Frontiers of Engineering Management >
Operation management of green ports and shipping networks: overview and research opportunities
Received date: 30 Nov 2018
Accepted date: 12 Mar 2019
Published date: 15 Jun 2019
Copyright
Global ports and maritime shipping networks are important carriers for global supply chain networks, but they are also the main sources of energy consumption and pollution. To limit ship emissions in ports and offshore areas, the International Maritime Organization, as well as some countries, has issued a series of policies. This study highlights the importance and necessity of investigating emergent research problems in the operation management of green ports and maritime shipping networks. Considerable literature related to this topic is reviewed and discussed. Moreover, a comprehensive research framework on green port and shipping operation management is proposed for future research opportunities. The framework mainly comprises four research areas related to emission control and grading policies. This review may provide new ideas to the academia and industry practitioners for improving the performance and efficiency of the operation management of green ports and maritime shipping networks.
Lu ZHEN , Dan ZHUGE , Liwen MURONG , Ran YAN , Shuaian WANG . Operation management of green ports and shipping networks: overview and research opportunities[J]. Frontiers of Engineering Management, 2019 , 6(2) : 152 -162 . DOI: 10.1007/s42524-019-0027-2
1 |
Acomi N, Acomi O (2014). The influence of different types of fuel over the energy efficiency operational index. Energy Procedia, 59: 243–248
|
2 |
Adland R, Fonnes G, Jia H, Lampe O D, Strandenes S P (2017). The impact of regional environmental regulations on empirical vessel speeds. Transportation Research Part D: Transport and Environment, 53: 37–49
|
3 |
Agarwal R, Ergun O (2008). Ship scheduling and network design for cargo routing in liner shipping. Transportation Science, 42(2): 175–196
|
4 |
Agarwal R, Ergun O (2010). Network design and allocation mechanisms for carrier alliances in liner shipping. Operations Research, 58(6): 1726–1742
|
5 |
Álvarez J F (2009). Joint routing and deployment of a fleet of container vessels. Maritime Economics & Logistics, 11(2): 186–208
|
6 |
Ančić I, Šestan A (2015). Influence of the required EEDI reduction factor on the CO2 emission from bulk carriers. Energy Policy, 84: 107–116
|
7 |
Ballini F, Bozzo R (2015). Air pollution from ships in ports: the socio-economic benefit of cold-ironing technology. Research in Transportation Business & Management, 17: 92–98
|
8 |
Bish E K, Chen F Y, Leong Y T, Nelson B L, Ng J W C, Simchi-levi D (2005). Dispatching vehicles in a mega container terminal. OR-Spektrum, 27(4): 491–506
|
9 |
Bouman E A, Lindstad E, Rialland A I, Strømman A H (2017). State-of-the-art technologies, measures, and potential for reducing GHG emissions from shipping—a review. Transportation Research Part D: Transport and Environment, 52: 408–421
|
10 |
Brouer B D, Alvarez J F, Plum C E M, Pisinger D, Sigurd M M (2014). A base integer programming model and benchmark suite for liner-shipping network design. Transportation Science, 48(2): 281–312
|
11 |
Buhaug Ø, Corbett J, Endresen Ø, Eyring V, Faber J, Hanayama S, Lee D, Lindstad H, Markowska A, Mjelde A (2009). Second IMO GHG Study. London: International Maritime Organization (IMO)
|
12 |
Chen K, Xu P, Yang Z (2017). Optimization model of liner transportation system considering cargo owner’s choice inertia. Journal of Management Sciences in China, 20(7): 104–114 (in Chinese)
|
13 |
Chen L, Yip T L, Mou J (2018). Provision of emission control area and the impact on shipping route choice and ship emissions. Transportation Research Part D: Transport and Environment, 58: 280–291
|
14 |
Chen Y, Leong Y T, Ng J W C, Demir E K, Nelson B L, Simchi-Levi D (1998). Dispatching automated guided vehicles in a mega container terminal. In: Proceedings of INFORMS Meeting, Montreal, Canada
|
15 |
Cheng H, Li B (2012). A comparative study of EEDI criterion methods. Shipbuilding of China, 53: 103–109 (in Chinese)
|
16 |
Cordeau J F, Laporte G, Legato P, Moccia L (2005). Models and tabu search heuristics for the berth allocation problem. Transportation Science, 39(4): 526–538
|
17 |
Davarzani H, Fahimnia B, Bell M, Sarkis J (2016). Greening ports and maritime logistics: a review. Transportation Research Part D: Transport and Environment, 48: 473–487
|
18 |
Doudnikoff M, Lacoste R (2014). Effect of a speed reduction of containerships in response to higher energy costs in sulphur emission control areas. Transportation Research Part D: Transport and Environment, 28: 51–61
|
19 |
Du Y, Chen Q, Lam J S L, Xu Y, Cao J X (2015). Modeling the impacts of tides and the virtual arrival policy in berth allocation. Transportation Science, 49(4): 939–956
|
20 |
Du Y, Chen Q, Quan X, Long L, Fung R Y K (2011). Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E: Logistics and Transportation Review, 47(6): 1021–1037
|
21 |
Fagerholt K, Gausel N T, Rakke J G, Psaraftis H N (2015). Maritime routing and speed optimization with emission control areas. Transportation Research Part C: Emerging Technologies, 52: 57–73
|
22 |
Fagerholt K, Psaraftis H N (2015). On two speed optimization problems for ships that sail in and out of emission control areas. Transportation Research Part D: Transport and Environment, 39: 56–64
|
23 |
Fransoo J C, Lee C Y (2013). The critical role of ocean container transport in global supply chain performance. Production and Operations Management, 22(2): 253–268
|
24 |
Giallombardo G, Moccia L, Salani M, Vacca I (2010). Modeling and solving the tactical berth allocation problem. Transportation Research Part B: Methodological, 44(2): 232–245
|
25 |
Gu Y, Wallace S W (2017). Scrubber: a potentially overestimated compliance method for the emission control areas: the importance of involving a ship’s sailing pattern in the evaluation. Transportation Research Part D: Transport and Environment, 55: 51–66
|
26 |
Hall W J (2010). Assessment of CO2 and priority pollutant reduction by installation of shoreside power. Resources, Conservation and Recycling, 54(7): 462–467
|
27 |
He J (2016). Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving. Advanced Engineering Informatics, 30(3): 390–405
|
28 |
He J, Huang Y, Yan W (2015a). Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Advanced Engineering Informatics, 29(1): 59–75
|
29 |
He J, Huang Y, Yan W, Wang S (2015b). Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption. Expert Systems with Applications, 42(5): 2464–2487
|
30 |
Heitmann N, Khalilian S (2011). Accounting for carbon dioxide emissions from international shipping: Burden sharing under different UNFCCC allocation options and regime scenarios. Marine Policy, 35(5): 682–691
|
31 |
Hou J (2017). Dynamic berth allocation problem with two types of shore power for containership based on rolling horizon strategy. In: Proceedings of the 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), Singapore, 144–149
|
32 |
Hu Q M, Hu Z H, Du Y (2014). Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 70: 1–10
|
33 |
Hu Z (2015). Multi-objective genetic algorithm for berth allocation problem considering daytime preference. Computers & Industrial Engineering, 89: 2–14
|
34 |
Imai S, Nishimura E, Papadimitriou S (2001). The dynamic berth allocation problem for a container port. Transportation Research Part B: Methodological, 35(4): 401–417
|
35 |
IMO (2008). Revised MARPOL Annex VI: Regulations for the prevention of air pollution from ships and NOx technical code. London: IMO Marine Environmental Protection Committee (MEPC)
|
36 |
Jia P, Govindan K, Kannan D (2015). Identification and evaluation of influential criteria for the selection of an environmental shipping carrier using DEMATEL: a case from India. International Journal of Shipping and Transport Logistics, 7(6): 719–741
|
37 |
Jin Z, Li N, Han J (2014). Fleet deployment optimization of container liner routes under the unbalance of supply and demand. Journal of Dalian Maritime University, 35(1): 21–28 (in Chinese)
|
38 |
Kavakeb S, Nguyen T T, McGinley K, Yang Z, Jenkinson I, Murray R (2015). Green vehicle technology to enhance the performance of a European port: a simulation model with a cost-benefit approach. Transportation Research Part C: Emerging Technologies, 60: 169–188
|
39 |
Kozan E, Preston P (2006). Mathematical modeling of container transfers and storage locations at seaport terminals. OR-Spektrum, 28(4): 519–537
|
40 |
Lee C Y, Song D P (2017). Ocean container transport in global supply chains: overview and research opportunities. Transportation Research Part B: Methodological, 95: 442–474
|
41 |
Lee L H, Chew E P, Tan K C, Wang Y (2010). Vehicle dispatching algorithm for container transshipment hubs. OR-Spektrum, 32(3): 663–685
|
42 |
Lee T, Chang Y, Lee P (2013). Economy-wide impact analysis of a carbon tax on international container shipping. Transportation Research Part A: Policy and Practice, 58: 87–102
|
43 |
Li B (2012). The status of energy saving and emission reduction and the methods of low carbon development in shipping industry. Journal of Engineering Studies, 4: 260–269 (in Chinese)
|
44 |
Li H, Zhang H, Wu G (2015). Analysis of EEDI influencing factors of ships and measures. China Water Transport, 15: 68–69 (in Chinese)
|
45 |
Lim A (1998). The berth planning problem. Operations Research Letters, 22(2–3): 105–110
|
46 |
Lindstad H, Asbjornslett B E, Stromman A H (2011). Reductions in greenhouse gas emissions and cost by shipping at lower speeds. Energy Policy, 39(6): 3456–3464
|
47 |
Liu D, Ge Y E (2018). Modeling assignment of quay cranes using queueing theory for minimizing CO2 emission at a container terminal. Transportation Research Part D: Transport and Environment, 61: 140–151
|
48 |
Lu R, Turan O, Boulougouris E, Banks C, Incecik A (2015). A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping. Ocean Engineering, 110: 18–28
|
49 |
Lv J, Mao H (2017). An optimization model of liner ship fleet deployment under sulfur emission control area and carbon emission restriction. Journal of Dalian Maritime University, 43(1): 101–105 (in Chinese)
|
50 |
Meng Q, Wang S, Andersson H, Thun K (2014). Containership routing and scheduling in liner shipping: overview and future research directions. Transportation Science, 48(2): 265–280
|
51 |
Murty K G, Liu J, Wan Y, Linn R (2005). A decision support system for operations in a container terminal. Decision Support Systems, 39(3): 309–332
|
52 |
Norstad I, Fagerholt K, Laporte G (2011). Tramp ship routing and scheduling with speed optimization. Transportation Research Part C: Emerging Technologies, 19(5): 853–865
|
53 |
Panagakos G P, Stamatopoulou E V, Psaraftis H N (2014). The possible designation of the Mediterranean Sea as a SECA: a case study. Transportation Research Part D: Transport and Environment, 28: 74–90
|
54 |
Peng Y, Wang W, Song X, Zhang Q (2016). Optimal allocation of resources for yard crane network management to minimize carbon dioxide emissions. Journal of Cleaner Production, 131: 649–658
|
55 |
Pjevčević D, Vladisavljević I, Vukadinović K, Teodorović D (2011). Application of DEA to the analysis of AGV fleet operations in a port container terminal. Procedia: Social and Behavioral Sciences, 20: 816–825
|
56 |
Psaraftis H N, Kontovas C A (2013). Speed models for energy-efficient maritime transportation: a taxonomy and survey. Transportation Research Part C: Emerging Technologies, 26: 331–351
|
57 |
Psaraftis H N, Kontovas C A (2014). Ship speed optimization: concept, models and combined speed-routing scenarios. Transportation Research Part C: Emerging Technologies, 44: 52–69
|
58 |
Rizaldi A, Wasesa M, Rahman M N (2015). Yard cranes coordination schemes for automated container terminals: an agent-based approach. Procedia Manufacturing, 4: 124–132
|
59 |
Sha M, Zhang T, Lan Y, Zhou X, Qin T, Yu D, Chen K (2017). Scheduling optimization of yard cranes with minimal energy consumption at container terminals. Computers & Industrial Engineering, 113: 704–713
|
60 |
Shi Y (2016). Reducing greenhouse gas emissions from international shipping: is it time to consider market-based measures. Marine Policy, 64: 123–134
|
61 |
Sun X, Yan X, Wu B, Song X (2013). Analysis of the operational energy efficiency for inland river ships. Transportation Research Part D: Transport and Environment, 22: 34–39
|
62 |
Talavera A M, Barron J G G, Passamani C M T C (2016). Optimization of vessel and quay crane emissions during the hoteling phase. In: Proceedings of the 7th International Conference on Information, Intelligence, Systems & Applications (IISA), Chalkidiki, Greece: IEEE, 1–10
|
63 |
UNCTAD (2011). Review of Maritime Transport 2011. United Nations, New York and Geneva
|
64 |
UNCTAD (2015). Review of Maritime Transport 2015. United Nations, New York and Geneva
|
65 |
Vaishnav P, Fischbeck P S, Morgan M G, Corbett J J (2016). Shore power for vessels calling at US ports: benefits and costs. Environmental Science & Technology, 50(3): 1102–1110
|
66 |
Venturini G, Iris C, Kontovas C A, Larsen A (2017). Multi-port berth allocation problem with speed optimization and emission considerations. Transportation Research Part D: Transport and Environment, 54: 142–159
|
67 |
Wang F, Huang J, Liu Z (2017). Port management and operations: emerging research hotspots and development. Journal of Management Sciences in China, 20(5): 111–126 (in Chinese)
|
68 |
Wang F, Lim A (2007). A stochastic beam search for the berth allocation problem. Decision Support Systems, 42(4): 2186–2196
|
69 |
Wang H, Mao X, Rutherford D (2015a). Costs and Benefits of Shore Power at the Port of Shenzhen. The International Council on Clean Transportation
|
70 |
Wang K, Fu X, Luo M (2015b). Modeling the impacts of alternative emission trading schemes on international shipping. Transportation Research Part A: Policy and Practice, 77: 35–49
|
71 |
Wang S, Meng Q (2012a). Liner ship fleet deployment with container transshipment operations. Transportation Research Part E: Logistics and Transportation Review, 48(2): 470–484
|
72 |
Wang S, Meng Q (2012b). Sailing speed optimization for container ships in a liner shipping network. Transportation Research Part E: Logistics and Transportation Review, 48(3): 701–714
|
73 |
Wang S, Meng Q, Liu Z (2013). A note on “berth allocation considering fuel consumption and vessel emissions”. Transportation Research Part E: Logistics and Transportation Review, 49(1): 48–54
|
74 |
Wang T, Lu L, Wang X (2016). Cooperation mode of port and shipping companies with the introduction of price compensation mechanism. Resource Development & Market, 32: 1409–1414 (in Chinese)
|
75 |
Wang T, Wang X, Meng Q (2018). Joint berth allocation and quay crane assignment under different carbon taxation policies. Transportation Research Part B: Methodological, 117: 18–36
|
76 |
Winkel R, Weddige U, Johnsen D, Hoen V, Papaefthimiou S (2016). Shore side electricity in Europe: potential and environmental benefits. Energy Policy, 88: 584–593
|
77 |
Xia J, Li X, Ma H, Xu Z (2015). Joint planning of fleet deployment, speed optimization, and cargo allocation for liner shipping. Transportation Science, 49(4): 922–938
|
78 |
Yu H, Ge Y E, Chen J, Luo L, Tan C, Liu D (2017a). CO2 emission evaluation of yard tractors during loading at container terminals. Transportation Research Part D: Transport and Environment, 53: 17–36
|
79 |
Yu S, Wang C (2015). Ship speed optimization under different carbon emission control policies. Journal of Dalian Maritime University, 41(3): 45–50 (in Chinese)
|
80 |
Yu S, Wang S, Zhen L (2017b). Quay crane scheduling problem with considering tidal impact and fuel consumption. Flexible Services and Manufacturing Journal, 29(3–4): 345–368
|
81 |
Zhang S, Zhang S (2008). Trading mechanism framework for international maritime greenhouse gas emissions. China Maritime Safety, 9: 60–63 (in Chinese)
|
82 |
Zhao Y (2015). Axle-spoke container shipping network design problem under competitive environment. Chinese Journal of Management Science, 23(7): 103–112 (in Chinese)
|
83 |
Zhao Y, Duan H, Kuang H (2015). Axle-spoke container shipping network design considering CO2 emissions. Journal of Systems Engineering, 30: 383–393 (in Chinese)
|
84 |
Zhen L (2017). Optimization of Container Port Operation Management. Beijing: Science Press (in Chinese)
|
85 |
Zheng J, Gao Z, Yang D, Sun Z (2015). Network design and capacity exchange for liner alliances with fixed and variable container demands. Transportation Science, 49(4): 886–899
|
86 |
Zhu M, Zhen H, Gan A (2016). Optimization of liner fleet deployment under carbon emission right trading. Journal of Transportation Systems Engineering and Information Technology, 16(1): 202–208
|
87 |
Zis T, Angeloudis P, Bell M G H, Psaraftis H N (2016). Payback period for emissions abatement alternatives: role of regulation and fuel prices. Transportation Research Record: Journal of the Transportation Research Board, 2549(1): 37–44
|
/
〈 | 〉 |