Browsing by Subject "Location-allocation"

Now showing 1 - 2 of 2

Open Access
The green network design problem
(Elsevier, 2019) Dükkancı, Okan; Bektaş, T.; Kara, Bahar Y.; Faulin, J.; Grassman, S. E.; Juan, A. A.; Hirsch, P.
Logistics activities are at the heart of world trade, but they also have unintended consequences on the environment due to the use of land, energy, and other types of natural resources. The significant energy usage by the more traditional means of transportation results in emissions, one of the most prominent of all negative externalities, that in turn causes air pollution affecting human health. One way to reduce such externalities is the (re-)design of the overall network on which logistics activities take place, giving rise to green network design problems, where the minimization of emissions is an integral and explicit part of the objective. The aim of this chapter is to present an overview and a classification of green network design problems arising at different levels of decision making, from operational to strategic, and will present definitions, optimization models, and practical applications for some of the key problems in this category.
Open Access
Leveraging large-scale data for supply chain network design: a location-allocation model for Rwanda
(2024-08) Gürkan, Zeynep Göze
Clean cooking strategies are significant contributors to the enhancement of development and sustainability. Regarding the lower emission levels compared to biomass usage, we consider Liquefied Petroleum Gas (LPG) a clean cooking strategy to promote, especially in developing countries. Hence, we design large-scale supply chain operations for the LPG distribution in Rwanda. This involves addressing the location-allocation problem of facilities by utilizing a large dataset on the location and LPG demand of each rooftop by formulating a Mixed-Integer Linear Programming (MILP) model. In order to decrease the size of the problem, we propose three methods. First of all, we design the system independent of time index. Next, we use the agglomerative hierarchical clustering-based heuristic approach to cluster the rooftops and locate retailers on the distance-constrained geomedian point of each cluster. Finally, we propose to decompose the formulated MILP model to get adequate solutions in less time. For computational analysis, we compare the system configurations with different retailer locations obtained by the village centroid approach and agglomerative hierarchical clustering based-heuristic approach. In addition, we investigate whether the existing system configuration can be extended when the projected increase in yearly LPG demand is introduced. Moreover, we conduct a sensitivity analysis to show the trade-off between the infrastructure and transportation costs due to the volatility in diesel fuel prices. Finally, we compare the results and performances of the main model and the decomposed model.