Problem Size Research Articles

A machine learning approach for predicting the best heuristic for a large scaled Capacitated Lotsizing Problem

Abstract For some NP-hard lotsizing problems, many different heuristics exist, but they have different solution qualities and computation times depending on the characteristics of the instance. The computation times of the individual heuristics increase significantly with the problem size, so that testing all available heuristics for large instances requires extensive time. Therefore, it is necessary to develop a method that allows a prediction of the best heuristic for the respective instance without testing all available heuristics. The Capacitated Lotsizing Problem (CLSP) is chosen as the problem to be solved, since it is a fundamental model in the field of lotsizing, well researched and several different heuristics exist for it. The CLSP addresses the problem of determining lotsizes on a production line given limited capacity, product-dependent setup costs, and deterministic, dynamic demand for multiple products. The objective is to minimize setup and inventory holding costs. Two different forecasting methods are presented. One of them is a two-layer neural network called CLSP-Net. It is trained on small CLSP instances, which can be solved very fast with the considered heuristics. Due to the use of a fixed number of wisely chosen features that are relative, relevant, and computationally efficient, and which leverage problem-specific knowledge, CLSP-Net is also capable of predicting the most suitable heuristic for large instances.

A fast local search for the identical parallel machine scheduling problem with the position‐based deteriorating effect and maintenance

AbstractIn this paper, we develop a fast local search for the identical parallel machine scheduling problem with maintenance activities and the position‐based deteriorating under the maximum lateness minimization. Our approach allows us to calculate criterion values in a constant time per solution in a neighbourhood. The application is given on the basis of the Nawaz–Ensore–Ham method, iterative local search, tabu search, and a family of memetic search algorithms, where their efficiency equipped with our method is compared with their classical versions. The computational experiments confirm the theoretical analysis that our approach essentially overwhelms the typical implementation and speeds up the related algorithms over 200 times for reasonable problem sizes. Thereby it allows us to search for significantly larger solution space at the same time and, therefore, radically improve the criterion values of the obtained solutions.

Multithreaded and GPU-Based Implementations of a Modified Particle Swarm Optimization Algorithm with Application to Solving Large-Scale Systems of Nonlinear Equations

This paper presents a novel Graphics Processing Unit (GPU) accelerated implementation of a modified Particle Swarm Optimization (PSO) algorithm specifically designed to solve large-scale Systems of Nonlinear Equations (SNEs). The proposed GPU-based parallel version of the PSO algorithm uses the inherent parallelism of modern hardware architectures. Its performance is compared against both sequential and multithreaded Central Processing Unit (CPU) implementations. The primary objective is to evaluate the efficiency and scalability of PSO across different hardware platforms with a focus on solving large-scale SNEs involving thousands of equations and variables. The GPU-parallelized and multithreaded versions of the algorithm were implemented in the Julia programming language. Performance analyses were conducted on an NVIDIA A100 GPU and an AMD EPYC 7643 CPU. The tests utilized a set of challenging, scalable SNEs with dimensions ranging from 1000 to 5000. Results demonstrate that the GPU accelerated modified PSO substantially outperforms its CPU counterparts, achieving substantial speedups and consistently surpassing the highly optimized multithreaded CPU implementation in terms of computation time and scalability as the problem size increases. Therefore, this work evaluates the trade-offs between different hardware platforms and underscores the potential of GPU-based parallelism for accelerating SNE solvers.

Fleet size problem for one-way electric carsharing services considering customers’ waiting tolerance and waiting stress

Fleet size problem for one-way electric carsharing services considering customers’ waiting tolerance and waiting stress

P-749. Prognostic Determinants in Patients with Necrotizing Fasciitis in a Referral Hospital in Nicaragua

Abstract Background Necrotizing fasciitis is a serious condition in which timely diagnosis and therapeutic approaches, both medical and surgical, play an essential role in prognosis. In Nicaragua, the prognostic determinants for this clinical condition in different hospital institutions have not been formally evaluated. The aim of this study was to establish factors that could be determinant for the prognosis of patients with necrotizing fasciitis in a national referral hospital.Table 1.Organ Failure at the Admission in Patients with Necrotizing Fasciitis Methods This is a retrospective, cross-sectional, analytical study. It was conducted with patients over 15 years of age with a diagnosis of necrotizing fasciitis hospitalized between January 201 and December 2023 in Dr. Fernando Vélez Paiz Hospital in Managua, Nicaragua. Clinical factors such as comorbidities or organ failure on admission, as well as the LRINEC score, and the times of administration of the first dose of antibiotic and surgical intervention were analyzed and correlated with the clinical outcome of the patients.Table 2.Potentials Prognosis Determinants in Necrotizing Fasciitis that were Evaluated Results A total of 45 patients were included in the study, 60% of whom were women. The mean age was 56.0 ± 14.6 years-old. 86.7% of patients had at least one comorbidity. At the time of diagnosis, 31.1% of patients had renal insufficiency, 24.4% had shock, and 15.6% had altered state of consciousness (Table 1). 25 patients died for a mortality rate of 44.4%. The mean time from suspected diagnosis and administration of the first dose of antibiotics was 2.5 ± 1.4 hours (Table 2). Only 20% of patients received the first dose of the antibiotic at the first hour. 17.8% of patients underwent surgery within 6 hours of diagnosis. There was no association between prognosis and the time of initiation of antibiotic therapy and the time of initiation of surgical treatment after diagnosis of necrotizing fasciitis, probably due to a problem of sample size. 60% of the deceased had LRINEC ≥ 8 points, while only 28% in the group of survivors, OR(95%CI): 3.85 (1.10-13.46) (Table 3).Table 3.LRINEC and Mortality in Patients with Necrotizing Fasciitis Conclusion Patients with necrotizing fasciitis mostly have comorbidity, mainly diabetes, and almost a quarter of them have shock and a third have renal failure. A LRINEC score greater than or equal to 8 points significantly increases the risk of death in this type of patient. Disclosures All Authors: No reported disclosures

Parallel Primal-Dual Method with Linearization for Structured Convex Optimization

This paper presents the Parallel Primal-Dual (PPD3) algorithm, an innovative approach to solving optimization problems characterized by the minimization of the sum of three convex functions, including a Lipschitz continuous term. The proposed algorithm operates in a parallel framework, simultaneously updating primal and dual variables, and offers potential computational advantages. This parallelization can greatly accelerate computation, particularly when run on parallel computing platforms. By departing from traditional primal-dual methods that necessitate strict parameter constraints, the PPD3 algorithm removes reliance on the spectral norm of the linear operator, significantly reducing the computational burden associated with its evaluation. As the problem size grows, calculating the spectral norm, which is essential for many primal-dual methods, becomes progressively more expensive. In addition, adaptive step sizes are computed to accelerate the convergence process. In contrast to most primal-dual approaches that employ a fixed step size constrained by a global upper limit throughout all iterations, the adaptive step size is typically greater and may result in faster convergence. An O(1/k) ergodic convergence rate is proved theoretically. Applications in Fused LASSO and image inpainting demonstrate the method’s efficiency in computation time and convergence rate compared to state-of-the-art algorithms.

MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today’s Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this paper, we consider the challenge of scheduling a RT application modelled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTGs on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6 % and area usage reductions of 46.3 % are possible compared to full Software SW and HW execution, respectively.

Priority rule-based heuristics for distributed multi-project scheduling considering global resource failures

In fields such as construction engineering, maintenance services, and globalized manufacturing, enterprises widely adopt a distributed management mode to coordinate and manage multiple projects sharing global resources. Under this management mode, the distributed resource-constrained multi-project scheduling problem (DRCMPSP) has gradually become a core issue in both project management practices and academic research. This article investigates the intricacies of global resource uncertainty within the DRCMPSP framework, meticulously portraying resource failures through a scenario-oriented approach. With the aim of obtaining a high-quality baseline schedule and adeptly countering the consequences of global resource failures, a comprehensive two-phase DRCMPSP planning-repairing model is established. Additionally, a heuristic algorithm, grounded in a Graded Scoring Rule (GSR) and Repairing Graded Scoring Rule (RGSR), is proposed to solve the model. Extensive simulation experiments carried out on the MPSPLIB problem library demonstrate that the GSR/RGSR excels in handling large-scale instances. As global resource conflicts increase, the competition among projects for resources intensifies, and the utilization of GSR/RGSR proves to be significantly more effective in mitigating project delays and reducing stability costs in comparison to other rules. Furthermore, the GSR/RGSR demonstrates remarkable adaptability to variations in problem size and resource conflict intensity, delivering more consistent and dependable optimization outcomes.

Multi-GPU Acceleration for Finite Element Analysis in Structural Mechanics

This work evaluates the computing performance of finite element analysis in structural mechanics using modern multi-GPU systems. We can avoid the usual memory limitations when using one GPU device for many-core computing using multiple GPUs for scientific computing. We use a GPU-awareness MPI approach implementing a suitable smoothed aggregation multigrid for preconditioning an iterative distributed conjugate gradient solver for GPU computing. We evaluate the performance and scalability of different models, problem sizes, and computing resources. We take an efficient multi-core implementation as the reference to assess the computing performance of the numerical results. The numerical results show the advantages and limitations of using distributed many-core architectures to address structural mechanics problems.

Robust Co-Optimization of Medium- and Short-Term Electrical Energy and Flexibility in Electricity Clusters

The increasing penetration of distributed renewable energy sources introduces challenges in maintaining balance within power systems. Civic energy initiatives offer a promising solution by decentralizing balancing responsibilities to local areas, with energy clusters serving as an example of such communities. This article proposes a novel mixed-integer linear programming (MILP) model for optimizing the energy mix within a cluster, addressing both planned balancing (day-ahead) and unplanned real-time adjustments. The proposed approach focuses on mid-term decision-making, including the integration of additional wind energy sources into the cluster and the procurement of new demand-side response (DSR) contracts, that allow for short-term planned and unplanned balancing. While increased wind energy enhances the system’s renewable capacity, it also raises operational stiffness, whereas DSR contracts provide the flexibility necessary for effective system balancing. The model incorporates risk aversion by employing Conditional Value at Risk (CVaR) as a risk measure, enabling a nuanced evaluation of trade-offs between cost and risk. The interactive framework allows decision-makers to tailor solutions by adjusting confidence levels and assigning weights to cost and risk metrics. A representative numerical example, based on a typical energy cluster in Poland, illustrates the model’s applicability. This case study demonstrates that the model responds intuitively to varying decision-maker preferences and can be efficiently solved for practical problem sizes.

Coordinate Partitioning for Difficult Euclidean Max-Sum Diversity Problems

Divide and Conquer: Solving the Euclidean Diversity Problem Through Coordinate Partitioning A common technique for solving difficult optimization problems is to break them into easier-to-manage pieces, yet it is rarely obvious how to do so. This is especially true for the Euclidean max-sum diversity problem, which becomes increasingly difficult as the number of coordinates grows despite the problem size remaining the same. In “Coordinate Partitioning for Difficult Euclidean Max-Sum Diversity Problems,” Spiers, Bui, and Loxton use an old result by Schoenberg to transform Euclidean distances into their squared counterparts, enabling a functional decomposition of the problem’s objective. The authors introduce novel partitioning strategies as well as a globally convergent cutting-plane algorithm, which can effectively solve high-dimension instances. They also present a new class of challenging instances characterized by locations on the surface of a hyperball.

Research on Node Fault Diagnosis in Wireless Sensor Networks Based on a Constrained Data Maximum Entropy BN Parameter Learning Algorithm

Currently, power communication wireless sensor networks (WSNs) exhibit the characteristics of uncertainty and complexity. Furthermore, the application environment is more complex, resulting in nodes damaging easily, power communication breakdown, and economic losses. Dynamic monitoring of power communication WSN nodes is necessary. To this end, a constraint data maximum entropy Bayesian network (BN) parameter learning algorithm is used to solve the problem of small data sample size in monitoring and improve the quality of power communication WSN node fault diagnosis. After the latest verification, it is found that the correct rate of WSN node fault diagnosis under small data set is as follows: in normal conditions, the fault rate is 96%, the sensor fault is 94%, the power supply fault is 100%, the wireless communication fault is 90%, and the processor fault is 91%. It can be seen that even under small sample data sets, better diagnostic accuracy can be brought.

Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming

To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one has to rely on classical exact methods like integer programming. State-of-the-art integer programming algorithms can compute strong relaxation bounds even for hard instances, but may have to enumerate a large number of subproblems for determining an optimum solution. If the potential of quantum computing realizes, it can be expected that in particular finding high-quality solutions for hard problems can be done fast. Still, near-future quantum hardware considerably limits the size of treatable problems. In this work, we go one step into integrating the potentials of quantum and classical techniques for combinatorial optimization. We propose a hybrid heuristic for the weighted maximum-cut problem and for quadratic unconstrained binary optimization. The heuristic employs a linear programming relaxation, rendering it well-suited for integration into exact branch-and-cut algorithms. For large instances, we reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. Moreover, we improve the applicability of depth-1 QAOA, a parameterized quantum algorithm, by deriving a parameter estimate for arbitrary instances. We present numerous computational results from real quantum hardware.

Finite-Choice Logic Programming

Logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that vary along some set of choices while maintaining structural and logical constraints—there is no single canonical model. The notion of stable models for logic programs with negation has successfully captured programmer intuition about the set of valid solutions for such problems, giving rise to a family of programming languages and associated solvers known as answer set programming. Unfortunately, the definition of a stable model is frustratingly indirect, especially in the presence of rules containing free variables. We propose a new formalism, finite-choice logic programming, that uses choice, not negation, to admit multiple solutions. Finite-choice logic programming contains all the expressive power of the stable model semantics, gives meaning to a new and useful class of programs, and enjoys a least-fixed-point interpretation over a novel domain. We present an algorithm for exploring the solution space and prove it correct with respect to our semantics. Our implementation, the Dusa logic programming language, has performance that compares favorably with state-of-the-art answer set solvers and exhibits more predictable scaling with problem size.

Prefabrication Between Tradition and Innovation: The First Nucleus of Mirafiori Sud in Turin

In Italy, the start of prefabrication and building industrialization experiences took place slowly and late compared to other European countries. The debate between the main workers in the construction sector and the representatives of the economic and political world on post-war reconstruction was oriented towards a substantial reconfirmation of traditional construction methods. Added to the productive backwardness of the Italian construction industry was the difficulty of supplying materials and the opposition of much of the academic and professional culture to the experimentation and introduction of industrialized systems. The problems posed by the severe housing deficit of the post-war and following years laboriously paved the way for the first national experiments with prefabrication and building industrialization systems. Due to the need to act urgently to contain construction costs – a relevant problem given the size of the housing problem – the rules of the 1963 Gescal were explicitly addressed to the use of industrialized and prefabricated construction systems. The Gescal years allowed Italy to start large-scale experimentation and application of prefabrication. Among the public interventions of the early 1960s, the construction of the first nucleus of the Mirafiori Sud district in Turin stands out for its peculiar, almost experimental dimension between tradition and innovation.

Pengaruh Perekonomian Mikro dan Makro terhadap Pertumbuhan Ekonomi Indonesia

Finding out how the macro and micro economies affect Indonesia's economic growth is the aim of this study. The problem with the national economy is the scope of macroeconomics. There will surely be difficulties when a country's economy expands. The nation's state governments must address that impediment since it is an internal problem. The problems of inflation and unemployment cannot be separated from the problems of Indonesia's population size and quality due to a variety of obstacles. Of course, an autonomous nation implements its policies to deal with these problems. Whether a policy is successful or not, it is something to aim for. A skilled labor force capable of importing and exporting goods that can make the nation visible to the rest of the world can support the country's economic growth.

Multimodal deep learning-based feature fusion for object detection in remote sensing images

Object detection is an important computer vision task, which is developed from image classification task. The difference is that it is no longer only to classify a single type of object in an image, but to complete the classification and positioning of multiple objects that may exist in an image at the same time. Classification refers to assigning category labels to the object, and positioning refers to determining the vertex coordinates of the peripheral rectangular box of the object. Therefore, object detection is more challenging and has broader application prospects, such as automatic driving, face recognition, pedestrian detection, medical detection etc,. Object detection can also be used as the research basis for more complex computer vision task such as image segmentation, image description, object tracking and action recognition. In traditional object detection, the feature utilization rate is low and it is easy to be affected by other environmental factors. Hence, this paper proposes a multimodal deep learning-based feature fusion for object detection in remote sensing images. In the new model, cascade RCNN is the backbone network. Parallel cascade RCNN network is utilized for feature fusion to enhance feature expression ability. In order to solve the problem of different segmentation shapes and sizes, the central part of the network adopts multi-coefficient cascaded hollow convolution to obtain multi-receptive field features without using pooling mode and preserving image information. Meanwhile, an improved self36 attention combined receptive field strategy is used to obtain both low-level features with marginal details and high-level features with global semantics. Finally, we conduct experiments on DOTA set including ablation experiments and comparison experiments. The experimental results show that the mean Average Precision (mAP) and other indexes have been greatly improved, and its performance is better than the state-of-the-art detection algorithms. It has a good application prospect in the remote sensing image object detection task.

Compiler Sequence Optimization Using Machine Learning Prediction Method

Compiler optimization is crucial in improving program performance by improving execution speed, reducing memory usage, and minimizing energy consumption. Nevertheless, modern compilers, such as LLVM, with their numerous optimization passes, present a significant challenge in identifying the most effective sequence for optimizing a program. This study addresses the complex problem of determining optimal compiler optimization sequences within the LLVM framework, which encompasses 64 optimization passes, causing in an immense search space of 264264. Identifying the ideal sequence for even simple code can be an arduous task, as the interactions between passes are intricate and unpredictable. The primary objective of this research is to utilize machine-learning techniques to predict effective optimization sequences that outperform the default -O2 and -O3 optimization flags. The methodology involves generating 2,000 sequences per program and picking the one that achieves the shortest execution time. Three machine learning models—K-Nearest Neighbor (KNN), Decision Tree (DT), and Feedforward Neural Network (FFNN)—were employed to predict the optimization sequences based on features extracted from programs during execution. The study used benchmarks from Polybench, Shootout, and Stanford suites, each with varying problem sizes, to validate the proposed technique. The results demonstrate that the KNN model produced optimization sequences with superior performance compared to DT and FFNN. On average, KNN achieved execution times that were 2.5 times faster than those achieved using the O3 optimization flag. This research contributes to the field by programming the process of selecting optimal compiler sequences, which significantly reduces execution time and eliminates the need for manual tuning. It highlights the potential of machine learning in compiler optimization, offering a robust and scalable approach to improving program performance and setting the foundation for future advancements in the domain.

Cognitive and Neural Differences in Exact and Approximate Arithmetic Using the Production Paradigm: An fNIRS Study.

This study investigated the cognitive and neural mechanisms of exact and approximate arithmetic using fNIRS technology during natural calculation processes (i.e., the production paradigm). Behavioral results showed (1) a significantly longer reaction time for exact arithmetic compared to approximate arithmetic, and (2) both exact and approximate arithmetic exhibited a problem size effect, with larger operands requiring more time. The fNIRS results further revealed differences in the neural bases underlying these two arithmetic processes, with exact arithmetic showing greater activation in the L-SFG (left superior frontal gyrus, CH16), while approximate arithmetic exhibited problem size effect in the right hemisphere. Additionally, larger operands registered more brain activities in the R-DLPFC (right dorsolateral prefrontal cortex, CH4), R-SFG (right superior frontal gyrus, CH2), and PMC and SMA (pre- and supplementary motor cortexes, CH3) compared to smaller operands in approximate arithmetic. Moreover, correlation analysis found a significant correlation between approximate arithmetic and semantic processing in the R-PMC and R-SMA (right pre- and supplementary motor cortexes). These findings suggest a neural dissociation between exact and approximate arithmetic, with exact arithmetic processing showing a dominant role in the left hemisphere, while approximate arithmetic processing was more sensitive in the right hemisphere.

PHYSICS BASED ALGORITHMS FOR FUZZY TRANSPORTATION PROBLEM

Transportation problems are computationally challenging due to their nondeterministic polynomial-time hard (NP-hard) nature, especially when uncertainties in costs, supply, and demand are involved. The fuzzy transportation problem addresses these uncertainties by representing them as fuzzy numbers, requiring robust methods for ranking and solving optimization models. Traditional exact methods become impractical for such problems, leading to the adoption of metaheuristic approaches. This study introduces three novel physics-based algorithms: one single-point-based and two population-based methods. These algorithms leverage principles from gravitational attraction, electromagnetism, and water flow dynamics, incorporating innovative neighborhood structures tailored to the fuzzy nature of transportation problems. The proposed methods were tested on diverse problem sizes and compared against established optimization techniques, such as genetic algorithms and commercial software, using Python implementations. An experimental design methodology was employed to optimize parameter tuning, enhancing computational efficiency and reducing experimentation time. Results demonstrate that the physics-based algorithms achieve competitive performance in terms of solution quality, computational efficiency, and adaptability to uncertainty. These findings underscore the potential of physics-based methods to advance optimization in fuzzy transportation and other domains with inherent uncertainties, paving the way for further research in hybrid and multi-objective approaches.