This paper investigates a novel routing problem of a multi-robot station in a manufacturing cell. In the existing literature, the objective is to minimize the cycle time or energy consumption separately. The routing problem considered in this paper aims to reduce the cycle time and energy consumption jointly for each robot while avoiding collisions between these robots. For this routing problem, we propose a new flexible time-space network model that allows us to reduce energy consumption while minimizing the cycle time. The corresponding optimization problem is Mixed-Integer Nonlinear Programming (MINLP). For addressing its computational complexity, this paper designs a metaheuristic algorithm tailored to the studied problem and proposes an ϵ-constraint algorithm to study the trade-off between these two objectives. We conduct industrially relevant simulation experiments of case studies to show its effectiveness, in comparison to a conventional method, two state-of-the-art solvers, and two commonly-used metaheuristics. The results show that the proposed methodology can reduce energy consumption by up to 30% without compromising the cycle time. Meanwhile, the proposed algorithm can provide efficient solutions within a reasonable computation time. Note to Practitioners-This paper is motivated by the problem of improving energy efficiency when routing cooperative robots in a manufacturing station. In current approaches for routing multi-robot stations, the cycle time and energy consumption are minimized separately. This paper focuses on the movement of the robot end-effector and its connected joint and suggests a new approach to minimize these two objectives jointly by proposing a new mathematical model. The resulting planning problem is computationally intractable. A customized metaheuristic algorithm is thus designed for efficiently solving this planning problem. Our meta-heuristic algorithm is integrated with the ϵ-constraint method to study the relationship between these two objectives. Simulation experiments suggest that this approach can reduce energy consumption considerably, for the shortest cycle time, compared with the current approaches. In future research, the movements of multi-joints will be investigated whereby 3-D collision-free trajectory planning will be considered.

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

Operations of Automated Guided Vehicles (AGVs) are desired to be more energy-efficient while maintaining high transport productivity, motivated by the green production requirements. This paper investigates a new energy-efficient planning problem for determining conflict-free paths of the AGVs in its transport roadmap. In this problem, the vehicle path and transport time in the roadmap are jointly optimized, based on a flexible time–space network (FTSN). We provide the mathematical problem formulation of the energy-efficient path planning problem. The resulting optimization problem is proved to be a non-convex mixed-integer nonlinear programming which is computationally intractable. We further propose a hybrid metaheuristic that integrates the genetic algorithm and estimation of the distribution algorithm to improve its computational efficiency. Numerical results show the effectiveness of the developed algorithm based on the FTSN framework, compared to the existing metaheuristics, the conventional path planning method, and a commercial solver. The proposed method has a wide application in improving energy use of material handling, providing a guiding significance on promoting cleaner production of flexible manufacturing systems.