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

Meisam Nasrollahi; Jafar Razmi; Reza Ghodsi

doi:10.7307/ptt.v30i6.2779

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

DOI: https://doi.org/10.7307/ptt.v30i6.2779

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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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.

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