Flexible time–space network formulation and hybrid metaheuristic for conflict-free and energy-efficient path planning of automated guided vehicles. (20th April 2023)

• Record Type: Journal Article
• Authors: Xin, Jianbin; Wei, Liuqian; D'Ariano, Andrea; Zhang, Fangfang; Negenborn, Rudy
• Is Part Of: Journal of cleaner production. Volume 398(2023)
• Publication Date: 2023-04-20
• Subjects: Automated guided vehicles -- Energy efficiency -- Flexible time–space network model -- Hybrid metaheuristic; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2023.136472
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Highlights: We investigate conflict-free and energy-efficient path planning of AGVs. We propose an MINLP model to optimize productivity and energy consumption jointly. A customized hybrid metaheuristic is developed to improve computational efficiency. The proposed method reduces energy consumption without decreasing productivity.