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Frontiers of Engineering Management    2019, Vol. 6 Issue (3) : 368-383     https://doi.org/10.1007/s42524-019-0004-9
RESEARCH ARTICLE
A model for the evaluation of environmental impact indicators for a sustainable maritime transportation systems
Lizzette PÉREZ LESPIER1, Suzanna LONG2(), Tom SHOBERG3, Steven CORNS2
1. Department of Analytics, Information Systems & Supply Chain, University of North Carolina Wilmington, 601 S College Rd, Wilmington, NC 28402, USA
2. Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, 223 600 W, 14th Street, Rolla, MO 65401, USA
3. U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, MO 65401, USA
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Abstract

Maritime shipping is considered the most efficient, low-cost means for transporting large quantities of freight over significant distances. However, this process also causes negative environmental and societal impacts. Therefore, environmental sustainability is a pressing issue for maritime shipping management, given the interest in addressing important issues that affect the safety, security, and air and water quality as part of the efficient movement of freight throughout the coasts and waterways and associated port facilities worldwide. In-depth studies of maritime transportation systems (MTS) can be used to identify key environmental impact indicators within the transportation system. This paper develops a tool for decision making in complex environments; this tool will quantify and rank preferred environmental impact indicators within a MTS. Such a model will help decision-makers to achieve the goals of improved environmental sustainability. The model will also provide environmental policy-makers in the shipping industry with an analytical tool that can evaluate tradeoffs within the system and identify possible alternatives to mitigate detrimental effects on the environment.

Keywords environmental sustainability      maritime transportation system      environmental impact indicators      fuzzy analytic hierarchy process      fuzzy TOPSIS      decision-making tool     
在线预览日期:    发布日期: 2019-09-04
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Lizzette PÉREZ LESPIER
Suzanna LONG
Tom SHOBERG
Steven CORNS
引用本文:   
Lizzette PÉREZ LESPIER,Suzanna LONG,Tom SHOBERG, et al. A model for the evaluation of environmental impact indicators for a sustainable maritime transportation systems[J]. Front. Eng, 2019, 6(3): 368-383.
网址:  
http://journal.hep.com.cn/fem/EN/10.1007/s42524-019-0004-9     OR     http://journal.hep.com.cn/fem/EN/Y2019/V6/I3/368
Fig.1  Function of a TFN A
Expert Source(s)
E1 Duru et al. (2012)
E2 Gudmundsson (2001)
E3 Lai et al. (2011)
E4 Peris-Mora et al. (2005)
E5 Jeon & Amekudzi (2005), Rodrigue et al. (2013)
E6 Lister (2015), Lun et al. (2016)
Tab.1  Select heavily cited expert literature used for the evaluation of criteria and alternatives
Notations Environmental Performance Criteria
C1 Use of green design in ships, engines and machinery
C2 Use of clean technologies such as, low sulfur fuel and option to alternate energy (fuel type)
C3 Reuse and recycle of resources used in shipping
C4 Ballast water treatment and residue/waste/spill control
C5 Logistic and scheduling efficiency for such as reduction of idle and waiting times
C6 Use of environmentally friendly shipping equipment and facilities
Tab.2  Criteria from the FAHP that are most connected to improved environmental performance of MTS
Notations Environmental Performance Alternatives
A1 Reduction of release of substances as defined by MARPOL Annex 1-6
A2 Management of water ballast violations
A3 Contained spill of hazardous materials
A4 Reduction of environmental deficiencies
Tab.3  Environmental performance alternatives determined by FTOPSIS for environmentally sustainable MTS
Fig.2  DMCE model framework
Linguistic Variables Triangular Fuzzy Numbers Reciprocal Triangular Fuzzy Numbers
Absolute (A) (7/2, 4, 9/2) (2/9, 1/4, 2/7)
Very Strong (VS) (5/2, 3, 7/2) (2/7, 1/3, 2/5)
Fairly Strong (FS) (3/2, 2, 5/2) (2/5, 1/2, 2/3)
Weak (W) (2/3, 1, 3/2) (2/3, 1, 3/2)
Equal (E) (1, 1, 1) (1, 1, 1)
Tab.4  Values of TFNs (Tolga et al., 2005)
Criteria Experts C1 C2 C3 C4 C5 C6
C1 E1 E E FS VS E VS
E2 E W W W VS E
E3 E FS FS FS A E
E4 E E E W FS FS
E5 E W E W E E
E6 E W W E W E
C2 E1 E-1 E W E E A
E2 W-1 E VS FS VS FS
E3 FS-1 E FS E FS E
E4 E-1 E VS FS FS VS
E5 W-1 E FS W E FS
E6 W-1 E VS VS W E
C3 E1 FS-1 W-1 E E W W
E2 W-1 VS-1 E E W E
E3 FS-1 FS-1 E W E E
E4 E-1 VS-1 E E FS FS
E5 E-1 FS-1 E W E E
E6 W-1 VS-1 E W W W
C4 E1 VS-1 E-1 E-1 E E FS
E2 W-1 FS-1 E-1 E E E
E3 FS-1 E-1 W-1 E FS E
E4 W-1 FS-1 E-1 E VS FS
E5 W-1 W-1 W-1 E FS E
E6 E-1 VS-1 W-1 E E E
C5 E1 E-1 W-1 W-1 E-1 E A
E2 VS-1 W-1 W-1 E-1 E E
E3 A-1 E-1 E-1 FS-1 E E
E4 FS-1 FS-1 FS-1 VS-1 E VS
E5 E-1 E-1 E-1 FS-1 E FS
E6 W-1 W-1 W-1 E-1 E E
C6 E1 VS-1 A-1 W-1 FS-1 A-1 E
E2 E-1 FS-1 E-1 E-1 E-1 E
E3 E-1 E-1 E-1 E-1 E-1 E
E4 FS-1 VS-1 FS-1 FS-1 VS-1 E
E5 E-1 FS-1 E-1 E-1 FS-1 E
E6 E-1 E-1 W-1 E-1 E-1 E
Tab.5  Pairwise comparisons of criteria via linguistic variables
Criteria Experts C1 C2 C3 C4 C5 C6
C1 E1 (1,1,1) (1,1,1) (3/2, 2, 5/2) (5/2, 3, 7/2) (1,1,1) (5/2, 3, 7/2)
E2 (1,1,1) (2/3, 1, 3/2) (2/3, 1, 3/2) (2/3, 1, 3/2) (5/2, 3, 7/2) (1,1,1)
E3 (1,1,1) (3/2, 2, 5/2) (3/2, 2, 5/2) (3/2, 2, 5/2) (7/2, 4, 9/2) (1,1,1)
E4 (1,1,1) (1,1,1) (1,1,1) (2/3, 1, 3/2) (3/2, 2, 5/2) (3/2, 2, 5/2)
E5 (1,1,1) (2/3, 1, 3/2) (1,1,1) (2/3, 1, 3/2) (1,1,1) (1,1,1)
E6 (1,1,1) (2/3, 1, 3/2) (2/3, 1, 3/2) (1,1,1) (2/3, 1, 3/2) (1,1,1)
C2 E1 (1,1,1) (1,1,1) (2/3, 1, 3/2) (1,1,1) (1,1,1) (7/2, 4, 9/2)
E2 (2/3, 1, 3/2) (1,1,1) (5/2, 3, 7/2) (3/2, 2, 5/2) (5/2, 3, 7/2) (3/2, 2, 5/2)
E3 (2/5, 1/2, 2/3) (1,1,1) (3/2, 2, 5/2) (1,1,1) (3/2, 2, 5/2) (1,1,1)
E4 (1,1,1) (1,1,1) (5/2, 3, 7/2) (3/2, 2, 5/2) (3/2, 2, 5/2) (5/2, 3, 7/2)
E5 (2/3, 1, 3/2) (1,1,1) (3/2, 2, 5/2) (2/3, 1, 3/2) (1,1,1) (3/2, 2, 5/2)
E6 (2/3, 1, 3/2) (1,1,1) (5/2, 3, 7/2) (5/2, 3, 7/2) (2/3, 1, 3/2) (1,1,1)
C3 E1 (2/5, 1/2, 2/3) (2/3, 1, 3/2) (1,1,1) (1,1,1) (2/3, 1, 3/2) (2/3, 1, 3/2)
E2 (2/3, 1, 3/2) (2/7, 1/3, 2/5) (1,1,1) (1,1,1) (2/3, 1, 3/2) (1,1,1)
E3 (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (1,1,1) (2/3, 1, 3/2) (1,1,1) (1,1,1)
E4 (1,1,1) (2/7, 1/3, 2/5) (1,1,1) (1,1,1) (3/2, 2, 5/2) (3/2, 2, 5/2)
E5 (1,1,1) (2/5, 1/2, 2/3) (1,1,1) (2/3, 1, 3/2) (1,1,1) (1,1,1)
E6 (2/3, 1, 3/2) (2/7, 1/3, 2/5) (1,1,1) (2/3, 1, 3/2) (2/3, 1, 3/2) (2/3, 1, 3/2)
C4 E1 (2/7, 1/3, 2/5) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (3/2, 2, 5/2)
E2 (2/3, 1, 3/2) (2/5, 1/2, 2/3) (1,1,1) (1,1,1) (1,1,1) (1,1,1)
E3 (2/5, 1/2, 2/3) (1,1,1) (2/3, 1, 3/2) (1,1,1) (3/2, 2, 5/2) (1,1,1)
E4 (2/3, 1, 3/2) (2/5, 1/2, 2/3) (1,1,1) (1,1,1) (5/2, 3, 7/2) (3/2, 2, 5/2)
E5 (2/3, 1, 3/2) (2/3, 1, 3/2) (2/3, 1, 3/2) (1,1,1) (3/2, 2, 5/2) (1,1,1)
E6 (1,1,1) (2/7, 1/3, 2/5) (2/3, 1, 3/2) (1,1,1) (1,1,1) (1,1,1)
C5 E1 (1,1,1) (2/3, 1, 3/2) (2/3, 1, 3/2) (1,1,1) (1,1,1) (7/2, 4, 9/2)
E2 (2/7, 1/3, 2/5) (2/3, 1, 3/2) (2/3, 1, 3/2) (1,1,1) (1,1,1) (1,1,1)
E3 (2/9, 1/4, 2/7) (1,1,1) (1,1,1) (2/5, 1/2, 2/3) (1,1,1) (1,1,1)
E4 (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (2/7, 1/3, 2/5) (1,1,1) (5/2, 3, 7/2)
E5 (1,1,1) (1,1,1) (1,1,1) (2/5, 1/2, 2/3) (1,1,1) (3/2, 2, 5/2)
E6 (2/3, 1, 3/2) (2/3, 1, 3/2) (2/3, 1, 3/2) (1,1,1) (1,1,1) (1,1,1)
C6 E1 (2/7, 1/3, 2/5) (2/9, 1/4, 2/7) (2/3, 1, 3/2) (2/5, 1/2, 2/3) (2/9, 1/4, 2/7) (1,1,1)
E2 (1,1,1) (2/5, 1/2, 2/3) (1,1,1) (1,1,1) (1,1,1) (1,1,1)
E3 (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1) (1,1,1)
E4 (2/5, 1/2, 2/3) (2/7, 1/3, 2/5) (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (2/7, 1/3, 2/5) (1,1,1)
E5 (1,1,1) (2/5, 1/2, 2/3) (1,1,1) (1,1,1) (2/5, 1/2, 2/3) (1,1,1)
E6 (1,1,1) (1,1,1) (2/3, 1, 3/2) (1,1,1) (1,1,1) (1,1,1)
Tab.6  Pairwise comparisons of criteria via TFNs
Criteria C1 C2 C3 C4 C5 C6
C1 (1,1,1) (0.874,1.122,1.427) (1,1.260,1.554) (1.018,1.348,1.758) (1.435,1.698,1.973) (1.246,1.348,1.435)
C2 (0.701,0.891,1.145) (1,1,1) (1.692,2.182,2.717) (1.246,1.513,1.789) (1.246,1.513,1.789) (1.643,1.906,2.149)
C3 (0.644,0.794,1) 0.368,0.458,0.591) (1,1,1) (0.816,1,1.225) (0.874,1.122,1.427) (0.935,1.122,1.334)
C4 (0.569,0.742,0.983) (0.559,0.661,0.802) (0.816,1,1.225) (1,1,1) (1.334,1.513,1.672) (1.145,1.260,1.357)
C5 (0.507,0.589,0.697) (0.701,0.891,1.145) (0.701,0.891,1.145) (0.598,0.661,0.750) (1,1,1) (1.536,1.698,1.844)
C6 (0.697,0.742,0.802) (0.465,0.525,0.609) (0.750,0.891,1.070) (0.737,0.794,0.874) (0.542,0.589,0.651) (1,1,1)
Tab.7  Fuzzy geometric mean of pairwise comparison
Criteria Weight Low Weight Med Weight Upper
C1 0.146 0.201 0.274
C2 0.168 0.233 0.317
C3 0.103 0.142 0.197
C4 0.121 0.159 0.211
C5 0.112 0.148 0.197
C6 0.093 0.117 0.150
Tab.8  Fuzzy synthetic extent with respect to its criteria
Criteria BNP Rank
C1- Green design 0.207 2
C2- Clean technologies 0.239 1
C3- Reuse and recycle 0.147 5
C4- Residue, waste and spill control 0.164 3
C5- Logistic and scheduling efficiency 0.152 4
C6- Green equipment and facilities 0.120 6
Tab.9  Best BNP or crisp values of criteria
Linguistic Variables Triangular Fuzzy Numbers
Very Poor (VP) (0 ,0, 2)
Poor (P) (1, 2, 3)
Medium Poor (MP) (2, 3.5, 5)
Fair (F) (4, 5, 6)
Medium Good (MG) (5, 6.5, 8)
Good (G) (7 ,8, 9)
Very Good (VG) (8, 10, 10)
Tab.10  Linguistic variables for rating (Shukla et al., 2014).
Criteria Alternatives Experts Rating
E1 E2 E3 E4 E5 E6
C1 A1 VG VG G G G VG
A2 F P MP MP P F
A3 G G MP VG F F
A4 VG G VG VG G G
C2 A1 VG VG VG VG G VG
A2 G G VG VG F G
A3 G F G G VG G
A4 VG VG G G G VG
C3 A1 G VG VG G G F
A2 G G VG G VG G
A3 VG G MP G G G
A4 G G G VG G MP
C4 A1 G VG G G MG VG
A2 VG VG G VG G VG
A3 VG G G G VG G
A4 G G VG G VG G
C5 A1 VG G G VG VG G
A2 VP P MG F F MG
A3 G G MG F G F
A4 VG G G MG VG MG
C6 A1 VG G VG VG G VG
A2 MP F P P F MP
A3 G MP MP MP G MP
A4 VG G G MG VG G
Tab.11  Rating the alternatives in linguistic terms
Criteria Alternatives Experts Rating
E1 E2 E3 E4 E5 E6
C1 A1 (8,10,10) (8,10,10) (7,8,9) (7,8,9) (7,8,9) (8,10,10)
A2 (4,5,6) (1,2,3) (2,3.5,5) (2,3.5,5) (1,2,3) (4,5,6)
A3 (7,8,9) (7,8,9) (2,3.5,5) (8,10,10) (4,5,6) (4,5,6)
A4 (8,10,10) (7,8,9) (8,10,10) (8,10,10) (7,8,9) (7,8,9)
C2 A1 (8,10,10) (8,10,10) (8,10,10) (8,10,10) (7,8,9) (8,10,10)
A2 (7,8,9) (7,8,9) (8,10,10) (8,10,10) (4,5,6) (7,8,9)
A3 (7,8,9) (4,5,6) (7,8,9) (7,8,9) (8,10,10) (7,8,9)
A4 (8,10,10) (8,10,10) (7,8,9) (7,8,9) (7,8,9) (8,10,10)
C3 A1 (7,8,9) (8,10,10) (8,10,10) (7,8,9) (7,8,9) (8,10,10)
A2 (7,8,9) (7,8,9) (8,10,10) (7,8,9) (8,10,10) (7,8,9)
A3 (8,10,10) (7,8,9) (2,3.5,5) (7,8,9) (7,8,9) (7,8,9)
A4 (7,8,9) (7,8,9) (7,8,9) (8,10,10) (7,8,9) (2,3.5,5)
C4 A1 (7,8,9) (8,10,10) (7,8,9) (7,8,9) (5,6.5,8) (8,10,10)
A2 (8,10,10) (8,10,10) (7,8,9) (8,10,10) (7,8,9) (8,10,10)
A3 (8,10,10) (7,8,9) (7,8,9) (7,8,9) (8,10,10) (7,8,9)
A4 (7,8,9) (7,8,9) (8,10,10) (7,8,9) (8,10,10) (7,8,9)
C5 A1 (8,10,10) (7,8,9) (7,8,9) (8,10,10) (8,10,10) (7,8,9)
A2 (0,0,2) (1,2,3) (5,6.5,8) (4,5,6) (4,5,6) (5,6.5,8)
A3 (7,8,9) (7,8,9) (5,6.5,8) (4,5,6) (7,8,9) (4,5,6)
A4 (8,10,10) (7,8,9) (7,8,9) (5,6.5,8) (8,10,10) (5,6.5,8)
C6 A1 (8,10,10) (7,8,9) (8,10,10) (8,10,10) (7,8,9) (8,10,10)
A2 (2,3.5,5) (4,5,6) (1,2,3) (1,2,3) (4,5,6) (2,3.5,5)
A3 (7,8,9) (2,3.5,5) (2,3.5,5) (2,3.5,5) (7,8,9) (2,3.5,5)
A4 (8,10,10) (7,8,9) (7,8,9) (5,6.5,8) (8,10,10) (7,8,9)
Tab.12  Translating linguistic terms into fuzzy terms
Criteria Alternatives
A1 A2 A3 A4
C1 (7,8,9) (1,3.500,6) (2,6.583,10) (7,9,10)
C2 (7, 9.667,10) (4,8.167,10) (4,7.833,10) (7,9,10)
C3 (7,9,10) (7,8.667,10) (2,7.583,10) (2,7.583,10)
C4 (5,8.417,10) (7,9.333,10) (7,8.667,10) (7,8.667,10)
C5 (7,9,10) (0,4.167,8) (4,6.750,9) (5,8.167,10)
C6 (7,9.33,10) (1,3.500,6) (2,5,9) (5,8.417,10)
Tab.13  FDM
Alternatives Criteria
C1 C2 C3 C4 C5 C6
A1 (0.145,0.186,0.207) (0.167,0.231,0.239) (0.103,0.133,0.147) (0.082,0.138,0.164) (0.107,0.137,0.152) (0.084,0.112,0.120)
A2 (0.021,0.072,0.124) (0.096,0.195,0.239) (0.103,0.128,0.147) (0.115,0.153,0.164) (0,0.064,0.122) (0.012,0.042,0.072)
A3 (0.041,0.136,0.207) (0.096,0.187,0.239) (0.029,0.112,0.147) (0.115,0.142,0.164) (0.061,0.103,0.137) (0.024,0.060,0.108)
A4 (0.145,0.186,0.207) (0.167,0.215,0.239) (0.029,0.112,0.147) (0.115,0.142,0.164) (0.076,0.124,0.152) (0.060,0.101,0.120)
Tab.14  WNFDM
Criteria FPIS FNIS
C1 0.207 0.021
C2 0.239 0.096
C3 0.147 0.029
C4 0.164 0.082
C5 0.152 0.000
C6 0.120 0.012
Tab.15  FPIS and FNIS per criterion
Distance C1 C2 C3 C4 C5 C6 Sum
d(A1 to FPIS) 0.033 0.036 0.023 0.043 0.024 0.018 0.178
d(A2 to FPIS) 0.122 0.075 0.024 0.025 0.090 0.071 0.407
d(A3 to FPIS) 0.090 0.076 0.062 0.027 0.053 0.057 0.364
d(A4 to FPIS) 0.033 0.038 0.062 0.027 0.041 0.032 0.231
Tab.16  Distance between the alternatives and the FPIS with respect to each criterion
Distance C1 C2 C3 C4 C5 C6 Sum
d(A1 to FNIS) 0.139 0.105 0.087 0.050 0.116 0.082 0.578
d(A2 to FNIS) 0.058 0.087 0.085 0.057 0.069 0.034 0.389
d(A3 to FNIS) 0.110 0.085 0.072 0.053 0.091 0.054 0.465
d(A4 to FNIS) 0.139 0.100 0.072 0.053 0.106 0.074 0.544
Tab.17  Distance between the alternatives and the FNIS with respect to each criterion
Alternative d+ d- d+ + d- CC Rank
A1- Reduction release of substances: MARPOL 0.178 0.578 0.755 0.765 1
A2- Manage of water ballast violations 0.407 0.389 0.796 0.489 4
A3- Contained spill of hazardous materials 0.364 0.465 0.830 0.561 3
A4- Reduction of environmental deficiencies 0.231 0.544 0.775 0.702 2
Tab.18  CC of alternatives and their respective ranking
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