Search Results (286)

Carbon cap-and-trade is a practical policy in guiding manufacturers to produce economic and environmental production plans. However, previous studies on carbon cap-and-trade are from a macro level to guide manufacturers to make production plans, rather than from a perspective of specific production scheduling, which leads to a lack of theoretical guidance for manufacturers to develop reasonable production scheduling schemes for specific production orders. This article investigates a specific scheduling problem in a flexible job-shop environment that considers the carbon cap-and-trade policy, aiming to provide guidance for specific production scheduling (i.e., resource allocation). In the proposed problem, carbon emissions have an upper limit. A penalty will be generated if the emissions overpass the predetermined cap. To satisfy the carbon emission cap, the manufacturer can trade carbon credits or adopt outsourcing strategy, that is, outsourcing partial orders to partners at the expense of outsourcing costs. To solve the proposed model, a novel and efficient memetic algorithm (NEMA) is proposed. An initialization method and four local search operators are developed to enhance the search ability. Numerous experiments are conducted and the results validate that NEMA is a superior algorithm in both solution quality and efficiency. Full article

A green scheduling problem is proposed in this work, where both constraints on intermediate storage capacity and job transportation requirements are simultaneously considered. An improved discrete pathfinder algorithm (IDPFA) with multi-neighborhood local search is proposed to minimize the maximum completion time and total energy consumption. The algorithm addresses the green flow shop scheduling problem with limited buffers and automated guided vehicle (GFSSP_LBAGV). Firstly, based on the machine speed constraints, the transportation time for moving jobs by the automated guided vehicle (AGV) is incorporated to establish a mathematical model. Secondly, the core idea of the pathfinder algorithm (PFA) is applied to the evolutionary process of the discrete PFA, where three different crossover operations are used to replace the exploration process of the pathfinder, the influence of the pathfinder on the followers, and the mutual learning among the followers. Then, a multi-neighborhood local search is employed to conduct a detailed exploration of high-quality solution spaces. Finally, extensive standard test sets are used to verify the effectiveness of the proposed IDPFA in solving GFSSP_LBAGV. Full article

The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solving large instances. We propose a deep reinforcement learning (DRL) approach to minimize makespan in FJSP-JPC. The proposed method employs a heterogeneous disjunctive graph to represent the system state and a multi-head graph attention network for feature extraction. An actor–critic framework, trained using proximal policy optimization (PPO), is adopted to make operation sequencing and machine assignment decisions. The effectiveness of the proposed method is validated through comparisons with several classic dispatching rules and a state-of-the-art DRL approach. Additionally, the contributions of key mechanisms, such as information diffusion, node features, and action space, are analyzed through a full factorial design of experiments. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

With the advancement of logistics technology, Automated Guided Vehicles (AGVs) have been widely adopted in manufacturing enterprises due to their high flexibility and stability, particularly in flexible and discrete manufacturing domains such as tire production and electronic assembly. However, existing studies seldom systematically consider practical constraints such as limited AGV transport resources, AGV charging requirements, and charging station capacity limitations. To address this gap, this paper proposes a flexible job shop production-logistics collaborative scheduling model that incorporates transport and charging constraints, aiming to minimize the maximum makespan. To solve this problem, an improved PPO algorithm—CRGPPO-TKL—has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. Experimental results demonstrate that the proposed method outperforms composite dispatching rules and mainstream DRL methods across multiple scheduling scenarios, achieving an average improvement of 8.2% and 10.5% in makespan, respectively. Finally, sensitivity analysis verifies the robustness of the proposed method with respect to parameter combinations. Full article

Dynamic scheduling in hybrid flexible job shops (HFJSs) presents a critical challenge in modern manufacturing systems, particularly under dynamic and uncertain conditions. These systems often exhibit inherent structural and behavioral symmetry, such as uniform machine–job relationships and repeatable event response patterns. To leverage this, we propose a time Petri nets (TPNs) model that integrates time and logic constraints, capturing symmetric processing and setup behaviors across machines as well as dynamic job and machine events. A transition select coding mechanism is introduced, where each transition node is assigned a normalized priority value in the range [0, 1], preserving scheduling consistency and symmetry during decision-making. Furthermore, we develop a symmetry-aware time Petri nets-based improved genetic algorithm (TPGA) to solve both static and dynamic scheduling problems in HFJSs. Experimental evaluations show that TPGA significantly outperforms classical dispatching rules such as Shortest Job First (SJF) and Highest Response Ratio Next (HRN), achieving makespan reductions of 23%, 10%, and 13% in process, discrete, and hybrid manufacturing scenarios, respectively. These results highlight the potential of exploiting symmetry in system modeling and optimization for enhanced scheduling performance. Full article

This study investigates the integration of finite capacity scheduling with POLCA-based workload control in high-mix, low-volume production environments. We propose a proactive scheduling approach that embeds POLCA constraints into a constraint programming (CP) model, aiming to reconcile the trade-offs between utilization efficiency and system responsiveness. The proposed methodology is evaluated in two phases. First, a simplified job shop simulation compares a traditional reactive POLCA implementation with the CP-based proactive approach under varying system configurations, demonstrating significant reductions in lead times, tardiness, and deadlock occurrences. Second, an industrial case study in an aerospace manufacturing firm validates the practical applicability of the approach by retrospectively comparing the CP model against an existing commercial scheduler. The results underscore that the integrated framework not only enhances scheduling performance through improved workload control but also provides a more stable operational environment. Full article

The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots. Full article

To address the multi-objective flexible job shop scheduling problem in rolling production mode (FJSP-RPM), this study proposes a Multi Objective Improved of Salp Swarm Algorithm (MISSA) that simultaneously optimizes equipment utilization and total tardiness. The MISSA generates initial population through various heuristic strategies to improve the initial population quality. The exploitation capability of the algorithm is enhanced through the global crossover strategy and variety of local search strategies. In terms of improvement strategies, the MISSA (using all three strategies) outperforms other incomplete variant algorithms (using only two strategies) in three metrics: Generational Distance (GD), Inverted Generational Distance (IGD), and diversity metric, achieving superior results in 9 test cases, 8 test cases, and 4 test cases respectively. When compared with NSGA2, NSGA3, and SPEA2 algorithms, the MISSA demonstrates advantages in 8 test cases for GD, 8 test cases for IGD, and 7 test cases for the diversity metric. Additionally, the distribution of the obtained solution sets is significantly better than that of the comparative algorithms, which validats the effectiveness of the MISSA in solving FJSP-RPM. Full article

This study addresses the assembly job shop scheduling problem (AJSSP) with parallel machines. In an assembly job shop, product structures are represented through hierarchical tree diagrams, where components and subassemblies are sequentially assembled to form the final product. A mixed-integer linear programming (MILP) model is formulated to minimize the total completion time. A dominance relations-based variable neighborhood search (DR-VNS) is proposed for solving AJSSP with parallel machines. The proposed approach integrates dominance relations among operations in the initialization phase and employs tailored neighborhood structures to address sequencing and assignment challenges, thereby enhancing the generation of neighboring solutions. Experimental studies conducted on test cases of varying scales and complexities demonstrate the effectiveness of the proposed algorithms in solving the AJSSP with parallel machines. Full article

In view of the Flexible Job-shop Scheduling Problem (FJSP) under multi-product and variable-batch production modes, this paper presents an intelligent scheduling approach based on a heterogeneity-enhanced graph neural network combined with deep reinforcement learning. By constructing a heterogeneity-enhanced incidence graph to dynamically represent the scheduling state, the proposed method effectively captures both the dependencies among operations and the interaction features between operations and machines. Moreover, the Proximal Policy Optimization (PPO) algorithm is leveraged to achieve end-to-end optimization of scheduling decisions. Specifically, the FJSP is formulated as a Markov Decision Process. A heterogeneous enhanced graph neural network architecture is designed to extract deep features from operation nodes, machine nodes, and their heterogeneous relationships. Then, a policy network generates joint actions for operation assignment and machine selection, while the PPO algorithm iteratively refines the scheduling policy. Finally, the method is validated in an aerospace component machining workshop scenario and the benchmark dataset. Experimental results demonstrate that, compared with traditional dispatching rules and existing deep reinforcement learning techniques, the proposed approach not only achieves superior scheduling performance but also maintains an excellent balance between response efficiency and scheduling quality. Full article

The job shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization challenge that plays a crucial role in manufacturing systems. Deep reinforcement learning has shown great potential in solving this problem. However, it still has challenges in reward function design and state feature representation, which makes it suffer from slow policy convergence and low learning efficiency in complex production environments. Therefore, a human feedback-based large language model-assisted deep reinforcement learning (HFLLMDRL) framework is proposed to solve this problem, in which few-shot prompt engineering by human feedback is utilized to assist in designing instructive reward functions and guiding policy convergence. Additionally, a self-adaptation symbolic visualization Kolmogorov–Arnold Network (KAN) is integrated as the policy network in DRL to enhance state feature representation, thereby improving learning efficiency. Experimental results demonstrate that the proposed framework significantly boosts both learning performance and policy convergence, presenting a novel approach to the JSSP. Full article

With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. In addition, a two-stage approach based on a forensic-based investigation (TSFBI) is proposed to solve the problem. In the first stage, an improved multi-objective FBI algorithm is used to obtain the Pareto front solutions of this model, in which a hybrid real and integer encoding–decoding method is used for exploring the solution space and a fast non-dominated sorting method for improving efficiency. In the second stage, a multi-criteria decision analysis method based on an analytic hierarchy process (AHP) is used to select the optimal solution from the Pareto front solutions. Finally, experiments validated the TSFBI algorithm, showing its potential for smart manufacturing. Full article

Dynamic scheduling for flexible job shops under machine breakdown is a complex and challenging problem due to its valuable application in real-life productions. However, prior studies have struggled to perform well in changeable scenarios. To address this challenge, this paper introduces a dual-objective deep reinforcement learning (DRL) to solve this problem. This algorithm is based on the Double Deep Q-network (DDQN) and incorporates the attention mechanism. It decouples action relationships in the action space to reduce problem dimensionality and introduces an adaptive weighting method in agent decision-making to obtain high-quality Pareto front solutions. The algorithm is evaluated on a set of benchmark instances and compared with state-of-the-art algorithms. The experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms regarding machine offset and total tardiness, demonstrating more excellent stability and higher-quality solutions. At the same time, the actual use of the algorithm is verified using cases from real enterprises, and the results are still better than those of the multi-objective meta-heuristic algorithm. Full article

This article aims to review the industrial applications of AI-based intelligent system algorithms in the manufacturing sector to find the latest methods used for sustainability and optimisation. In contrast to previous review articles that broadly summarised existing methods, this paper specifically emphasises the most recent techniques, providing a systematic and structured evaluation of their practical applications within the sector. The primary objective of this study is to review the applications of intelligent system algorithms, including metaheuristics, evolutionary algorithms, and learning-based methods within the manufacturing sector, particularly through the lens of optimisation of workflow in the production lines, specifically Job Shop Scheduling Problems (JSSPs). It critically evaluates various algorithms for solving JSSPs, with a particular focus on Flexible Job Shop Scheduling Problems (FJSPs), a more advanced form of JSSPs. The manufacturing process consists of several intricate operations that must be meticulously planned and scheduled to be executed effectively. In this regard, Production scheduling aims to find the best possible schedule to maximise one or more performance parameters. An integral part of production scheduling is JSSP in both traditional and smart manufacturing; however, this research focuses on this concept in general, which pertains to industrial system scheduling and concerns the aim of maximising operational efficiency by reducing production time and costs. A common feature among research studies on optimisation is the lack of consistent and more effective solution algorithms that minimise time and energy consumption, thus accelerating optimisation with minimal resources. Full article