Dataset

Step 1: We begin by defining our fundamental geographical unit of analysis, such as the population centroid, and then create aggregated demand zones, with information on overall demand provided at each zone. We divided the city of Delhi into 1KM x 1KM square grids using ArcGIS software and plotted the centroid of each grid, obtaining the latitude and longitude of each centroid location. These centroids serve as customer zones that collection centers must service. Step 2: Search for potential collection sites. We consider gasoline pumps/gas stations, Residential Welfare Association offices in Delhi, and vehicle dealer locations in Delhi as potential collection center sites for our problem. Gas stations, vehicle dealerships, and gasoline pumps were located using Google Maps, and the addresses of residential societies were obtained from the Delhi government's official website. Step 3: Create a network-based demand zone for each demand node. The network demand area determines the distance customers are willing to travel to return their used batteries. We know the locations of all demand and potential collection sites in our scenario. Using Microsoft Bing APIs, we calculated the distance between all demand locations and collection sites. Step 4: Uncover the most suitable locations for facilities like collection centers within each demand region and identify the optimal incentives to be offered through various collection channels to encourage customers to return the used batteries.