Towards a framework for smart resilient logistics

In order to remain competitive, logistics companies are forced to provide smart solutions within a network that is characterized by complexity and heterogeneity. The advancements of sensing and communication technologies stimulate logistics organizations to improve their business performances by using more advanced decision support tools. This research is devoted to improve logistics decision making by exploiting the enormous datasets originating from IoT networks in combination with Big Data Analytics. The main aim is to develop a resilient planning framework that stimulates logistics planners to combine both human experiences and pattern recognition mechanisms (e.g., machine learning, data mining, etc.). In this paper, four research deliverables are proposed to pursue this vision: (1) a state-of-the-art overview of modern decision support tools to enhance logistics resilience and efficiency; (2) the development of dynamic optimization algorithms using real-time data; (3) the construction of data-driven algorithms
to identify, assess and resolve the presence of logistical disturbances and; (4) the formulation of resilient planning framework that enables real-life implementations of the
algorithms developed. A brief overview of the required research activities is given as well, including a visualization of the activities’ coherency. This paper concludes with a
description of the preliminary results and some future research directions.