ABSTRACT
At present times, online shopping websites have become more popular because of the latest advancements in wireless communication technologies and smartphones. A massive amount of data have been generated by online shopping websites, where big data analytics has been employed for explaining the large-scale data gathering, which significantly exceeds the abilities of conventional models. At the same time, it has made the logistic industry more successful, with a massive number of packages being transported to consumers. This alteration in the nature of shopping makes transportation of a massive quantity of packets essential. For addressing this need, in this chapter, a new artificial intelligence and Internet of Things (IoT) based logistic transportation planning model is presented by the hybridization of fuzzy logic with modified particle swarm optimization (HFMPSO) algorithm. The proposed model initially used IoT-based barcode reader to access the details of the package. The proposed transportation planning algorithm involves three main phases: partitioning of packages, route planning using HFMPSO algorithm, and package insertion. The HFMPSO model has been tested using a set of performance measures under diverse aspects. The experimental outcome clearly verified the superior performance of the HFMPSO model over the compared methods in a significant way.
