scholarly journals Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder

1998 ◽  
Vol 6 (1) ◽  
pp. 61-80 ◽  
Author(s):  
Emma Hart ◽  
Peter Ross ◽  
Jeremy Nelson
Keyword(s):  
Traditional Approach ◽  
Real Life ◽  
Population Based ◽  
Hill Climbing ◽  
Distinct Advantage ◽  
Scheduling Problem ◽  
Flexible System ◽  
Real World Problem ◽  
Large Numbers ◽  
Permutation Schedule

This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

scholarly journals Solving the nuclear dismantling project scheduling problem by combining mixed-integer and constraint programming techniques and metaheuristics

2021 ◽  
Author(s):  
Felix Hübner ◽  
Patrick Gerhards ◽  
Christian Stürck ◽  
Rebekka Volk
Keyword(s):  
Constraint Programming ◽  
Project Scheduling ◽  
Computational Study ◽  
Real Life ◽  
Mixed Integer ◽  
Extensive Literature ◽  
Scheduling Problem ◽  
Multiple Modes ◽  
Project Scheduling Problem ◽  
Optimisation Techniques

AbstractScheduling of megaprojects is very challenging because of typical characteristics, such as expected long project durations, many activities with multiple modes, scarce resources, and investment decisions. Furthermore, each megaproject has additional specific characteristics to be considered. Since the number of nuclear dismantling projects is expected to increase considerably worldwide in the coming decades, we use this type of megaproject as an application case in this paper. Therefore, we consider the specific characteristics of constrained renewable and non-renewable resources, multiple modes, precedence relations with and without no-wait condition, and a cost minimisation objective. To reliably plan at minimum costs considering all relevant characteristics, scheduling methods can be applied. But the extensive literature review conducted did not reveal a scheduling method considering the special characteristics of nuclear dismantling projects. Consequently, we introduce a novel scheduling problem referred to as the nuclear dismantling project scheduling problem. Furthermore, we developed and implemented an effective metaheuristic to obtain feasible schedules for projects with about 300 activities. We tested our approach with real-life data of three different nuclear dismantling projects in Germany. On average, it took less than a second to find an initial feasible solution for our samples. This solution could be further improved using metaheuristic procedures and exact optimisation techniques such as mixed-integer programming and constraint programming. The computational study shows that utilising exact optimisation techniques is beneficial compared to standard metaheuristics. The main result is the development of an initial solution finding procedure and an adaptive large neighbourhood search with iterative destroy and recreate operations that is competitive with state-of-the-art methods of related problems. The described problem and findings can be transferred to other megaprojects.

scholarly journals Metaheuristics for Order Scheduling Problem with Unequal Ready Times

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jan-Yee Kung ◽  
Jiahui Duan ◽  
Jianyou Xu ◽  
I-Hong Chung ◽  
Shuenn-Ren Cheng ◽  
...  
Keyword(s):  
Real Life ◽  
Experimental Tests ◽  
Service Systems ◽  
Scheduling Problem ◽  
Due Dates ◽  
Order Scheduling ◽  
Customer Order ◽  
Scheduling Model ◽  
Unequal Ready Times ◽  
Customer Order Scheduling

In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different products, convene as a product development team, and that team completes a product design only after all the modules have been designed. In real-life situations, a customer order can have some requirements including due dates, weights of jobs, and unequal ready times. Once encountering different ready times, waiting for future order or job arrivals to raise the completeness of a batch is an efficient policy. Meanwhile, the literature releases that few studies have taken unequal ready times into consideration for order scheduling problem. Motivated by this limitation, this study addresses an OS scheduling model with unequal order ready times. The objective function is to find a schedule to optimize the total completion time criterion. To solve this problem for exact solutions, two lower bounds and some properties are first derived to raise the searching power of a branch-and-bound method. For approximate solution, four simulated annealing approaches and four heuristic genetic algorithms are then proposed. At last, several experimental tests and their corresponding statistical outcomes are also reported to examine the performance of all the proposed methods.

scholarly journals P456 Drug survival of thiopurine–allopurinol combination therapy in a real-life population-based cohort

2018 ◽  
Vol 12 (supplement_1) ◽  
pp. S335-S335
Author(s):  
J E Kreijne ◽  
R C de Veer ◽  
N K de Boer ◽  
G Bouma ◽  
G Dijkstra ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Real Life ◽  
Population Based ◽  
Drug Survival

scholarly journals Sustainable education in Russia

2020 ◽  
Vol 208 ◽  
pp. 09032
Author(s):  
Yuliya Masalova
Keyword(s):  
Sustainable Development ◽  
Professional Education ◽  
Traditional Approach ◽  
Real Life ◽  
Modern World ◽  
Russian Higher Education ◽  
Key Factor ◽  
Integral Element ◽  
Higher Professional Education

In the modern world, many countries recognize that education should act in the interests and for the benefit of the ideas of sustainable development. At the same time, sustainable development itself becomes an integral element of quality education and its key factor. In Russia, the “national strategy of education for sustainable development” has been formed, which provides for a reorientation from ensuring that students have certain knowledge to the ability to analyse real problems and find possible solutions for them. At the same time, the traditional approach to teaching based on the study of specific subjects remains, but it is important to ensure that each student is able to carry out an interdisciplinary analysis of real-life situations. Currently, a project on “Key areas of development of Russian education for achieving the goals and objectives of sustainable development in the education system” until 2035 has been developed and published, which contains indicators that allow us to assess the achievement of the goals set out in it. However, this document hardly presents the level of higher professional education and its contribution to sustainable development. The article will examine the role of Russian higher education in the concept of sustainable development.

scholarly journals Blood Coagulation Algorithm: A Novel Bio-Inspired Meta-Heuristic Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3011
Author(s):  
Drishti Yadav
Keyword(s):  
Blood Coagulation ◽  
Heuristic Algorithms ◽  
Cooperative Behavior ◽  
Real Life ◽  
Search Space ◽  
Population Based ◽  
Test Analysis ◽  
Search Spaces ◽  
Consistent Performance ◽  
The Comparative Study

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems.

scholarly journals INFLUENCE ON HOMEOSTASIS AS THE CRITERION FOR SELECTING SINGLE NUCLEOTIDE POLYMORPHISMS FOR THE STUDY OF METABOLIC SYNDROM IN THE KAZAKH POPULATION

REPORTS ◽  
2020 ◽  
Vol 5 (333) ◽  
pp. 86-93
Author(s):  
V.V. Benberin ◽  
◽  
T.A. Voshchenkova ◽  
A.A. Nagymtayeva ◽  
A.S. Sibagatova ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Traditional Approach ◽  
Population Based ◽  
Malignant Neoplasms ◽  
Nucleotide Polymorphisms ◽  
Genetic Determinants ◽  
Single Nucleotide ◽  
Sequence Of Events ◽  
Kazakh Population ◽  
Symptom Complex

Metabolic syndrome (MS) is increasingly cited as the world's leading health risk. The sequence of events toward multimorbidity in most cases passes through MS. According to the research, MS heritability ranges from 23 to 27% in Europeans, and 51 to 60% in Asians. The purpose of the review: to form a strategy for the selection of single nucleotide polymorphisms (SNPs) for the study of MS in the Kazakh population based on the effect of SNPs on homeostasis indicators The stable symptom complex of MS is a complicated dynamic system of successive accumulations of dysmetabolic disorders of homeostasis. This system starts the development of subsequent age-associated diseases), such as cardiometabolic, neurodegenerative, and malignant neoplasms. The system for selecting SNPs for the MS study, proposed on the basis of the concept of homeostasis dysfunction, assumes, in conditions of limited resources, to see the greatest level of their influence within the conditional framework of three genetic models of homeostasis dysregulation: insulin resistance , oxidative stress, and chronic inflammation. This approach is fundamentally different from the traditional approach involving candidate genes. It is expected that scientific research in this direction will contribute not only to the understanding of general biological processes, but also to the targeted search for genetic determinants and for new opportunities for personalized interventions.

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