With increasing concerns about global warming caused by greenhouse gasses (GHGs), organizations have become more responsible for their operations. According to the U.S. Environmental Protection Agency (EPA), companies with a supply chain (SC) generate about 42% of GHGs in their transportation (30%) and inventory systems (12%), which makes mitigating climate change through a green supply chain (GSC) management a reasonable solution.
To design a GSC, we model the SC as a customer and store network, with customers driving in cars to and from stores and the retailer resupplying the stores from a central warehouse. The number and location of stores are determined to find a low-cost and low emission configuration for the SC.
The key findings are (1) SCs with more small stores generate less emission than ones with fewer large stores; (2) when minimizing the operating cost is more important than mitigating GHG emissions, fewer large stores are preferred than having more small stores; (3) a SC with two warehouses reduces the number of open stores in a large area such as Puerto Rico.
Our contributions are (1) building a model of a GSC based on population data; (2) modeling a GSC in a two-echelon network which can be solved simultaneously using the k-median approach; (3) evaluating the effect of multiple warehouses on the overall GHGs emissions; (4) managing the incompleteness and inaccuracy of the data through implementing the compromise Decision Support Problem construct to identify satisficing solutions.
The model mentioned earlier highlights the important parameters that impact the green GHG emissions reduction from a SC that describe in this paper. We also discuss how this approach can be employed for other design problems, including manufacturing and healthcare.