Due to poor predictability of resources and difficulty in perception of task execution status, traditional Automatic Guide Vehicle (AGV) scheduling systems need a lot of extra time in the charging process. To solve this problem, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed, including four functions, namely the knowledge support system, the scheduling model, the scheduling optimization, and the scheduling simulation. With the features of virtual reality data interaction, symbiosis, and fusion from the digital twin technology, the proposed DTDAS method can solve the AGV charging problem in the AGV scheduling system, effectively improving the operating efficiency of the workshop. An AGV scheduling process in a discrete manufacturing workshop is taken as a case study to verify the effectiveness of the proposed method. The results show that, compared with the traditional AGV scheduling method, the DTDAS method proposed in this article can reduce makespan 10.7% and reduce energy consumption by 1.32%.

The Capacitated Vehicle Routing Problem (CVRP) is a classic combinatorial optimization problem in logistics and distribution, with significant theoretical and practical importance. To address the limitations of traditional evolutionary algorithms—particularly their use of fixed operator selection and simplistic search strategies—this paper proposes a Q-learning-based evolutionary algorithm (QEA). By incorporating a reinforcement learning mechanism, the QEA adaptively selects among multiple neighborhood search operators, effectively balancing global exploration and local exploitation. In addition, a novel insertion-based crossover operator and a set of diverse neighborhood search strategies are designed to further enhance solution quality and search efficiency. Experimental results on a variety of standard CVRP benchmark instances show that the QEA demonstrates a superior performance and strong robustness, significantly outperforming several representative state-of-the-art algorithms for solving the CVRP. These results confirm the effectiveness and practical value of the proposed method.

The Distributed No-Wait Flow Shop Scheduling Problem (DNWFSP) arises in various manufacturing contexts, such as chemical production and electronic assembly, where strict no-wait constraints and multi-factory coordination are required. Solving the DNWFSP involves determining the allocation of jobs to factories and the no-wait processing sequences within each factory, making it a highly complex combinatorial problem. To address the limitations of existing methods—including poor initial solution quality, limited neighborhood exploration, and a tendency to converge prematurely—this paper proposes a Multi-Neighborhood Adaptive Tabu Search Algorithm (MNATS). The MNATS integrates a balance–lookahead NEH initializer (BL-NEH), an adaptive neighborhood local search (ANLS) strategy, and an Adaptive Tabu-Guided Perturbation (ATP) strategy. Experimental results on multiple benchmark instances demonstrate that MNATS algorithm significantly outperforms several state-of-the-art algorithms in terms of solution quality and robustness.

In distributed manufacturing systems, minimizing completion time and improving resource utilization are critical for enhancing operational efficiency. Conventional scheduling models for centralized flowshops struggle to capture the complexity of distributed heterogeneous systems, while existing studies often overlook the combined challenges of heterogeneous factories, no-wait constraints, and sequence-dependent setup times (SDST). This study focuses on the distributed heterogeneous no-wait permutation flowshop scheduling problem with SDST (DHNPFSP-SDST), which is proven NP-hard via polynomial reduction to the classic permutation flowshop scheduling problem (PFSP). We first establish a bi-objective optimization model to simultaneously minimize makespan and total machine non-working time, serving as a standard experimental foundation. The core innovation is a large language model-assisted non-dominated sorting genetic algorithm (LLM-NSGAII), Through a structured prompt framework, LLM-NSGAII leverages LLM’s zero-shot in-context learning to dynamically orchestrate selection, crossover, and mutation operations—replacing the fixed operators of traditional NSGAII. Experiments on extended benchmarks show that when compared with mainstream, multi-objective algorithms demonstrate competitiveness across most instances and provide a proof of concept for integrating LLMs with evolutionary algorithms, opening new avenues for algorithmic optimization.

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment.