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Nowadays, reducing total costs while enhancing customer satisfaction is a major task for many supply chain systems. To deal with this issue, the ‘Physical Internet’ (PI) paradigm can be represented as a potential replacement for the current logistics system. This paper devoted the cost reduction and lead time improvement in a PI-SCN using a hybrid framework based on an artificial neural network (ANN) and an improved slime mould algorithm metaheuristic. To address the performance of the proposed framework, a real-case study in Morocco is considered. The new trainer ISMA’s performance has been investigated regarding five recent metaheuristics. The experimental results highlight the effectiveness of ISMA according to other metaheuristics for training Feed-forward Neural Networks (FNNs) to converge speed and to avoid local minima.

International audience; In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

This paper proposes Dynamic Multiple Depots Vehicle Routing (DMDVR) to explore the feasible solution of routing transportation between PI-hubs and retailers in the same cluster. The routing solution in this research is constructed by the daily forecasting demand of a commodity crop, Pineapple, from Thailand’s northern region. Each route is composed of a starting hub, the number of retailers, and an ending hub. The authors propose Mixed Integer Linear Programming (MILP) to construct the routes considering inventory and truck capacity constraints. Besides, another solution method is proposed by using a heuristic method named “Iterated Random Heuristic”. The empirical results are evaluated by using the total distribution cost and computational time. The routing transportation of this research is based on the daily delivery transportation from PI-hubs to retailers. The results show that Iterated Random Heuristic with Nearest Neighbor Search generates near-optimal solutions within a short computational time.

International audience; This paper investigates a dynamic pricing problem for less-than-truckload (LTL) carriers during several auction periods in Physical Internet (PI), in consideration of peak demand forecasting. PI can be considered as the interconnection of logistics networks via open PI-hubs, in which there are plenty of requests to be transported. These requests continuously arrive at different auction periods with various quantity. Carriers can bid for these requests through participating several rounds of auction. In a dynamic environment, a major problem for the carrier is how to decide the bidding price to maximize his profit. Besides, to make better decision, when determining the bidding price in current period, carrier should forecast the possible requests in next periods, especially facing with peak/off-peak time periods. This paper proposes a dynamic pricing model considering the forecasted quantity of requests in the next auction periods to optimize the bidding price and maximize the total profit. A numerical study is conducted to evaluate the model and study how the future requests influence the current pricing decision, including the influence to the bidding price and profit.

Centralisation and decentralisation are the two common organisations in freight transport. The first relies on a central authority who optimises and establishes transport plans for all carriers for global-interest, while the second lets carriers optimise their own transport plans for their self-interest. The outcome-efficiency and effectiveness-could be different. This paper aims to use the concept of Price of Anarchy (PoA) to compare the outcome of the two organisations. Due to the complexity of actual freight transport market, this paper adapts the gamification methodology to investigate the two organisations. A freight transport game was developed for simulation. The outcome of the two simulated organisations-centralisation or decentralisation-are then compared. The results show that the centralisation outperforms in terms of global efficiency and effectiveness; while decentralisation is better individual incentive. However, the PoA varies depending on information revealed. Copyright © 2019 IFAC

This paper investigates inventory management problems for fast-moving consumer goods (FMCG) with the Physical Internet (interconnected logistic services) where goods are stored and distributed in an interconnected and open network of hubs. Unlike current hierarchical inventory model where the source assignment is predetermined for each replenishment order either from the retailers or from the warehouses, the PI-inventory model enables multiple source selection options to each order and transshipments of inventories among hubs, resulting in complete pooling of inventories within the network. We adopt a continuous review (Q, R) ordering policy for each facility and propose four dynamic source selection strategies related to the supplying nodes' inventory levels, the distance and lead time between the supplying and the ordering nodes. The objective is to determine the optimal replenishment policies for hubs in order to minimize the total logistic costs of the distribution system. A nonlinear optimization model is proposed and a heuristic using simulated annealing is applied. A simulation study based on two categories of FMCG is taken to evaluate the performance of the proposed inventory control policies. Our results suggest that compared to current centralized inventory control policy, the PI-inventory control model with proposed source selection strategies can significantly reduce the total costs as well as the average inventory level of the network of hubs while reaching the same end customer service level.

The paper presents results of research on modeling, analysis and optimization in a supply chain. It shows modern approaches to the model supply chain: multimodal, based on the idea of Physical Internet and based on Cyber-Physical Internet. The main goal of this paper is to present the method which enables identifying and simplifying optimization of cross-docking terminal with use of the multimodal approach. The asynchronous multimodal process approach is shown as a complement to current concepts of Physical Internet. As an example of multimodal process thinking, the solution for an optimization task in a cross-docking hub is also presented.

Prefabrication could provide various benefits for housing production, including lower the cost, improve the management efficiency and quality, ensure the safety, and realize sustainability. However, to fully acquire these benefits in real world construction process, challenges still exist on real-time tracing and tracking of the prefabrication components, and getting real-time interactions among all the stockholders. Physical Internet (PI), as has been well adopted in logistics, could be an effective solution for real-time visibility. Based on the prefabrication practice in Hong Kong, this paper proposes the architecture of an PI-enabled prefabricated housing construction which upgrades and transforms the prefabrication construction so that the logistics echelons could be seamlessly integrated and synchronized, together with a PI-enabled decision support system (DSS) which uses Internet of Things (IoT) and cloud techniques for designing the architecture and a rich set of services and tools to assist different decision makers.. Firstly, three stages of prefabrication construction have been analyzed in detail to point out current challenges. Secondly, the technical framework of PI-enabled Building Information Modelling (BIM) Platform (PIBIMP) is developed. Finally, a case study is given to demonstrate the working process of our proposed solution, and the effectiveness of it has also been verified.