Abstract

Interoperability in logistics is a prerequisite for realizing data pipelines and the Physical Internet. Forecasting data, real time data, and actual positions of shipments, containers, and transport means shared via events have to be harmonized and are expected to improve all types of processes, support synchromodal planning, and improve risk analysis from a compliance and resilience perspective. Technically, several solutions are implemented by organizations and innovations have been validated in so-called Living Labs or demonstrators in various projects. These solutions do not yet provide open systems required for a (global) data pipeline. A federation of solutions is required to construct data pipelines and to support sustainable development of applications on smart devices allowing Small and Medium sized Enterprises to collaborate. This paper proposes a set of platform services and so-called platform protocols to allow interoperability of different platforms for constructing a data pipeline. The proposed services and protocols further extend existing interoperability solutions and services for supply and logistics. © IFIP International Federation for Information Processing 2015. Document type: Part of book or chapter of book

Abstract

Physical Internet based supply chains create open, global logistics systems that enable new types of collaboration among participants. The open system allows the logistical examination of vehicle technology innovations such as the platooning concept. This article explores the multiple platoon collaboration. For the reconfiguration of two platoons a heuristic and a reinforcement learning (RL) based models have been developed. To our knowledge, this work is the first attempt to apply an RL-based decision model to solve the problem of controlling platoon cooperation. Vehicle exchange between platoons is provided by a virtual hub. Depending on the various input parameters, the efficiency of the model was examined through numerical examples in terms of the target function based on the transportation cost. Models using platoon reconfiguration are also compared to the cases where no vehicle exchange is implemented. We have found that a reinforcement learning based model provides a more efficient solution for high incoming vehicle numbers and low dispatch interval, although for low vehicle numbers heuristics model performs better. Document type: Article

Abstract

International audience; On the forward side, the growth of E-commerce in recent years substantially generates additional packets and parcels for distribution; meanwhile, on the reverse side, collecting returned goods is also becoming a preoccupation of sustainability, especially in metropolitan areas. Inspired by the concepts of crowdsourcing and the Physical Internet, in this paper, we propose an innovative solution that seeks to exploit the extra loading capacity and constant mobility from taxis in metropolitan areas to collect and delivery the e-commerce returns from final consumption points back to retailers. We assume that, on one hand, e-retailers will have incentive to outsource this task; on the other hand, taxi drivers will also be motivated because they can earn a little extra money from the shipments that they have fulfilled. As an alternative to the traditional ways, the solution proposed is more sustainable because it could simultaneously reduce the economical (pickup and transportation costs), environmental (CO 2 emissions, energy consumption, traffic congestion in city), and social (the wastes of the impulse buying, reduced incitation of online shopping) impacts resulted from reverse flows management in metropolitan areas. As the first qualitative and quantitative study of the concept, this paper uses open databases of taxi GPS traces and locations of shops in a large city in China for investigating the feasibility and viability of the solution proposed. Two collection strategies are proposed and evaluated by an optimization-based simulation model. The results generate several useful insights to the implementability and managerial issues of the concept.

Abstract

International audience; Urban logistics is becoming more complicated and costlier due to new challenges in recent years. Since the main problem lies on congestion, the clean vehicle is not necessarily the most effective solution. There is thus a need to redesign the logistics networks in the city. This paper proposes a methodology to evaluate different distribution schemes in the city among which we find the most efficient and sustainable one. External impacts are added to the analysis of schemes, including accident, air pollution, climate change, noise, and congestion. An optimization model based on an analytical model is developed to optimize transportation means and distribution schemes. Results based on Bordeaux city show that PI scheme improves the performances of distribution.