Parallelized Research Articles

Production scheduling with multi-robot task allocation in a real industry 4.0 setting

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise scheduling essential for seamless operations. However, research often oversimplifies the Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot Flexible Job Shop (MRFJS) scheduling problem with limited buffers. It involves non-identical parallel machines and robots with varying capabilities overseeing material handling under blocking conditions. The case study is based on a real Industry 4.0 scenario, where the layout restricts each robotic arm’s access, requiring strategic buffer placement for part transfers. A Mixed-Integer Programming (MILP) model aims to minimize makespan, followed by a new Genetic Algorithm (GA) using Roy and Sussman’s Alternative Graph. Computational tests on various scales and real data from a manufacturing plant demonstrate the GA’s efficacy in solving complex scheduling problems in real-world production settings. Based on the data, the Proposed Genetic Algorithm (PGA), with an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared to the Basic Genetic Algorithm (BGA), with an average ARD of 0.38%. This percentage indicates that the PGA significantly outperforms in solving complex scheduling problems.

Unrelated parallel machine scheduling with random rework and limited preemption

Unrelated parallel machine scheduling with random rework and limited preemption

Deterioration-Aware Collaborative Energy-Efficient Batch Scheduling and Maintenance for Unrelated Parallel Machines Based on Improved MOEA/D

Deterioration-Aware Collaborative Energy-Efficient Batch Scheduling and Maintenance for Unrelated Parallel Machines Based on Improved MOEA/D

Suppression of photospheric velocity fluctuations in strongly magnetic O stars in radiation-magnetohydrodynamic simulations

O stars generally show clear signs of strong line broadening (in addition to rotational broadening) in their photospheric absorption lines (typically referred to as macroturbulence), believed to originate in a turbulent sub-surface zone associated with enhanced opacities due to the recombination of iron-group elements (at T ∼ 150-200 kK). O stars with detected global magnetic fields also display such macroturbulence; the sole exception to this is NGC 1624-2, which also has the strongest (by far) detected field of the known magnetic O stars. It has been suggested that this lack of additional line broadening is due to NGC 1624-2's exceptionally strong magnetic field, which might be able to suppress the turbulent velocity field generated in the iron opacity peak zone. We tested this hypothesis based on two-dimensional (2D), time-dependent, radiation magnetohydrodynamical (RMHD) box-in-a-star simulations for O stars that encapsulate the deeper sub-surface atmosphere (down to T ∼ 400 kK), the stellar photosphere, and the onset of the supersonic line-driven wind in one unified approach. To study the potential suppression of atmospheric velocity fluctuations, we extended our previous non-magnetic O star radiation-hydrodynamic (RHD) simulations to include magnetic fields of varying strengths and orientations. We used MPI-AMRVAC which is a Fortran 90 based publically available parallel finite volume code that is highly modular. We used the recently added RMHD module to perform all our simulations here. For moderately strong magnetic cases (∼ 1 kG), the simulated atmospheres are highly structured and characterised by large root-mean-square velocities, and our results are qualitatively similar to those found in previous non-magnetic studies. By contrast, we find that a strong horizontal magnetic field in excess of 10 kG can indeed suppress the large velocity fluctuations, and can thus stabilise (and thereby also inflate) the atmosphere of a typical early O star in the Galaxy. On the other hand, an equally strong radial field is only able to suppress horizontal motions, and as a consequence these models exhibit significant radial fluctuations. Our simulations provide an overall physical rationale as to why NGC 1624-2 with its strong ∼ 20 kG dipolar field lacks the large macroturbulent line broadening that all other known slowly rotating early O stars exhibit. However, our simulations also highlight the importance of field geometry for controlling the atmospheric dynamics in massive and luminous stars that are strongly magnetic, tentatively suggesting latitudinal dependence of macroturbulence and basic photospheric parameters.

A bi-objective Genetic Algorithm for flexible flow shop scheduling: A real-world application in the electrical industry

The electrical sector forces manufacturing companies of electrical solutions to continually innovate and implement new processes for greater efficiency. The growing demand for electrical energy, as well as the need to adapt to hybrid operations that combine multi-project operation models with continuous production models, requires efficient workflow management. Accordingly, this article proposes a Genetic Algorithm (GA) approach for solving the scheduling problem in a Flexible Hybrid Flow Shop (FHFS) environment considering a transfer batch approach to minimize makespan and total tardiness. The approach is inspired by a real-world application in the electrical industry and also accounts for unrelated parallel machines, precedence, release times, and due dates for jobs at each production center as key constraints. Three real-data scenarios were generated and evaluated. In the first scenario, a 7 % improvement in makespan was observed compared to real execution times. In Scenario 2, the makespan improved significantly by 33 %, and only 17.4 % of jobs were delayed, compared to 96 % in the real data. Likewise, GA showed a lightly better performance over Tabu Search (TS) in 3.01 % for makespan while the delayed jobs found by GA were 25 % below those obtained by TS. These results highlight the potential of the proposed method to improve overall production efficiency, not only in the electrical sector but also in similar industries.

A new workspace estimation method for heavy-load parallel kinematic machine considering mechanism deformation and motor loading performance

Abstract The estimation of workspace for parallel kinematic machines (PKMs) typically relies on geometric considerations, which is suitable for PKMs operating under light load conditions. However, when subjected to heavy load, PKMs may experience significant deformation in certain postures, potentially compromising their stiffness. Additionally, heavy load conditions can impact motor loading performance, leading to inadequate motor loading in specific postures. Consequently, in addition to geometric constraints, the workspace of PKMs under heavy load is also constrained by mechanism deformation and motor loading performance. This paper aims at developing a new heavy load 6-PSS PKM for multi-degree of freedom forming process. Additionally, it proposes a new method for estimating the workspace, which takes into account both mechanism deformation and motor loading performance. Initially, the geometric workspace of the machine is predicted based on its geometric configuration. Subsequently, the workspace is predicted while considering the effects of mechanism deformation and motor loading performance separately. Finally, the workspace is synthesized by simultaneously accounting for both mechanism deformation and motor loading performance, and a new index of workspace utilization rate is proposed. The results indicate that the synthesized workspace of the machine diminishes as the load magnitude and load arm increase. Specifically, under a heavy load magnitude of 6000 kN and a load arm of 200 mm, the utilization rate of the synthesized workspace is only 9.9% of the geometric workspace.

Certain Domination Parameters and Their Resolving Versions of Fractal Cubic Networks

Networks are designed to communicate, operate, and allocate tasks to respective commodities. Operating supercomputers became challenging, which was handled by the network design commonly known as hypercube, denoted by Qn. In a recent study, the hypercube networks were insufficient to hold supercomputers’ parallel processors. Thus, variants of hypercubes were discovered to produce an alternative to the hypercube. A new variant of the hypercube, the fractal cubic network, can be used as the best alternative in the case of hypercubes. Our research investigates that the fractal cubic network is a rooted product of two graphs. We try to determine its domination and resolving domination parameters, which could be applied to resource location and broadcasting-related problems.

HighP5: Programming using Partitioned Parallel Processing Spaces

HighP5 is a new high-level parallel programming language designed to help software developers to achieve three objectives simultaneously: programmer productivity, program portability, and superior program performance. HighP5 enables this by fostering a new programming paradigm that we call hardware-cognizant parallel programming. The paradigm uses a uniform hardware abstraction and a declarative programming syntax to allow programmers to write hardware feature-sensitive efficient programs without delving into the detail of those feature implementations. This paper is the first comprehensive description of HighP5's design rationale, language grammar, and core features. It also discusses the runtime behavior of HighP5 programs. In addition, the paper presents preliminary results on program performance from HighP5 compilers on three different architectural platforms: shared-memory multiprocessors, distributed memory multi-computers, and hybrid GPU/multi-computers.

SCHEME FOR CALCULATING UNSTEADY FLOWS OF HEAT-CONDUCTING GAS IN A THREE-TEMPERATURE APPROXIMATION

A numerical modeling technique for unsteady flows of heat-conducting gas in a three-temperature approximation is presented. The methodology is based on the foundational principles of S.K. Godunov. For time integration, each time step is computed by splitting the governing equations into hyperbolic and parabolic subsystems. The first subsystem is solved using a generalized Godunov scheme, while the second uses an explicit-iterative Chebyshev scheme. Adaptive, curvilinear moving grids are used for discretization, and the discrete scheme is formulated in curvilinear coordinates, preserving the symmetries of the differential problem. The methodology is implemented as a parallel code for multiprocessor computers. Its primary purpose is to support computational studies in controlled thermonuclear fusion, though it can also be applied to other areas of computational aerogas dynamics.

Benders decomposition for the multi-agent location and scheduling problem on unrelated parallel machines

Benders decomposition for the multi-agent location and scheduling problem on unrelated parallel machines

End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients. In this retrospective multicenter study, brain DWI studies from January 2017 to April 2020 from Center 1, from June 2020 to December 2020 from Center 2, and from November 2019 to April 2020 from Center 3 were included. Four radiologists labeled images into five classes: anterior cerebral artery (ACA), middle cerebral artery (MCA), posterior circulation (PC), and watershed (WS) regions, as well as normal images. Additionally, for Center 1, clinical information was encoded as a domain knowledge vector to incorporate into image embeddings. 3D convolutional neural network (CNN) and attention gate integrated versions for direct 3D encoding, long short-term memory (LSTM-CNN), and time-distributed layer for slice-based encoding were employed. Balanced classification accuracy, macro averaged f1 score, AUC, and interrater Cohen's kappa were calculated. Overall, 624 DWI MRIs from 3 centers were utilized (mean age, interval: 66.89 years, 29-95 years; 345 male) with 439 patients in the training, 103 in the validation, and 82 in the test sets. The best model was a slice-based parallel encoding model with 0.88 balanced accuracy, 0.80 macro-f1 score, and an AUC of 0.98. Clinical domain knowledge integration improved the performance with 0.93 best overall accuracy with parallel stream model embeddings and support vector machine classifiers. The mean kappa value for interrater agreement was 0.87. Developed end-to-end deep learning models performed well in classifying affected regions from stroke in DWI. The end-to-end deep learning model with a parallel stream encoding strategy for classifying stroke regions in DWI has performed comparably with radiologists.

Parallelization of particle-based reaction-diffusion simulations using MPI.

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.

CFD Code Parallelization on GPU and the Code Portability

AbstractGoal of this paper is to develop a fully functional parallel Computational Fluid Dynamics (CFD) code that is optimized to run on a single Graphics Processing Unit (GPU). This is achieved by writing the code in FORTRAN and OpenACC (Open Accelerators), providing them with an easily portable, platform independent code. Existing CFD code is significantly modified to allow for parallel asynchronous execution. Also, due to strong recursive dependencies in Tridiagonal Matrix Algorithm (TDMA) solver, it is replaced with Jacobi, which provides fast execution in environments with large number of parallel cores. In this research a computer code for simulation of 2D flow of water through the axisymmetric channel is used as a base for development. The parallel code is executed on GPU, single, and multicore Central Processing Unit (CPU), and the execution times are compared between platforms. Even though that Jacobi solver performs worse on single core computers, compared to its Gauss‐seidel counterpart, it is used to provide a baseline for comparison. In this work, it is shown that computation on finer grids takes less time on GPU than on CPU. The computation time increase with the number of cells in grid on GPU should follow the observed linear trend until the GPUs physical limitations are reached depending on memory size and core count.

Concurrent Bering Sea and Labrador Sea ice melt extremes in March 2023: a confluence of meteorological events aligned with stratosphere–troposphere interactions

Abstract. Today's Arctic is characterized by a lengthening of the sea ice melt season, as well as by fast and at times unseasonal melt events. Such anomalous melt cases have been identified in Pacific and Atlantic Arctic sector sea ice studies. Through observational analyses, we document an unprecedented, concurrent marginal ice zone melt event in the Bering Sea and Labrador Sea in March of 2023. Taken independently, variability in the cold-season ice edge at synoptic timescales is common. However, such anomalous, short-term ice loss over either region during the climatological sea ice maxima is uncommon, and the tandem ice loss that occurred qualifies this as a rare event. The atmospheric setting that supported the unseasonal melt events was preceded by a sudden stratospheric warming event amidst background La Niña conditions that led to positive tropospheric height anomalies across much of the Arctic and the development of anomalous mid-troposphere ridges over the ice loss regions. These large-scale anticyclonic centers funneled extremely warm and moist airstreams onto the ice causing melt. Further analysis identified the presence of atmospheric rivers within these warm airstreams whose characteristics likely contributed to this bi-regional ice melt event. Whether such a confluence of anomalous wintertime events associated with troposphere–stratosphere coupling may occur more often in a warming Arctic remains a research area ripe for further exploration.

Type synthesis of a new class of 4-3T1R(y) parallel mechanisms for parallel machine tools design

Unlike previous design procedures for parallel machine tools (PMTs), this paper first analyzes the path and posture requirements of the tools in universal processing, then proposes and constructs a new class of PMs that can provide the required path and posture. This opens up new ideas for the design of PMTs. Based on the reciprocal screw theory, a type synthesis process of asymmetrical lower-mobility PMs was proposed. Through this process, a novel class of four-limbed parallel mechanisms (PMs) providing three translations and one rotation around a horizontal axis (y-axis), denoted as 4-3T1R(y) PMs, was successfully proposed and constructed. Such PMs can be applied to the design of PMTs, especially the design of micro-electrical discharge machining (micro-EDM) PMTs. The structural design of a new 2RRC-2CRU PM, which was a member of 4-3T1R(y) PMs, was performed, and its mobility was analyzed in detail to demonstrate the efficiency of the proposed type synthesis process. The inverse, forward, and velocity kinematics problems of the 2RRC-2CRU PM were solved for further applications.

Are Speech Sound Difficulties Risk Factors for Difficulties in Language and Reading Skills? A Systematic Review and Meta-Analysis.

Children with speech sound difficulties often require educational psychology services, yet systematic reviews examining the association between these difficulties and language or reading problems are lacking. This meta-analysis examines whether these children are at higher risk of language and reading difficulties compared to their peers. The study analyzed 290 effect sizes from cross-sectional and longitudinal studies that compared language and reading skills between children with speech sound difficulties and controls. Additionally, we evaluated 37 effect sizes from correlational studies in general populations to examine the relationship between speech sound skills and language or reading skills. Children with speech sound difficulties showed significant concurrent language (Hedges' g = -0.60) and reading (Hedges' g = -0.58) problems. Correlational studies also demonstrated a relationship between speech sound skills and language (r = .23) and reading (r = .23) skills. Phonological awareness and study quality were significant moderators. Longitudinal studies showed persistent or increasing group differences over time in language (Hedges' g = -0.85) and reading (Hedges' g = -0.50). These findings were consistent regardless of the severity or types of speech sound difficulties, nonverbal IQ, country, age, and publication year. However, a precision-effect test and the precision-effect estimate with standard errors analysis suggested a potential decrease in effect size due to publication bias from small sample sizes in primary studies. Children with speech sound difficulties are at an increased risk of language and reading difficulties, emphasizing the need for broader language assessments and early interventions to mitigate future academic difficulties. https://doi.org/10.23641/asha.27849828.

An Optimization Model for Production Scheduling in Parallel Machine Systems

The efficiency and quality of the manufacturing industry are greatly influenced by production scheduling, which makes it a crucial aspect. A well-designed production scheduling scheme can significantly enhance manufacturing efficiency and reduce enterprise costs. This paper presents a tailored optimization model designed to address a more complex production scheduling problem that incorporates parallel machines and preventive maintenance. The proposed solutions aim to achieve a balance between job sequence and machine reliability, considering the minimum maintenance cost rate for determining maintenance cycles of deteriorating machines in real manufacturing scenarios. Furthermore, the objective of minimizing the maximum completion time guides machine assignment and job sequence based on maintenance constraints. The innovation lies in the introduction of a greedy algorithm that utilizes a water injection model to address this NP-hard integrated problem. A pre-distribution model is constructed using the water injection model, and its solution is utilized as input for constructing the production scheduling model, which aids in determining machine assignment and job sequence. This algorithm demonstrates remarkable effectiveness and efficiency, enabling the achievement of an optimal solution. A numerical example is presented to illustrate the computational process, accompanied by an extensive discussion of the results showcasing improved performance. Furthermore, the optimization model developed in this paper can be adapted to tackle the production scheduling problem with modifications tailored for parallel machines.

Application of LightGBM Algorithm in Production Scheduling Optimization on Non-Identical Parallel Machines

Production scheduling plays a decisive role in supply chain management, directly influencing the operational efficiency and competitiveness of companies. This study explores the effectiveness of the LightGBM algorithm for production scheduling on non-identical parallel machines, comparing it to algorithms such as logistic regression, KNN, decision tree, and XGBoost. LightGBM was chosen for its speed of execution and its ability to handle large amounts of data. The results show that LightGBM outperforms the other models in terms of RMSE, MAE, explained variance score, and R² score for regression tasks, as well as in classification accuracy for certain features. Its superiority is attributed to its ability to efficiently handle data complexity while reducing computational complexity through its leaf tree growth technique. This study highlights LightGBM's potential for improving the efficiency of supply chain management systems and the challenges associated with computational scalability for large datasets. The results suggest that LightGBM is a robust and effective solution to optimize production scheduling, paving the way for future research in this field.

Cooperative game theory based coordinated scheduling of production and transportation of parallel machines with deterioration effects

Abstract Cooperative game theory is used to study the production and transportation scheduling problem of parallel machines with deterioration effects. Jobs with an initial scheduling order are processed on parallel machines and then transported to customers. Penalties are incurred for early or late completion if the jobs are not completed on the due dates. Considering that jobs can rearrange the scheduling order in the coalition through cooperation to bring about cost savings, the cooperative game model is constructed by considering the jobs as players and taking the maximum cost savings of the coalition as the characteristic function. It is proven that when jobs have the same normal processing time and deterioration rate, the cooperative game has a non-empty core, and a core allocation can be obtained from the β-rule allocation method. A Dual Replay Buffer Double DQN (DRB-DDQN) algorithm is designed to get the optimal schedule of the coalition. Experiments demonstrate the effectiveness of the DRB-DDQN and β-rule allocation method in solving the game scheduling problem.

A weighted distribution-free model for parallel machine scheduling with uncertain job processing times

A weighted distribution-free model for parallel machine scheduling with uncertain job processing times