Search Results (403)

The rapid growth of online shopping has increased demand for home deliveries, leading to sustainability issues and logistical challenges such as labour shortages and congestion. Autonomous delivery vehicles, including drones, street robots, autonomous vans, and mobile vending machines, are emerging as potential solutions. Understanding consumers’ perceptions of these technologies is critical for sustainable implementation. This exploratory study aimed to examine consumer reactions to emerging autonomous delivery services, providing insights into how consumers may respond to autonomous delivery systems encompassing multiple vehicle modes and the resulting policy implications. Eight online focus groups (n = 55) were conducted with a diverse range of participants to examine community attitudes to autonomous delivery services. Participants were shown videos depicting various autonomous delivery methods to foster informed responses. Thematic analysis of the transcripts identified recurring themes relating to participants’ preferences, concerns, and expectations. While participants had some concerns, they were largely receptive to using autonomous delivery services. Positive reactions centred around: (i) convenience, (ii) cost reductions, and (iii) novelty. Identified concerns included: (i) job losses, (ii) practical limitations of the delivery devices, (iii) degradation of urban environments, and (iv) facilitation of unhealthy lifestyles. Overall, the results suggest autonomous delivery systems have the potential to be popular, and proactive government policies are thus likely to be needed to ensure they are implemented in a manner that aligns with community expectations and minimises any negative sustainability outcomes. Full article

Developing high-quality and reliable solver code for the job shop scheduling problem (JSSP) remains a challenging and expertise-intensive task because generated code must stay executable, produce feasible schedules, and achieve strong scheduling results. This paper proposes a large language model (LLM)-driven multi-agent evolution framework for scheduling solver code generation, where LLMs act as hyper-heuristics for program-space search under external evaluation. The framework forms a closed-loop process with three collaborating agents. A seed heuristic generation agent uses a structured constraint template and a shared solver skeleton to synthesize, screen, and diversify seed programs to construct a competitive initial code pool. An evolutionary operator agent updates the pool through program-space crossover and best-so-far mutation. A code reflection agent analyzes solver code and maintains trajectory-aware reflective memory to generate structured guidance for later revision. Experiments on standard JSSP benchmarks show that the framework outperforms representative metaheuristics across heterogeneous instance families and scales while reaching best-known reference quality on a subset of instances. Ablation results further confirm the contributions of the initialization design and the reflection-guided revision mechanism. More broadly, the proposed framework helps reduce manual heuristic design effort and offers a practical approach to production scheduling optimization in intelligent manufacturing environments. Full article

The Flexible Job-shop Scheduling Problem (FJSP) plays an important role in production and processing in Smart Manufacturing Systems. Unlike the traditional Job-shop Scheduling Problem (JSP), the additional flexibility in machine selection enlarges the search space and increases scheduling difficulty, particularly for large-scale instances. Existing algorithms improve either convergence speed or solution quality, but maintaining both remains difficult as problem size grows. This paper presents a Hybrid Local Search Algorithm (HLS-FJSP), integrating Greedy Search, Genetic Algorithm, and Tabu Search into a two-phase optimization framework. Control parameters and a process monitoring mechanism are used to adjust the search behavior during different optimization stages. Computational experiments on benchmark instances show that the proposed method obtains competitive makespan results compared with several existing algorithms. The results also show stable improvement capability when used for further optimization of existing schedules. Full article

Market competition is increasingly intense and sustainable development has attracted widespread attention. The flexible job shop scheduling problem requires the collaborative optimization of production efficiency and machine energy consumption. This scheduling problem has high solution complexity. It is difficult to balance multiple conflicting objectives and obtain stable scheduling results with traditional optimization methods. A Dual-Layer Proximal Policy Optimization algorithm (DL-PPO) based on a hierarchical decision-making mechanism is proposed to achieve the collaborative optimization of production efficiency and energy consumption in solving the Energy-Aware Flexible Job Shop Scheduling Problem (EA-FJSP). First, a hierarchical scheduling framework based on DL-PPO is designed to solve the EA-FJSP. In this framework, the high-level controller selects sub-objectives from a global optimization perspective, while the low-level controller executes feasible dispatching rules according to the selected sub-objectives. Twelve key state features extracted from four dimensions, time, energy consumption, job, and machine, are used to construct a multi-dimensional state space. These features enable a comprehensive state representation of the scheduling environment and provide accurate input for the DL-PPO. The global optimization objective is decomposed into four sub-objectives employing a goal decoupling policy. Four dedicated reward functions are designed for the sub-objectives to guide the low-level controller to make optimal decisions in terms of time and energy consumption, thereby achieving multi-objective collaborative optimization. Considering the two decisions of job selection and machine assignment in solving the EA-FJSP, twenty dual-decision-point dispatching rules are designed as the action space for the low-level controller to achieve the global optimization objective. Finally, the effectiveness, applicability, and superiority of the DL-PPO in EA-FJSP are demonstrated through comparisons with dispatching rules and other deep reinforcement learning methods. Full article

Job shop scheduling holds significant importance due to its relevance and impact on various industrial and manufacturing systems. Aiming at the job shop scheduling problem with random machine breakdowns, a multi-objective optimization model is established, which considers both the makespan and expected makespan delay simultaneously. Considering that the expected makespan delay cannot be calculated analytically, this paper proposes a symbolic regression-based construction method, which can automatically learn a surrogate evaluation measure. Then, a multi-objective evolutionary algorithm is proposed for solving this model, where the constructed surrogate evaluation measure is used to replace the expected makespan delay for fitness evaluation, to achieve rapid evaluation and efficient optimization. Finally, extensive simulation experiments are conducted on 40 benchmark problems of job shop scheduling, which verify the effectiveness of the proposed method and its advantages in computational efficiency. Full article

Aiming at the inherent limitations of the basic White Shark Optimizer (WSO), such as insufficient population diversity, unbalanced global and local search mechanisms, and weak convergence in the later stage, this paper proposes an Improved White Shark Optimizer (IWSO). The algorithm is improved from the following three aspects: Firstly, the Tent chaotic map is introduced to replace the traditional random initialization in the population initialization stage. Secondly, an adaptive nonlinear convergence factor and a dynamic inertia weight adjustment strategy are designed to focus on the fine search in the neighborhood of the optimal solution. Thirdly, the Levy flight perturbation mechanism and the elite opposition-based learning strategy are integrated to expand the search range and further accelerate the convergence speed. To verify the effectiveness and superiority of the IWSO algorithm, the CEC2017 test suite is selected for simulation experiments, and the IWSO is systematically compared with seven other representative swarm intelligence algorithms. The experimental results show that the IWSO is significantly superior to all comparison algorithms in multiple evaluation indicators, including minimum makespan, average convergence value, standard deviation, and successful convergence rate, on scheduling instances of different scales and difficulties. The convergence curve remains leading throughout the iteration process and shows a smoother convergence trend. The multi-strategy enhanced white shark optimizer proposed in this paper effectively overcomes the inherent defects of the basic algorithm, significantly improves the solution accuracy and convergence efficiency of the job shop scheduling problem, and has high theoretical research value and practical engineering application prospects. In the future, the multi-strategy improved White Shark Optimizer will be extended to multi-objective job shop scheduling, dynamic disturbance job shop scheduling, and large-scale production scheduling scenarios with numerous workpieces and machines. Full article

Against the backdrop of mass customization, research interest in hybrid flow shop scheduling for standard and customized part production has been on the rise. However, most extant studies focus on single-shop scheduling optimization, and the inter-shop coordination mechanism for heterogeneous multi-shop systems remains underexplored. This paper investigates a heterogeneous hybrid flow shop scheduling problem featuring a distributed flow shop for standardized parts and a flexible job shop for customized parts, with the dual objectives of minimizing makespan and total cost. For this problem with the core complexity of heterogeneous cross-shop production reliance and conflicting dual-objective optimization, we propose a multi-objective grey wolf optimizer (MOGWO) combined with problem-specific local search strategies. Computational experiments on a set of test instances are carried out to evaluate the MOGWO’s performance, which is further compared with four classic multi-objective evolutionary algorithms of analogous algorithmic frameworks. Experimental results confirm that the proposed algorithm achieves superior solution quality and convergence efficiency for the multi-objective heterogeneous hybrid flow shop scheduling problem under study. Full article

This paper addresses an integrated production–transportation scheduling problem in assembly workshops, encompassing the processes of part machining, material handling via handling resources, and final synchronous assembly. The finite buffer capacities of production resources can cause blocking, thereby reducing efficiency. Material handling resources are subject to different service area restrictions, and some share safety zones with production resources, preventing simultaneous processing. To address this, a mixed-integer programming model is formulated with makespan and total empty travel time as bi-objective optimization targets. Since the mixed-integer linear programming (MILP) model faces difficulties in solving medium- and large-scale instances, an improved memetic NSGA-II algorithm (IMNSGA-II) is proposed. The algorithm adopts a three-segment chromosome encoding and incorporates a VNS-SA local search mechanism within the global evolutionary framework of NSGA-II. Small-scale computational experiments using Gurobi are first used to verify the correctness of the model. Decoupling experiments further demonstrate the necessity of integrated optimization: compared with phased baseline methods, IMNSGA-II reduces makespan and empty travel time by approximately 10.16% and 12.33%, respectively. In ablation and comparative experiments, results based on hypervolume (HV) and inverted generational distance (IGD) show that the proposed method achieves better convergence, diversity, and overall Pareto front quality than multiple baseline algorithms. These experiments confirm the effectiveness of the proposed model and algorithm. Full article

The flexible job shop scheduling problem (FJSP) is a typical combinatorial optimization problem in smart manufacturing. Although existing methods have considered machine competition relationships, they lack explicit structured modeling of machine competition relationships induced by candidate operations and are not systematically integrated across state representation, representation learning, and decision-making processes. To address this, this paper proposes a competition-aware dual-attention deep reinforcement learning method. We construct a dynamic heterogeneous graph representation, where machine competition is modeled as state-dependent edges instantiated via a 3D competition tensor, transforming machine competition relationships into structured information, thereby enhancing the model’s ability to characterize complex resource competition patterns. On this basis, we have designed the Competition-Aware Dual-Attention Network (CADAN), which injects competition information into both the attention computation and representation learning processes via a dual-path mechanism, enabling more expressive modeling of machine competition relationships, and which introduces a head-wise competition bias to capture heterogeneous competition patterns. Furthermore, we have developed an adaptive decision head to refine the scores of candidate actions. Our experimental results demonstrate that the proposed method outperforms classical dispatching rules and achieves competitive or superior performance compared with representative evolutionary and learning-based methods on synthetic datasets, public benchmark datasets, and a real-world industrial machining scenario involving mechanical transmission components. Full article

To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model that minimizes both makespan and total energy consumption. An Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is proposed to solve this model. In the population initialization phase, chaotic mapping is integrated with multiple heuristic rules to generate a high-quality and uniformly distributed initial population. Furthermore, an enhanced elite selection mechanism is employed to effectively prevent premature convergence. Subsequently, adaptive crossover and mutation operators are designed to enable differentiated evolution across sub-populations, effectively coordinating global exploration and local exploitation. Finally, experimental results on the Brandimarte and Hurink benchmark datasets demonstrate the superiority of the proposed algorithm in terms of convergence and diversity, providing a robust solution for optimizing green industrial production scheduling. Full article

This paper addresses the flexible job-shop scheduling problem (FJSP) as a constrained combinatorial optimization task with a large discrete action space. Although action-masked reinforcement learning has shown promise for such problems, the effect of structured vector-state encoding in scheduling has received less attention. The main contribution of this work is a structured block-wise state representation and a multi-branch feature extraction module for action-masked Proximal Policy Optimization (PPO). The proposed representation decomposes the scheduling state into three heterogeneous components capturing resource availability, operation readiness, and temporal attributes of operation–machine alternatives. Instead of flattening these signals into a single vector, the proposed encoder processes each block separately before aggregation, with the aim of preserving semantic structure during policy learning. To isolate the effect of representation design, we compare the proposed multi-branch encoder with a baseline single-branch multilayer perceptron under identical PPO hyperparameters and training conditions. Experiments on the Brandimarte MK benchmark suite show that the proposed architecture yields a lower best-achieved makespan on nine of ten instances and improves the best baseline result by up to 27.84%. Additional validation on selected Behnke and Geiger instances indicates that the BR encoder’s advantage extends to larger FJSP cases while preserving sub-second inference. Full article

Robot swarm manufacturing is a promising direction in smart manufacturing that aggregates multiple robots to collaboratively complete production jobs; however, achieving conflict-free scheduling remains a significant challenge. Traditional methods struggle to address this issue since robot swarms are inherently prone to conflicts. This article puts forward a non-sorted metaheuristic method to solve it. First, the conflict-free robot swarm manufacturing problem—integrating a multi-objective optimization problem (MOP), a flexible job-shop scheduling problem (FJSP) for job processing, and a flow-shop scheduling problem (FSP) for robot travel—is formulated as a multi-objective job-flow-shop scheduling problem (MJFSP). The robot swarm must accomplish all manufacturing jobs while achieving high manufacturing performance, energy efficiency, and conflict-free operations. Second, a non-sorted metaheuristic algorithm based on an artificial plant community (APC) is proposed. It employs a sequential-pairwise single-elimination tournament system (SSTS) to select elites with a time complexity of $O\left(n\right)$, which scales linearly with the population size $\left(n\right)$. This surpasses the sorting-based elite selection with polynomial time complexity employed in most metaheuristic methods, such as the $O(n^2)$ of the non-dominated sorting genetic algorithm-III (NSGA-III). Third, an MJFSP benchmark dataset is built, and the experimental results uncover the complex dependencies between the FJSP for job processing and the FSP for robot traveling. The proposed method improves the makespan by up to 13.10% and reduces non-loaded energy consumption by up to 13.49%, achieving zero collision time and an average solution time 11.18% faster than NSGA-III. Full article

Sustainable manufacturing requires schedules that balance environmental responsibility with delivery reliability. This paper studies the Carbon-Aware Flexible Job Shop Scheduling Problem with Tardiness Penalty (CAFJSP-T), where total carbon emissions and total tardiness penalty are the primary objectives. We propose a Policy-based Rough Optimization with a Large Neighborhood Search (Pro-LNS) framework integrating Proximal Policy Optimization (PPO) and adaptive Large Neighborhood Search (LNS). PPO constructs a feasible schedule by selecting operation-machine assignments from job-readiness, machine-availability, earliest-completion, and critical-path features. This policy-generated schedule provides a structurally informed incumbent, enabling LNS to avoid unguided search and focus destroy-and-repair refinement on high-impact operations. Both phases use the same normalized scalarized carbon-tardiness objective, which guides PPO rewards and LNS removal, reinsertion, and acceptance while preserving precedence, eligibility, and capacity constraints. Experiments on small, medium, and large workcenter benchmarks show strong due-date performance and controlled carbon emissions. Under equal objective weighting, Pro-LNS achieves a median optimality gap of 6.12% relative to the exact formulation, with all instances within 14%, while requiring 4.08 s on average and at most 10.51 s. Comparisons with PPO-only, Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Genetic Algorithm (GA) schedulers show that Pro-LNS attains the best weighted scalarized objective across representative instance-weight settings. Friedman and Holm-corrected Wilcoxon tests confirm significant improvements over all competitors, with average weighted-objective gains of 4.90%, 7.25%, 8.81%, and 9.51% over PPO-only, A2C, SAC, and GA, respectively. These results demonstrate that Pro-LNS is an effective and computationally practical hybrid approach for carbon-aware, tardiness-sensitive flexible job shop scheduling. Full article

In recent years, with the advancement of production technology in the manufacturing industry, the scheduling problems that rely on modeling in real-world scenarios have gradually evolved into complex process flows. Aiming at the limited problem modeling capabilities of existing scheduling problems, this study proposes Multi-Stage Flexible Job Shop Scheduling Problem (MS-FJSP). MS-FJSP alters the fixed operation processing sequence of jobs in conventional scheduling problems and introduces staged processing to incorporate flexible constraints on operation selection. Furthermore, MS-FJSP modifies the constraint of unique machine compatibility, enabling arbitrary adjustments to machine combinations according to processing requirements. To address the complex flexibility and large-scale solution space of MS-FJSP, we propose a particle swarm optimization algorithm based on double neighborhood tabu search (TS-PSO). Specifically, the PSO algorithm determines a superior neighborhood structure for this problem, while the TS algorithm improves and optimizes the solution quality within the neighborhood of this solution structure. We verify the algorithm’s performance using a dataset consisting of 12,000 MS-FJSP instances and an MS-FJSP instance modeled from a real-world scheduling scenario. Experimental results demonstrate that TS-PSO can achieve excellent solution quality within a reasonable time, and MS-FJSP possesses efficient modeling capability for real-world scheduling scenarios. Full article

In this study, a special version of the Flexible Job Shop Scheduling Problem with equally and consistently batching constraints (hereafter called ECBFJSP) is considered, which involves multiple aspects of coordination, such as machine selection, process sorting, and batch splitting, which is highly complex and places strict demands on the optimization strategy. To effectively meet this challenge, this study constructs a dual-action deep reinforcement learning algorithm framework based on the Enhanced Heterogeneous Graph Neural Network (EHGNN). First, an enhanced heterogeneous graph and EHGNN model for the ECBFJSP is innovatively proposed. By integrating multi-dimensional node features such as work order priority, machine tool processing capability, and process constraints, dynamic feature aggregation of various types of information is achieved with the help of GATs and GRUs. The model can output context-aware representations containing global resource constraints, greatly improving the joint optimization efficiency of job scheduling and batch partitioning and significantly enhancing the adaptability of the dual-action decision framework to the complexity of the ECBFJSP. At the decision-making mechanism level, this study designed a dual-action decision space of process sequencing–machine selection action and batch partitioning action and used the DAPPO algorithm to collaboratively optimize the dual-action strategy to ensure the stability and efficiency of the decision-making process. The experimental data results show that compared with traditional algorithms, the proposed intelligent decision framework performs better in scheduling quality when solving the ECBFJSP, which fully verifies the significant effectiveness and practicality of the framework in solving the ECBFJSP. Full article