Abstract.

A novel dynamic rectangular packing model for a resource allocation problem, which has many applications in Logistics Internet-of-Things (L-IoT) is considered in this study. We use an optimization approach to deal with an IoT-based problem, whose objective is to maximize the profit obtained from packing the data demand over a sequence of time frames, while satisfying several quality of service constraints. We propose a nonlinear integer programming model for the problem that optimizes demand partitioning and rectangle packing simultaneously, for the first time in literature. By introducing effective upper and lower bounds for this practically important problem, a computational experimentation is designed for assessing the model and bound performances. An extensive discussion and recommendations for policy-making are included based on the computational results.

Keywords:

bin packing, optimization, logistics Internet-of-Things, nonlinear integer programming.