Selection Of Optimal Solution Research Articles

Выбор решений при проектировании сложных систем на основе анализа вариантов со случайными весами

A new one-parameter approach to the selection of optimal solutions for the design of complex systems is proposed. The approach is based on the analysis of a tree of variants with random weights (here weight is a certain non-negative quantity: for example, cost, mass, energy consumption, etc.). At the root of the method suggested in the work lies the fact that the tree of variants forms a matroid, on which the optimal solution can be found using the greedy algorithm. The basis of the method proposed in the paper is the fact that in case of non-negative values of the mathematical expectation and variance of the elements of the variants tree they can be considered as components of vectors belonging to a semiring. It is shown that the appropriate definition of the operations of addition and multiplication makes it possible to define a function on the semiring. This function satisfies the norm axioms of vectors and coincides in structure with the expression for the upper bound of the confidence interval. After determining the weight of the tree elements through the introduced norm function the upper bound of the confidence interval of the variant tree with the minimum weight was found. The approach suggested in the work can be used at various stages of designing complex systems, including, among other things, the development of system profiles, and makes it possible to increase the validity of the decisions made.

Sustainable refurbishment for school buildings: a literature review

PurposeAny building refurbishment is challenging and school buildings offer no exception. They are increasingly in need of refurbishment due to their age and evolving teaching and learning. The purpose of this paper is to present an overview of literature on sustainable refurbishment so as to identify key lessons from selected successful refurbishment projects. The review findings are expected to contribute to the development of refurbishment plans in an effective and innovative manner that should extend building’s service life, focus on resource efficiency, and comfort their users. It will also contribute to knowledge base of refurbishment and suggest future directions for research.Design/methodology/approachThe paper has undertaken a literature review on the sustainability assessment of buildings and frameworks for sustainable refurbishment. Besides, the work also provides a review of recent successful refurbishment projects to collect and structure systems experiences which can be adopted in developing a sustainable refurbishment strategy for school buildings.FindingsFindings include three groups of lessons in terms of reasons, process and barriers in the selected refurbishment projects that assist stakeholders to prepare a suitable refurbishment plan for their school buildings. The potential of 3D scanners and BIM applications in the refurbishment process will also be reviewed in order to develop a proposed framework of 3D scanner vs BIM for the refurbishment process. Recommendations highlight the role of a national strategy as a driving factor for applying the advantages of information technology to enhance optimal solution selection processes to get better and more sustainable results.Originality/valueThe conceptual framework for 3D scanner and BIM applications within sustainable refurbishment for school buildings is currently under researched, and the findings aimed to address such a gap when considering 3D scanners and BIM applications in the refurbishment process.

Logistics-based fleet design for complex transportation scenarios

ABSTRACTA new integrated method for the preliminary design of a fleet of ships for a new trade is presented. Data from two apparently separate types of analysis, based on logistics and naval architecture, are merged within an integrated logistics-fleet design framework. The procedure carries out a preliminary screening of the design space based on logistics criteria. Then, possible fleet configurations and ship designs are investigated, providing information to shipyards and ship owners for an effective selection of optimal solutions. Interactions between logistics evaluations and classic ship design assessments are highlighted. Results from a realistic test case concerning compressed natural gas transportation are presented and discussed.

Программный комплекс автоматизации расчетов по Полярным правилам и выбора рациональных конструктивных решений

Программный комплекс автоматизации расчетов по Полярным правилам и выбора рациональных конструктивных решений

Optimization-Oriented Resource Allocation Management for Vehicular Fog Computing

Vehicular fog computing, which extends the mobile cloud paradigm, is usually composed of stable infrastructures, a large volume of vehicles, portable devices, and robust networks. As a service-providing platform, it is significant to quickly obtain the required service with the aim to correctly save the energy of the corresponding nodes and effectively improve the network survivability. However, the limited capacity of components makes such a situation more complicated. This paper aims to reduce serving time by allocating the available bandwidth to four kinds of services. A utility model is built according to the above-mentioned serving methods and is solved through a two-step approach. For the first step, all the sub-optimal solutions are provided based on a Lagrangian algorithm. For the second step, an optimal solution selection process is presented and analyzed. A numerical simulation is executed to illustrate the allocation results and the optimal utility model while optimizing the survivability.

ANALYSIS OF EXISTING METHODS AND MODELS WHEN RATIONALE OF ORGANIZATIONAL-TECHNOLOGICAL SOLUTIONS OF CONSTRUCTION OBJECTS

Purpose. A purpose to analyses methods and models, which ground the organization and technology decisions of building of objects, in particular, bridges and transporting tunnels, is put in the article. Methodology. A normative document which is used presently has whole row failings, conditioned by imperfection of information, arbitrariness of form of presentation of information, besides practically does not take into account the requirements of automation. In modern conditions of construction on the optimality of organizational and technological solutions to a variety of factors, some of which are the causes of risk, and the second arises as measures to reduce the impact of the risk. These circumstances require the development of an effective system that minimizes their impact on the selection of optimal organizational and technological solutions. To minimize the time and cost of construction for a given quality of construction works based on the selected methods of organization and technology of production of construction works. Findings. The creation of modern simulation models based on engineering principles that will allow to create optimal conditions to address the complex issues of the reliability of organizational and technological solutions in the construction of objects, in particular, bridges and transporting tunnels. Originality. Has been proved in the conditions of market relations the optimality of organizational and technological solutions can only be achieved at the account of risks arising from changes in the organizational and technological environment of project and construction organizations and other participants of investment and construction activities. Practical value. A simulation technique, which creates additional possibility at estimation of risk due to that does possible appearance of casual scenarios, has developed.

Model of the Multi-Criteria Analysis of the Infrastructure of the Railway Line at the Stage of Initiation of the Project of High-Speed Railway

Предлагается модель анализа текущего состояния существующей инфраструктуры железнодорожной магистрали с целью последующего принятия организационно-технических решений на этапе инициации проекта высокоскоростной железнодорожной магистрали. Описываются возможные современные способы реализации высокоскоростного сообщения и их отличия в проектных и организационных решениях. Представлены основные элементы инфраструктуры железнодорожной магистрали, отдельно выделены факторы, влияющие на увеличение скорости движения. Представлена методика анализа, этапы и последовательность его проведения, принцип определения объектов анализа, возможные способы сбора данных по объектам анализа, перечень отраслевых геоинформационных и автоматизированных систем сбора и обработки оперативной информации по текущему состоянию инфраструктуры и эксплуатационной работе, методика обработки, обобщения, систематизации полученных данных для последующего анализа. Разработан общий алгоритм комплексного анализа технических и эксплуатационных параметров магистрали в виде последовательной блок-схемы. Данная модель актуальна при проведении анализа состояния инфраструктуры для всех существующих железнодорожных магистральных линий на территории России при выборе оптимальных и эффективных проектных решений совместно с экономическим обоснованием в процессе разработки комплексных мероприятий по внедрению высокоскоростного сообщения.

Modeling and analysis of profit based self scheduling of GENCO in electricity markets with renewable energy penetration and emission constraints

Abstract In the rapidly evolving modern grid technologies and deregulated electricity markets, sustainability needs are given a prime importance besides achieving energy sufficiency. To investigate the same, a multi-objective function, including both economic as well as environmental interests is formulated. Pertaining to the inclusion of renewable energy technologies (RETs), constraints and penetration bounds are introduced. In addition, to examine the tradeoff criteria in preservation of environmental and economic interests, pareto sets are obtained and also, a simple optimality condition for selection of near optimal solution from the obtained pareto set is proposed. Further, various scheduling alternatives/scenarios and cases are proposed to analyze the scheduling scenarios against operation strategies of different markets. Simulation results are presented and discussed in detail. Observations of the pareto sets revealed that, RET schedules in pareto dominant solutions are affected by scheduling scenario. The intervention of RET affected the optimal tradeoff between economic and environmental interests. Therefore, optimal scheduling pertaining to particular scenario affects the energy schedule of both conventional, RET and thereby affecting resultant profit and emissions.

Abstract 207: Reliable identification of mutations in bottom-up proteomics

Abstract Knowledge of missense mutations is of significant biological importance as it provides valuable insights into alterations of phenotype. While identification of reference proteins is routinely performed in proteomics, identification of mutations remains still fairly uncommon. Well-established methods for reference peptides are generally insufficient for altered ones; e.g., utilizing mutant-augmented database search usually results in inadmissible proportion of false positives. This undesirable situation is, however, rather natural consequence of peptide fragmentation, its computational modelling and evaluation of their correspondence. Often, there are many peptides having the same or similar agreement with acquired spectrum, thereby preventing agreement-based decision. These situations happen frequently in identification of mutant peptides (e.g., homologous peptides with PTMs, semi-specific peptides), therefore their reliable identification requires additional treatment. To deeper understand properties of identification, we have formally studied generalized version of the identification problem and derived its optimal solution under particular assumptions. In proteomics, the selection of optimal solution can be always found in finite time; moreover, the strategy is straightforward to implement, enables parallelization and provides guarantees over claimed interpretations. The solution utilizes almost exclusively spectral data of product ions without additional LC/MS information. In practice, however, it is beneficial to employ precursor isotopic distribution analysis for correction of non-monoisotopic selection of precursor as that would otherwise systematically result in artefacts. The behaviour of proposed method was validated in a variety of scenarios that could be broadly categorized as direct (known spectral content) and indirect (partial knowledge of sample content). For direct validation, synthetic combinatorial peptide library of 400 peptides (all coded amino acids in 12. and 13. codon of KRAS peptide) was measured and interpreted; the sensitivity and specificity obtained was 0.81 and 0.99 respectively. Indirect validation was, at first, performed on in-house samples with RNA-Seq data available (HCT116), giving 97 missense RNA-Seq supported mutations. Similarly, the analysis was done on proteome data of NCI60 cell lines, resulting on average in 23 SNVs with maximum of 56 SNVs (HCC-2998). Identified alterations correspond, in general, to highly expressed genes. In summary, the approach was shown to reliably identify missense mutations in a range of cancer cell lines; its wide applicability enhances interpretation of standard bottom-up proteomics data. Citation Format: Miroslav Hruska, Lakshman Varanasi, Jiri Voller, Petr Dzubak, Marian Hajduch. Reliable identification of mutations in bottom-up proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 207. doi:10.1158/1538-7445.AM2017-207

Pareto Optimal Solution Selection for a Multi-Site Supply Chain Planning Problem Using the VIKOR and TOPSIS Methods

In this paper, a multi-objective, multi-product, multi-period production and transportation planning problem in the context of a multi-site supply chain is proposed. The developed model attempts simultaneously to maximize the profit and to maximize the product quality level. The objective of this paper is to provide the decision maker with a front of Pareto optimal solutions and to help him to select the best Pareto solution. To do so, the epsilon-constraint method is adopted to generate the set of Pareto optimal solutions. Then, the technique for order preference by similarity to ideal solution (TOSIS) is used to choose the best compromise solution. The multi-criteria optimization and compromise solution (VIKOR), a commonly used method in multiple criteria analysis, is applied in order to evaluate the selected solutions using TOPSIS method. This paper offers a numerical example to illustrate the solution approach and to compare the obtained results using TOSIS and VIKOR methods.

Optimal design of plate-fin heat exchanger by combining multi-objective algorithms

In this paper, a multi-objective optimization method is proposed which is a combination of Genetic algorithm, Differential Evolution and Adaptive Simulated Annealing algorithms. This technique is intended to generalize and improve the robustness of the three population based algorithms. In optimization problems, it is essential to keep the balance between local and global search abilities of algorithms. In the current method, DE, GA and ASA algorithms are linked in the variation stage to enrich the searching behavior and enhance the diversity of the population. The performance of the proposed DE-GA-ASA is tested against benchmark problems for multi-objectives and compared with two widely recognized vector optimizers. Next, the proposed technique is successfully implemented to optimize the design of plate-fin heat exchanger. The effectiveness of the present method is illustrated by comparing with various case studies. Some of the earlier case studies violated the constraints and/or only focused on single objective optimization. Results show that DE-GA-ASA method can be used effectively for the optimal design of plate-fin heat exchanger. Moreover, the effect of variation of fin and heat exchanger parameters on the optimal design is also investigated. Hot, cold and no-flow length of the heat exchanger, fin offset length, fin height and fin length are introduced as the optimization variables to obtain maximum heat transfer rate and minimum total annual cost. The investment cost and operating costs are independently optimized to provide a detailed investigation on the effect of fin and heat exchanger geometry parameters on their variation. Furthermore, a multi-criteria decision making method, TOPSIS is introduced for the selection of final optimal solution from the set of non-dominated solutions.

A fast hypervolume driven selection mechanism for many-objective optimisation problems

Abstract Solutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a “many-objective optimisation problem”. A problem of this category introduces many challenges, one of which is the effective and efficient selection of optimal solutions. The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an effective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a non-dominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computational cost. The present study proposes a novel hypervolume driven selection mechanism for many-objective problems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indicator for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-off between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world efficiency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.

Multicriteria Analysis of Selected Building Thermal Insulation Solutions

Abstract The purpose of this paper is to present and analyse the decision-making problem faced by a future house owner - selection of the optimal solution of building thermal insulation in relation to the selected criteria, both related to costs and future benefits. The problem of selecting the best solutions in the construction sector is widely discussed in the science literature. In this paper, the authors decided to solve the raised problem by using the Entropy method.

Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model

Road traffic noise is one of the most significant environmental impacts generated by transport systems. To this regard, the recent implementation of the European Environmental Noise Directive by Public Administrations of the European Union member countries has led to various noise action plans (NAPs) for reducing the noise exposure of EU inhabitants. Every country or administration is responsible for applying criteria based on their own experience or expert knowledge, but there is no regulated process for the prioritization of technical measures within these plans. This paper proposes a multi-criteria decision methodology for the selection of suitable alternatives against traffic noise in each of the road stretches included in the NAPs. The methodology first defines the main criteria and alternatives to be considered. Secondly, it determines the relative weights for the criteria and sub-criteria using the fuzzy extended analytical hierarchy process as applied to the results from an expert panel, thereby allowing expert knowledge to be captured in an automated way. A final step comprises the use of discrete multi-criteria analysis methods such as weighted sum, ELECTRE and TOPSIS, to rank the alternatives by suitability. To illustrate an application of the proposed methodology, this paper describes its implementation in a complex real case study: the selection of optimal technical solutions against traffic noise in the top priority road stretch included in the revision of the NAP of the regional road network in the province of Almeria (Spain).

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

Flood protection as a key-component of the environmental restoration of Canal del Dique, Colombia

Canal del Dique is a man-made distributary of Rio Magdalena. After its widening in 1980’s environmental degradation caused by abundant sediment load and changes to hydrology took a catastrophic form. In 2010, the Canal’s dike breached and 35,000 ha of land were flooded. In 2013 a huge effort to restore the environment in the Canal del Dique system and to flood-proof the villages was started. An integrated approach was adopted to provide an optimal solution for flood control, environment, fresh water supply and navigation. In order to prepare restoration plans, an extensive hydrologic and hydraulic assessment was carried out. 1D, 2D and 3D numerical models were developed to answer different questions, to evaluate different alternatives and to enable selection of optimal solutions. To assess the flood risk, a hindcast of 2010 flood was carried out. A solution was designed in which the inlet of water from the Magdalena River is regulated by a control structure, managed by an automated system based on SCADA and Delft-FEWS flood forecasting software and advanced statistics.

Optimizing Facility Location Decisions Considering Health Impact on Local Population

Minimizing environmental impacts due to logistics activities have long been recognized as part of a way to promote a sustainable supply chain. Facility location decisions play a critical role in strategic design of supply chain and hence have a long-term impact on its cost and environmental performance. In this study, Operations research and Life-cycle impact assessment techniques are used to estimate the burden on local population's health caused by facility location decisions. Specifically, our primary goal is to minimize the impacts on local population's health quantified in terms of Disability Adjusted Life Year (DALY). A mixed integer linear programming model is developed based on a case study located in Can Tho city, Vietnam. Geographic information system (GIS) mapping software is used to estimate the number of affected population. The results of the study show that population density along different parts of transportation routes is the main factor influencing the selection of optimal solutions.

Prioritization of NFRs using Multi-Criteria Decision Making Methods

software engineering is divided into two parts functional requirements (FRs) and non-functional requirements (NFRs) objective of this paper is to classify the Prioritization of Non Functional Requirements (NFRs) by using multi criteria decision making (MCDM) methods. Recently the MCDM are very important for selection of best optimal solution among the different substitutes. Decision making methods (DMM) are selection tools for the managers or decision makers to make future better plans by using qualitative or quantitative data. In this research we take an example of Institute examination system (IES) a general idea about DMM and comparison between the two important models, Prioritization of (NFRs) and (MCDM) by using Analytical Hierarchy Process (AHP) method. The research was done by using the information in the literature and expert review. This paper can be used by academics as a foundation for further research and development in the area of decision making models. Decision makers can use this paper for choosing the right DMM in a variety of constraints, such as money and time etc. It can also be used for further development in making standard operational decision making procedures in critical situations. Keywords-functional Requirements (NFRs), Multi Criteria Decision

Parametric Study along with Selection of Optimal Solutions in Wire Cut Machining of Titanium (Gr2)

This paper investigates the relationship of process parameters in wire electrical discharge machining of titanium alloy with brass wire as tool electrode. Wire electrical discharge machining (WEDM) is used to cut conductive metals of any hardness or that difficult to cut with conventional methods. The process performances such as material removal rate (MRR) and surface finish (Ra) were evaluated by giving specific input parameters which practiced to obtain optimal response. The difficulty in machine tool industry is to predict the expected output performance for the desired input variables by the way of conducting more number of experiments for different machining parameters, which leads to the increase in consumption of electric power, material and time. To overcome this phenomenon, parametric investigation was made on WED machining on titanium alloy by using Taguchi’s method.

Parametric study along with selection of optimal solutions in dry wire cut machining of cemented tungsten carbide (WC-Co)

This work deals with parametric study of dry wire EDM (WEDM) process of cemented tungsten carbide. Experiments have been conducted using air as dielectric medium to investigate effects of pulse on time, pulse off time, gap set voltage, discharge current and wire tension on cutting velocity (CV) surface roughness (SR) and oversize (OS). Firstly, a series of exploratory experiments were carried out to identify appropriate gas and its pressure. Afterward, preliminary experiments were conducted to investigate effects of process parameters on dry WEDM characteristics and find appropriate ranges for each factor. Then a central composite rotatable method was employed to design experiments based on response surface methodology (RSM). Empirical models were developed to create relationships between process factors and responses by considering to analysis of variances (ANOVA). To increase the predictability of the process, intelligent models have been developed based on back-propagation neural network (BPNN) and accuracy of these models was compared with mathematical models based on root mean square error (RMSE) and prediction error percent (PEP). In order to select optimal solutions in the cases of single-objective and multi-objectives optimization problems, optimization includes two main approaches. First approach was based on mathematical model and desirability function. Also second approach was designed based on neural network and particle swarm optimization. These approaches were applied in both cases of single-objective and multi-objectives optimization problems and their results were compared with together. Results indicated that selection of air at inlet pressure of 1.5bar is really appropriate for conducting experiments of next stages. Also, the BPNN creates more accurate prediction rather than mathematical model. Moreover, the BPNN-PSO approach was more efficient in optimization of process rather than mathematical model-desirability function in respect with validation tests.