Sustainable Logistics Network Optimization From Dry Ports to Seaport: A Case Study From Pakistan

Abstract

Several considerations are explored in each mode of transportation for the safe, quick, and environmentally friendly movement of goods. To obtain the advantages in all transportation modes, it is critical to develop appropriate models and algorithms. By expanding our understanding of the planning of bi-objective optimization problems, this study contributes to the field of real-time green logistics analysis. The paper provides case studies based on road and road-rail transportation from dry ports to a seaport in Pakistan. Analysis of the cases shapes the economic and environmental planning of transport. Because there is little research on this topic in Pakistan, the innovative aspect is to reduce the cost and greenhouse gas (GHG) emissions of road and intermodal freight transportation between dry ports and seaport. A mixed-integer linear programming model is used for the formulation of the bi-objective problem. Pareto front solutions are provided to trade-off the cost and GHG emissions in delivering goods from dry ports to seaport in Pakistan. The study uses real-life data from one of the largest logistics service providers in Pakistan. Results show that intermodal transportation is 82.6% more economical and 78% greener compared with road freight transportation. Further, this study could be extended by including other factors such as noise, air pollution, and traffic accidents.

Keywords

rail freight rail transportation freight intermodal railroads sustainability and resilience transportation and sustainability air quality and greenhouse gas mitigation air quality modeling emissions and air quality management emissions modeling

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References

Iannone

The Private and Social Cost Efficiency of Port Hinterland Container Distribution Through a Regional Logistics System. Transportation Research Part A: Policy and Practice, Vol. 46, No. 9, 2012, pp. 1424–1448. https://doi.org/10.1016/j.tra.2012.05.019.

Zondag

Bucci

Gützkow

de Jong

Port Competition Modeling Including Maritime, Port, and Hinterland Characteristics. Maritime Policy and Management, Vol. 37, No. 3, 2010, pp. 179–194. https://doi.org/10.1080/03088831003700579

Chen

S. L.

Jeevan

Cahoon

Malaysian Container Seaport-Hinterland Connectivity: Status, Challenges and Strategies. Asian Journal of Shipping and Logistics, Vol. 32, No. 3, 2016, pp. 127–138. https://doi.org/10.1016/j.ajsl.2016.09.001.

Talley

W. K.

M. W.

Hinterland Transport Chains: Determinant Effects on Chain Choice. International Journal of Production Economics, Vol. 185, 2017, pp. 175–179. https://doi.org/10.1016/j.ijpe.2016.12.026.

Meersman

Sys

van de Voorde

Vanelslander

Road Pricing and Port Hinterland Competitiveness: An Application to the Hamburg–Le Havre Range. International Journal of Sustainable Transportation, Vol. 10, No. 3, 2016, pp. 170–179. https://doi.org/10.1080/15568318.2013.878771.

Kotsios

Folinas

Analysis and Comparison of Road Freight Transport Cost in 20 European Countries. International Journal of Applied Logistics, Vol. 10, No. 1, 2019, pp. 13–26. https://doi.org/10.4018/ijal.2020010102.

Frémont

Franc

Hinterland Transportation in Europe: Combined Transport Versus Road Transport. Journal of Transport Geography, Vol. 18, No. 4, 2010, pp. 548–556. https://doi.org/10.1016/j.jtrangeo.2010.03.009.

Bai

Liu

Higgins

Chiu

Y. P.

Chen

Transportation Cost Modeling of Containerized Soybean Exports in the United States. Transportation Research Record: Journal of the Transportation Research Board, 2017. 2611: 19–31.

Seedah

D. P. K.

Harrison

Blaze

J. R.

Basic Tool Kit for Estimation of Intermodal Rail Cost. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2261: 134–142.

10.

Harris

Naim

Palmer

Potter

Mumford

Assessing the Impact of Cost Optimization Based on Infrastructure Modelling on CO₂ Emissions. International Journal of Production Economics, Vol. 131, No. 1, 2011, pp. 313–321. https://doi.org/10.1016/j.ijpe.2010.03.005.

11.

Mustapa

S. I.

Bekhet

H. A.

Analysis of CO₂ Emissions Reduction in the Malaysian Transportation Sector: An Optimisation Approach. Energy Policy, Vol. 89, 2016, pp. 171–183. https://doi.org/10.1016/j.enpol.2015.11.016.

12.

Lättilä

Henttu

Hilmola

O. P.

Hinterland Operations of Sea Ports Do Matter: Dry Port Usage Effects on Transportation Costs and CO₂ Emissions. Transportation Research Part E: Logistics and Transportation Review, Vol. 55, 2013, pp. 23–42. https://doi.org/10.1016/j.tre.2013.03.007.

13.

Pritchard

Preston

Armstrong

Making Meaningful Comparisons Between Road and Rail – Substituting Average Energy Consumption Data for Rail With Empirical Analysis. Transportation Planning and Technology, Vol. 38, No. 1, 2015, pp. 111–130. https://doi.org/10.1080/03081060.2014.976985.

14.

Sousa

Roseta-Palma

Martins

L. F.

Economic Growth and Transport: On the Road to Sustainability. Natural Resources Forum, Vol. 39, No. 1, 2015, pp. 3–14. https://doi.org/10.1111/1477-8947.12060.

15.

Hulagu

Celikoglu

H. B.

Environment-Friendly School Bus Routing Problem With Heterogeneous Fleet: A Large-Scale Real Case. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 4, 2020, pp. 3461–3471. https://doi.org/10.1109/TITS.2020.3036696.

16.

Barnhart

Laporte

. Handbooks in Operations Research | Management Science: Transportation. Elsevier, Amsterdam, The Netherlands, 2006.

17.

TaillardÉ

. A Heuristic Column Generation Method for the Heterogeneous Fleet VRP. RAIRO - Operations Research, Vol. 33, No. 1, 1999, pp. 1–14. https://doi.org/10.1051/ro:1999101.

18.

Potvin

J. Y.

Bengio

The Vehicle Routing Problem With Time Windows Part II: Genetic Search. INFORMS Journal on Computing, Vol. 8, No. 2, 1996, pp. 165–172. https://doi.org/10.1287/IJOC.8.2.165.

19.

Hulagu

Atasavar

Celikoglu

H. B.

Green Routing Plan for University Shuttle Services Using Mixed Integer Linear Programming. Proc., 5th International Forum on Research and Technologies for Society and Industry: Innovation to Shape the Future, Florence, Italy, IEEE, New York, 2019, pp. 471–476.

20.

Back

Fogel

Michalewicz

. Handbook of Evolutionary Computation. IOP Publishing Ltd, Bristol, UK, 1997.

21.

Steuer

R. E.

Multiple Criteria Optimization: Theory, Computation and Application.Wiley, New York, 1986.

22.

Hulagu

Celikoglu

H. B.

An Electric Vehicle Routing Problem With Intermediate Nodes for Shuttle Fleets. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 2, 2020, pp. 1223–1235. https://doi.org/10.1109/TITS.2020.3023673.

23.

Wang

Yin

Shi

Liu

On-Vehicle Emission Measurement of a Light-Duty Diesel Van at Various Speeds at High Altitude. Atmospheric Environment, Vol. 81, 2013, pp. 263–269. https://doi.org/10.1016/J.ATMOSENV.2013.09.015.

24.

Kim

W. G.

Kim

C. K.

Lee

J. T.

Kim

J. S.

Yun

C. W.

Yook

S. J.

Fine Particle Emission Characteristics of a Light-Duty Diesel Vehicle According to Vehicle Acceleration and Road Grade. Transportation Research Part D: Transport and Environment, Vol. 53, 2017, pp. 428–439. https://doi.org/10.1016/J.TRD.2017.04.041.

25.

Wyatt

D. W.

Tate

J. E.

The Impact of Road Grade on Carbon Dioxide (CO₂) Emission of a Passenger Vehicle in Real-World Driving. Transportation Research Part D: Transport and Environment, Vol. 32, 2014, pp. 160–170. https://doi.org/10.1016/J.TRD.2014.07.015.

26.

Ramos

García-Contreras

Armas

Performance, Combustion Timing and Emissions From a Light Duty Vehicle at Different Altitudes Fueled With Animal Fat Biodiesel, GTL and Diesel Fuels. Applied Energy, Vol. 182, 2016, pp. 507–517. https://doi.org/10.1016/J.APENERGY.2016.08.159.

27.

Prati

M. V.

Meccariello

Della Ragione

Costagliola

M. A.

Real Driving Emissions of a Light-Duty Vehicle in Naples. Influence of Road Grade. SAE Technical Papers. Proc., 12th International Conference on Engines & Vehicles, Naples, Italy, 2015.

28.

Wang

Huang

Impacts of Road Grade on Fuel Consumption of Light Vehicles by Use of Google Earth DEM. Proc., International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC, Xi’an, China, IEEE, New York, 2015, pp. 360–363.

29.

Ragione

L. D.

Giovanni

. The Influence of Road Gradient in an Integrated Approach of Real Driving Cycles and Emissions Factors Model. Transportation Research Procedia, Vol. 14, 2016, pp. 3179–3188. https://doi.org/10.1016/j.trpro.2016.05.258.

30.

Gallus

Kirchner

Vogt

Benter

Impact of Driving Style and Road Grade on Gaseous Exhaust Emissions of Passenger Vehicles Measured by a Portable Emission Measurement System (PEMS). Transportation Research Part D: Transport and Environment, Vol. 52, 2017, pp. 215–226. https://doi.org/10.1016/J.TRD.2017.03.011.

31.

Barr

. Economics of the Welfare State.Oxford University Press, Oxford, 2020.

32.

Sawaragai

Nakayama

Tanino

Theory of Multi-Objective Optimization. Academic Press Inc., London, 1985.

33.

Konak

Coit

D. W.

Smith

A. E.

Multi-Objective Optimization Using Genetic Algorithms: A Tutorial. Reliability Engineering and System Safety, Vol. 91, No. 9, 2006, pp. 992–1007. https://doi.org/10.1016/j.ress.2005.11.018.

34.

Delgado

Cuellar

M. P.

Pegalajar

M. C.

Multiobjective Hybrid Optimization and Training of Recurrent Neural Networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 38, No. 2, 2008, pp. 381–403. https://doi.org/10.1109/TSMCB.2007.912937.

35.

Jones

Mirrazavi

Tamiz

Multi-Objective Meta-Heuristics: An Overview of the Current State-of-the-Art. European Journal of Operational Research, Vol. 137, No. 1, 2002, pp. 1–9. https://doi.org/10.1016/S0377-2217(01)00123-0

36.

Srinivas

Deb

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, Vol. 2, No. 3, 1994, pp. 221–248. https://doi.org/10.1162/evco.1994.2.3.221

37.

Deb

Pratap

Agarwal

Meyarivan

A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, 2002, pp. 182–197. https://doi.org/10.1109/4235.996017.

38.

Leong

W. F.

Yen

G. G.

Dynamic Population Size in PSO-Based Multiobjective Optimization. Proc., IEEE International Conference on Evolutionary Computation, IEEE, New York, Vancouver, BC, Canada, 2006, pp. 1718–1725.

39.

Knowles

J. D.

Corne

D. W.

Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, Vol. 8, No. 2, 2000, pp. 149–172. https://doi.org/10.1162/106365600568167

40.

Lei

Shi

Overview of Multi-Objective Optimization Methods. Journal of Systems Engineering and Electronics, Vol. 15, No. 2, 2004, pp. 142–146.

41.

Van Veldhuizen

D. A.

Lamont

G. B.

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation, Vol. 8, No. 2, 2000, pp. 125–147. https://doi.org/10.1162/106365600568158

42.

Zou

Chen

Liu

Kang

A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 38, No. 5, 2008, pp. 1402–1412. https://doi.org/10.1109/TSMCB.2008.926329.

43.

Bandyopadhyay

Pal

S. K.

Aruna

Multiobjective GAs, Quantitative Indices, and Pattern Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 34, No. 5, 2004, pp. 2088–2099. https://doi.org/10.1109/TSMCB.2004.834438.

44.

Griffis

S. E.

Bell

J.E.

Closs

D. J.

Metaheuristics in Logistics and Supply Chain Management. Journal of Business Logistics, Vol. 33, No. 2, 2012, pp. 90–106. https://doi.org/10.1111/J.0000-0000.2012.01042.X

45.

Demir

Hrušovský

Jammernegg

van Woensel

Green Intermodal Freight Transportation: Bi-Objective Modelling and Analysis. International Journal of Production Research, Vol. 57, No. 19, 2019, pp. 6162–6180. https://doi.org/10.1080/00207543.2019.1620363.

46.

Demir

Burgholzer

Hrušovský

Arikan

Jammernegg

Van Woensel

A Green Intermodal Service Network Design Problem with Travel Time Uncertainty. Transportation Research Part B: Methodological, Vol. 93, 2016, pp. 789–807. https://doi.org/10.1016/j.trb.2015.09.007.

47.

Holland

J. H.

Adaptation in Natural and Artificial Systems, 1st ed. University of Michigan Press, Ann Arbor, 1975.

48.

Tardif

Fuel Efficiency Benchmarking in Canada’s Trucking Industry: Results of an Industry Survey. Ottawa, Ontario: Natural Resources Canada. https://publications.gc.ca/site/eng/294517/publication.htmlAccessed December 5, 2021.