A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study
Keywords:
healthcare, greenhouse effect, supply network, carbon emissions, Monte Carlo
Abstract
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.
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[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
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[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
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[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
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Published
2018-12-27
How to Cite
1.
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 22Apr.2019];30(6):693-08. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/2779
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