Search Results (464)

This paper investigates the energy-efficient collaborative scheduling of dual-trolley quay cranes (DTQCs) and automated guided vehicles (AGVs) in automated container terminals (ACTs). Considering operational constraints such as mixed bidirectional flows, limited buffers, precedence constraints, and deadlocks, this complex logistical system is formally characterized as a blocking hybrid flow shop scheduling problem (BHFSSP-BFLB). To systematically minimize the total energy consumption, a mathematical framework grounded in a mixed-integer programming model is developed. To solve the model efficiently, an improved genetic algorithm (IGA) is proposed featuring a two-layer encoding approach to respect precedence and mitigate deadlocks. Furthermore, an active scheduling strategy based on machine idle time insertion is incorporated during decoding to shorten the makespan without increasing energy consumption. Numerical experiments demonstrate that the IGA can significantly decrease the makespan while reducing total energy consumption: compared with a standard genetic algorithm (GA) without active scheduling, the proposed IGA reduces the makespan by 32.35% on average. In addition, the makespan under energy minimization is within 1.5% of that under makespan minimization, indicating that energy optimization yields an almost minimal makespan. Sensitivity analysis further evaluates the effects of DTQC-AGV configurations and buffer capacities, offering practical insights for decision-makers. Full article

In this work, we study the multiresource flexible job-shop scheduling problem (MRFJSSP), which relaxes the standard “simultaneous occupation” policy described in the literature. This policy implies that a job operation starts only when all its assigned necessary resources are available and releases them simultaneously. In contrast, our approach assumes that a job operation begins simultaneously across all assigned resources, although these resources may not be occupied for the same duration. This variant (which we will call “early resource release”) was first formally proposed in the scheduling literature more than twenty years ago, but to the best of our knowledge, it has not been empirically tested. Thus, to tackle this problem, we formulate a constraint programming (CP) model adopting a multi-mode resource-constrained project scheduling problem (MMRCPSP) representation. We tested our approach on 65 instances of the MRFJSSP where the precedence relationships between operations of a given job follow a linear order. We prove optimality in 44 instances with an average optimality gap of 9.37%. Additionally, we contributed eight new lower bounds for the same set of instances in the literature when considering the simultaneous occupation policy. Full article

This paper addresses the limitations of genetic algorithms in solving the Flexible Job-Shop Scheduling Problem (FJSP) including slow convergence, susceptibility to local optima, and sensitivity to parameter settings. The paper proposes an Improved Genetic Algorithm based on Reinforcement Learning (IGARL). First, a hybrid population selection mechanism that combines the Queen Bee Mating Flight (QBMF) strategy with the Tournament Selection (TS) method is introduced. This mechanism significantly accelerates convergence by optimizing the population structure. Second, a dynamic population update strategy based on tunnel vision, termed the Solution Space Diversity Awakening (SSDA) strategy, is developed. When the population becomes trapped in local optima, this strategy intelligently triggers random perturbations and introduces high-potential individuals to enhance the algorithm’s ability to escape local optima and promote population diversity. Third, a novel multi-Q-table reinforcement learning framework is embedded within the iterative process to dynamically adjust key genetic algorithm parameters (such as selection, mutation, and crossover rates) and enable multi-dimensional performance evaluation, thereby effectively guiding the search toward better solutions. Experimental results demonstrate that the IGARL algorithm achieves a 10% to 60% improvement in convergence speed on Brandimarte benchmark instances, with solution quality significantly surpassing that of the basic genetic algorithm. Moreover, the fluctuation of the average optimal solution remains within 20%, indicating strong stability and robustness. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article

The opto-mechanical automated manufacturing process, characterized by stringent process constraints, dynamic disturbances, and conflicting optimization objectives, presents significant control challenges for traditional scheduling and control approaches. We formulate the scheduling problem within a closed-loop control paradigm and propose a novel bi-level intelligent control framework integrating Deep Reinforcement Learning (DRL) and Bayesian Optimization (BO). The core of our approach is a bi-level intelligent control framework. An inner DRL agent acts as an adaptive controller, generating control actions (scheduling decisions) by perceiving the system state and learning a near-optimal policy through a carefully designed reward function, while an outer BO loop automatically tunes the DRL’s hyperparameters and reward weights for superior performance. This synergistic BO-DRL mechanism facilitates intelligent and adaptive decision-making. The proposed method is extensively evaluated against standard meta-heuristics, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on a complex 20-jobs × 20-machines flexible job shop scheduling benchmark specific to opto-mechanical automated manufacturing. The experimental results demonstrate that our BO-DRL algorithm significantly outperforms these benchmarks, achieving reductions in makespan of 13.37% and 25.51% compared to GA and PSO, respectively, alongside higher machine utilization and better on-time delivery. Furthermore, the algorithm exhibits enhanced convergence speed, superior robustness under dynamic disruptions (e.g., machine failures, urgent orders), and excellent scalability to larger problem instances. This study confirms that integrating DRL’s perceptual decision-making capability with BO’s efficient parameter optimization yields a powerful and effective solution for intelligent scheduling in high-precision manufacturing environments. Full article

As one of the major sources of global carbon emissions, the manufacturing industry urgently requires green transformation. The utilization of renewable energy in production workshop offers a promising route toward zero-carbon manufacturing. However, renewable energy fluctuations and dynamic workshop events make efficient scheduling increasingly challenging. This paper introduces a low-carbon and energy-efficient dynamic flexible job shop scheduling problem oriented towards renewable energy integration, and develops a multi-agent deep reinforcement learning framework for dynamic and intelligent production scheduling. Inspired by the Proximal Policy Optimization (PPO) algorithm, a routing agent and a sequencing agent are designed for machine assignment and job sequencing, respectively. Customized state representations and reward functions are also designed to enhance learning performance and scheduling efficiency. Simulation results demonstrate that the proposed method achieves superior performance in multi-objective optimization, effectively balancing production efficiency, energy consumption, and carbon emission reduction across various job shop scheduling scenarios. Full article

Most multi-objective studies on distributed hybrid flow shops that include tardiness-related objectives focus solely on optimizing makespan alongside a single tardiness objective. However, in real-world scenarios with strict contractual deadlines or high penalty costs for delays, minimizing both total tardiness and the number of tardy jobs becomes critically important. This paper addresses this gap by prioritizing tardiness-related objectives while simultaneously optimizing makespan, total tardiness, and the number of tardy jobs. It investigates a distributed hybrid flow shop scheduling problem (DHFSP), which has some symmetries on machines. We propose an improved multi-objective memetic algorithm incorporating Q-learning (IMOMA-QL) to solve this problem, featuring (1) a hybrid initialization method that generates high-quality, diverse solutions by balancing all three objectives; (2) a multi-factory SB2OX crossover operator preserving high-performance job sequences across factories; (3) six problem-specific neighborhood structures for efficient solution space exploration; and (4) a Q-learning-guided variable neighborhood search that adaptively selects neighborhood structures. Based on extensive numerical experiments across 100 generated instances and a comprehensive comparison with four comparative algorithms, the proposed IMOMA demonstrates its effectiveness and proves to be a competitive method for solving the DHFSP. Full article

High variability in multi-product manufacturing environments and rapidly changing customer demands make decentralized coordination of work-in-process (WIP) and production resources increasingly important. However, the intrinsic rigidity of conventional centralized and monolithic manufacturing execution systems (MESs) renders them unsuitable for such highly dynamic environments. To address this limitation, this study proposes an agent-based distributed, decentralized MES architecture. The manufacturing execution process is realized through collaboration among constituent agents based on an employment network (EmNet). Specifically, three types of agents are introduced: WIPAgents (representing WIPs), PAgents (representing processing resources), and MHAgents (representing material-handling resources). Collaboration among agents (e.g., collaborator discovery, partner selection, and data sharing/exchange) is facilitated by a data-space-based collaboration platform which was introduced in our prior work. To validate the proposed architecture, we built a digital-twin-based simulation testbed and conducted simulation experiments. The experimental results confirm the validity and operational feasibility of the proposed architecture. Full article

Conventional large fleets of Automated Guided Vehicles (AGVs) suffer from issues related to the network environment, including handoff latency and interference. Recently, 5G technology has emerged as a practical tool to resolve these network issues. Consequently, there is a growing trend toward deploying large AGV fleets based on 5G technology. Typically, AGVs are controlled by an AGV control system (ACS), which is responsible for tasks such as path planning and AGV scheduling. AGV scheduling is the process of assigning the right task to the right vehicle at the right time. This process has a significant impact on the performance of an AGV fleet, particularly for large-scale fleets. However, existing AGV scheduling approaches hardly consider traffic congestion, which often occurs in large fleets. To fill this gap, this study proposes a simulation-based congestion-aware AGV scheduling approach for large AGV fleets. The proposed approach is characterized by three components: congestion functions, congestion penalties, and congestion-aware scheduling rules. Congestion functions are employed to compute the degree of congestion at a specific point or area within the shop floor. Congestion penalties represent the loss incurred when a vehicle traverses a specific segment within the AGV path network. Congestion-aware scheduling rules provide the decision-making logic for task and vehicle dispatching. We outline the components and apply them to a discrete event simulation (DES) model containing an AGV fleet. The experimental results demonstrate that the proposed approach reduces the inefficiencies of the AGV system caused by traffic congestion. Full article

The dynamic flexible job shop scheduling problem (DFJSP) with machine faults, considering the recovery condition and variable processing time, is studied to determine the rescheduling scheme when machine faults occur in real time. The Monte Carlo Tree Search (MCTS) algorithm with reinforcement learning and the relational-enhanced graph attention network (MGRL) is presented to address the DFJSP with machine faults, considering the recovery condition and variable processing time. The MCTS with the skip-node restart strategy, which utilizes local optimal solutions found during the Monte Carlo sampling process, is designed to enhance the optimization efficiency of MCTS in real time. A relational graph attention network (RGAT), a relational-enhanced and transformer-integrated graph network in the MGRL, is designed to analyze the scheduling disjunctive graph, guide the Monte Carlo sampling method to improve sampling efficiency, and enhance the quality of MCTS optimization decisions. Experimental results demonstrate the effectiveness of the RGAT and the skip-node restart strategy. Further application analysis results show that the MGRL is optimal among all comparison methods when algorithms solve the DFJSP. Full article

Population initialization significantly influences metaheuristic algorithm performance, yet random initialization dominates despite problem-specific needs. This study investigates initialization strategies for the Job Shop Scheduling Problem (JSSP), an NP-hard combinatorial optimization challenge in manufacturing systems, addressing the gap in understanding how different initialization approaches affect solution quality and reliability in constrained discrete problems. The research employs a two-phase experimental design using Particle Swarm Optimization (PSO) on Taillard benchmark instances. Phase 1 evaluates seventeen initialization methods across four categories: random-based, problem-specific heuristics, hybrid methods, and adaptive strategies. Each method is tested through 30 independent runs on six problem instances. Phase 2 develops machine learning-enhanced initialization using variational autoencoders (VAEs) trained on high-quality solutions from successful traditional methods, comparing three VAE variants against conventional approaches. Results show all random-based methods failed completely, while only First-In-First-Out and Most Work Remaining heuristics succeeded consistently among traditional approaches. VAE-based methods achieved 100% solution validity (540/540) versus 97% for traditional methods (349/360), with statistical significance (χ² = 14.27, p < 0.001). The Friedman test confirmed performance differences (χ² = 19.87, p < 0.001, Kendall’s W = 0.828), with VAE methods achieving lower mean ranks and makespan reductions. Despite starting with lower initial diversity, VAE methods exhibited larger diversity increases during optimization, suggesting structured initialization enables more effective exploration than random dispersion. Full article

Digital Twins (DTs) integrated with Artificial Intelligence (AI) are emerging as transformative tools in smart manufacturing. By bridging the physical and virtual domains, DTs enable real-time monitoring, predictive analytics, and autonomous decision-making. Originally conceived as advanced simulation models, DTs have evolved significantly with the incorporation of AI, which enhances their ability to acquire process knowledge, optimize scheduling, and autonomously control system variables. This evolution transforms DTs from passive representations into prescriptive, self-optimizing systems. AI-driven DTs support a wide range of applications, including predictive maintenance, process optimization, quality control, and dynamic scheduling, using techniques such as deep reinforcement learning and convolutional neural networks. These capabilities have been successfully deployed across industrial domains such as CNC machining, robotics, and industrial printing, yielding substantial improvements in efficiency, reliability, and responsiveness. Despite these advancements, the full realization of intelligent DTs relies heavily on the availability of high-fidelity, real-time data and a seamless alignment between physical systems and their digital counterparts. This literature survey provides a state-of-the-art review of AI-driven DTs in manufacturing, highlighting their key applications, challenges, and emerging research directions that will shape the future of intelligent and adaptive manufacturing systems. To present a structured perspective on the evolution and scalability of AI-driven DTs, the application case studies are organized according to four integration levels—machine, cell, shop floor, and enterprise—highlighting how these technologies scale from individual assets to fully interconnected manufacturing ecosystems. Full article

Job-shop scheduling plays a pivotal role in sustainable manufacturing because scheduling decisions strongly influence energy consumption, machine utilization, and environmental performance. Traditional job-shop scheduling research has mainly optimized makespan, throughput, and tardiness; however, growing sustainability pressures and Industry 4.0 technologies have shifted attention toward energy-conscious scheduling. This review systematically analyzes 2083 publications retrieved from SCOPUS, Web of Science, and IEEE Xplore to map the evolution of energy-efficient job-shop scheduling (EEJSS) models, methods, and industrial applications. Compared with prior surveys, this work contributes a sector-specific analysis, an updated classification of energy-aware models, and the first structured mapping of EEJSS research to sustainability and Industry 4.0 capabilities. Further, challenges such as computational complexity, absence of standardized energy benchmarks, limited industrial deployment, and narrow sustainability metrics are addressed. Overall, this review consolidates the state of EEJSS and positions energy-aware scheduling as a foundational enabler of low-carbon, resilient, and intelligent manufacturing systems. Full article

This study proposes an optimization framework based on Multi-agent Deep Reinforcement Learning (MADRL), conducting a systematic exploration of FJSP under dynamic scenarios. The research analyzes the impact of two types of dynamic disturbance events—machine failures and order insertions—on the Dynamic Flexible Job Shop Scheduling Problem (DFJSP). Furthermore, it integrates process selection agents and machine selection agents to devise solutions for handling dynamic events. Experimental results demonstrate that, when solving standard benchmark problems, the proposed multi-objective DFJSP scheduling method, based on the 3DQN algorithm and incorporating an event-triggered rescheduling strategy, effectively mitigates disruptions caused by dynamic events. Full article

This study addresses a critical gap in Energy-Efficient Permutation Flow Shop Scheduling (EPFSP) by integrating the often-overlooked time-accumulative equipment degradation inherent in practical manufacturing. This research formalizes and solves the EPFSP with machine deterioration (EPFSP-DEM), aiming to simultaneously minimize the makespan and total energy consumption. To achieve this objective, this study proposes a Diversity-Constrained Imperialist Competitive Algorithm (DCICA) featuring several novel mechanisms. In DCICA, a differentiated assimilation is developed to improve diversity of the population; a knowledge-guided revolution is designed to allocate computing resources efficiently; the convergence and diversity metrics are defined to evaluate the search quality on assimilation and revolution; a novel imperialist competition is also given to enhance information exchanges among empires and strengthen the search for some worse solutions. Finally, extensive experiments are conducted, and the results demonstrate that DCICA outperforms the existing algorithms in solving the investigated problem. Full article