of container trucks on different roads and in different schemes.
6. Conclusion This paper studies a new way to alleviate traffic congestion caused by container trucks in port cities. The ULS is proposed as a new transportation system, meanwhile, the port-convergence-station within this system is designed. This paper mainly considers the problem of the convergence of the ULS and ports, discusses the construction of port-convergence-stations. Aiming at improving transportation efficiency around port area, a robust optimization model is established to minimize the total cost of all scenarios for the optimal location. Moreover, numerical experiments based on the particularity of Shanghai Waigaoqiao Port Area is conducted to intuitively illustrate that the ULS can effectively alleviate traffic congestion, and three connection schemes between the ULS and the ports are designed. Besides, we obtained the target function value of 8.33 billion CNY, and select Location 3 among the alternative locations to establish the port-convergence-station of Scheme I by experiment. Finally, the effectiveness of the three schemes to reduce congestion is verified by simulation. Consequently, the ULS and the port-convergence-station are a new way to solve the problem of congestion in the port city. However, the study has its own limitation. The ULS is a complex integrated service system, which involves multi-disciplinary contents, such as urban planning, civil engineering, risk, environmental factors and so on. This paper only considers the factors related to the warehousing and distribution of logistics, so multi-disciplinary contents can be future research directions. On the other hand, in order to obtain more accurate and reasonable location plan through the model, data should also be determined according to the real situation. References
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Acknowledgements This work was financially supported by National Natural Science Foundation of China (No. 71471110 and No.71631007), Science and Technology Commission capacity construction project of Shanghai (No.16040501500) and Science and technology innovation action plan social development project of Shanghai (No.16DZ1201402).
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