Search Results (128)

The rapid proliferation of distributed energy resources (DERs), including photovoltaics, wind power, battery energy storage, and electric vehicles, has transformed traditional power systems into highly decentralized and data-rich environments. Virtual power plants (VPPs) have emerged as a key mechanism for aggregating these heterogeneous assets and enabling coordinated control, market participation, and grid-support functions. Recent advances in artificial intelligence (AI) have further elevated the scalability, autonomy, and responsiveness of VPP operations. This paper presents a comprehensive review of AI for VPPs, organized around a taxonomy of machine learning, deep learning, reinforcement learning, and hybrid approaches, and examines how these methods map to core VPP functions such as forecasting, scheduling, market bidding, aggregation, and ancillary services. In parallel, we analyze enabling architectural frameworks—including centralized cloud, distributed edge, hybrid cloud–edge collaboration, and emerging 5G/LEO satellite communication infrastructures—that support real-time data exchange and scalable deployment of intelligent control. By integrating methodological, functional, and architectural perspectives, this review highlights the evolution of VPPs from rule-based coordination to intelligent, autonomous energy ecosystems. Key research challenges are identified in data quality, model interpretability, multi-agent scalability, cyber-physical resilience, and the integration of AI with digital twins and edge-native computation. These findings outline promising directions for next-generation intelligent VPPs capable of delivering secure, flexible, and self-optimizing DER aggregation at scale. Full article

This paper considers the two parallel-machine scheduling problem with intree-precedence constraints where machines are subject to non-availability constraints. In the literature, this problem is considered to be an open problem of unknown complexity. The proposed solution proves that the problem under consideration has polynomial complexity. Periods of machine unavailability are predetermined, and both task execution and inter-task communication are modeled as requiring one unit of time. The optimization criterion central to this study is the minimization of the makespan. Such a scheduling challenge is directly applicable to manufacturing environments, where production equipment can be intermittently offline for reasons such as unscheduled repairs or planned preventative maintenance. Adopting a unit-time task model offers a valuable framework for subsequently scheduling larger, preemptable jobs.This work presents a new method, called Scheduling Intrees with Unavailability Constraints (SIwUC), which operates by aggregating tasks into distinct groups. The analysis establishes that the SIwUC algorithm produces optimal schedules and reveals how the underlying problem architecture and its solutions demonstrate a symmetrical property in the distribution of tasks across the two parallel machines. This paper demonstrates that the proposed SIwUC algorithm builds optimal schedules and highlight how the problem structure and its solutions exhibit a form of symmetry in balancing task allocation between the two parallel machines. Full article

In the context of intelligent manufacturing, the integrated scheduling problem of dual rail-guided vehicles (RGVs) and multiple parallel processing equipment in flexible manufacturing systems has gained increasing importance. This problem exhibits spatiotemporal coupling and dynamic constraint characteristics, making traditional optimization methods ineffective at finding optimal solutions. At the problem formulation level, the dual resource scheduling task is modeled as a mixed-integer optimization problem. An intelligent scheduling framework based on action mask-constrained Proximal Policy Optimization (PPO) deep reinforcement learning is proposed to achieve integrated decision-making for production equipment allocation and RGV path planning. The approach models the scheduling problem as a Markov Decision Process, designing a high-dimensional state space, along with a multi-discrete action space that integrates machine selection and RGV motion control. The framework employs a shared feature extraction layer and dual-head Actor-Critic network architecture, combined with parallel experience collection and synchronous parameter update mechanisms. In computational experiments across different scales, the proposed method achieves an average makespan reduction of 15–20% compared with numerical methods, while exhibiting excellent robustness under uncertain conditions including processing time fluctuations. Full article

The cloud manufacturing (CMfg) platform serves as a centralized hub for allocating and scheduling tasks to distributed resources. It features a concrete two-agent model that addresses real-world industrial needs: the first agent handles long-term flexible tasks, while the second agent manages urgent short-term tasks, both sharing a common due date. The second agent employs multitasking scheduling, which allows for the flexible suspension and switching of tasks. This paper addresses a novel scheduling problem aimed at minimizing the total weighted completion time of the first agent’s jobs while guaranteeing the second agent’s due date. For single-machine cases, a polynomial algorithm provides an efficient baseline; for parallel machines, an exact branch-and-price approach is developed, where the polynomial method informs the pricing problem and structural properties accelerate convergence. Computational results demonstrate significant improvements: the branch-and-price solves large-sized instances (up to 40 jobs) within 7200 s, outperforming CPLEX, which fails to find solutions for instances with more than 15 jobs. This approach is scalable for industrial cloud manufacturing applications, such as automotive parts production, and is capable of handling both design validation and quality inspection tasks. Full article

On GPU-based clusters, the training workloads of machine learning (ML) models, particularly neural networks (NNs), are often structured as Directed Acyclic Graphs (DAGs) and typically deployed for parallel execution across heterogeneous GPU resources. Efficient scheduling of these workloads is crucial for optimizing performance metrics such as execution time, under various constraints including GPU heterogeneity, network capacity, and data dependencies. DAG-structured ML workload scheduling could be modeled as a Nonlinear Integer Program (NIP) problem, and is shown to be NP-complete. By leveraging a positive correlation between Scheduling Plan Distance (SPD) and Finish Time Gap (FTG) identified through an empirical study, we propose to develop a Running Time Gap Strategy for scheduling based on Whale Optimization Algorithm (WOA) and Reinforcement Learning, referred to as WORL-RTGS. The proposed method integrates the global search capabilities of WOA with the adaptive decision-making of Double Deep Q-Networks (DDQN). Particularly, we derive a novel function to generate effective scheduling plans using DDQN, enhancing adaptability to complex DAG structures. Comprehensive evaluations on practical ML workload traces collected from Alibaba on simulated GPU-enabled platforms demonstrate that WORL-RTGS significantly improves WOA’s stability for DAG-structured ML workload scheduling and reduces completion time by up to 66.56% compared with five state-of-the-art scheduling algorithms. Full article

This paper considers the scheduling problem of uniform parallel machines. The objective is to minimize the makespan. This problem holds practical significance and is inherently NP-hard. Therefore, solutions of the exact formulation are limited to small-sized instances. As the problem size increases, the exact formulation struggles to find optimal solutions within a reasonable time. To address this challenge, an arc flow formulation is proposed, aiming to solve larger instances. The arc flow formulation creates a pseudo-polynomial number of variables, with its size being significantly influenced by the problem’s bounds. Therefore, bounds from the literature are utilized, and symmetry-breaking rules are applied to reduce the size of the arc flow graph. To test the effectiveness of the proposed arc flow formulation, it was compared with a mathematical formulation from the literature on small instances with up to 30 jobs. Computational results showed that the arc flow formulation outperforms the mathematical formulation from the literature, solving all cases within a few seconds. Additionally, on larger benchmark instances, the arc flow formulation solved 84.27% of the cases to optimality. The maximum optimality gap does not exceed 0.072% for the instances not solved to optimality. Full article

For the unrelated parallel machine scheduling problem, an improved Spider Monkey Optimization algorithm incorporating a variable neighborhood search (VNS) mechanism (VNS-SMO) is proposed to minimize the makespan, total tardiness, and total energy consumption. The VNS-SMO incorporates six types of neighborhood searches based on the objective characteristics to strengthen the optimization performance of the algorithm. To verify the effectiveness and superiority of VNS-SMO, first, Taguchi experiments were used to determine the algorithm parameters, and then three instances of different scales were solved and compared with the traditional algorithms NSGA-II, PSO, and SMO. The experimental results indicate that VNS-SMO significantly outperforms the comparison algorithms on IGD, NR, and C-matrix metrics, fully demonstrating its comprehensive advantages in convergence, distribution, and diversity. Full article

This paper addresses the multi-objective scheduling problem for unrelated parallel batch processing machines under different job arrival times. We propose a dual-list feature-driven (DLFD) heuristic algorithm to simultaneously minimize the completion time, total delay time, and total energy consumption. Firstly, the heuristic selects a machine based on the machine’s capacity and energy consumption characteristics. Secondly, a job is selected from two candidate job lists governed by machine capacity and batch processing time constraints, thereby reducing the search space and improving solution quality. To validate the effectiveness of the DLFD heuristic, experiments of three different scales were designed to compare its performance against classic composite dispatching rules. The results demonstrate that the proposed heuristic achieves a significantly superior Pareto front compared to the traditional rules and exhibits strong robustness in solving problems of various scales. Full article

To solve the unrelated parallel batch processing machine scheduling problem (UPBPMSP) with dynamic job arrivals, heterogeneous processing times, and machine heterogeneity, this paper presents an improved artificial bee colony (IABC) algorithm aimed at minimizing the makespan. Three improvements include the following: (1) a hybrid encoding scheme that combines machine allocation coefficients and priority weights, allowing for flexible consideration of machine capabilities and dynamic job priorities; (2) a dual-mode variable neighborhood search strategy to optimize machine allocation and job sequencing simultaneously; (3) a dynamic weight tournament selection mechanism to enhance population diversity and avoid premature convergence. Experimental results show that IABC reduces the makespan by 5% to 25% compared to traditional ABC and genetic algorithms (GAs), with the most significant advantages observed in concentrated job arrival scenarios. Statistical tests confirm that the improvements are statistically significant, validating the effectiveness of the proposed algorithm. Full article

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

Efficient scheduling of work orders in weaving looms is crucial for improving production efficiency and meeting tight delivery deadlines in the textile industry. This study proposes a genetic algorithm (GA)-based model to optimize work order assignments, minimize type changeover durations, and balance machine workloads. The model uses real-world ERP data, supports job splitting for parallel production, and dynamically classifies type changes into variant, linked warp, and full setup changes. Experimental results show significant improvements in planning time, changeover reduction, and delivery performance. The proposed GA approach offers a scalable and intelligent solution that can be readily adopted for modern textile manufacturing challenges. Full article

Ensuring the safety, reliability, and longevity of Lithium-ion (Li-ion) batteries is crucial for sustainable integration of Electric Vehicles (EVs) within Intelligent Transportation Systems (ITSs). However, thermal stress and degradation-induced anomalies can cause sudden performance failures, posing critical operational and safety risks. Capturing complex, non-linear, and high-dimensional patterns remains challenging for traditional Machine Learning (ML) models. We propose a hybrid anomaly detection method that incorporates a Variational Quantum Neural Network (VQNN), which uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to learn complex non-linear patterns. The VQNN is integrated with Isolation Forest (IF) and a Median Absolute Deviation (MAD)-based spike characterisation method to form a Quantum Anomaly Detector (QAD). This method distinguishes between normal and anomalous spikes in battery behaviour. Using an Arrhenius-based model, we simulate how the State of Health (SoH) and voltage of a Li-ion battery reduce as temperatures increase. We perform experiments on NASA battery datasets and detect abnormal spikes in 14 out of 168 cycles, corresponding to $8.3$% of the cycles. The QAD achieves the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.9820$, outperforming the baseline IF model by $7.78\%$. We use ML to predict the SoH and voltage changes when the temperature varies. Gradient Boosting (GB) achieves a voltage Mean Squared Error (MSE) of $0.001425$, while Support Vector Regression (SVR) achieves the highest $R^2$ score of $0.9343$. These results demonstrate that Quantum Machine Learning (QML) can be applied for anomaly detection in Battery Management Systems (BMSs) within intelligent transportation ecosystems and could enable EVs to autonomously adapt their routing and schedule preventative maintenance. With these capabilities, safety will be improved, downtime minimised, and public confidence in sustainable transport technologies increased. Full article

Batch processing machines (BPMs) are extensively present in high energy-consuming manufacturing processes such as casting, and they show some symmetries on adjacent batches and jobs within each batch. Preventive maintenance (PM) is very important for the stable running and energy saving of BPMs; however, PM in a parallel BPM shop is seldom studied. In this study, the energy-efficient parallel BPM scheduling problem with PM is considered and an imperialist competitive algorithm with three empires (TEICA) is presented to minimize makespan and total energy consumption. To obtain high-quality solutions, the number of empires is not used as a parameter and fixed at 3, a new way is applied to construct three initial empires, each of which has a new structure like two imperialists, a new assimilation is given, and an adaptive imperialist competition is implemented based on historical competition data. A number of computational experiments are conducted on 108 instances. The computational results show that the new strategies of TEICA are effective; TEICA can provide better results than all comparative methods on more than 90% instances of the considered BPM scheduling problem, and TEICA may be an effective way to solve other BPM scheduling problem. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

This paper examines the impact of two storage policies—dedicated storage (D-SLAP) and randomized storage (R-SLAP)—on warehouse operational efficiency. It integrates the Storage Location Assignment Problem (SLAP) with the unrelated parallel machine scheduling problem (UPMSP), which represents the scheduling of the material handling equipment (MHE). This integration is intended to elucidate the interplay between storage strategies and scheduling performance. The considered evaluation metrics include transportation cost, average waiting time, and total tardiness, while accounting for product arrival and demand schedules, precedence constraints, and transportation expenses. Additionally, considerations such as MHE eligibility, resource requirements, and available storage locations are incorporated into the analysis. Given the complexity of the combined problem, a tailored Non-dominated Sorting Genetic Algorithm (NSGA-II) was developed to assess the performance of the two storage policies across various randomly generated test instances of differing sizes. Parameter tuning for the NSGA-II was conducted using the Taguchi method to identify optimal settings. Experimental and statistical analyses reveal that, for small-size instances, both policies exhibit comparable performance in terms of transportation cost and total tardiness, with R-SLAP demonstrating superior performance in reducing average waiting time. Conversely, results from large-size instances indicate that D-SLAP surpasses R-SLAP in optimizing waiting time and tardiness objectives, while R-SLAP achieves lower transportation cost. Full article