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Original scientific paper
https://doi.org/10.7307/ptt.v30i6.2779

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

Meisam Nasrollahi   ORCID icon orcid.org/0000-0001-5404-9940 ; 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

Fulltext: english, PDF (1 MB) pages 693-708 downloads: 194* cite
APA 6th Edition
Nasrollahi, M., Razmi, J. & Ghodsi, R. (2018). A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study. Promet - Traffic&Transportation, 30 (6), 693-708. https://doi.org/10.7307/ptt.v30i6.2779
MLA 8th Edition
Nasrollahi, Meisam, et al. "A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study." Promet - Traffic&Transportation, vol. 30, no. 6, 2018, pp. 693-708. https://doi.org/10.7307/ptt.v30i6.2779. Accessed 27 Nov. 2021.
Chicago 17th Edition
Nasrollahi, Meisam, Jafar Razmi and Reza Ghodsi. "A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study." Promet - Traffic&Transportation 30, no. 6 (2018): 693-708. https://doi.org/10.7307/ptt.v30i6.2779
Harvard
Nasrollahi, M., Razmi, J., and Ghodsi, R. (2018). 'A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study', Promet - Traffic&Transportation, 30(6), pp. 693-708. https://doi.org/10.7307/ptt.v30i6.2779
Vancouver
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]. 2018 [cited 2021 November 27];30(6):693-708. https://doi.org/10.7307/ptt.v30i6.2779
IEEE
M. Nasrollahi, J. Razmi and R. Ghodsi, "A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study", Promet - Traffic&Transportation, vol.30, no. 6, pp. 693-708, 2018. [Online]. https://doi.org/10.7307/ptt.v30i6.2779

Abstracts

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.

Hrčak ID: 214141

URI
https://hrcak.srce.hr/214141

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