| Citation: | BAO Tian-tian, LIAN Feng, YANG Zhong-zhen. Research review of shipping management[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 55-69. doi: 10.19818/j.cnki.1671-1637.2020.04.004
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In summary, regarding the three research hotspots of shipping market analysis, shipping operation management, and green shipping, this article will further summarize the corresponding research progress, explore and analyze their research scope, research objects, research methods, and propose future research directions.
The ups and downs of the shipping market directly affect the survival and development of shipping companies, and research on the shipping market has always been highly valued by the academic community. Sorescu et al[10]It is pointed out that the supply and demand relationship in the shipping market determines market freight rates, and shipping companies and shippers will make corresponding adjustments based on freight rates to balance the supply and demand in the shipping market. This is the freight rate adjustment mechanism of the shipping market supply and demand. Therefore, many scholars analyze the operating characteristics of the shipping market around freight rates and judge the future trend of the shipping market.
In a broad sense, the shipping market includes both the freight market and the ship market. The freight market is a place where shipping companies and shippers engage in supply and demand transactions for shipping goods, while the ship market is related to shipping supply and thus affects the relationship between shipping supply and demand[11]Due to their respective characteristics, freight market research focuses on analyzing the volatility of freight rates, predicting freight rates, and studying market pricing and impact mechanisms around the keyword of freight rates. Ship market research mainly analyzes the mutual influence of freight rates and ship market prices such as shipbuilding market, second-hand ship market, and dismantling market, as follows.
(1) Regarding the volatility of freight rates, Xu et al[12]We calculated the volatility of freight rates in the dry bulk market and found a positive correlation between fleet size growth and freight rate volatility; Yin et al[13]Analyzed the long-term causal relationship between spot freight and forward freight agreements, and found that exogenous variables such as fleet size and fuel prices have a significant impact on freight rate volatility; Adland and others[14]The linkage of regional spot freight rates under discrete and continuous time conditions was studied, and the correlation between the term structure of freight fluctuations, regional freight rates, and market factors was analyzed; Wu Huahua and others[15]Analyzed the fluctuation cycle characteristics of the Baltic Sea Index and pointed out that the length of seasonal fluctuations shows a significant trend of shortening.
(4) Regarding the mutual influence between freight rates and ship market prices, Dai et al[24]An analysis was conducted on the impact of freight rates, shipyard capacity, shipbuilding costs, exchange rates, and second-hand ship prices on the cost of new ships of different types in the dry bulk shipbuilding market; Adland and others[25]Analyzed the prices of the freight market, shipbuilding market, second-hand ship market, and dismantling market during the same period, indicating that the volatility of shipbuilding market prices is influenced by freight market prices; Bai et al[26]An analysis was conducted on the relationship between supply, demand, freight rates, and the prices of new and second-hand ships in the Very Large Gas Carrier (VLGC) market, indicating that freight rates directly affect the prices of second-hand ships and indirectly affect the prices of new ships.
Table 1Summarized the research results of the shipping market, divided into research scope, research themes, research objects, and research methods.
| 研究范围 | 研究主题 | 研究对象 | 研究方法 | 文献来源 |
| 货运市场 | 运价波动性 | 干散货市场 | 向量自回归(Vector Autoregression, VAR)模型、向量误差校正模型(Vector Error Correction Model, VECM)、自回归广义自回归条件异方差(Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity, AR-GARCH)模型、集成连续自回归(Integrated Continuous Autoregressive, ICAR)模型、经验模态分解-小波分析(Empirical Mode Decomposition-Wavelet Analysis, EMD-WA)模型 | [12]~[15] |
| 运价预测 | 干散货市场 | 自回归移动平均(Autoregressive Moving Average, ARMA)模型、霍尔特-温特(Holt-Winters)非季节模型、非线性自回归动态网络(Non-linear Autoregressive Dynamic Network, NARNET)模型、具有外部输入的非线性自回归神经网络(Non-linear Autoregressive Neural Network with External Input, NARXNET)模型 | [16]、[19] | |
| 班轮市场 | 差分整合移动平均自回归(Autoregressive Integrated Moving Average, ARIMA)模型、自回归条件异方差(Autoregressive Conditional Heteroscedasticity, ARCH)模型 | [17] | ||
| 油运市场 | 人工神经网络(Artificial Neural Network, ANN)模型、自适应遗传算法(Adaptive Genetic Algorithm, AGA) | [18] | ||
| 市场定价及影响机制 | 班轮市场 | 合作博弈理论、价格方程和回归分析、二元广义自回归条件异方差(Generalized Autoregressive Conditional Heteroscedasticity, GARCH)模型 | [20]、[21]、[23] | |
| 干散货市场 | 价格博弈理论 | [22] | ||
| 船舶市场 | 运价与船舶市场价格的相互影响 | 造船市场、二手船市场、拆船市场 | GARCH模型、单方程模型、结构方程建模 | [24]~[26] |
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Fleet planning refers to the systematic arrangement by shipping companies of the purchase, use, renewal, and disposal of ships during the planning period based on future transportation needs and their own capabilities, in order to determine the optimal fleet size and structure, better adapt to changes in the shipping market, and avoid risks[4]According to different focuses, it can be divided into two categories: planning problems for newly established fleets and fleet renewal problems based on existing fleets.
Network design and route allocation are aimed at determining the number of routes in the network, the ports of call and their sequence for each route, the size and quantity of ships configured on each route, etc., based on the given transportation tasks between OD ports during the planning period (such as one year, six months, etc.), in order to maximize the profit or minimize the cost of the route network[27]Structurally, shipping networks can be divided into branch route networks, multi port berthing route networks, hub and spoke route networks, and hybrid route networksFigure 4.
(3) For hub and spoke airline networks（Figure 4 (c)), Gelareh et al[51-52]Considering the competition among shipping companies, a mixed integer linear programming model is proposed, and a Lagrangian decomposition algorithm is designed to optimize hub port location and network design. Furthermore, a mixed integer linear programming model is constructed with the goal of maximizing total revenue, while optimizing the route allocation, network design, and hub port location for ship transportation; Asgari et al[53]Considering the cooperation and competition among stakeholders in the maritime transportation system, a liner transportation network design model was constructed based on game theory with the goal of minimizing shipping costs for shipping companies and maximizing hub port revenue; Zheng et al[54-56]Introducing the concept of main ports, considering constraints such as multiple types of containers and transportation time, a mixed integer programming model was established. By distinguishing between fixed and variable demands, an integrated mixed integer linear programming model for capacity sharing in liner alliances was proposed to achieve hub and spoke network design for liner transportation. Based on the development characteristics of Yangtze River shipping, a mixed integer linear programming model was constructed with the goal of minimizing ship operating costs and container loading and unloading costs, and a hub and spoke network for Yangtze River shipping was designed; Moon and others[57]We studied the hub and spoke network of irregular ship transportation, established an integer linear programming model, proposed a genetic algorithm based on local search, and optimized the hub port location, network design, and route allocation of irregular ship transportation.
(1) For the transportation of bulk industrial materials, Hennig et al[64]Considering the situation of batch transportation of goods, a path flow model with continuous cargo volume was established to optimize the tanker route, cargo volume, and arrival and departure time at the port. Regarding shipping safety and risk issues, Siddiqui et al[65]Considering the high risk of crude oil transportation, a dual objective mixed integer optimization model was established to minimize operating costs and transportation risks, and optimize the intercontinental route selection and ship scheduling plan of the oil tanker fleet. With the development of the maritime supply chain, De Assis et al[66]We studied the scheduling problem of shuttle oil tankers from drilling platforms to docks, considering variable transportation time, proposed a mixed integer linear programming model, and designed a heuristic algorithm based on rolling time domain and relaxation fixed, while achieving inventory management and path optimization for oil tanker transportation; Li et al[67]Taking into account the multi-layered shipping network for iron ore transportation in the Yangtze River, dynamic iron ore prices and transportation costs, and timely demand from steel plants, a mixed integer programming model was constructed with the goal of minimizing the total cost of the entire supply chain to determine the optimal ship scheduling plan.
(2) For irregular shipping, Norstad et al[68]The functional relationship between ship speed and fuel consumption rate has been clarified, and an optimization model for irregular ship scheduling considering speed optimization has been proposed, indicating that considering speed changes greatly improves the scheduling scheme for irregular ship transportation; Tang Lei and others[69]Considering the nonlinear impact of ship speed on voyage time and cost, a nonlinear network programming model with variable speed is proposed, and a two-stage solution algorithm based on set partitioning is designed; Yu et al[70]Considering the uncertainty of demand for goods in the spot market, seasonal fluctuations, and non navigable conditions caused by adverse weather conditions, a mixed integer programming model was established to obtain the optimal voyage revenue, vessel route, voyage, and voyage time; Jiang Zhenfeng and others[71]Considering the selection behavior of shippers and the spatiotemporal distribution characteristics of transportation demand, based on a discrete selection model, a non scheduled ship scheduling model is constructed with the goal of maximizing the long-term revenue of carriers.
(3) For liner shipping, Shou Yongyi and others[72]Based on the spatiotemporal network of port time periods and round-trip voyages, an integer programming mathematical model for liner scheduling was established with the objectives of minimizing liner variable costs, route capacity gaps, and total absolute deviation of liner voyages; Wang et al[73]Assuming that transportation demand is a decreasing continuous function of transportation time, a mixed integer nonlinear non convex optimization model for liner scheduling is established. In response to the problem of empty container transportation caused by imbalanced demand for goods, Song and others[74]Designed an integer programming method based on two-stage shortest path and an integer programming method based on two-stage heuristic algorithm to achieve joint optimization of cargo routing and empty container transportation. The former is suitable for small-scale problems, while the latter is used to solve large-scale situations; Jeong et al[75]A mixed integer programming model was established to determine the optimal number and path of empty container transportation for a bilateral trade two-way four tiered container supply chain. Regarding shipping safety and risk issues, Li et al[76]A multi-stage stochastic programming method for real-time ship schedule recovery of liner is proposed, considering the conventional uncertainty caused by probabilistic events and the sudden interruption of transportation caused by accidental events; Reinhardt et al[77]Considering the risks of navigation in pirate areas and other practical situations, a mixed integer programming model for optimizing liner speed is established to balance the robustness of ship scheduling and fuel consumption.
| 研究范围 | 研究主题 | 研究目标 | 模型类型 | 求解算法 | 文献来源 |
| 战略层 | 新组建船队规划 | 总成本最小化 | 整数规划、动态规划、非线性规划 | 遗传算法、动态规划算法、集合划分算法 | [28]、[30]、[32] |
| 总收益最大化 | 动态规划 | 最短路径算法、技术经济性分析 | [29]、[31] | ||
| 船队更新 | 总成本最小化 | 随机规划、随机混合整数规划 | Xpress-MP软件、滚动时域算法 | [33]、[34]、[36] | |
| 多目标最优 | 动态规划 | 多目标离散粒子群优化算法 | [35] | ||
| 总收益最大化 | 整数规划 | 设计求解算法 | [38] | ||
| 战术层 | 分支航线网络 | 总成本最小化 | 整数规划、随机VRP、混合整数线性规划 | 遗传算法、适应邻域搜索算法 | [39]~[41] |
| 货运需求最大化 | 整数规划 | 智能启发式算法 | [42] | ||
| 多港挂靠航线网络 | 总成本最小化 | 混合整数非线性规划、混合整数规划 | CPLEX软件、滚动时域算法、GAMS/OSICPLEX求解器 | [44]、[46]、[48]~[50] | |
| 总收益最大化 | 混合整数规划、两阶段模型 | 遗传算法、CPLEX软件、BCB算法 | [43]、[45]、[47] | ||
| 轴辐式航线网络 | 总成本最小化 | 整数规划、混合整数规划 | 遗传算法、CPLEX软件 | [54]~[57] | |
| 总收益最大化 | 混合整数线性规划 | 分解算法 | [52] | ||
| 市场份额最大化 | 混合整数线性规划 | 拉格朗日分解算法 | [51] | ||
| 多目标最优 | 博弈论 | 区间分支定界法 | [53] | ||
| 混合式航线网络 | 总成本最小化 | 混合整数线性规划、混合整数非线性规划 | CPLEX软件、分段线性逼近函数、分支定界法、拉格朗日分解算法 | [58]~[62] | |
| 总收益最大化 | 混合整数非线性规划 | 二阶锥规划(Second-Order Cone Programming, SOCP)算法、禁忌搜索算法 | [63] | ||
| 运作层 | 大宗工业物资运输 | 总成本最小化 | 混合整数规划 | Xpress-MP软件、基于滚动时域和松弛-固定的启发式算法、动态规划算法 | [64]、[66]、[67] |
| 多目标最优 | 混合整数规划 | CPLEX软件 | [65] | ||
| 不定期船运输 | 总收益最大化 | 混合整数非线性规划、非线性规划、混合整数规划 | 集合划分算法、多起始点局部搜索启发式算法、遗传算法 | [68]~[71] | |
| 班轮运输 | 总成本最小化 | 整数规划、随机规划、混合整数规划 | 最短路和两阶段启发式算法、基于向后价值迭代的最优控制策略、加速粒子群优化(Accelerated Particle Swarm Optimization, APSO)算法 | [74]~[75] | |
| 总收益最大化 | 混合整数非线性规划 | 分支定界法 | [73] | ||
| 多目标最优 | 整数规划、混合整数规划 | 蚁群算法和邻域搜索技术、CPLEX软件 | [72]~[77] |
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The development of the shipping industry also brings serious environmental pollution. According to statistics from the Hong Kong Environmental Protection Department in 2015, nitrogen oxides (NO) produced by water transportationx）Sulfur oxides (SO)x）Particulate matter (PM) accounts for 37%, 59%, and 39% of the total emissions in Hong Kong, respectively[79]The greenhouse gases and air pollutants generated by shipping activities can lead to environmental problems such as climate change, acid rain, and human health problems such as asthma and lung cancer. Therefore, IMO, some developed countries, and China have successively legislated to strictly control the emissions of various pollutants in the shipping industry, such as Annex VI of the International Convention for the Prevention of Pollution from Ships formulated by IMO and the Implementation Plan for Ship Air Pollutant Emission Control Zones issued by China. In recent years, green shipping has received widespread attention from the academic community, defined as "an efficient maritime transportation method with minimal damage to health and ecology", and it is widely believed that implementing green shipping can effectively control pollution emissions and achieve environmental friendliness[8, 80]At present, green shipping measures mainly include three categories: technical measures, operational measures, and market-based measures[81]To comply with environmental regulations, shipping companies need to choose and take corresponding measures.
(3) Market based measures mainly refer to the market mechanisms adopted by governments or organizations through incentive or punitive economic measures to enhance the emission reduction enthusiasm of shipping companies, such as emission trading plans, carbon or fuel taxes, etc. These measures themselves cannot directly improve energy efficiency, but they usually have a certain degree of coercive power and are an important supplement to the first two types of emission reduction measures. Regarding carbon or fuel taxes, Lee and others[99]The Global Trade Analysis Project Energy (GTAP-E) model was used to quantitatively analyze the impact of carbon taxes on container traffic, carbon emissions, and average freight costs on different shipping routes. The results showed that the imposition of carbon taxes would have a significant impact on three shipping routes: China/United States, Asia/United States, and South America/China; Kosmas and others[100]Based on the spider web principle, analyze the economic and environmental impacts of shipping fuel taxes, including unit tax and ad valorem tax. Regarding the Maritime Emissions Trading Scheme (METS), Zhu et al[101]Explored the potential impact of METS on the composition and carbon emissions of shipping fleets, established a stochastic optimization model for fleet planning, and demonstrated that METS can incentivize shipping companies to use more energy-efficient and efficient vessels; Dai et al[102]We studied the modeling problem of operating costs and carbon emissions under different shipping networks, and found that when the emission charging standards exceed the threshold, the shipping network will be redesigned to offset the emission reduction effect of the plan; Gu et al[103]We studied the impact of METS on fleet operations and carbon emissions reduction, taking into account the uncertainty of fuel prices and charter rates. We established an optimization model for fleet composition and route allocation, indicating that when fuel prices are low, emission quota costs are high, or METS global coverage is high, METS measures can significantly reduce carbon emissions in the short term.
| 研究范围 | 研究主题 | 研究对象 | 研究方法 | 文献来源 |
| 技术性措施 | 成本效益比较 | SOx、碳减排措施 | 减排成本估计、现金流建模、净现值估计、两阶段随机优化模型、边际减排成本曲线 | [82]、[83]、[85]、[86]、[88] |
| 减排效果比较 | SOx减排措施 | 年燃油消耗量估计 | [84] | |
| 综合评价 | SOx、NOx减排措施 | TOPSIS、AHP、FAHP、证据理论 | [87]、[89]、[90] | |
| 营运性措施 | 减排效果分析 | 减速航行 | 利润最大化方程、实证分析、t检验 | [91]、[92]、[97] |
| 绿色航运运营管理优化 | 航速、船舶配载、燃油切换位置、网络结构 | GSRSP、航速优化模型、航运网络设计模型 | [93]~[96]、[98] | |
| 基于市场的措施 | 实施效果分析 | 国际碳税、航运燃油税 | GTAP-E模型、蛛网原理 | [99]、[100] |
| 运营管理优化 | 海事排放贸易计划、欧盟的排放收费计划 | 船队规划模型、航运网络设计模型、船队构成和航线配船优化模型 | [101]~[103] |
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(3) Further research on shipping management under uncertain conditions should not only consider conventional uncertainties such as transportation demand, freight rates, fuel prices, and port operations, but also take into account shipping safety and risk factors such as adverse weather, dangerous goods, sudden destructive events, piracy, as well as uncertainties caused by complex international environments such as the China US trade war and geopolitics.
(4) Based on the powerful self-learning ability of artificial intelligence algorithms, fully utilizing shipping big data such as AIS, further exploring more accurate and efficient analysis methods and model solving algorithms is one of the key directions to solve more complex shipping management problems in the future.
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| 9. | 杨忠振，杨云茜，辛旭. 全球性重大灾害事件背景下港航运营管理研究综述. 交通运输工程学报. 2023(05): 1-18 . 本站查看 | |
| 10. | 盛进路，唐柳，郑婉媚，张雅茹. 船舶岸电政策量化评价研究. 中国航海. 2023(04): 132-140 . | |
| 11. | 周洋，杨发财，李世安，沈秋婉，杨国刚. 燃料电池动力船舶安全问题及对策探讨. 舰船科学技术. 2022(04): 91-96 . | |
| 12. | 王超，李一帆，顾永恒，姚晓霞，常佳，丛晓男. 中国内陆港建设对亚欧通道运输服务贸易脆弱性影响研究. 长安大学学报(社会科学版). 2022(02): 69-77 . | |
| 13. | 余珍，李瀛，肖金龙，蒋仲廉. 基于CiteSpace的航运经济知识图谱构建与分析. 中国水运. 2022(12): 17-18 . | |
| 14. | 兰金金，李碧珍. 能源转型视域下航运业绿色减排的发展研究. 物流科技. 2022(16): 73-77 . | |
| 15. | 余珍，李瀛，肖金龙，蒋仲廉. 基于CiteSpace的航运经济知识图谱构建与分析. 中国水运. 2022(23): 17-18 . | |
| 16. | 严新平，李晨，刘佳仑，游旭，王树武，马枫. 新一代航运系统体系架构与关键技术研究. 交通运输系统工程与信息. 2021(05): 22-29+76 . | |
| 17. | 侯慧，陈洋洋，谢长君，吴细秀，谢坤，范则阳. 基于内部能量管理和外部路径规划协同的水下无人航行器能耗优化. 电工电能新技术. 2021(10): 27-36 . |
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BAO Tian-tian, LIAN Feng, YANG Zhong-zhen. Research review of shipping management[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 55-69. doi: 10.19818/j.cnki.1671-1637.2020.04.004
| 研究范围 | 研究主题 | 研究对象 | 研究方法 | 文献来源 |
| 货运市场 | 运价波动性 | 干散货市场 | 向量自回归(Vector Autoregression, VAR)模型、向量误差校正模型(Vector Error Correction Model, VECM)、自回归广义自回归条件异方差(Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity, AR-GARCH)模型、集成连续自回归(Integrated Continuous Autoregressive, ICAR)模型、经验模态分解-小波分析(Empirical Mode Decomposition-Wavelet Analysis, EMD-WA)模型 | [12]~[15] |
| 运价预测 | 干散货市场 | 自回归移动平均(Autoregressive Moving Average, ARMA)模型、霍尔特-温特(Holt-Winters)非季节模型、非线性自回归动态网络(Non-linear Autoregressive Dynamic Network, NARNET)模型、具有外部输入的非线性自回归神经网络(Non-linear Autoregressive Neural Network with External Input, NARXNET)模型 | [16]、[19] | |
| 班轮市场 | 差分整合移动平均自回归(Autoregressive Integrated Moving Average, ARIMA)模型、自回归条件异方差(Autoregressive Conditional Heteroscedasticity, ARCH)模型 | [17] | ||
| 油运市场 | 人工神经网络(Artificial Neural Network, ANN)模型、自适应遗传算法(Adaptive Genetic Algorithm, AGA) | [18] | ||
| 市场定价及影响机制 | 班轮市场 | 合作博弈理论、价格方程和回归分析、二元广义自回归条件异方差(Generalized Autoregressive Conditional Heteroscedasticity, GARCH)模型 | [20]、[21]、[23] | |
| 干散货市场 | 价格博弈理论 | [22] | ||
| 船舶市场 | 运价与船舶市场价格的相互影响 | 造船市场、二手船市场、拆船市场 | GARCH模型、单方程模型、结构方程建模 | [24]~[26] |
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| 研究范围 | 研究主题 | 研究目标 | 模型类型 | 求解算法 | 文献来源 |
| 战略层 | 新组建船队规划 | 总成本最小化 | 整数规划、动态规划、非线性规划 | 遗传算法、动态规划算法、集合划分算法 | [28]、[30]、[32] |
| 总收益最大化 | 动态规划 | 最短路径算法、技术经济性分析 | [29]、[31] | ||
| 船队更新 | 总成本最小化 | 随机规划、随机混合整数规划 | Xpress-MP软件、滚动时域算法 | [33]、[34]、[36] | |
| 多目标最优 | 动态规划 | 多目标离散粒子群优化算法 | [35] | ||
| 总收益最大化 | 整数规划 | 设计求解算法 | [38] | ||
| 战术层 | 分支航线网络 | 总成本最小化 | 整数规划、随机VRP、混合整数线性规划 | 遗传算法、适应邻域搜索算法 | [39]~[41] |
| 货运需求最大化 | 整数规划 | 智能启发式算法 | [42] | ||
| 多港挂靠航线网络 | 总成本最小化 | 混合整数非线性规划、混合整数规划 | CPLEX软件、滚动时域算法、GAMS/OSICPLEX求解器 | [44]、[46]、[48]~[50] | |
| 总收益最大化 | 混合整数规划、两阶段模型 | 遗传算法、CPLEX软件、BCB算法 | [43]、[45]、[47] | ||
| 轴辐式航线网络 | 总成本最小化 | 整数规划、混合整数规划 | 遗传算法、CPLEX软件 | [54]~[57] | |
| 总收益最大化 | 混合整数线性规划 | 分解算法 | [52] | ||
| 市场份额最大化 | 混合整数线性规划 | 拉格朗日分解算法 | [51] | ||
| 多目标最优 | 博弈论 | 区间分支定界法 | [53] | ||
| 混合式航线网络 | 总成本最小化 | 混合整数线性规划、混合整数非线性规划 | CPLEX软件、分段线性逼近函数、分支定界法、拉格朗日分解算法 | [58]~[62] | |
| 总收益最大化 | 混合整数非线性规划 | 二阶锥规划(Second-Order Cone Programming, SOCP)算法、禁忌搜索算法 | [63] | ||
| 运作层 | 大宗工业物资运输 | 总成本最小化 | 混合整数规划 | Xpress-MP软件、基于滚动时域和松弛-固定的启发式算法、动态规划算法 | [64]、[66]、[67] |
| 多目标最优 | 混合整数规划 | CPLEX软件 | [65] | ||
| 不定期船运输 | 总收益最大化 | 混合整数非线性规划、非线性规划、混合整数规划 | 集合划分算法、多起始点局部搜索启发式算法、遗传算法 | [68]~[71] | |
| 班轮运输 | 总成本最小化 | 整数规划、随机规划、混合整数规划 | 最短路和两阶段启发式算法、基于向后价值迭代的最优控制策略、加速粒子群优化(Accelerated Particle Swarm Optimization, APSO)算法 | [74]~[75] | |
| 总收益最大化 | 混合整数非线性规划 | 分支定界法 | [73] | ||
| 多目标最优 | 整数规划、混合整数规划 | 蚁群算法和邻域搜索技术、CPLEX软件 | [72]~[77] |
DownLoad:
CSV
| 研究范围 | 研究主题 | 研究对象 | 研究方法 | 文献来源 |
| 技术性措施 | 成本效益比较 | SOx、碳减排措施 | 减排成本估计、现金流建模、净现值估计、两阶段随机优化模型、边际减排成本曲线 | [82]、[83]、[85]、[86]、[88] |
| 减排效果比较 | SOx减排措施 | 年燃油消耗量估计 | [84] | |
| 综合评价 | SOx、NOx减排措施 | TOPSIS、AHP、FAHP、证据理论 | [87]、[89]、[90] | |
| 营运性措施 | 减排效果分析 | 减速航行 | 利润最大化方程、实证分析、t检验 | [91]、[92]、[97] |
| 绿色航运运营管理优化 | 航速、船舶配载、燃油切换位置、网络结构 | GSRSP、航速优化模型、航运网络设计模型 | [93]~[96]、[98] | |
| 基于市场的措施 | 实施效果分析 | 国际碳税、航运燃油税 | GTAP-E模型、蛛网原理 | [99]、[100] |
| 运营管理优化 | 海事排放贸易计划、欧盟的排放收费计划 | 船队规划模型、航运网络设计模型、船队构成和航线配船优化模型 | [101]~[103] |
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| 研究范围 | 研究主题 | 研究对象 | 研究方法 | 文献来源 |
| 货运市场 | 运价波动性 | 干散货市场 | 向量自回归(Vector Autoregression, VAR)模型、向量误差校正模型(Vector Error Correction Model, VECM)、自回归广义自回归条件异方差(Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity, AR-GARCH)模型、集成连续自回归(Integrated Continuous Autoregressive, ICAR)模型、经验模态分解-小波分析(Empirical Mode Decomposition-Wavelet Analysis, EMD-WA)模型 | [12]~[15] |
| 运价预测 | 干散货市场 | 自回归移动平均(Autoregressive Moving Average, ARMA)模型、霍尔特-温特(Holt-Winters)非季节模型、非线性自回归动态网络(Non-linear Autoregressive Dynamic Network, NARNET)模型、具有外部输入的非线性自回归神经网络(Non-linear Autoregressive Neural Network with External Input, NARXNET)模型 | [16]、[19] | |
| 班轮市场 | 差分整合移动平均自回归(Autoregressive Integrated Moving Average, ARIMA)模型、自回归条件异方差(Autoregressive Conditional Heteroscedasticity, ARCH)模型 | [17] | ||
| 油运市场 | 人工神经网络(Artificial Neural Network, ANN)模型、自适应遗传算法(Adaptive Genetic Algorithm, AGA) | [18] | ||
| 市场定价及影响机制 | 班轮市场 | 合作博弈理论、价格方程和回归分析、二元广义自回归条件异方差(Generalized Autoregressive Conditional Heteroscedasticity, GARCH)模型 | [20]、[21]、[23] | |
| 干散货市场 | 价格博弈理论 | [22] | ||
| 船舶市场 | 运价与船舶市场价格的相互影响 | 造船市场、二手船市场、拆船市场 | GARCH模型、单方程模型、结构方程建模 | [24]~[26] |
| 研究范围 | 研究主题 | 研究目标 | 模型类型 | 求解算法 | 文献来源 |
| 战略层 | 新组建船队规划 | 总成本最小化 | 整数规划、动态规划、非线性规划 | 遗传算法、动态规划算法、集合划分算法 | [28]、[30]、[32] |
| 总收益最大化 | 动态规划 | 最短路径算法、技术经济性分析 | [29]、[31] | ||
| 船队更新 | 总成本最小化 | 随机规划、随机混合整数规划 | Xpress-MP软件、滚动时域算法 | [33]、[34]、[36] | |
| 多目标最优 | 动态规划 | 多目标离散粒子群优化算法 | [35] | ||
| 总收益最大化 | 整数规划 | 设计求解算法 | [38] | ||
| 战术层 | 分支航线网络 | 总成本最小化 | 整数规划、随机VRP、混合整数线性规划 | 遗传算法、适应邻域搜索算法 | [39]~[41] |
| 货运需求最大化 | 整数规划 | 智能启发式算法 | [42] | ||
| 多港挂靠航线网络 | 总成本最小化 | 混合整数非线性规划、混合整数规划 | CPLEX软件、滚动时域算法、GAMS/OSICPLEX求解器 | [44]、[46]、[48]~[50] | |
| 总收益最大化 | 混合整数规划、两阶段模型 | 遗传算法、CPLEX软件、BCB算法 | [43]、[45]、[47] | ||
| 轴辐式航线网络 | 总成本最小化 | 整数规划、混合整数规划 | 遗传算法、CPLEX软件 | [54]~[57] | |
| 总收益最大化 | 混合整数线性规划 | 分解算法 | [52] | ||
| 市场份额最大化 | 混合整数线性规划 | 拉格朗日分解算法 | [51] | ||
| 多目标最优 | 博弈论 | 区间分支定界法 | [53] | ||
| 混合式航线网络 | 总成本最小化 | 混合整数线性规划、混合整数非线性规划 | CPLEX软件、分段线性逼近函数、分支定界法、拉格朗日分解算法 | [58]~[62] | |
| 总收益最大化 | 混合整数非线性规划 | 二阶锥规划(Second-Order Cone Programming, SOCP)算法、禁忌搜索算法 | [63] | ||
| 运作层 | 大宗工业物资运输 | 总成本最小化 | 混合整数规划 | Xpress-MP软件、基于滚动时域和松弛-固定的启发式算法、动态规划算法 | [64]、[66]、[67] |
| 多目标最优 | 混合整数规划 | CPLEX软件 | [65] | ||
| 不定期船运输 | 总收益最大化 | 混合整数非线性规划、非线性规划、混合整数规划 | 集合划分算法、多起始点局部搜索启发式算法、遗传算法 | [68]~[71] | |
| 班轮运输 | 总成本最小化 | 整数规划、随机规划、混合整数规划 | 最短路和两阶段启发式算法、基于向后价值迭代的最优控制策略、加速粒子群优化(Accelerated Particle Swarm Optimization, APSO)算法 | [74]~[75] | |
| 总收益最大化 | 混合整数非线性规划 | 分支定界法 | [73] | ||
| 多目标最优 | 整数规划、混合整数规划 | 蚁群算法和邻域搜索技术、CPLEX软件 | [72]~[77] |
| 研究范围 | 研究主题 | 研究对象 | 研究方法 | 文献来源 |
| 技术性措施 | 成本效益比较 | SOx、碳减排措施 | 减排成本估计、现金流建模、净现值估计、两阶段随机优化模型、边际减排成本曲线 | [82]、[83]、[85]、[86]、[88] |
| 减排效果比较 | SOx减排措施 | 年燃油消耗量估计 | [84] | |
| 综合评价 | SOx、NOx减排措施 | TOPSIS、AHP、FAHP、证据理论 | [87]、[89]、[90] | |
| 营运性措施 | 减排效果分析 | 减速航行 | 利润最大化方程、实证分析、t检验 | [91]、[92]、[97] |
| 绿色航运运营管理优化 | 航速、船舶配载、燃油切换位置、网络结构 | GSRSP、航速优化模型、航运网络设计模型 | [93]~[96]、[98] | |
| 基于市场的措施 | 实施效果分析 | 国际碳税、航运燃油税 | GTAP-E模型、蛛网原理 | [99]、[100] |
| 运营管理优化 | 海事排放贸易计划、欧盟的排放收费计划 | 船队规划模型、航运网络设计模型、船队构成和航线配船优化模型 | [101]~[103] |
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