<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10011929" mdate="2021-02-01 00:00:00"> <author>Vincenzo Capalbo and Gianpaolo Ghiani and Emanuele Manni</author> <title>The Role of Optimization and Machine Learning in eCommerce Logistics in 2030</title> <pages>294 - 298</pages> <year>2021</year> <volume>15</volume> <number>3</number> <journal>International Journal of Economics and Management Engineering</journal> <ee>https://publications.waset.org/pdf/10011929</ee> <url>https://publications.waset.org/vol/171</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Global ecommerce sales have reached unprecedented levels in the past few years. As this trend is only predicted to go up as we continue into the &amp;amp;rsquo;20s, new challenges will be faced by companies when planning and controlling ecommerce 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 nextgeneration planning algorithms namely scalability, modelandrun features and learning capabilities that will be fundamental to cope with the scale and complexity of logistics in the next decade.</abstract> <index>Open Science Index 171, 2021</index> </article>