Zengin araç rotalama: Bir lojistik firması için veri odaklı sezgisel uygulama

Changing online shopping behaviors have resulted in the emergence of different product and services that aim high customer satisfaction. In this thesis, we develop an alternative approach to solve problem of a logistics company, which operates solely for e-commerce transactions, using an Adaptive Large Neighborhood Search (ALNS) heuristic. To understand the nature of the distribution system and for the development of the solution procedure, we create, preprocess and analyze a dataset constructed from company's database that is used for daily operations. The proposed solution provides a prioritization mechanism for the deliveries based on certain specifications related to deliveries. To evaluate the performance of the proposed ALNS, we perform computational experiments using scenarios with real-life instances extracted from the dataset. Our results show that, the proposed ALNS can produce solutions with high quality regarding customer satisfaction.