Logistics 4.0: technologies for flexible and eco-efficient logistics

Abstract

In the future circular economy, the movement of raw materials, products, residues for recycling and production resources across all regions of the global is a key requirement. Logistics operations can already represent up to 35% of the logistics costs and this relative importance may grow even higher. Therefore, the optimal plan and management of the logistics operations can determine the competitiveness of the industry. Logistics also impact on the sustainability of the supply chains and condition the design and infrastructuring of future cities.Intelligent logistics systems have been developed in recent years to help to fulfill the transportation needs at minimum cost. Yet, under the new paradigm of Industry 4.0, large amounts of information will become available (close-to-real-time) from a multitude of sensors and similar technologies embedded in the freights, transportation vehicles and hubs. In this framework, this project aims to research new advanced optimization methods for flexible transportation planning, whose models may address uncertainty and dynamically adapt in respect to just-in-time data, and further address circular economy concerns (e.g. ecological impact, recycling). The emphasis of this research will be on the dynamic vehicle routing problem (dVRP), a variant from the traditional vehicle routing problem (VRP), in which it is possible to readjust the vehicles routes over time. To make use of just-in-time information to readjust dynamically the routes can save significant amount of costs and, at the same time, better attend customer demands. It has several applications from emergency to delivery services and also can better address routing problems under uncertainty. The dynamic vehicle routing problem is relatively new and recent literature reviews point out some aspects of the problem that can be more explored. Therefore, the objective of this research is to fill some gaps of the problem based on four branches. The first one is to standardize the research on the problem in terms of its taxonomy, applications, variants, approaches, instances and benchmarks, systematizing existing approaches for eco-efficiency in transportation planning. The second is the study of how uncertainty can be incorporated into the problem to deliver robust solutions in an adequate time. The third objective is to develop efficient solution techniques that solve the problem in a more tractable manner. This aspect is instrumental for developing future transportation systems for practical applications. The last objective is to apply the proposed models and methods to validate the benefits in a practical problem of a Portuguese retail e-commerce company. (AU)