Operation management of green ports and shipping networks: overview and research opportunities

doi:10.1007/s42524-019-0027-2

Front. Eng

2019, Vol. 6

Issue (2) : 152-162 https://doi.org/10.1007/s42524-019-0027-2

REVIEW ARTICLE

Operation management of green ports and shipping networks: overview and research opportunities

Lu ZHEN¹, Dan ZHUGE²(

), Liwen MURONG³, Ran YAN², Shuaian WANG²

¹. School of Management, Shanghai University, Shanghai 200444, China
². Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong 999077, China
³. Department of Systems Innovation, The University of Tokyo, Tokyo 113-8654, Japan

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Abstract

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.

Keywords maritime shipping port operations green port green shipping emission control areas

Corresponding Author(s): Dan ZHUGE

Just Accepted Date: 27 March 2019 Online First Date: 28 April 2019 Issue Date: 17 May 2019

Cite this article:

Lu ZHEN,Dan ZHUGE,Liwen MURONG, et al. Operation management of green ports and shipping networks: overview and research opportunities[J]. Front. Eng, 2019, 6(2): 152-162.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-019-0027-2
https://academic.hep.com.cn/fem/EN/Y2019/V6/I2/152

Tab.1 Framework of potential topics

1	NAcomi, O Acomi (2014). The influence of different types of fuel over the energy efficiency operational index. Energy Procedia, 59: 243–248 https://doi.org/10.1016/j.egypro.2014.10.373
2	RAdland, G Fonnes, HJia, O DLampe, S PStrandenes (2017). The impact of regional environmental regulations on empirical vessel speeds. Transportation Research Part D: Transport and Environment, 53: 37–49 https://doi.org/10.1016/j.trd.2017.03.018
3	RAgarwal, O Ergun (2008). Ship scheduling and network design for cargo routing in liner shipping. Transportation Science, 42(2): 175–196 https://doi.org/10.1287/trsc.1070.0205
4	RAgarwal, O Ergun (2010). Network design and allocation mechanisms for carrier alliances in liner shipping. Operations Research, 58(6): 1726–1742 https://doi.org/10.1287/opre.1100.0848
5	J FÁlvarez (2009). Joint routing and deployment of a fleet of container vessels. Maritime Economics & Logistics, 11(2): 186–208 https://doi.org/10.1057/mel.2009.5
6	IAnčić, AŠestan (2015). Influence of the required EEDI reduction factor on the CO2 emission from bulk carriers. Energy Policy, 84: 107–116 https://doi.org/10.1016/j.enpol.2015.04.031
7	FBallini, R Bozzo (2015). Air pollution from ships in ports: the socio-economic benefit of cold-ironing technology. Research in Transportation Business & Management, 17: 92–98 https://doi.org/10.1016/j.rtbm.2015.10.007
8	E KBish, F Y Chen, Y T Leong, B L Nelson, J W C Ng, D Simchi-levi (2005). Dispatching vehicles in a mega container terminal. OR-Spektrum, 27(4): 491–506 https://doi.org/10.1007/s00291-004-0194-2
9	E ABouman, E Lindstad, A IRialland, A HStrømman (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 https://doi.org/10.1016/j.trd.2017.03.022
10	B DBrouer, J F Alvarez, C E M Plum, D Pisinger, M MSigurd (2014). A base integer programming model and benchmark suite for liner-shipping network design. Transportation Science, 48(2): 281–312 https://doi.org/10.1287/trsc.2013.0471
11	ØBuhaug, J Corbett, ØEndresen, VEyring, JFaber, SHanayama, DLee, H Lindstad, AMarkowska, AMjelde (2009). Second IMO GHG Study. London: International Maritime Organization (IMO)
12	KChen, P Xu, ZYang (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	LChen, T L Yip, J Mou (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 https://doi.org/10.1016/j.trd.2017.07.003
14	YChen, Y T Leong, J W C Ng, E K Demir, B L Nelson, D Simchi-Levi (1998). Dispatching automated guided vehicles in a mega container terminal. In: Proceedings of INFORMS Meeting, Montreal, Canada
15	HCheng, B Li (2012). A comparative study of EEDI criterion methods. Shipbuilding of China, 53: 103–109 (in Chinese)
16	J FCordeau, G Laporte, PLegato, LMoccia (2005). Models and tabu search heuristics for the berth allocation problem. Transportation Science, 39(4): 526–538 https://doi.org/10.1287/trsc.1050.0120
17	HDavarzani, B Fahimnia, MBell, JSarkis (2016). Greening ports and maritime logistics: a review. Transportation Research Part D: Transport and Environment, 48: 473–487 https://doi.org/10.1016/j.trd.2015.07.007
18	MDoudnikoff, R Lacoste (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 https://doi.org/10.1016/j.trd.2014.03.002
19	YDu, Q Chen, J S LLam, YXu, J X Cao (2015). Modeling the impacts of tides and the virtual arrival policy in berth allocation. Transportation Science, 49(4): 939–956 https://doi.org/10.1287/trsc.2014.0568
20	YDu, Q Chen, XQuan, LLong, R Y K Fung (2011). Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E: Logistics and Transportation Review, 47(6): 1021–1037 https://doi.org/10.1016/j.tre.2011.05.011
21	KFagerholt, N T Gausel, J G Rakke, H N Psaraftis (2015). Maritime routing and speed optimization with emission control areas. Transportation Research Part C: Emerging Technologies, 52: 57–73 https://doi.org/10.1016/j.trc.2014.12.010
22	KFagerholt, H N Psaraftis (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 https://doi.org/10.1016/j.trd.2015.06.005
23	J CFransoo, C Y Lee (2013). The critical role of ocean container transport in global supply chain performance. Production and Operations Management, 22(2): 253–268 https://doi.org/10.1111/j.1937-5956.2011.01310.x
24	GGiallombardo, L Moccia, MSalani, IVacca (2010). Modeling and solving the tactical berth allocation problem. Transportation Research Part B: Methodological, 44(2): 232–245 https://doi.org/10.1016/j.trb.2009.07.003
25	YGu, S W Wallace (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 https://doi.org/10.1016/j.trd.2017.06.024
26	W JHall (2010). Assessment of CO2 and priority pollutant reduction by installation of shoreside power. Resources, Conservation and Recycling, 54(7): 462–467 https://doi.org/10.1016/j.resconrec.2009.10.002
27	JHe (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 https://doi.org/10.1016/j.aei.2016.04.006
28	JHe, Y Huang, WYan (2015a). Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Advanced Engineering Informatics, 29(1): 59–75 https://doi.org/10.1016/j.aei.2014.09.003
29	JHe, Y Huang, WYan, SWang (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 https://doi.org/10.1016/j.eswa.2014.11.016
30	NHeitmann, S Khalilian (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 https://doi.org/10.1016/j.marpol.2011.02.009
31	JHou (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	Q MHu, Z H Hu, Y Du (2014). Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 70: 1–10 https://doi.org/10.1016/j.cie.2014.01.003
33	ZHu (2015). Multi-objective genetic algorithm for berth allocation problem considering daytime preference. Computers & Industrial Engineering, 89: 2–14 https://doi.org/10.1016/j.cie.2015.04.035
34	SImai, E Nishimura, SPapadimitriou (2001). The dynamic berth allocation problem for a container port. Transportation Research Part B: Methodological, 35(4): 401–417 https://doi.org/10.1016/S0191-2615(99)00057-0
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	PJia, K Govindan, DKannan (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 https://doi.org/10.1504/IJSTL.2015.072684
37	ZJin, N Li, JHan (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	SKavakeb, T T Nguyen, K McGinley, ZYang, IJenkinson, RMurray (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 https://doi.org/10.1016/j.trc.2015.08.012
39	EKozan, P Preston (2006). Mathematical modeling of container transfers and storage locations at seaport terminals. OR-Spektrum, 28(4): 519–537 https://doi.org/10.1007/s00291-006-0048-1
40	C YLee, D P Song (2017). Ocean container transport in global supply chains: overview and research opportunities. Transportation Research Part B: Methodological, 95: 442–474 https://doi.org/10.1016/j.trb.2016.05.001
41	L HLee, E P Chew, K C Tan, Y Wang (2010). Vehicle dispatching algorithm for container transshipment hubs. OR-Spektrum, 32(3): 663–685 https://doi.org/10.1007/s00291-010-0199-y
42	TLee, Y Chang, PLee (2013). Economy-wide impact analysis of a carbon tax on international container shipping. Transportation Research Part A: Policy and Practice, 58: 87–102 https://doi.org/10.1016/j.tra.2013.10.002
43	BLi (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	HLi, H Zhang, GWu (2015). Analysis of EEDI influencing factors of ships and measures. China Water Transport, 15: 68–69 (in Chinese)
45	ALim (1998). The berth planning problem. Operations Research Letters, 22(2–3): 105–110 https://doi.org/10.1016/S0167-6377(98)00010-8
46	HLindstad, B E Asbjornslett, A H Stromman (2011). Reductions in greenhouse gas emissions and cost by shipping at lower speeds. Energy Policy, 39(6): 3456–3464 https://doi.org/10.1016/j.enpol.2011.03.044
47	DLiu, Y E Ge (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 https://doi.org/10.1016/j.trd.2017.06.006
48	RLu, O Turan, EBoulougouris, CBanks, AIncecik (2015). A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping. Ocean Engineering, 110: 18–28 https://doi.org/10.1016/j.oceaneng.2015.07.042
49	JLv, H Mao (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	QMeng, S Wang, HAndersson, KThun (2014). Containership routing and scheduling in liner shipping: overview and future research directions. Transportation Science, 48(2): 265–280 https://doi.org/10.1287/trsc.2013.0461
51	K GMurty, J Liu, YWan, RLinn (2005). A decision support system for operations in a container terminal. Decision Support Systems, 39(3): 309–332 https://doi.org/10.1016/j.dss.2003.11.002
52	INorstad, K Fagerholt, GLaporte (2011). Tramp ship routing and scheduling with speed optimization. Transportation Research Part C: Emerging Technologies, 19(5): 853–865 https://doi.org/10.1016/j.trc.2010.05.001
53	G PPanagakos, E VStamatopoulou, H NPsaraftis (2014). The possible designation of the Mediterranean Sea as a SECA: a case study. Transportation Research Part D: Transport and Environment, 28: 74–90 https://doi.org/10.1016/j.trd.2013.12.010
54	YPeng, W Wang, XSong, QZhang (2016). Optimal allocation of resources for yard crane network management to minimize carbon dioxide emissions. Journal of Cleaner Production, 131: 649–658 https://doi.org/10.1016/j.jclepro.2016.04.120
55	DPjevčević, IVladisavljević, KVukadinović, DTeodorović (2011). Application of DEA to the analysis of AGV fleet operations in a port container terminal. Procedia: Social and Behavioral Sciences, 20: 816–825 https://doi.org/10.1016/j.sbspro.2011.08.090
56	H NPsaraftis, C AKontovas (2013). Speed models for energy-efficient maritime transportation: a taxonomy and survey. Transportation Research Part C: Emerging Technologies, 26: 331–351 https://doi.org/10.1016/j.trc.2012.09.012
57	H NPsaraftis, C AKontovas (2014). Ship speed optimization: concept, models and combined speed-routing scenarios. Transportation Research Part C: Emerging Technologies, 44: 52–69 https://doi.org/10.1016/j.trc.2014.03.001
58	ARizaldi, M Wasesa, M NRahman (2015). Yard cranes coordination schemes for automated container terminals: an agent-based approach. Procedia Manufacturing, 4: 124–132 https://doi.org/10.1016/j.promfg.2015.11.022
59	MSha, T Zhang, YLan, XZhou, T Qin, DYu, KChen (2017). Scheduling optimization of yard cranes with minimal energy consumption at container terminals. Computers & Industrial Engineering, 113: 704–713 https://doi.org/10.1016/j.cie.2016.03.022
60	YShi (2016). Reducing greenhouse gas emissions from international shipping: is it time to consider market-based measures. Marine Policy, 64: 123–134 https://doi.org/10.1016/j.marpol.2015.11.013
61	XSun, X Yan, BWu, XSong (2013). Analysis of the operational energy efficiency for inland river ships. Transportation Research Part D: Transport and Environment, 22: 34–39 https://doi.org/10.1016/j.trd.2013.03.002
62	A MTalavera, J G G Barron, C M T C Passamani (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	PVaishnav, P S Fischbeck, M G Morgan, J J Corbett (2016). Shore power for vessels calling at US ports: benefits and costs. Environmental Science & Technology, 50(3): 1102–1110 https://doi.org/10.1021/acs.est.5b04860
66	GVenturini, C Iris, C AKontovas, ALarsen (2017). Multi-port berth allocation problem with speed optimization and emission considerations. Transportation Research Part D: Transport and Environment, 54: 142–159 https://doi.org/10.1016/j.trd.2017.05.002
67	FWang, J Huang, ZLiu (2017). Port management and operations: emerging research hotspots and development. Journal of Management Sciences in China, 20(5): 111–126 (in Chinese)
68	FWang, A Lim (2007). A stochastic beam search for the berth allocation problem. Decision Support Systems, 42(4): 2186–2196 https://doi.org/10.1016/j.dss.2006.06.008
69	HWang, X Mao, DRutherford (2015a). Costs and Benefits of Shore Power at the Port of Shenzhen. The International Council on Clean Transportation
70	KWang, X Fu, MLuo (2015b). Modeling the impacts of alternative emission trading schemes on international shipping. Transportation Research Part A: Policy and Practice, 77: 35–49 https://doi.org/10.1016/j.tra.2015.04.006
71	SWang, Q Meng (2012a). Liner ship fleet deployment with container transshipment operations. Transportation Research Part E: Logistics and Transportation Review, 48(2): 470–484 https://doi.org/10.1016/j.tre.2011.10.011
72	SWang, Q Meng (2012b). Sailing speed optimization for container ships in a liner shipping network. Transportation Research Part E: Logistics and Transportation Review, 48(3): 701–714 https://doi.org/10.1016/j.tre.2011.12.003
73	SWang, Q Meng, ZLiu (2013). A note on “berth allocation considering fuel consumption and vessel emissions”. Transportation Research Part E: Logistics and Transportation Review, 49(1): 48–54 https://doi.org/10.1016/j.tre.2012.07.002
74	TWang, L Lu, XWang (2016). Cooperation mode of port and shipping companies with the introduction of price compensation mechanism. Resource Development & Market, 32: 1409–1414 (in Chinese)
75	TWang, X Wang, QMeng (2018). Joint berth allocation and quay crane assignment under different carbon taxation policies. Transportation Research Part B: Methodological, 117: 18–36 https://doi.org/10.1016/j.trb.2018.08.012
76	RWinkel, U Weddige, DJohnsen, VHoen, S Papaefthimiou (2016). Shore side electricity in Europe: potential and environmental benefits. Energy Policy, 88: 584–593 https://doi.org/10.1016/j.enpol.2015.07.013
77	JXia, X Li, HMa, ZXu (2015). Joint planning of fleet deployment, speed optimization, and cargo allocation for liner shipping. Transportation Science, 49(4): 922–938 https://doi.org/10.1287/trsc.2015.0625
78	HYu, Y E Ge, J Chen, LLuo, CTan, D Liu (2017a). CO2 emission evaluation of yard tractors during loading at container terminals. Transportation Research Part D: Transport and Environment, 53: 17–36 https://doi.org/10.1016/j.trd.2017.03.014
79	SYu, C Wang (2015). Ship speed optimization under different carbon emission control policies. Journal of Dalian Maritime University, 41(3): 45–50 (in Chinese)
80	SYu, S Wang, LZhen (2017b). Quay crane scheduling problem with considering tidal impact and fuel consumption. Flexible Services and Manufacturing Journal, 29(3–4): 345–368 https://doi.org/10.1007/s10696-016-9248-4
81	SZhang, S Zhang (2008). Trading mechanism framework for international maritime greenhouse gas emissions. China Maritime Safety, 9: 60–63 (in Chinese)
82	YZhao (2015). Axle-spoke container shipping network design problem under competitive environment. Chinese Journal of Management Science, 23(7): 103–112 (in Chinese)
83	YZhao, H Duan, HKuang (2015). Axle-spoke container shipping network design considering CO2 emissions. Journal of Systems Engineering, 30: 383–393 (in Chinese)
84	LZhen (2017). Optimization of Container Port Operation Management. Beijing: Science Press (in Chinese)
85	JZheng, Z Gao, DYang, ZSun (2015). Network design and capacity exchange for liner alliances with fixed and variable container demands. Transportation Science, 49(4): 886–899 https://doi.org/10.1287/trsc.2014.0572
86	MZhu, H Zhen, AGan (2016). Optimization of liner fleet deployment under carbon emission right trading. Journal of Transportation Systems Engineering and Information Technology, 16(1): 202–208
87	TZis, P Angeloudis, M G HBell, H NPsaraftis (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 https://doi.org/10.3141/2549-05

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