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.
Highlights
Environmental changes and global warming are among the most important challenges humans have faced in the last hundred years [1, 2]
The presented problem is solved by NSGA-II, MOPSO, and multi-objective non-dominated ranked genetic algorithm (M-NRGA)
We provided a computational method for measuring transport related carbon emissions (TRCE) in a healthcare supply network
Summary
Environmental changes and global warming are among the most important challenges humans have faced in the last hundred years [1, 2]. Carbon emissions produced by human activities usually come from burning fossil fuels, e.g. oil, natural gas, coal, and wood [5]. To overcome this challenge, governments and industry sectors are working to understand their own carbon footprint and explore the required actions. In some real applications, the nature of retailer’s demand is imprecise In this case, fuzzy set theory (FST) provides a proper. Note that in most real applications of a supply chain problem there is not enough historical data for the previous demand values In these cases, using fuzzy numbers can be used to describe the decision models for the external demand of a network. The conclusion and future study considerations finalize the paper
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