The circular economy is gaining in importance globally and locally. The COVID-19 crisis, as an exceptional event, showed the limits and the fragility of supply chains, with circular economy practices as a potential solution during and post-COVID. Reverse logistics (RL) is an important dimension of the circular economy which allows management of economic, social, and environmental challenges. Transportation is needed for RL to effectively operate, but research study on this topic has been relatively limited. New digitalization opportunities can enhance transportation and RL, and therefore further enhance the circular economy. This paper proposes to review practical research and concerns at the nexus of transportation, RL, and blockchain as a digitalizing technology. The potential benefits of blockchain technology through example use cases on various aspects of RL and transportation activities are presented. This integration and applications are evaluated using various capability facets of blockchain technology, particularly as an immutable and reliable ledger, a tracking service, a smart contract utility, as marketplace support, and as tokenization and incentivization. We also briefly introduce the physical internet concept within this context. The physical internet paradigm proposed last decade, promises to also disrupt the blockchain, transportation, and RL nexus. We include potential research directions and managerial implications across the blockchain, transportation, and RL nexus.

Supply chains are complex, stochastic systems. Nowadays, logistics managers face two main problems: increasingly diverse and variable customer demand that is difficult to predict. Classical forecasting methods implemented in many business units have limitations with the fluctuating demand and the complexity of fully connected supply chains. Machine Learning methods have been proposed to improve prediction. In this paper, a Long Short-Term Memory (LSTM) is proposed for demand forecasting in a physical internet supply chain network. A hybrid genetic algorithm and scatter search are proposed to automate tuning of the LSTM hyperparameters. To assess the performance of the proposed method, a real-case study on agricultural products in a supply chain in Thailand was considered. Accuracy and coefficient of determination were the key performance indicators used to compare the performance of the proposed method with other supervised learnings: ARIMAX, Support Vector Regression, and Multiple Linear Regression. The results prove the better forecasting efficiency of the LSTM method with continuous fluctuating demand, whereas the others offer greater performance with less varied demand. The performance of hybrid metaheuristics is higher than with trial-and-error. Finally, the results of forecasting model are effective in transportation and holding costs in the distribution process of the Physical Internet.

In this paper, decentralized motion planning and scheduling of automated guided vehicles (AGVs) in a flexible manufacturing system (FMS) is proposed. A motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. The proposed strategy is based on two steps. The first step consists in planning a presumed trajectory to avoid collision conflicts previously detected by a central supervisor, enabling more appropriate decentralized scheduling by AGVs. The presumed trajectories, which represent the intentions of AGVs, are then exchanged with neighboring AGVs. The second step uses the presumed trajectories of neighbors to compute a collision-free trajectory according to the priority policy. Numerical and experimental results are provided to show the pertinence and the feasibility of the proposed strategy.

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.