A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study

  • Meisam Nasrollahi University of Tehran
  • Jafar Razmi University of Tehran
  • Reza Ghodsi Professor, Industrial Engineering Department, University of Tehran & Professor, Engineering Department, Central Connecticut State University, USA
Keywords: healthcare, greenhouse effect, supply network, carbon emissions, Monte Carlo


Measuring carbon emissions is an essential step in taking required action to fight global warming. This research presents a computational method for measuring transport related carbon emissions in a healthcare supply network. The network configuration significantly impacts carbon emissions. First, a multi-objective mathematical programing model is developed for designing a healthcare supply network in the form of a two-graph location routing problem under demand and fuel consumption uncertainty. Objective functions are minimizing total cost and minimizing total fuel consumption. In the presented model, the demand of each customer must be completely satisfied in each time period, and backlog is not permitted. The number and capacity of vehicles are determined, and vehicles are heterogeneous. Furthermore, fuel consumption depends on traveling distance, vehicle and road conditions, and the load of a vehicle. The centroid method is applied to face demand uncertainty. Next, a multi-objective non-dominated ranked genetic algorithm (M-NRGA) is proposed to solve the model. Then, a Monte Carlo based approach is presented for measuring 
transport-related carbon emissions based on fuel consumption in supply network. Finally, the proposed approach is applied to the case of a healthcare supply network in the Fars province in Iran. The obtained results illustrate that the proposed approach is a practical tool in designing healthcare supply networks and measuring transport-related carbon emissions in the network.

Author Biographies

Meisam Nasrollahi, University of Tehran
Meisam Nasrollahi is Ph.D. candidate at the Industrial engineering school of the University of Tehran. His research area is Healthcare supply network design, green supply chain, transportation network design and time series. He has published several papers in leading journals such as Management Science and Practice, Production & Operations Management, Applied Mathematics in Engineering, Management and Technology.
Jafar Razmi, University of Tehran

Prof. Dr. Jafar Razmi is Professor at Department of Industrial Engineering, College of Engineering, University of Tehran. His research area include Supply Chain Management, Production Planning, Operations Management, Operations Research, Health Engineering, etc. He has published in leading journals such as Omega, Transportation Research Part E: Logistics and Transportation Review, Expert systems with applications, Computers & Industrial Engineering, International Journal of Production Research, Soft Computing, International Journal of Production Economics, Journal of Cleaner Production.  

Reza Ghodsi, Professor, Industrial Engineering Department, University of Tehran & Professor, Engineering Department, Central Connecticut State University, USA
Dr. Reza Ghodsi is an associate professor of industrial engineering at university of Tehran & associate professor of Mechanical Engineering at the Engineering, Science and Technology school of the Central Connecticut State University in New Britain, Connecticut USA. His research area include Sustainable Energy; Advanced Manufacturing; Rapid Prototyping; Operations Research; Optimization; Data mining and analysis; Health Engineering. He has published in leading journals such as International Journal of Production Research, International Journal of Assembly Automation, International Journal of Management Practice, Expert Systems with Applications, International Journal of Operational Research, and Applied Mathematical Modeling.


[1] Martens P. Health and climate change: modelling the impacts of global warming and ozone depletion. 1st ed. Routledge; 2014.
[2] Ali A, Amin SE, Ramadan HH, Tolba MF. Enhancement of OMI aerosol optical depth data assimilation using artificial neural network. Neural Computing and Applications. 2013;23(7-8): 2267-2279. Available from: https://doi.org/10.1007/s00521-012-1178-9
[3] Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 2014. Available from: https://www.ipcc.ch/report/ar5/syr
[4] Peters GP, Andrew RM, Boden T, Canadell JG, Ciais P, Le Quere C, et al. The challenge to keep global warming below 2 °C. Nature Clim Change. 2013;3(1): 4-6. Available from: http://dx.doi.org/10.1038/nclimate1783
[5] Reddy PP. Causes of Climate Change. In: Climate Resilient Agriculture for Ensuring Food Security. 1st ed. Springer; 2015. p. 17-26.
[6] Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, et al. Greenhouse-gas emission targets for limiting global warming to 2°C. Nature. 2009;458(7242): 1158-1162. Available from: http://dx.doi.org/10.1038/nature08017
[7] Plambeck EL. Reducing greenhouse gas emissions through operations and supply chain management. Energy Economics. 2012;34(Suppl.1): 564-574. Available from: http://dx.doi.org/10.1016/j.eneco.2012.08.031
[8] IPCC. Mitigation of climate change: Contribution of working group III to the fourth assessment report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change; 2007. 851 p.
[9] Mula J, Peidro D, Poler R. The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. International Journal of Production Economics. 2010;128(1): 136-143. Available from: http://dx.doi.org/10.1016/j.ijpe.2010.06.007
[10] Selim H, Ozkarahan I. A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. International Journal of Advanced Manufacturing Technology. 2008;36(3-4): 401-418. Available from: https://doi.org/10.1007/s00170-006-0842-6
[11] Acquaye A, Genovese A, Barrett J, Koh SCL. Benchmarking carbon emissions performance in supply chains. Supply Chain Management: An International Journal. 2014;19(3): 306-321. Available from: http://www.emeraldinsight.com/10.1108/SCM-11-2013-0419
[12] O’Shea SJ, Allen G, Fleming ZL, Bauguitte SJ-B, Percival CJ, Gallagher MW, et al. Area fluxes of carbon dioxide, methane, and carbon monoxide derived from airborne measurements around Greater London: A case study during summer 2012. Journal of Geophysical Research: Atmospheres. 2014;119(8): 4940-4952. Available from: http://doi.wiley.com/10.1002/2013JD021269
[13] Benjaafar S, Li Y, Daskin M. Carbon Footprint and the Management of Supply Chains: Insights From Simple Models. IEEE Transactions on Automation Science and Engineering. 2012;10(1): 99-116. Available from: https://ieeexplore.ieee.org/document/6248180
[14] Carling K, Han M, Håkansson J, Meng X, Rudholm N. Measuring transport related CO 2 emissions induced by online and brick-and-mortar retailing. Transportation Research Part D. 2015;40: 28-42. Available from: http://dx.doi.org/10.1016/j.trd.2015.07.010
[15] Minx JC, Wiedmann T, Wood R, Peters GP, Lenzen M, Owen A, et al. Input–output analysis and carbon footprinting: an overview of applications. Economic Systems Research. 2009;21(3): 187-216. Available from: https://doi.org/10.1080/09535310903541298
[16] Jaegler A, Burlat P. Carbon friendly supply chains: a simulation study of different scenarios. Production Planning & Control. 2012;23(4): 269-278. Available from: https://doi.org/10.1080/09537287.2011.627656
[17] Tian Y, Liu Q. The Study about the Calculation Method of Product Carbon Footprint during the Flow Manufacturing Process. Journal of Low Carbon Economy. 2014;3: 1-6. Available from: http://dx.doi.org/10.12677/jlce.2014.31001
[18] Mogensen L, Kristensen T, Nguyen TLT, Knudsen MT, Hermansen JE. Method for calculating carbon footprint of cattle feeds–including contribution from soil carbon changes and use of cattle manure. Journal of Cleaner Production. 2014;73: 40-51. Available from: https://doi.org/10.1016/j.jclepro.2014.02.023
[19] Kaydani H, Najafzadeh M, Hajizadeh A. A new correlation for calculating carbon dioxide minimum miscibility pressure based on multi-gene genetic programming. Journal of Natural Gas Science and Engineering. 2014;21(1): 625-630. Available from: https://doi.org/10.1016/j.jngse.2014.09.013
[20] Ryu BY, Jung HJ, Bae SH. Development of a corrected average speed model for calculating carbon dioxide emissions per link unit on urban roads. Transportation Research Part D: Transport and Environment. 2015;34: 245-254. Available from: https://doi.org/10.1016/j.trd.2014.10.012
[21] Ortmeyer TH, Pillay P. Trends in transportation sector technology energy use and greenhouse gas emissions. Proceedings of the IEEE. 2001;89(12): 1837-1847. Available from: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=975921&isnumber=21066
[22] Morrow WR, Gallagher KS, Collantes G, Lee H. Analysis of policies to reduce oil consumption and greenhouse-
gas emissions from the US transportation sector. Energy Policy. 2010;38(3): 1305-1320. Available from: https://doi.org/10.1016/j.enpol.2009.11.006
[23] Lotfi MM, Tavakkoli-Moghaddam R. A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems. Applied Soft Computing. 2013;13(5): 2711-2726. Available from: https://doi.org/10.1016/j.asoc.2012.11.016
[24] Babazadeh A, Langerudi MF, Afkar N, Shahandashti KF. Parameter Selection in Particle Swarm Optimization for Transportation Network Design Problem. arXivpreprint arXiv:14127185. 2014. Available from: http://arxiv.org/ftp/arxiv/papers/1412/1412.7185.pdf
[25] Jang Y-J, Jang S-Y, Chang B-M, Park J. A combined model of network design and production/distribution planning for a supply network. Computers & Industrial Engineering. 2002;43(1-2): 263-281. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0360835202000748
[26] Pontrandolfo P, Okogbaa OG. Global manufacturing: A review and a framework for planning in a global corporation. International Journal of Production Research. 1999;37(1): 1-19. Available from: https://doi.org/10.1080/002075499191887
[27] Wong KF, Beasley JE. Vehicle routing using fixed delivery areas. Omega. 1984;12(6): 591-600. Available from: https://doi.org/10.1016/0305-0483(84)90062-8
[28] Norouzi N, Sadegh-Amalnick M, Alinaghiyan M. Evaluating of the particle swarm optimization in a periodic vehicle routing problem. Measurement. 2015;62: 162-169. Available from: https://doi.org/10.1016/j.measurement.2014.10.024
[29] Huang Z, Geng K. Local search for dynamic vehicle routing problem with time windows. In: Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2013 2nd International Symposium on. IEEE; 2013. p. 841-4.
[30] Baldacci R, Mingozzi A, Roberti R. Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. European Journal of Operational Research. 2012;218(1): 1-6. Available from: http://dx.doi.org/10.1016/j.ejor.2011.07.037
[31] Nadizadeh A, Hosseini Nasab H, Nasab HH. Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm. European Journal of Operational Research. 2014;238(2): 458-470. Available from: http://linkinghub.elsevier.com/retrieve/pii/S037722171400321X
[32] Vahdani B, Shekari DVN, Mousavi SM. Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications. 2016;30(3): 835-854. Available from: https://doi.org/10.1007/s00521-016-2696-7
[33] Ewbank H, Wanke P, Hadi-Vencheh A. An unsupervised fuzzy clustering approach to the capacitated vehicle routing problem. Neural Computing and Applications. 2015; 857-867. Available from: http://link.springer.com/10.1007/s00521-015-1901-4
[34] Dalfard VM, Kaveh M, Nosratian NE. Two meta-heuristic algorithms for two-echelon location-routing problem with vehicle fleet capacity and maximum route length constraints. -Neural Computing and Applications. 2013;23(7-8): 2341-2349. Available from: ttps://doi.org/10.1007/s00521-012-1190-0
[35] Escobar JW, Linfati R, Baldoquin MG, Toth P. A Granular Variable Tabu Neighborhood Search for the capacitated location-routing problem. Transportation Research Part B: Methodological. 2014;67: 344-356. Available from: https://doi.org/10.1016/j.trb.2014.05.014
[36] Hemmelmayr VC. Sequential and parallel large neighbourhood search algorithms for the periodic location routing problem. European Journal of Operational Research. 2015;243(1): 52-60. Available from: https://doi.org/10.1016/j.ejor.2014.11.024
[37] Wang F, Lai X, Shi N. A multi-objective optimization for green supply chain network design. Decision Support Systems. 2011;51(2): 262-269. Available from: http://dx.doi.org/10.1016/j.dss.2010.11.020
[38] Srivastava SK. Green supply chain management: a state of the art literature review. International journal of management reviews. 2007;9(1): 53-80. Available from: https://ieeexplore.ieee.org/document/6852800
[39] Rahimi M, Baboli A, Rekik Y. Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria. Transportation Research Part E: Logistics and Transportation Review. 2017;101: 59-83. Available from: http://dx.doi.org/10.1016/j.tre.2017.03.001
[40] Marcon E, Chaabane S, Sallez Y, Bonte T, Trentesaux D. Simulation Modelling Practice and Theory A multiagent system based on reactive decision rules for solving the caregiver routing problem in home health care. Simulation Modelling Practice and Theory. 2017;74: 134-151. Available from: http://dx.doi.org/10.1016/j.simpat.2017.03.006
[41] Mańdziuk J, Świechowski M. UCT in Capacitated Vehicle Routing Problem with traffic jams. Information Sciences. 2017;406: 42-56. Available from: https://doi.org/10.1016/j.ins.2017.04.020
[42] Puga MS, Tancrez J. A heuristic algorithm for solving large location – inventory problems with demand uncertainty. European Journal of Operational Research. 2017;259(2): 413-423. Available from: https://doi.org/10.1016/j.ejor.2016.10.037
[43] Dehghani E, Behfar nima, Jabalameli MS. Optimizing location, routing and inventory decisions in an integrated supply chain network under uncertainty. Journal of Industrial and Systems Engineering. 2016;9(4): 93-111.
Available from: http://www.jise.ir/article_16501_0ccd46eae583b5bc10ca6437f66f71ae.pdf
[44] Nasrollahi M, Amiri AS, Razmi J, Nasrollahi H. Bullwhip Effect in Different Network Configuration. Applied Mathematics in Engineering Management and Technology. 2015;3(4): 261-267.
[45] Safaei M, Thoben KD. Measuring and evaluating of the network type impact on time uncertainty in the supply networks with three nodes. Measurement: Journal of the International Measurement Confederation. 2014;56: 121-127. Available from: http://dx.doi.org/10.1016/j.measurement.2014.06.010
[46] Rezvani S. Ranking generalized exponential trapezoidal fuzzy numbers based on variance. Applied Mathematics and Computation. 2015;262: 191-198. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0096300315004889
[47] Mousavi SM, Alikar N, Niaki STA, Bahreininejad A. Two tuned multi-objective meta-heuristic algorithms for solving a fuzzy multi-state redundancy allocation problem under discount strategies. Applied Mathematical Modelling. 2015;39(22): 6968-6989. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0307904X15001298
[48] Al Jadaan O, Rajamani L, Rao CR. Non-dominated ranked genetic algorithm for solving multi-objective optimization problems. Journal of Theoretical and Applied Information Technology. 2008; 113-118. Available from: www.jatit.org/volumes/research-papers/Vol5No5/15Vol5No5.pdf
[49] Mousavi SM, Sadeghi J, Niaki STA, Tavana M. A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO. Applied Soft Computing Journal. 2016;43: 57-72. Available from: http://dx.doi.org/10.1016/j.asoc.2016.02.014
[50] Kilic HS, Zaim S, Delen D. Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Systems with Applications. 2015;42(5): 2343-2352. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0957417414006587
[51] Athawale VM, Chakraborty S. Facility Location Selection using PROMETHEE II Method. International Conference on Industrial Engineering and Operations Management Dhaka. January 9-10; 2010.
[52] amar.sci.org.ir [Internet]. Available from: https://amar.sci.org.ir/index_e.aspx
[53] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 2002;6(2): 182-197. Available from: https://ieeexplore.ieee.org/document/996017.
[54] Pasandideh SHR, Niaki STA, Asadi K. Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences. 2015;292: 57-74. Available from: http://dx.doi.org/10.1016/j.ins.2014.08.068
[55] Moore J, Chapman R. Application of particle swarm to multiobjective optimization. Department of Computer Science and Software Engineering Department, Auburn University. 1999; 1-4. Available from: http://goo.gl/NPkun
[56] Azadeh A, Ravanbakhsh M, Rezaei-Malek M, Sheikhalishahi M, Taheri-Moghaddam A. Unique NSGA-II and MOPSO Algorithms for Improved Dynamic CMS by Considering Human Factors. Applied Mathematical Modelling. 2017;48: 655-672. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0307904X17301221
[57] Montgomery DC, Runger GC. Applied statistics and probability for engineers. 6th ed. NJ: Wiley; 2007.
How to Cite
Nasrollahi M, Razmi J, Ghodsi R. A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study. Promet - Traffic & Transportation [Internet]. 27Dec.2018 [cited 21Jan.2019];30(6):693-08. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2779