TY - JFULL AU - Vincenzo Capalbo and Gianpaolo Ghiani and Emanuele Manni PY - 2021/4/ TI - The Role of Optimization and Machine Learning in e-Commerce Logistics in 2030 T2 - International Journal of Economics and Management Engineering SP - 293 EP - 298 VL - 15 SN - 1307-6892 UR - https://publications.waset.org/pdf/10011929 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 171, 2021 N2 - Global e-commerce sales have reached unprecedented levels in the past few years. As this trend is only predicted to go up as we continue into the ’20s, new challenges will be faced by companies when planning and controlling e-commerce logistics. In this paper, we survey the related literature on Optimization and Machine Learning as well as on combined methodologies. We also identify the distinctive features of next-generation planning algorithms - namely scalability, model-and-run features and learning capabilities - that will be fundamental to cope with the scale and complexity of logistics in the next decade. ER -