Locating Collection and Delivery Points Using the p-Median Location Problem

Background: Possible solutions to overcome the many challenges of home delivery are collection and points (CDPs). In addition commercial facilities, role CDPs can also be played by users’ households, providing a crowd storage service. Key decisions regarding relate their location, as well allocation users selected locations, so that distance from is minimal. Methods: this paper, described problem defined p-median solved for area city Belgrade, using heuristic “greedy” simulated annealing algorithm. Results: Fifty locations were allocated them distributed in over 950 zones. The individual distances between nearest sum these distances, multiplied number requests, obtained. An example modification presented way obtaining correspond different preferences operators and/or terms CDPs. Conclusions: User households used achieve various benefits. Locating CDPs, i.e., selecting problem, combination metaheuristic algorithms. addition, modifying medians, total average better managed. main contributions paper establishment potential framework analysis impact on customers, creation basis upgrading model implementation business practice.