Search Results (140)

Efficient workflow scheduling is essential for improving testing efficiency in large-scale Vehicle-to-Everything (V2X) protocol conformance testing. Existing directed acyclic graph (DAG) schedulers treat all test components uniformly, overlooking the inherent structural heterogeneity of V2X workflows which contain mixed independent and precedence-constrained components. This leads to cross-subsystem interference, low resource utilization, and extended makespan. This paper presents AOE–CP (AON DAG with Edge-Weighted Transformation and Critical Path Scheduling), a structure-aware hybrid scheduling architecture for V2X testing. Unlike existing heuristic-based improvements, AOE–CP achieves performance gains through domain-specific structural reorganization rather than new scheduling rules. It integrates three mechanisms: (i) structural decomposition to decouple independent and dependent components and resolve their conflicting optimization objectives; (ii) critical path thread isolation to eliminate cross-subsystem resource competition and guarantee zero critical path waiting time for V2X dominant-topology workflows; (iii) atomic operation-based time estimation supporting fully offline scheduling without runtime profiling overhead. Experiments show that, for large-scale workflow scenarios, AOE–CP reduces normalized makespan by 14–16% and 22–24% compared with HEFT (Heterogeneous Earliest Finish Time) and CPOP (Critical Path On Processor), respectively. Scheduling overhead is only 2.1 ms for 1000-component workflows. AOE-CP reaches makespan saturation with four threads, versus 12 for HEFT and 28 for CPOP, demonstrating superior resource efficiency and scalability. The framework can also be generalized to other mixed-dependency workflow scenarios. Full article

In most railway divisions the results of scheduled inspections of automation and telemechanics field devices are still recorded on paper. Such records reach engineering management with a delay, are easy to lose, and are difficult to verify. This paper examines the data transmission core of a digital inspection complex in which passive RFID tags identify both the equipment and the personnel, and the inspection record is delivered to a cloud server over a hybrid cellular architecture combining a failure-prone GSM channel with a reliable CDMA channel. To quantify the stability of such a system, a continuous-time Markov chain model is constructed in which the link is represented as an M/M/2/K queue with one unreliable server: both channels carry traffic in parallel, and during a GSM outage the CDMA channel alone sustains service. Records already admitted are preserved across a channel switch; only records arriving at a full shared buffer are rejected. This residual overflow loss stays below 0.1% at routine load with a buffer of m ≥ 5 and reaches about 2.8% only under post-incident overload. The model parameters were measured on an operating ESP32-based scanner complex piloted at Hamza station on 46 point machines. Calculations for three load scenarios show that increasing the local buffer beyond m = 5 yields diminishing returns while the delay grows, and that resilience is governed primarily by the presence of the redundant channel and adequate buffering, with the primary-channel recovery rate a secondary factor. Full article

Scheduling at the printing bottleneck of plastic woven packaging production is complicated by asymmetric sequence-dependent setup times (SDST) caused by color transitions and by mandatory non-preemptive shutdown windows. This study focuses on assigning and sequencing orders on parallel printing machines under these coupled operational constraints. To address this problem, we propose an Attention-Enhanced Dual-Critic Deep Reinforcement Learning framework with Action Masking (AE-DMAC). The scheduling problem is formulated as a bi-objective parallel-machine Markov Decision Process that considers makespan and total setup time under a fixed preference setting. A cross-attention module models the compatibility between the current machine state and pending orders to capture asymmetric SDST effects. Two critics separately estimate the efficiency- and setup-related value signals before they are combined for policy optimization, reducing interference between the two objectives. In addition, a deterministic action-feasibility mask removes assignments that would overlap the known shutdown window before action sampling. The framework is evaluated on industrially calibrated synthetic instances with 50, 150, and 300 orders scheduled on eight parallel printing machines. Experimental results show that AE-DMAC consistently improves makespan and normalized setup time per machine compared with the implemented heuristic, meta-heuristic, and Vanilla PPO baselines under the tested operating conditions. In the large-scale instance, AE-DMAC achieves an average makespan of 223.8 h and a normalized setup time of 568.4 min per machine, corresponding to reductions of 7.4% and 27.4%, respectively, relative to Vanilla PPO. The feasibility mask maintains zero shutdown-window violations in the evaluated deterministic setting, while the attention mechanism substantially reduces high-cost sequence-dependent color transitions. These results indicate that the proposed framework is a promising scheduling approach for parallel printing systems with asymmetric changeovers and known machine-unavailability windows. Full article

The hybrid flow shop scheduling problem (HFSP) with unrelated parallel machines (UPMs), sequence-dependent setup times (SDSTs), and inter-stage transportation times has recently emerged as a prominent research topic. To address this scheduling problem with the objective of minimizing the maximum completion time (makespan), this paper first formulates a mixed-integer linear programming (MILP) model based on the machine-position modeling idea. Exact solution analyses on small-scale instances reveal that the strong coupling effect of these triple constraints concentrates the computational bottleneck on the time-consuming proof of optimality, thereby underscoring the strongly NP-hard nature of the investigated HFSP-SDST-T problem. To efficiently solve large-scale instances, a novel adaptive co-evolutionary memetic algorithm (ACMA) is proposed. ACMA adopts a dual-population co-evolutionary framework, where a customized genetic algorithm (GA) is designed for global exploration and a Lévy flight-enhanced particle swarm optimization (PSO) improves local search capability. To dynamically balance exploration and exploitation, a Dynamic Role Allocation (DRA) mechanism is developed to adaptively reassign individuals between the two populations according to their evolutionary states. Moreover, a progressive two-stage memetic enhancement strategy is proposed to overcome premature convergence by sequentially activating deep variable neighborhood search (VNS) and a catastrophe-based diversification strategy, enabling adaptive responses to different stagnation levels. Extensive experiments, including ablation studies, comparisons with benchmark algorithms, and computational complexity analysis, are conducted on small- and large-scale instances. The results show that ACMA consistently obtains the exact optimal solutions obtained from the MILP model for small-scale instances and achieves competitive performance on large-scale complex instances. Furthermore, Wilcoxon signed-rank tests confirm the statistical significance of the performance differences, supporting the reliability of the experimental results. Full article

This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a single HFS model has received little attention. The problem is motivated by a real-world order sequencing problem in insulation board manufacturing, where all four constraints arise simultaneously from the production process. To formally characterize the problem, we develop a mixed-integer programming formulation that captures all operational constraints. For practical-scale problems, we propose several dispatching heuristics that can obtain sufficiently good solutions within a short computation time. We further develop a genetic algorithm as an independent solution approach to obtain high-quality solutions close to the optimum within a reasonable computation time. Computational experiments on instances generated based on real insulation board production characteristics demonstrate that the proposed algorithms outperform a benchmark greedy rule, and sensitivity analyses reveal the effects of setup time magnitude and the number of parallel machines on scheduling performance. Full article

The increasing computational demands of artificial intelligence (AI) inference at the edge require hardware accelerators capable of overcoming the von Neumann bottleneck while operating under power constraints. Conventional digital architectures based on multiply–accumulate (MAC) units are limited in energy efficiency and scalability for resource-constrained applications. This work presents a proof-of-concept AI accelerator based on analog frequency–domain computation implemented within a semi-systolic array architecture. The proposed approach exploits frequency mixing to perform multiplication and accumulation operations in hardware, enabling the execution of matrix–matrix operations, which constitute the General Matrix Multiplication (GEMM) methods that dominate the computational workload of convolutional and fully connected neural networks. The proposed system consists of a custom printed circuit board controlled by an ATmega328P microcontroller(Microchip Technology Inc., Chandler, AZ, USA) and a software stack designed to interface with standard machine learning frameworks such as PyTorch. The software layer enables neural network operations, including convolutional and fully connected layers, to be mapped onto hardware-executed matrix–matrix computations through an abstraction analogous to the General Matrix Multiplication (GEMM) functionality provided by Level-3 Basic Linear Algebra Subprograms (BLAS). Matrix multiplication and accumulation are partly performed directly by the hardware processing elements, while the software control unit coordinates data movement and computation scheduling. Although bias operations are not implemented in the current prototype, their comparatively low computational cost makes them less critical to the overall acceleration strategy. A quantization-aware mapping methodology constrained by analog-to-digital and digital-to-analog converter specifications is introduced to translate neural network operations into frequency–domain computations. The paper further describes the hardware architecture, communication protocols, software stack organization, and interactions between system components. In addition, the effects of analog nonidealities and error sources associated with frequency–domain multiplication are investigated, and simulations of the proposed processing elements are presented to evaluate the computational approach. Experimental and simulation results demonstrate the feasibility of performing dense linear algebra operations through analog frequency–domain processing and validate the operation of the processing elements. The study further explores converter resolution, frequency interference, and analog component nonidealities and provides a comparison with conventional digital and other low-power accelerator approaches. The results indicate that exploiting the inherent parallelism of analog computation offers a promising pathway toward ultra-low-power AI inference, making the proposed architecture a potential alternative for energy-constrained edge applications. Full article

This editorial introduces the Special Issue “Mathematical Methods and Operation Research in Planning, Scheduling and Supply Chain Operations Management” of the journal Mathematics. The Special Issue gathers nine peer-reviewed papers that, together, illustrate the breadth of contemporary operations research applied to the planning and control of modern industrial systems. The accepted contributions span the full methodological spectrum—from exact combinatorial optimisation (constraint programming, branch and bound, and mixed-integer programming) through heuristics, metaheuristics and reinforcement learning to worst-case performance analysis and stochastic models—and they address problems across the whole operations hierarchy, ranging from single- and parallel-machine scheduling, flow shops and flexible job shops to inventory systems and supply-chain logistics, with a recurrent emphasis on sustainability, robustness and real-world applicability. This editorial summarises the scope of the Special Issue and the individual contributions, and outlines some cross-cutting trends and directions for future research. Full article

In parallel CNC machining systems, machine deterioration can simultaneously increase energy-related operating costs, affect delivery performance, and change the timing of preventive maintenance. This study develops a reliability- and energy-aware decision-support framework for production-maintenance scheduling in a two-machine parallel CNC cell. The model integrates priority sequencing, reliability-based machine assignment during decoding, degradation-dependent energy cost, tardiness penalties, and threshold-triggered preventive maintenance in a unified cost-minimization formulation. A normalized reliability index is updated by a short-horizon exponential degradation function, and the energy term is amplified when machines operate in degraded states. Preventive maintenance is triggered when post-job reliability falls below a specified threshold, restoring the machine’s condition for subsequent production or the next planning horizon. The computational study combines application-inspired machining instances, decoder-space full enumeration for small cases, repeated GA/PSO comparisons, a new algorithm-budget sensitivity experiment, and adapted OR-Library weighted-tardiness benchmarks. Across 270 paired budget-sensitivity runs, GA obtained a lower total cost in 265 cases, whereas PSO retained a shorter average runtime in the matched-budget experiments. Across 90 adapted public-benchmark comparisons, GA obtained a lower total cost in 86 cases. These results show that the framework generates feasible schedules and reveals energy–maintenance–tardiness trade-offs. The algorithmic findings are interpreted as a quality–time trade-off under the tested scalarized cost model, not as a claim of universal algorithmic superiority. Full article

While the Triton compiler has revolutionized GPU kernel development, its deployment on general-purpose CPUs struggles to fully utilize the underlying hardware capabilities. This is primarily due to the semantic gap between Triton’s SPMD execution model and CPU vector architectures, which leads to suboptimal utilization of vector units during complex memory accesses. In this paper, we present a comprehensive compilation and runtime co-optimization framework for Triton-CPU, specifically targeting Vector Length Agnostic architectures (VLA) like ARM SVE. At the compiler level, we propose a novel semantic reconstruction and explicit base-offset decoupling strategy, enabling native VLA gather/scatter generation and eliminating scalar loop overheads. At the runtime level, we introduce a Machine Learning-driven thread scheduling model to optimally orchestrate the synergy between Thread-Level Parallelism and Vector-Level Parallelism. Extensive evaluations on an ARM-based multi-core processor demonstrate that our framework achieves up to a 2.0× throughput improvement for compute-bound GEMM operators (peaking at 346 GFLOPS), notably outperforming the hand-optimized OpenBLAS library by up to 1.54× at small-to-medium scales. Additionally, it delivers a 1.7× speedup for element-wise workloads. Furthermore, our optimizations saturate memory bandwidth (up to 55 GB/s) for memory-bound operators with zero compilation bloat, establishing a robust, high-performance foundation for deploying deep learning models on general-purpose CPUs. Full article

In flexible job shop scheduling, industrial robots undertake both workpiece transportation and loading/unloading operations. Equipping each machine with dedicated buffers tends to increase transportation workload and further intensify transport bottlenecks. Shared buffers are therefore introduced to temporarily store workpieces and relieve congestion in the production process. This paper establishes a transport-integrated flexible job shop scheduling model with shared buffer constraints, which minimizes makespan, total energy consumption, and machine load range simultaneously. Correspondingly, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is developed to achieve better solution performance. A time-window-based path-planning decoding scheme is constructed to address buffer constraints and transportation conflicts in the coordinated production and transportation process. In parallel, four initialization rules are designed to improve the quality and diversity of the initial population, and a variable neighborhood search algorithm (VNS) is embedded to enhance the local exploitation ability of the proposed algorithm. The performance of the presented method is evaluated through two groups of numerical experiments. The first group is carried out on extended benchmark instances. Comparisons with the conventional Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization algorithms (MOPSO) validate the efficacy of the proposed strategies and demonstrate the superiority of ENSGA-II in both solution quality and computational efficiency. Experimental results on real-world cases further illustrate that the proposed method can effectively solve the integrated scheduling problem in flexible manufacturing systems where industrial robots are employed as the main transport resources. Full article

Heavy-haul railways require efficient rail replacement because extreme axle loads and high-density transport accelerate rail wear. Traditional manual-led processes are limited by fragmented operations, high labor demand, and complex equipment scheduling, typically completing about 1 km of rail replacement within a 4 h maintenance window and requiring approximately 340 workers. This study is positioned as construction-process modeling, workflow organization, and simulation-supported feasibility analysis for an integrated rail-changing workflow, rather than the development or field validation of a fully mature rail-changing machine. The proposed workflow coordinates rail unloading, on-board welding, fastener disassembly, rail cutting, exchange-recovery, fastening, closure welding, and final inspection through a highly parallel construction organization. A process-level train-set configuration, including a tractor, a long-rail comprehensive transport vehicle, an exchange-recovery integrated transport vehicle, and a mobile welding vehicle, is used as an engineering carrier to support the closed-loop workflow of unloading, welding, exchange, and recovery. Based on engineering time-study analysis, field experience, expert consultation, and discrete-event simulation, the results indicate that the proposed workflow has the potential to complete a simulated 2 km rail-changing task within a single 4 h maintenance window with an estimated labor demand of 80–95 personnel under the specified assumptions. The study provides conceptual and simulation-supported feasibility evidence for construction-process organization, rather than field-validated machine performance, and offers a technical reference for improving the mechanization and coordination of heavy-haul railway maintenance. Full article

In panel-furniture manufacturing, dynamic scheduling is challenging because upstream motherboard release and parallel CUT-machine assignment are tightly coupled with downstream multibranch processing, finite-capacity buffers, and machine disturbances. This study investigates an event-driven scheduling problem in a multibranch panel-furniture workshop and aims to minimize the cumulative tardiness of all parts. To capture the structural dependencies of the shop floor, we construct a heterogeneous graph to represent the real-time production state. In this graph, candidate motherboards, parallel CUT machines, and downstream branch-level production units form different node types. The representation explicitly encodes motherboard–branch coupling features and machine–branch inflow relations, while aggregated branch-state features summarize detailed process-order information within each downstream branch. Based on this structured state representation, we develop a proximal policy optimization (PPO)-based actor–critic framework to learn adaptive motherboard release and CUT-assignment policies. We compare the proposed method with three heuristic rules and two PPO-based learning baselines under different machine-availability levels, buffer-capacity settings, and dataset scales. The results show that the proposed method achieves the best overall tardiness performance across the tested baselines and converges faster and more stably than the other PPO-based methods under different dataset scales and workshop conditions. These findings demonstrate the effectiveness of combining heterogeneous graph modeling with reinforcement learning for dynamic scheduling in multibranch panel-furniture production systems. Full article

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