A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study
| Autor: | Reza Ghodsi, Jafar Razmi, Meisam Nasrollahi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Operations research Total cost Computer science Global warming Monte Carlo method lcsh:TA1001-1280 healthcare Ocean Engineering greenhouse effect 02 engineering and technology supply network 020901 industrial engineering & automation Empirical research Greenhouse gas Genetic algorithm 0202 electrical engineering electronic engineering information engineering Supply network Fuel efficiency 020201 artificial intelligence & image processing carbon emissions lcsh:Transportation engineering Engineering (miscellaneous) Monte Carlo Civil and Structural Engineering |
| Zdroj: | Promet (Zagreb), Vol 30, Iss 6, Pp 693-708 (2018) Promet-Traffic&Transportation Volume 30 Issue 6 |
| ISSN: | 1848-4069 0353-5320 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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