Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Numerical Analysis and Computation
Title: Exact and Heuristic Methods for the Assembly Line Worker Assignment and Balancing Problem
Abstract: In traditional assembly lines, it is reasonable to assume that task execution times are the same for each worker. However, in sheltered work centres for disabled this assumption is not valid: some workers may execute some tasks considerably slower or even be incapable of executing them. Worker heterogeneity leads to a problem called the assembly line worker assignment and balancing problem (ALWABP). For a fixed number of workers the problem is to maximize the production rate of an assembly line by assigning workers to stations and tasks to workers, while satisfying precedence constraints between the tasks. This paper introduces new heuristic and exact methods to solve this problem. We present a new MIP model, propose a novel heuristic algorithm based on beam search, as well as a task-oriented branch-and-bound procedure which uses new reduction rules and lower bounds for solving the problem. Extensive computational tests on a large set of instances show that these methods are effective and improve over existing ones.
Extracted statement — has research problem: Assembly line worker assignment and balancing problem
Research field: Numerical Analysis and Computation
Title: Solving Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with FSS Algorithm
Abstract: Balancing assembly lines, a family of optimization problems commonly known as Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of problems of enormous practical interest to manufacturing industry due to the relevant frequency of this type of production paradigm. For this reason, many researchers on Computational Intelligence and Industrial Engineering have been conceiving algorithms for tackling different versions of assembly line balancing problems utilizing different methodologies. In this article, it was proposed a problem version referred as Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with the intention of including pressing issues of real assembly lines in the optimization problem, to which four versions were conceived. Heuristic search procedures were used, namely two Swarm Intelligence algorithms from the Fish School Search family: the original version, named "vanilla", and a special variation including a stagnation avoidance routine. Either approaches solved the newly posed problem achieving good results when compared to Particle Swarm Optimization algorithm.
Extracted statement — has research problem: Optimization problem
Research field: Numerical Analysis and Computation
Title: Iterative Beam Search for Simple Assembly Line Balancing with a Fixed Number of Work Stations
Abstract: The simple assembly line balancing problem (SALBP) concern s the assignment of tasks with pre-defined processing times to work stations that are arran ged in a line. Hereby, precedence constraints between the tasks must be respected. The optimi zation goal of the SALBP-2 variant of the problem concerns the minimization of the so-called cy cle time, that is, the time in which the tasks of each work station must be completed. In this work we p ropose to tackle this problem with an iterative search method based on beam search. The propose d algorithm is able to generate optimal solutions, respectively the best upper bounds, for 283 out of 302 test cases. Moreover, for 9 further test cases the algorithm is able to improve the c urrently best upper bounds. These numbers indicate that the proposed iterative beam search al gorithm is currently a state-of-the-art method for the SALBP-2
Extracted statement — has research problem: Simple assembly line balancing problem (SALBP)
Research field: Numerical Analysis and Computation
Title: Assembly line balancing with task division
Abstract: In a commonly-used version of the Simple Assembly Line Balancing Problem (SALBP-1) tasks are assigned to stations along an assembly line with a fixed cycle time in order to minimize the required number of stations. It has traditionally been assumed that the total work needed for each product unit has been partitioned into economically indivisible tasks. However, in practice, it is sometimes possible to divide particular tasks in limited ways at additional time penalty cost. Despite the penalties, task division where possible, now and then leads to a reduction in the minimum number of stations. Deciding which allowable tasks to divide creates a new assembly line balancing problem, TDALBP (Task Division Assembly Line Balancing Problem). We propose a mathematical model of the TDALBP, an exact solution procedure for it and present promising computational results for the adaptation of some classical SALBP instances from the research literature. The results demonstrate that the TDALBP sometimes has the potential to significantly improve assembly line performance.
Extracted statement — has research problem: Task division assembly line balancing problem
Research field: Operations Research, Systems Engineering and Industrial Engineering
Title: Supply chain network design under the risk of uncertain disruptions
Abstract: Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.
Extracted statement — has research problem: Supply chain
Research field: Organic Chemistry
Title: Solvent-Free Chelation-Assisted Catalytic C-C Bond Cleavage of Unstrained Ketone by Rhodium(I) Complexes under Microwave Irradiation
Abstract: A highly efficient C-C bond cleavage of unstrained aliphatic ketones bearing β-hydrogens with olefins was achieved using a chelation-assisted catalytic system consisting of (Ph 3 P) 3 RhCl and 2-amino-3-picoline by microwave irradiation under solvent-free conditions. The addition of cyclohexylamine catalyst accelerated the reaction rate dramatically under microwave irradiation compared with the classical heating method.
Extracted statement — has research problem: C-C bond cleavage
Research field: Science
Title: Less is more: Active learning with support vector machines
Abstract: We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks. We observe a number of benefits, the most surprising of which is that a SVM trained on a wellchosen subset of the available corpus frequently performs better than one trained on all available data. The heuristic for choosing this subset is simple to compute, and makes no use of information about the test set. Given that the training time of SVMs depends heavily on the training set size, our heuristic not only offers better performance with fewer data, it frequently does so in less time than the naive approach of training on all available data.
Extracted statement — has research problem: Active learning
Research field: Science
Title: Support vector machine active learning with applications to text classification
Abstract: Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
Extracted statement — has research problem: Active learning
Research field: Science
Title: Active learning using adaptive resampling
Abstract: Classi cation modeling (a.k.a. supervised learning) is an extremely useful analytical technique for developing predictive and forecasting applications. The explosive growth in data warehousing and internet usage has made large amounts of data potentially available for developing classi cation models. For example, natural language text is widely available in many forms (e.g., electronic mail, news articles, reports, and web page contents). Categorization of data is a common activity which can be automated to a large extent using supervised learning methods. Examples of this include routing of electronic mail, satellite image classi cation, and character recognition. However, these tasks require labeled data sets of su ciently high quality with adequate instances for training the predictive models. Much of the on-line data, particularly the unstructured variety (e.g., text), is unlabeled. Labeling is usually a expensive manual process done by domain experts. Active learning is an approach to solving this problem and works by identifying a subset of the data that needs to be labeled and uses this subset to generate classi cation models. We present an active learning method that uses adaptive resampling in a natural way to signi cantly reduce the size of the required labeled set and generates a classi cation model that achieves the high accuracies possible with current adaptive resampling methods.
Extracted statement — has research problem: Active learning
Research field: Science
Title: Toward optimal active learning through sampling estimation of error reduction
Abstract: This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expected future error is intractable. Our approach is made feasible by taking a sampling approach to estimating the expected reduction in error due to the labeling of a query. In experimental results on two real-world data sets we reach high accuracy very quickly, sometimes with four times fewer labeled examples than competing methods.
Extracted statement — has research problem: Active learning
Research field: Science
Title: A probabilistic active support vector learning algorithm
Abstract: The paper describes a probabilistic active learning strategy for support vector machine (SVM) design in large data applications. The learning strategy is motivated by the statistical query model. While most existing methods of active SVM learning query for points based on their proximity to the current separating hyperplane, the proposed method queries for a set of points according to a distribution as determined by the current separating hyperplane and a newly defined concept of an adaptive confidence factor. This enables the algorithm to have more robust and efficient learning capabilities. The confidence factor is estimated from local information using the k nearest neighbor principle. The effectiveness of the method is demonstrated on real-life data sets both in terms of generalization performance, query complexity, and training time.
Extracted statement — has research problem: Active learning
Research field: Science
Title: Active learning using pre-clustering
Abstract: The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary, better performance can be achieved by taking into account the prior data distribution. The main contribution of the paper is a formal framework that incorporates clustering into active learning. The algorithm first constructs a classifier on the set of the cluster representatives, and then propagates the classification decision to the other samples via a local noise model. The proposed model allows to select the most representative samples as well as to avoid repeatedly labeling samples in the same cluster. During the active learning process, the clustering is adjusted using the coarse-to-fine strategy in order to balance between the advantage of large clusters and the accuracy of the data representation. The results of experiments in image databases show a better performance of our algorithm compared to the current methods.
Extracted statement — has research problem: Active learning
Research field: Science
Title: Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
Abstract: Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.
Extracted statement — has research problem: Active learning
Research field: Science
Title: Cytogenetic abnormalities in adult acute lymphoblastic leukemia: correlations with hematologic findings outcome A Collaborative Study of the Group Francais de Cyto- genetique Hematologique
Abstract: Cytogenetic analyses performed at diagnosis on 443 adult patients with acute lymphoblastic leukemia (ALL) were reviewed by the Groupe Français de Cytogénétique Hématologique, correlated with hematologic data, and compared with findings for childhood ALL. This study showed that the same recurrent abnormalities as those reported in childhood ALL are found in adults, and it determined their frequencies and distribution according to age. Hyperdiploidy greater than 50 chromosomes with a standard pattern of chromosome gains had a lower frequency (7%) than in children, and was associated with the Philadelphia chromosome (Ph) in 11 of 30 cases. Tetraploidy (2%) and triploidy (3%) were more frequent than that in childhood ALL. Hypodiploidy 30-39 chromosomes (2%), characterized by a specific pattern of chromosome losses, might be related to the triploid group that evoked a duplication of the 30-39 hypodiploidy. Both groups shared similar hematologic features. Ph+ ALL (29%) peaked in the 40- to 50-year-old age range (49%) and showed a high frequency of myeloid antigens (24%). ALL with t(1;19) (3%) occurred in young adults (median age, 22 years). In T-cell ALL (T-ALL), frequencies of 14q11 breakpoints (26%) and of t(10;14)(q24;q11) (14%) were higher than those in childhood ALL. New recurrent changes were identified, ie, monosomies 7 present in Ph-ALL (17%) and also in other ALL (8%) and two new recurrent translocations, t(1;11)(p34;pll) in T-ALL and t(1;7)(q11-21;q35-36) in Ph+ ALL. The ploidy groups with a favorable prognostic impact were hyperdiploidy greater than 50 without Ph chromosome (median event-free survival [EFS], 46 months) and tetraploidy (median EFS, 46 months). The recurrent abnormalities associated with better response to therapy were also significantly correlated to T-cell lineage. Among them, t(10;14)(q24;q11) (median EFS, 46 months) conferred the best prognostic impact (3-year EFS, 75%). Hypodiploidy 30-39 chromosomes and the related triploidy were associated with poor outcome. All Ph-ALL had short EFS (median EFS, 5 months), and no additional change affected this prognostic impact. Most patients with t(1;19) failed therapy within 1 year. Patients with 11q23 changes not because of t(4;11) had a poor outcome, although they did not present the high-risk factors found in t(4;11).
Extracted statement — has research problem: acute lymphoblastic leukemia (ALL)
Research field: Science
Title: How Would Monetary Policy Matter in the Proposed African Monetary Unions? Evidence from Output and Prices
Abstract: We analyze the effects of monetary policy on economic activity in the proposed African monetary unions. Findings broadly show that: (1) but for financial efficiency in the EAMZ, monetary policy variables affect output neither in the short-run nor in the long-term and; (2) with the exception of financial size that impacts inflation in the EAMZ in the short-term, monetary policy variables generally have no effect on prices in the short-run. The WAMZ may not use policy instruments to offset adverse shocks to output by pursuing either an expansionary or a contractionary policy, while the EAMZ can do with the ‘financial allocation efficiency’ instrument. Policy implications are discussed.
Extracted statement — has research problem: African monetary unions
Research field: Science
Title: Understanding post-adoptive agile usage: An exploratory cross- case analysis
Abstract: The widespread adoption of agile methodologies raises the question of their continued and effective usage in organizations. An agile usage model consisting of innovation, sociological, technological, team, and organizational factors is used to inform an analysis of post-adoptive usage of agile practices in two major organizations. Analysis of the two case studies found that a methodology champion and top management support were the most important factors influencing continued usage, while innovation factors such as compatibility seemed less influential. Both horizontal and vertical usage was found to have significant impact on the effectiveness of agile usage.
Extracted statement — has research problem: Agile Usage
Research field: Science
Title: An Ant Colony Optimization Approach to Test Sequence Generation for Statebased Software Testing
Abstract: Properly generated test suites may not only locate the defects in software systems, but also help in reducing the high cost associated with software testing, ft is often desired that test sequences in a test suite can be automatically generated to achieve required test coverage. However, automatic test sequence generation remains a major problem in software testing. This paper proposes an ant colony optimization approach to automatic test sequence generation for state-based software testing. The proposed approach can directly use UML artifacts to automatically generate test sequences to achieve required test coverage.
Extracted statement — has research problem: Ant Colony Optimization
Research field: Science
Title: Automatic Mutation Test Input Data Generation via Ant Colony
Abstract: Fault-based testing is often advocated to overcome limitations ofother testing approaches; however it is also recognized as beingexpensive. On the other hand, evolutionary algorithms have beenproved suitable for reducing the cost of data generation in the contextof coverage based testing. In this paper, we propose a newevolutionary approach based on ant colony optimization for automatictest input data generation in the context of mutation testingto reduce the cost of such a test strategy. In our approach the antcolony optimization algorithm is enhanced by a probability densityestimation technique. We compare our proposal with otherevolutionary algorithms, e.g., Genetic Algorithm. Our preliminaryresults on JAVA testbeds show that our approach performed significantlybetter than other alternatives.
Extracted statement — has research problem: Ant Colony Optimization
Research field: Science
Title: A Non- Pheromone based Intelligent Swarm Optimization Technique in Software Test Suite Optimization
Abstract: In our paper, we applied a non-pheromone based intelligent swarm optimization technique namely artificial bee colony optimization (ABC) for test suite optimization. Our approach is a population based algorithm, in which each test case represents a possible solution in the optimization problem and happiness value which is a heuristic introduced to each test case corresponds to the quality or fitness of the associated solution. The functionalities of three groups of bees are extended to three agents namely Search Agent, Selector Agent and Optimizer Agent to select efficient test cases among near infinite number of test cases. Because of the parallel behavior of these agents, the solution generation becomes faster and makes the approach an efficient one. Since, the test adequacy criterion we used is path coverage; the quality of the test cases is improved during each iteration to cover the paths in the software. Finally, we compared our approach with Ant Colony Optimization (ACO), a pheromone based optimization technique in test suite optimization and finalized that, ABC based approach has several advantages over ACO based optimization.
Extracted statement — has research problem: Ant Colony Optimization
Research field: Science
Title: Variable Strength Interaction Testing with an Ant Colony System Approach
Abstract: Interaction testing (also called combinatorial testing) is an cost-effective test generation technique in software testing. Most research work focuses on finding effective approaches to build optimal t-way interaction test suites. However, the strength of different factor sets may not be consistent due to the practical test requirements. To solve this problem, a variable strength combinatorial object and several approaches based on it have been proposed. These approaches include simulated annealing (SA) and greedy algorithms. SA starts with a large randomly generated test suite and then uses a binary search process to find the optimal solution. Although this approach often generates the minimal test suites, it is time consuming. Greedy algorithms avoid this shortcoming but the size of generated test suites is usually not as small as SA. In this paper, we propose a novel approach to generate variable strength interaction test suites (VSITs). In our approach, we adopt a one-test-at-a-time strategy to build final test suites. To generate a single test, we adopt ant colony system (ACS) strategy, an effective variant of ant colony optimization (ACO). In order to successfully adopt ACS, we formulize the solution space, the cost function and several heuristic settings in this framework. We also apply our approach to some typical inputs. Experimental results show the effectiveness of our approach especially compared to greedy algorithms and several existing tools.
Extracted statement — has research problem: Ant Colony Optimization