Intelligent scheduling plays a crucial role in minimizing transportation expenses and enhancing overall efficiency. However, most of the existing scheduling models fail to comprehensively account for the requirements of urban development, as exemplified by the vehicle routing problem with time windows (VRPTW), which merely specifies the minimization of path length. This paper introduces a new model of the heterogeneous green city vehicle routing problem with time windows (HGCVRPTW), addressing challenges in urban logistics. The HGCVRPTW model considers carriers with diverse attributes, recipients with varying tolerance for delays, and fluctuating road congestion levels impacting carbon emissions. To better deal with the HGCVRPTW, an adaptive multi-objective genetic algorithm based on the greedy initialization strategy (AMoGA-GIS) is proposed, which includes the following three advantages. Firstly, considering the impact of initial information on the search process, a greedy initialization strategy (GIS) is proposed to guide the overall evolution during the initialization phase. Secondly, the adaptive multiple mutation operators (AMMO) are introduced to improve the diversity of the population at different evolutionary stages according to their success rate of mutation. Moreover, we built a more tailored testing dataset that better aligns with the challenges faced by the HGCVRPTW. Our extensive experiments affirm the competitive performance of the AMoGA-GIS by comparing it with other state-of-the-art algorithms and prove that the GIS and AMMO play a pivotal role in advancing algorithmic capabilities tailored to the HGCVRPTW.

Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed “DataMatrix-ArUco-Hybrid” (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called “FiMa-Reader”, which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots’ positioning with high precision in various indoor lighting conditions.

In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings.

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.

With the rapid advancement of mobile robotics technology, Simultaneous Localization and Mapping (SLAM) has become indispensable for enabling robots to autonomously navigate and construct maps of unknown environments in real time. Traditional SLAM algorithms, such as the Extended Kalman Filter (EKF) and FastSLAM, have shown commendable performance in certain applications. However, they encounter significant limitations when dealing with nonlinear systems and non-Gaussian noise distributions, especially in dynamic and complex environments coupled with high computational complexity. To address these challenges, this study proposes an enhanced particle filtering method leveraging particle swarm optimization (PSO) to improve the accuracy of pose estimation and the efficacy of map construction in SLAM algorithms. We begin by elucidating the foundational principles of FastSLAM and its critical role in empowering robots with the ability to autonomously explore and map unknown territories. Subsequently, we delve into the innovative integration of PSO with FastSLAM, highlighting our novel approach of designing a bespoke fitness function tailored to enhance the distribution of particles. This innovation is pivotal in mitigating the degradation issues associated with particle filtering, thereby significantly improving the estimation accuracy and robustness of the SLAM solution in various operational scenarios. A series of simulation experiments and tests were conducted to substantiate the efficacy of the proposed method across diverse environments. The experimental outcomes demonstrate that, compared to the standard particle filtering algorithm, the PSO-enhanced particle filtering effectively mitigates the issue of particle degeneration, ensuring reliable and accurate SLAM performance even in challenging, unknown environments.