No abstract

a b s t r a c tBenchmarking is comparing the output of different systems for a given set of input data in order to improve the system's performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events.

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.

No abstract

No abstract

Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.

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.

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 collisionfree trajectory according to the priority policy. Numerical and experimental results are provided to show the pertinence and the feasibility of the proposed strategy.