From the perspective of supply-side reform in China, it is hard for COSCO Shipping, a merged company with a strong shipping capacity, to abandon the container shipping market. Meanwhile, the new company could cooperate with new strategic ports along the Maritime Silk Road in liner service. Against this backdrop, this paper aims to optimize the liner shipping network (LSN) from strategic, tactical, and operational levels and help the merged shipping company adjust its operational measures according to market changes. The optimization towards different levels of decision-making process is a new research of highly practical values. Specifically, this paper created two-phase optimization models for LSN based on the selection of hub ports. In Network Assessment (NA) phase, the LSNs of two types of hub ports selected are designed and assessed on strategic and tactical levels, and the primary and secondary routes are identified; in Network Operation (NO) phase, the “path-based flow” formulations are proposed from the operational level, considering operational measures including demand rejection and flow integration. The models in both phases are mixed-integer linear programming (MILP), but are solved by different tools: CPLEX for the NA phase models and the Genetic Algorithm (GA) for the NO phase models due to the computational complexity of the latter problem. Then, a computational experiment is performed on the LSN of COSCO Shipping on the Persian Gulf trade lane. The results have proved the effectiveness of the methodology and inspired important countermeasures for the merged shipping company.
The global demand for container shipping had been rapidly increasing from the birth of the containership in the 1950s to the outbreak of the subprime crisis [
In order to deal with the oversupply issue, governments and shipping industry have been making efforts to conduct supply-side reform. The supply-side reform consists of a series of parallel measures and regulations, including annual capacity limits and mergers of shipping companies. The most intuitive way is to directly control the growth of freight capacity. For example, the Chinese government is imposing a gradually stringently macrocontrol to maritime freight capacity. Currently, any expansion of fleet that transport bulk liquid hazardous goods need to be scrutinized [
In comparison with the annual capacity limit that seems in lack of mature practice, mergers and alliances is an obvious trend in recent years leading to the concentration of shipping capacity. There have been several successful cases of mergers in the maritime industry. The largest five carriers handled 27% of all TEUs in 1996, 46% in 2008, and 64% in 2017 [
The rationale of the COSCO/CSCL merger is entirely sound as they both have designed many similar services, and the unnecessary competition has deteriorated their financial performance. Besides eliminating competition, there are more benefits awaited the shipping companies through optimizing their LSNs after mergers, which is investigated in this paper. In practice, after mergers, the LSNs of the acquired shipping companies need to firstly go through strict assessment, then considering adjusting the services. The Network Assessment (NA) phase and Network Operation (NO) phase differ greatly in the content and process of the decision-making of the shipping companies [
In this paper, two-phase optimization models are proposed to investigate the decision-making process in NA and NO phases, aimed at maximizing the actual profits of a shipping company in the context of supply-side reform, for the LSN based on strategic ports, investigating the decision-making process in NA and NO phases. Various factors are considered to better reflect the NA phase and NO phase in practice, such as the cooperation with different hub ports, the transshipment of cargoes, the rejection of unprofitable demand, and the fluctuation of demands and freight rates.
The remainder of this paper is organized as follows: Section
There are three decision-making levels for the shipping companies to design LSN: strategic, tactical, and operational [
Most existing literature on the optimization of the LSN is devoted to the strategic and tactical levels. Wang and Meng [
From the above discussion, it is clear that the strategic and tactical decisions are often an input to the operational optimization. The idea of combining different levels of decision-making has been absorbed in some studies in recent years, known as two-phase optimization. By generating the set of routes firstly, the container flows can be optimized based on the given set of routes in the second phase [
This research fills in the gap in the existing literature and makes contributions to the research in LSN design problem as follows. Firstly, we investigate the LSN design problem for shipping companies under the context of supply-side reform. Various measures of supply-side reform are considered in this paper, including the macrocontrol of capacity and the mergers of shipping companies. The decision-making process is divided into NA phase and NO phase, and two-phase optimization models for the LSN are developed accordingly. Secondly, we look for alternative solutions to the LSN design problem in the NO phase with a GA-based algorithm. The proposed method can efficiently solve the “path-based flow” formulations. Thirdly, this paper gives out several countermeasures of shipping companies from the perspective of supply-side reform in China, e.g., the selection of hub ports, demand rejection, and the idea of flow integration. In addition, the scenario analyses reveal how shipping companies can flexibly adjust their operational measures according to the actual market indicators such as demand and freight rates.
We consider the LSN optimization for a shipping company in the context of supply-side reform, typically a merger or acquisition. NA and NO phases after a merger are analyzed: selecting the most profitable route in the NA phase from all the similar preset routes that have been designed by different acquired shipping companies, and figuring out the optimal plan of flowing cargoes in the NO phase according to the actual shipping market. The objectives of both phases are to maximize profits. Detailed information about the two phases is stated in Section
The elements of LSN are defined as follows to avoid ambiguity: Port calls: a typical liner shipping route usually contains at least several fixed ports calls, thus also named as multiport calling (MPC) service [ Hub ports: when operating along a liner service, the containerships are allowed to call twice at hub ports, but only once at any other ports. As commonly observed in practice, each route is limited to one single hub port. The shipping companies can cooperate with different hub ports, which can be classified as traditional hub ports (THPs) and emerging hub ports (EHPs). In addition, hub ports are able to transship cargoes due to better facilities. Routes: the route in the LSN may have 10–20 legs, where a leg is a directed arc between two consecutive ports [ Cargo flows: cargo flow refers to the move of cargoes on a leg. A flow path is the directed path consisted of all the legs between the origin port and the destination port. Demands: there are several pairs of origin and destination (O-D pairs) of cargoes along a route, generating shipping demands. The market changes are represented by the variation of demands and freight rates for container shipping [
Suppose two shipping companies, represented by A and B, respectively, are merged into a new shipping company C. In the NA phase, there are already similar routes established by the acquired shipping companies A and B. Such similar preset routes may be initiatively designed to satisfy the demand in the same regions, which leads to unnecessary competition. Despite the similarities, the selection of hub ports contributes to the differences among the routes. For instance, A has established a cooperative relationship with traditional hub ports (THP); i.e., the containerships operated by A are allowed to call twice at the THP. However, B noticed that the shipping demands generated from Emerging Hub Ports (EHP) are growing rapidly, thus is more willing to cooperate with EHP [
The assessment is based on the prediction regarding the quantities of demands
The assessment results in the NA phase based on predicted demand give out a rough principle that more cargoes should flow on the primary route. In the NO phase, in order to start operation in practice, shipping company C needs to depict more detailed plans on how to adjust cargo flows, which involve how to pick up, unload, and transship containers at any port of call according to the actual market situation.
As shown in Figure
Two-phase decision-making process for merged shipping company. (a) The LSN design problem in the NA phase and (b) the LSN operation problem in the NO phase.
The flow path of any shipping demand from origin port
In NO phase, the decision-making is based on actual demands and freight rates, which may have a deviation
The assumptions of the models are listed here as follows: Without considering the impact of natural disasters and local wars on the LSN, any demand between an O-D port pair is a long-standing issue that changes with the global trade. Without considering the difference between types of containerships, the voyage expense incurred by containership deployment is fixed, and all containerships sail at the agreed speed [ There is no limit on the loading/unloading capacities of all ports, that is, any port can handle the maximum containership capacity. The terminal handling charges are fixed on each port, but vary among all ports [ The emission regulations of MARPOL-VI and EU-ETS on ports and containerships are not considered, as their impacts are restricted to certain areas and are negligible for long-haul liner services [
The LSN design problem in the NA phase based on hub ports selected as THPs is formulated as Model (I). The notations used the model in the NA phase are shown in Table
Notations of model in NA phase.
Set of all nodes in the LSNs | |
Set of all traditional hub ports (THP) | |
Set of all emerging hub ports (EHP) | |
Set of all origin ports of demands | |
Set of all destination ports of demands | |
Set of all available legs in the LSNs | |
Capacity of any deployed containership | |
Voyage expense of operating on leg ( | |
Transit time of operating on leg ( | |
Ω | Maximum containership capacity for a voyage circle controlled by the government |
Qod | Quantity of demand between origin port |
eod | Freight rate of transporting unit demand between origin port |
Expected total revenue, which can be calculated as | |
Fixed transit time for the total legs in a voyage cycle | |
(Binary) 1 iff the leg ( | |
The number of containers to be transported on leg ( | |
The number of deployed containerships |
Having defined the notations, we have Model (I) as follows:
Objective function (
Unlike the set of the THPs in constraints (
The LSN design problem in the NA phase based on hub ports which are the EHPs is given as Model (II).
The LSN design problem in the NO phase to determine the optimal cargo flows is formulated as Model (III). As defined in Section
For any path
In Model (III), we define
Objective function (
The resulting models (I)∼(III) are all MILP problems. Models (I)∼(II) will be solved by the standard solver such as CPLEX [
The proposed solution approach can be stated as follows: CPLEX explores the space of containership deployment and route design and finds feasible solutions. From every solution, a valid LSN configuration is derived. Once a valid configuration is found, the problems of selecting the demands and switching the paths are solved for this configuration by the GA-based algorithm, and the optimal flows and paths are found, for that network configuration. By this algorithm, a set of candidate solutions (populations) is retained in each iteration (a.k.a. generation or trial), and the best populations are identified based on the principle of “survival of the fittest” through genetic operations as selection, crossover, and mutation, forming a new generation of candidate solutions. This process is repeated until reaching the maximum number of iterations
Flowchart of the proposed GA-based algorithm to model (III).
Coding: the solution representation directly bears on the GA performance. Considering the features of decision variables with the inclusion of two terms: “path-based flow,” the solution is subjected to natural number encoding. Here, each solution is divided into two terms. The first term refers to the possible cargo flow on the path
A typical solution to model (III).
Fitness function: each solution satisfying the constraints is deemed as a chromosome. This paper attempts to minimize the difference between the operation costs and the temporal revenues. Here, the fitness function is set up based on the reciprocal of the objective function in equation (
Selection: before crossover, two parent chromosomes are selected based on fitness. Then, a roulette selection procedure is adopted for our solution framework. First, calculate the fitness
Crossover: a single point crossover operator is used. In each crossover, we randomly select a cut-point in the chromosome and exchange the right parts of the two selected parent chromosomes to generate one or more children. The crossover probability is set as
Mutation: through mutation, a new solution can be derived from an old solution. The mutation operator is employed in each generation of chromosomes at an equal probability (mutation rate)
An example of crossover and mutation.
Infeasible solution disposing: after crossover and mutation, if the solution to a chromosome is infeasible, the above steps are repeated from Step
To assess the performance of the proposed algorithm on solving different test problems, the well-known standard dataset of the Persian Gulf trade lane that consists of 14 ports of COSCO Shipping in 2018 is used in the experiments. All data are generated from real information without distorting the original structure. The voyage distance ( The THP The voyage expense per containership of any leg is calculated as The transit time of any leg Considering that the government may control the freight capacity growth of maritime industry, we assume that the annual containership capacity that COSOCO Shipping can provide is limited at 1560000 (TEU/YEAR), according to the average containership capacity of COSCO Shipping in the past ten years. In other words, even if all the deployable containerships of COSCO Shipping are allocated to serve the investigated Persian Gulf trade lane with all the containerships full loaded for a whole year, the annual freight volume carried in the Persian Gulf trade lane cannot exceed 1560000 (TEU/YEAR). Therefore, in order to meet the annual capacity limit, the maximum containership capacity for a voyage circle is Ω = 1560000/(365/W) (TEU). The demand between each O-D port pair is Qod ∊ [772, 79562] (TEU) and the freight rate of the corresponding demand is expected to be eod ∊ [8.46, 1885.28] (USD/TEU). The loading/unloading expense at any port is set as ci ∊ [1.21, 2.45] (USD/TEU). Within the designed transit time for a voyage circle
The results of models (I)∼(II) are calculated by ILOG-CPLEX 12.5. Given the fixed limit of annual containership capacity controlled by the government, if the transit time of a voyage circle
30 different {
The test results of 30 different {
Ω (TEU) | − | Gap (%) | Time (s) | − | Gap (%) | Time (s) | ||
---|---|---|---|---|---|---|---|---|
− | 1823446452 | 0.07 | 3.08 | |||||
1837997487 | 0.32 | 2.95 | ||||||
5 | 95 | 406027 | 1817209621 | 0.08 | 1.42 | 1838152024 | 0.08 | 2.66 |
6 | 98 | 418849 | 1817209621 | 0.17 | 3.31 | 1838286035 | 0.09 | 2.84 |
7 | 101 | 431671 | 1817209621 | 0.08 | 1.70 | 1838393254 | 0.06 | 2.78 |
8 | 104 | 444493 | 1827488337 | 0.16 | 2.58 | 1841772518 | 0.05 | 2.39 |
9 | 107 | 457315 | 1827478457 | 0.12 | 2.33 | 1842149323 | 0.11 | 2.36 |
10 | 110 | 470137 | 1827488337 | 0.10 | 1.53 | 1842149323 | 0.09 | 2.94 |
11 | 113 | 482959 | 1828105883 | 0.42 | 2.34 | 1842149323 | 0.11 | 2.72 |
12 | 116 | 495781 | 1828105883 | 0.09 | 1.89 | 1842149323 | 0.06 | 3.16 |
13 | 119 | 508603 | 1828105883 | 0.46 | 2.31 | 1842149323 | 0.01 | 2.84 |
14 | 122 | 521425 | 1828105883 | 0.11 | 1.52 | 1842149323 | 0.07 | 2.97 |
15 | 125 | 534247 | 1828105883 | 0.14 | 3.81 | 1842149323 | 0.11 | 3.00 |
16 | 128 | 547068 | 1828105883 | 0.04 | 3.05 | 1842149323 | 0.10 | 3.70 |
17 | 131 | 559890 | 1828105883 | 0.05 | 3.22 | 1842149323 | 0.35 | 2.86 |
18 | 134 | 572712 | 1828105883 | 0.01 | 3.34 | 1842149323 | 0.17 | 3.53 |
19 | 137 | 585534 | 1828105883 | 0.13 | 4.30 | 1845244453 | 0.14 | 2.38 |
20 | 140 | 598356 | 1828105883 | 0.08 | 3.56 | 1845244453 | 0.04 | 2.45 |
21 | 143 | 611178 | 1831418092 | 0.41 | 2.30 | 1847951008 | 0.13 | 4.00 |
22 | 146 | 624000 | 1831418092 | 0.05 | 2.58 | 1847951008 | 0.32 | 2.89 |
23 | 149 | 636822 | 1836229052 | 0.37 | 2.78 | |||
24 | 152 | 649644 | 1836229052 | 0.18 | 4.45 | |||
To compare the maximum predicted profits in NA phase, the
The results of LSNs (
The total profit is fixed and predicted against the demands and freight rates between the origin and destination ports. Actually, the optimization of
After comparing the predicted profits, we took
The convergence of LSN in NO phase (
In the NO phase, the actual profit of COSCO Shipping is 907399279.57 (USD) when Δ
The results of LSN in NO phase (
Δ | Δ | Δ | Δ | Δ | Δ | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | −14 | 4 | −7 | 9 | −5 | ||||||
2 | −8 | 91.82 | 12 ⟶ 8 | −5 | −8 | 83.07 | 11 ⟶ 2 | −8 | −17 | ||
8 | −6 | 91.68 | 3 ⟶ 14 | 1 | −12 | 82.68 | 12 ⟶ 2 | −10 | −2 | ||
6 | −14 | 91.56 | 1 ⟶ 12 | −23 | −7 | 82.56 | 3 ⟶ 11 | 8 | −20 | ||
100 | 5 ⟶ 11 | 9 | −9 | −5 | −2 | 82.29 | 13 ⟶ 1 | −10 | −10 | ||
5 | −10 | 91.02 | 13 ⟶ 5 | 10 | −16 | 81.73 | 6 ⟶ 12 | 8 | −9 | ||
−7 | −7 | 90.59 | 2 ⟶ 10 | −10 | −8 | 81.10 | 13 ⟶ 2 | −10 | −20 | ||
100 | 8 ⟶ 12 | −2 | −10 | 6 | −10 | 79.80 | 6 ⟶ 10 | −2 | −7 | ||
10 | −16 | 2 | −3 | −1 | −13 | ||||||
10 | −6 | 90.01 | 11 ⟶ 8 | −9 | −19 | 79.13 | 9 ⟶ 8 | −8 | −4 | ||
9 | −8 | 89.53 | 12 ⟶ 6 | −1 | −7 | 6 | −12 | ||||
2 | −7 | −1 | −13 | 77.02 | 12 ⟶ 1 | −7 | −4 | ||||
1 | −11 | 89.03 | 5 ⟶ 14 | −9 | −3 | 76.62 | 14 ⟶ 3 | −5 | −10 | ||
100 | 12 ⟶ 5 | 6 | −12 | 88.89 | 8 ⟶ 14 | 9 | −11 | −5 | −2 | ||
9 | −5 | 88.55 | 8 ⟶ 13 | 1 | −8 | 75.78 | 10 ⟶ 1 | −9 | −17 | ||
7 | −4 | 88.45 | 6 ⟶ 14 | −6 | −4 | 75.34 | 10 ⟶ 2 | −4 | −6 | ||
100 | 13 ⟶ 6 | 10 | −11 | 88.39 | 6 ⟶ 13 | −7 | −2 | 74.71 | 1 ⟶ 11 | −5 | −17 |
−8 | −9 | 87.92 | 3 ⟶ 13 | 0 | −18 | 73.18 | 2 ⟶ 14 | 2 | −8 | ||
3 | −11 | 4 | −2 | 72.72 | 11 ⟶ 5 | −8 | −11 | ||||
2 | −6 | 87.76 | 11 ⟶ 3 | 9 | −5 | 72.71 | 1 ⟶ 13 | −7 | −7 | ||
4 | −7 | 86.89 | 10 ⟶ 3 | 10 | −20 | −10 | −20 | ||||
10 | −7 | 86.73 | 13 ⟶ 3 | −5 | −3 | 72.01 | 1 ⟶ 14 | 0 | −9 | ||
95.84 | 5 ⟶ 13 | 10 | −2 | 86.49 | 2 ⟶ 11 | 10 | −11 | 69.76 | 11 ⟶ 1 | −2 | −6 |
95.58 | 8 ⟶ 10 | 6 | −3 | 86.10 | 5 ⟶ 12 | −2 | −20 | 69.41 | 1 ⟶ 10 | 2 | −10 |
95.30 | 8 ⟶ 11 | 5 | −13 | 85.77 | 5 ⟶ 10 | 6 | −6 | −8 | −7 | ||
−9 | −9 | 7 | −13 | −6 | −12 | ||||||
94.30 | 12 ⟶ 3 | 9 | −7 | 85.54 | 3 ⟶ 10 | −3 | −20 | 52.64 | 14 ⟶ 8 | −4 | −5 |
9 | −20 | 84.85 | 10 ⟶ 5 | 1 | −6 | −12 | −20 | ||||
93.28 | 3 ⟶ 12 | 10 | −11 | 84.53 | 2 ⟶ 12 | −10 | −8 | 41.11 | 10 ⟶ 6 | −8 | −17 |
−8 | −10 | 84.24 | 2 ⟶ 13 | −1 | −14 | −5 | −11 | ||||
92.87 | 13 ⟶ 8 | −1 | −9 | 5 | −6 | 16.54 | 14 ⟶ 5 | −10 | −13 | ||
92.54 | 14 ⟶ 1 | 2 | −5 | 83.63 | 14 ⟶ 2 | 1 | −12 | 2.72 | 10 ⟶ 8 | −7 | −11 |
In addition to Δ
The LSN in NO phase ( Scenario 1: all Δ Scenario 2: all Δ Scenario 3: all Δ
Under Scenarios 1–3, the actual profits of COSCO Shipping are 902148715.92(USD), 896171319.02(USD), and 900705361.54(USD), respectively, down by 0.58%, 1.24%, and 0.74% from those in Scenario 0 (see Figure
The actual profits of COSCO Shipping in Scenarios 1–3.
Under Scenarios 1–3, the overall demand acceptance rates of COSCO Shipping are 90.91%, 89.33%, and 90.79%, respectively, up by 4.68%, 2.86%, and 4.54% from those in Scenario 0 (see Figure
The overall demand acceptance rate of COSCO Shipping in Scenarios 1–3.
Finally, the results indicate that the shipping companies should attach more importance to EHPs when designing and optimizing the LSNs. On the one hand, EHPs are more likely to generate demand because they usually locate in rapidly developing economies. Scenario 3 assumes an increase of [5%, 15%] in the demands that take the EHPs as the origin and destination ports. The results show that the EHPs contributed to the 1.44% growth in demand, which leads to a 0.51% increase in the actual profits of shipping companies. On the other hand, shipping companies should increase the acceptance rate for the demands taking the EHPs as the origin and destination ports, as shown in Table
The results of demand acceptance rate of COSCO Shipping in Scenario 3.
12 ⟶ 1 | 100 | ||||||
1 ⟶ 10 | 100 | 5 ⟶ 10 | 100 | 12 ⟶ 2 | 94.59 | ||
1 ⟶ 11 | 88.36 | 5 ⟶ 11 | 97.36 | 12 ⟶ 3 | 75.22 | ||
1 ⟶ 12 | 58.72 | 5 ⟶ 12 | 85.31 | 12 ⟶ 4 | 96.80 | ||
1 ⟶ 13 | 94.67 | 5 ⟶ 13 | 85.95 | 12 ⟶ 5 | 100 | ||
1 ⟶ 14 | 92.95 | 5 ⟶ 14 | 98.90 | ||||
12 ⟶ 7 | 85.42 | ||||||
2 ⟶ 10 | 91.20 | 12 ⟶ 8 | 34.14 | ||||
2 ⟶ 11 | 100 | 10 ⟶ 1 | 93.78 | 13 ⟶ 1 | 98.42 | ||
2 ⟶ 12 | 98.72 | 10 ⟶ 2 | 100 | 13 ⟶ 2 | 80.48 | ||
2 ⟶ 13 | 66.33 | 10 ⟶ 3 | 98.11 | 13 ⟶ 3 | 96.02 | ||
2 ⟶ 14 | 95.04 | 10 ⟶ 4 | 88.41 | 13 ⟶ 4 | 93.05 | ||
10 ⟶ 5 | 96.29 | 13 ⟶ 5 | 72.51 | ||||
3 ⟶ 10 | 87.15 | 7 ⟶ 10 | 98.01 | ||||
3 ⟶ 11 | 95.77 | 7 ⟶ 11 | 97.02 | 10 ⟶ 7 | 79.55 | 13 ⟶ 7 | 34.47 |
3 ⟶ 12 | 96.02 | 7 ⟶ 12 | 82.89 | 10 ⟶ 8 | 83.06 | 13 ⟶ 8 | 96.46 |
3 ⟶ 13 | 90.79 | 7 ⟶ 13 | 87.13 | 11 ⟶ 1 | 87.90 | 14 ⟶ 1 | 76.08 |
3 ⟶ 14 | 95.95 | 7 ⟶ 14 | 88.28 | 11 ⟶ 2 | 41.85 | 14 ⟶ 2 | 39.21 |
11 ⟶ 3 | 86.12 | 14 ⟶ 3 | 91.62 | ||||
4 ⟶ 10 | 84.56 | 8 ⟶ 10 | 82.82 | 11 ⟶ 4 | 61.80 | 14 ⟶ 4 | 84.47 |
4 ⟶ 11 | 100 | 8 ⟶ 11 | 95.76 | 11 ⟶ 5 | 84.72 | 14 ⟶ 5 | 93.58 |
4 ⟶ 12 | 98.95 | 8 ⟶ 12 | 100 | ||||
4 ⟶ 13 | 91.74 | 8 ⟶ 13 | 96.66 | 11 ⟶ 7 | 91.30 | 14 ⟶ 7 | 82.76 |
4 ⟶ 14 | 100 | 8 ⟶ 14 | 86.66 | 11 ⟶ 8 | 79.34 | 14 ⟶ 8 | 66.21 |
This paper aims to help COSCO Shipping address the LSN design problem with several hub ports to cooperate in regions along the Maritime Silk Road from the perspective of supply-side reform in China. For this purpose, we proposed two-phase optimization models for the LSN from strategic, tactical, and operational levels. Unlike traditional optimization approaches, our work divides the decision-making process into Network Assessment (NA) phase and Network Operation (NO) phase and considers external factors like market changes and hub port cooperation. In addition, our analyses highlighted two crucial operational measures: demand rejection and flow integration.
The optimization models for both phases are MILPs. The models in the NA phase are programmed in CPLEX, and those in the NO phase are solved by a GA-based algorithm. In light of the assessment of designing LSNs by cooperating with different types of hub ports based on predictions in the NA phase, a “path-based flow” model in the NO phase is specially developed and a set of easy-to-implement GA-based algorithm is designed to compute optimal solutions efficiently. Then, a computational experiment is performed on the Persian Gulf trade lane of COSCO Shipping. The experimental results prove the effectiveness of the GA and inspire the following countermeasures.
Firstly, when designing LSNs based on the cooperation with hub ports in the NA phase, the merged shipping company should increase the number of legs in the designed LSNs, e.g., calling twice at hub ports, in order to save the total installation cost. More importantly, the total installation cost could be further reduced by adjusting the selection of hub ports from THPs to EHPs. Secondly, the shipping company should reject more cargoes when the actual market is not satisfied, i.e., both quantities and freight rates of demands are lower. The scenario analyses show that the LSNs optimization measures including demands rejection and flow integration can efficiently help the shipping companies reduce the negative impacts of depressed market. Thirdly, the shipping company should increase the demand acceptance rate for the demands taking the hub ports, especially the EHPs as the origin and destination ports. In general, both the design and operation of LSNs should be flexibly adjusted according to demand prediction. If some ports are expected to generate greater demands than others, adjusting the hub of LSNs and accept more demand related to these EHPs could achieve better performance.
It must be noted that this study does not tackle all the decision-making problems at strategic, tactical, and operational levels of LSPs in NA and NO phases. To further optimize the LSNs, the future research will dig deep into the following issues: better prediction of future demand helps identify the emerging ports and optimize the LSNs; greater understanding of LSN structures, which consist of butterfly services, pendulum services, and even more complex services, helps explore more flexible and cost-efficient solutions; the operation adjustment after shipping company mergers or forming alliances deserves more attention.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work was supported in part by National Natural Science Foundation of China (Grant nos. 72072017, 71902016, and 71831002), Foundation for Humanities and Social Sciences of Ministry of Education of China (Grant no. 18YJC630261), Natural Science Foundation of Liaoning Province of China (Grant no. 2020-hylh-41, 2020-BS-213), and Social Science Foundation of Liaoning Province of China (Grant no. L19AGL012).