6,324 research outputs found
Operational Research in Education
Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions
Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
The large number of possible configurations of modern software-based systems,
combined with the large number of possible environmental situations of such
systems, prohibits enumerating all adaptation options at design time and
necessitates planning at run time to dynamically identify an appropriate
configuration for a situation. While numerous planning techniques exist, they
typically assume a detailed state-based model of the system and that the
situations that warrant adaptations are known. Both of these assumptions can be
violated in complex, real-world systems. As a result, adaptation planning must
rely on simple models that capture what can be changed (input parameters) and
observed in the system and environment (output and context parameters). We
therefore propose planning as optimization: the use of optimization strategies
to discover optimal system configurations at runtime for each distinct
situation that is also dynamically identified at runtime. We apply our approach
to CrowdNav, an open-source traffic routing system with the characteristics of
a real-world system. We identify situations via clustering and conduct an
empirical study that compares Bayesian optimization and two types of
evolutionary optimization (NSGA-II and novelty search) in CrowdNav
Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'
In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems
Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'
In the power and energy systems area, a progressive increase of literature
contributions containing applications of metaheuristic algorithms is occurring.
In many cases, these applications are merely aimed at proposing the testing of
an existing metaheuristic algorithm on a specific problem, claiming that the
proposed method is better than other methods based on weak comparisons. This
'rush to heuristics' does not happen in the evolutionary computation domain,
where the rules for setting up rigorous comparisons are stricter, but are
typical of the domains of application of the metaheuristics. This paper
considers the applications to power and energy systems, and aims at providing a
comprehensive view of the main issues concerning the use of metaheuristics for
global optimization problems. A set of underlying principles that characterize
the metaheuristic algorithms is presented. The customization of metaheuristic
algorithms to fit the constraints of specific problems is discussed. Some
weaknesses and pitfalls found in literature contributions are identified, and
specific guidelines are provided on how to prepare sound contributions on the
application of metaheuristic algorithms to specific problems
Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics
Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.
Multi-Objective Multi-mode Time-Cost Tradeoff modeling in Construction Projects Considering Productivity Improvement
In today's construction industry, poor performance often arises due to
various factors related to time, finances, and quality. These factors
frequently lead to project delays and resource losses, particularly in terms of
financial resources. This research addresses the Multimode Resource-Constrained
Project Scheduling Problem (MRCPSP), a real-world challenge that takes into
account the time value of money and project payment planning. In this context,
project activities exhibit discrete cost profiles under different execution
conditions and can be carried out in multiple ways. This paper aims to achieve
two primary objectives: minimizing the net present value of project costs and
project completion times while simultaneously improving the project's
productivity index. To accomplish this, a mathematical programming model based
on certain assumptions is proposed. Several test cases are designed, and they
are rigorously evaluated using the methodology outlined in this paper to
validate the modeling approach. Recognizing the NP-hard nature of this problem,
a multi-objective genetic algorithm capable of solving large-scale instances is
developed. Finally, the effectiveness of the proposed solution is assessed by
comparing it to the performance of the NSGA-II algorithm using well-established
efficiency metrics. Results demonstrate the superior performance of the
algorithm introduced in this study.Comment: 40 pages, 20 figures, 7 table
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Solving the minimum labelling spanning tree problem using hybrid local search
Given a connected, undirected graph whose edges are labelled (or coloured), the minimum
labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest
number of distinct labels (or colours). In recent work, the MLST problem has been shown
to be NP-hard and some effective heuristics (Modified Genetic Algorithm (MGA) and Pilot
Method (PILOT)) have been proposed and analyzed. A hybrid local search method, that we
call Group-Swap Variable Neighbourhood Search (GS-VNS), is proposed in this paper. It is
obtained by combining two classic metaheuristics: Variable Neighbourhood Search (VNS) and
Simulated Annealing (SA). Computational experiments show that GS-VNS outperforms MGA
and PILOT. Furthermore, a comparison with the results provided by an exact approach shows
that we may quickly obtain optimal or near-optimal solutions with the proposed heuristic
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