Operations research and Optimization

El embalse Carrizal es una obra realizada sobre el río Tunuyán, que nace a la vida del oasis en el año 1971, acompañada de importantes proyectos de desarrollo económico e integración territorial. La inexistencia de una legislación adecuada a las necesidades generadas por esta infraestructura, la ineficiencia en su administración y la desconexión puesta de manifiesto entre el embalse y su zona de influencia, son las causas que provocan la falta de equipamiento turístico-recreativo adecuado y el aprovechamiento óptimo del recurso agua. En el caso de Potrerillos , se diferencian 3 zonas en donde el impacto de la presa puede producir cambios importantes, potenciando su desarrollo o provocando la aparición de externalidades negativas y mayores gastos de los previstos. Estas son : la márgenes del embalse, en donde se espera, al igual que en El Carrizal, el

This paper presents the Driver and Navigator Algorithm (DNA), a novel population-based metaheuristic inspired by the cooperative interaction between a driver and navigator during dynamic navigation tasks. The DNA algorithm divides the optimization process into two complementary phases: exploration, guided by the navigator synthesizing global and local information, and exploitation, driven by the driver's real-time, localized decisions. The population consists of candidate solutions representing drivers, each paired with a randomly selected navigator that integrates information from a random peer, the current best, and the worst solutions to suggest exploratory moves. Exploration updates utilize sinusoidal modulation to enable smooth, adaptive search steps, while exploitation performs fine-grained, time-damped local refinements mimicking cautious maneuvering. The algorithm employs simple mathematical operations and trigonometric functions, ensuring computational efficiency with linear complexity in population size, problem dimensionality, and iteration count. To validate DNA's effectiveness, extensive simulations are conducted on 23 benchmark functions categorized as unimodal, high-dimensional multimodal, and fixeddimensional multimodal. The results demonstrate that DNA consistently outperforms nine recent metaheuristic competitors in solution quality, convergence speed, stability, and robustness. It achieves superior mean and best values across all categories, with negligible standard deviations indicating high repeatability. Boxplot analyses further confirm DNA's low dispersion, minimal fluctuations, and resistance to outliers. These characteristics enable a reliable performance in both simple and complex multimodal landscapes. Overall, the DNA algorithm offers a biologically inspired, interpretable, and scalable approach that balances global exploration and local exploitation, making it a promising method for diverse real-world optimization challenges.

This article presents the problem of quickest descent, or the Brachistochrone curve, that may be solved by the calculus of variations and the Euler-Lagrange equation. The cycloid is the quickest curve and also has the property of isochronism by which Huygens improved on Galileo's pendulum.

The traveling salesman problem, or TSP for short, is easy to state: given a nite number of cities" along with the cost of travel between each pair of them, nd the cheapest way of visiting all the cities and returning to your starting point. The travel costs are symmetric in the sense that traveling from city X t o c i t y Y costs just as much a s t r a v eling from Y to X; the way of visiting all the cities" is simply the order in which the cities are visited. In this report we consider the relaxed version of the TSP where we ask only for a tour of low cost. This is a preliminary version of a chapter of planned monograph on the TSP.

Objective: This research aims to propose a large-scale vehicle routing model for the distribution network of a food industry product and apply the model in a real-world case study.
Methods: A mathematical model is formulated to minimize the total variable transportation costs.  Considering the complexity of the model, a constrained clustering algorithm is used to decompose the problem. Then, vehicles are assigned to demand clusters according to their capacity. Finally, each cluster's symmetric traveling salesman problem (TSP) is solved using a genetic algorithm. The parameters of the proposed genetic algorithm were calibrated based on its widespread application in solving symmetric TSPs. A conservative approach was adopted to ensure the solution's validity by evaluating a worst-case scenario considering the highest node demands.
Results: By applying the proposed algorithm to the case study, over 2,000 demand nodes across Tehran were grouped into 91 clusters. Then, based on the demand level of each cluster, the vehicles are assigned, consisting of 26 small and 65 large cars. Within each cluster, the assigned vehicle followed an optimized route among the nodes, designed based on the optimal tour generated by solving the cluster-specific TSP using the genetic algorithm, and then returned to the central warehouse.
Conclusion: Comparing the results with the current situation, the size of the proposed transportation fleet showed a 40% reduction. Additionally, reducing fleet size and optimizing the routes improved the total distribution network costs by 25%. Given the model's computational efficiency, this improvement is considered satisfactory.

This paper investigates on analysing the methods and techniques of constructing new modern buildings by utilizing ultra-low energy. This study involves types of solutions which can be implemented by a well-organized way. It emphasizes mainly on climate change & various other efforts to reduce construction hazards. One of the main tasks of the construction industry is to increase the strength and reliability of structures while reducing construction costs & ill-effects caused by it. In these techniques the energy use of constructing structures is being reduced to a very far extent. The paper enlightens on topics such as passive house, porous concrete & a well-organized drainage system (SUDS). Hence, there is feasibility to adopt these advanced construction techniques.

Bu çalışmada, yapay sinir ağları kullanılarak altın fiyat değişim yönü tahmin edilmiştir. Altın fiyatındaki değişimlerin tahmini araştırmacılar ve özellikle yatırımcı olan karar vericiler açısından önem taşıyan bir konudur. Literatürde altın fiyatını etkileyen parametrelerin belirlenmesi ve altın değerinin tahmin edilmesi amacıyla farklı yöntemler ile yapılmış birçok çalışma bulunmaktadır. Tahmin yöntemleri, kantitatif ve kalitatif yöntemler olarak ikiye ayrılmaktadır. Bu çalışmada kantitatif yöntemler arasında yer alan yapay sinir ağları kullanılarak altın fiyatındaki değişim yönünün (Artış -Azalış) tahmin edilmesi hedeflenmiştir. Çalışmada 2007 ile 2015 yılları arasındaki aylık veriler kullanılmıştır. Çalışmada bağımlı değişken olarak Türkiye'deki altının gram fiyatı esas alınmış ve bağımsız değişkenler olarak ham petrol fiyatı, dolar endeksi, dolar kuru, Standard&Poor's 500 endeksi, BIST100 endeksi, Türkiye enflasyon, tahvil ve faiz oranları, ABD enflasyon, tahvil ve faiz oranları, gümüş ve bakır fiyatları kullanılmıştır. Çalışmada, bağımlı değişkenin gelecekteki değerinin tahmininin yapılması için, bağımsız değişkenlerin bir ay önceki değerleri kullanılmıştır. Çalışma neticesinde gram altın fiyatındaki değişim yönünün tahmin başarım oranı %75,24 olarak bulunmuştur.

This paper aims to analyze the impact of drought on the life cycle of barge transportation. The LCA method was used to quantify the environmental impact of barge transportation services on the Madeira River/Brazil: Transportation Operation, Barge Fleet, and Waterway Infrastructure. A model for barge convoy formation was developed as a function of river water level variation. From this, the transport operation was simulated, considering the loading of grains from the Madeira River in 2021, as well as the respective fuel consumption and CO2Eq emissions. The results indicate that barge transportation is more harmful to the environment during drought, since only a convoy of nine barges is allowed to navigate, and its energy efficiency is compromised due to the longer travel time and lower loading capacity in one trip. The intense use of this barge convoy implied an increase of 22.25% in CO2Eq emissions when compared to the full river.

This paper is intended to present optimal route finding system for road network application. It is used to operate in complicated mapping situations and new unknown environments. This technique can be implemented to find the optimal path from source to destination in region of map. This system use A* and Dijkstra algorithms to draw plan and develop the Geographic Information System(GIS) assisted optimal road network route finding and give shortest path. The optimal (or) shortest path based on distance or time or cost. And then evaluates the performance of these two algorithms.

The delay in the physical progress of construction creates additional costs, missed deadlines, and quality issues. The research aimed to estimate the physical progress of the project by using unmanned aerial vehicles (UAVs) and building information modeling (BIM). The methodology comprised capturing 848 high-resolution images of the Civil Engineering Laboratory construction site at the National University of Jaen, Cajamarca, Peru, using the Phantom 4 RTK drone. The photographs were processed using Agisoft 2.0.1 software, resulting in a point cloud. This was then imported into ReCap Pro 2023 software, which was used to assess the quality of the points. The Revit 2023 software was subsequently utilized to establish the phase parameters, linking the BIM model with the point cloud, filtering the model, and eventually exporting it to the Power BI 2023 software. The work's estimated progress utilizing the proposed methodology was 42.82%, which was not statistically significant compared to the Public Works Information System (INFOBRAS) of 43.14%. This allows for the automation of customary processes, the identification of crucial issues, and prompt decision-making. The study's originality lies in the suggestion of integrating aerial imagery with drones and BIM modeling for the real-time and precise estimation of work progression. This method provides a precise and effective substitute for traditional techniques for gauging the tangible advancement of projects.

Recent financial researches showed that technical indicators are useful tools for stock prediction. Technical indicators are used to generate trading signals (buy/sell) signals. The main problem of an indicator usage is to determine its appropriate parameters. In this paper a new GA based technique for optimizing the parameters of a collection of technical indicators over two objective functions Sharpe ratio and annual profit is proposed. The technique handles four indicators DEMAC (Double Exponential Moving Average Crossovers), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and MARSI (Moving Average RSI) indicators. The technique was tested on 30 years of historical data of DJIA (Dow Jones Industrial Average) stock index. Results showed that the optimized parameters obtained by the proposed technique improved the profits obtained by the indicators with their typical parameters, the Buy and Hold strategy and the random strategy.

The work done in this investigation is modeling to optimization product mix in industrial settings of resources allocation and utilization in poultry feed production. Most poultry feeds producers in Nigeria do not lend themselves to flexible production process which is important for them to manage the use of resources for effective optimal production. This research focuses on linear optimization for achieving productmix optimization in terms of the product identification and the right quantity in feed brand production for better profit and optimum firm performance. The computational experiments in this research contains data and information on the units item costs, unit contribution margin, maximum resources capacity, individual products absorption rate and other constraints that are particular to each of the six products produced in the company employed as case study. In data analysis, linear programming model was employed with the aid of software to analyze the data. After solving...

Сторчак Анатолій, аспірант, Гальченко Володимир, д.т.н., проф., Тичков Володимир, к.т.н., доц., Руслана Трембовецька, к.т.н., доц. Шляхом вихрострумового неруйнівного контролю є можливість визначати електрофізичні характеристики об'єктів контролю (ОК), зокрема електричної провідності (ЕП) і магнітної проникності (МП). Завдяки кореляційним зв'язкам ЕП та МП з фізико-механічними властивостями матеріалу в свою чергу можна отримати інформацію про такі властивості приповерхневого шару матеріалу як: в'язкість, пластичність, твердість, теплоємність, міцність, а крім того, хімічний і фазовий склад; степінь концентрації водню в поверхневих шарах ОК із легких сплавів на основі титану, цирконію; виявити ділянки ОК, які знаходилися під впливом високих температур, зробити оцінку їх розмірів та рівня температурних впливів. Тому визначення просторових розподілів ЕП та МП в об'ємі циліндричних ОК на основі вимірювань вихрострумовими перетворювачами (ВСП) є актуальною науково-технічною задачею, що має суттєве практичне значення. Постановка задачі. Припускається, що матеріал циліндричного ОК має неперервну зміну електрофізичних параметрів матеріалу в приповерхневій зоні уздовж його радіусу. Необхідно знайти профіль радіального розподілення електрофізичних параметрів ЕП та МП матеріалу ОК на основі вимірювань амплітуди та фази сигналу ВСП. Розв'язок задачі. Задача ідентифікації профілів розподілення МП та ЕП матеріалу ОК відноситься до класу обернених задач (ОЗ). Її розв'язок отримується апроксимаційним методом з використанням апріорної інформації. Тому є сенс виконувати пошук розв'язку сформульованої ОЗ в декілька рознесених у часі етапів: -"точний" розв'язок прямої електродинамічної задачі взаємодії квазістаціонарного електромагнітного поля, яке згенеровано прохідним ВСП, з циліндричним ОК. Вибір відповідної математичної моделі, її програмна реалізація та верифікація, а також приклад генерації навчальної вибірки, створеної на основі плану з використанням ЛПτ-послідовностей Соболя, були описані в публікації [1]; -побудова засобами багатовимірної апроксимації, яка ґрунтується на техніці сурогатного моделювання, апріорної моделі-замісника (метамоделі), що є значно менш ресурсоємною та наближено з прийнятною точністю відтворює "точну". В якості метамоделі було використано глибоку штучну нейронну мережу (НМ) як універсальний засіб апроксимації. Реалізація та результати цього етапу наведені в статті [2]; -власне розв'язок ОЗ на базі проведених вимірювань із використанням ВСП та сурогатної НМ-моделі, створеної на попередньому етапі. Для цього необхідна інверсія НМ. Головною проблемою при цьому стає невизначеність -одному вхідному значенню може відповідати декілька комбінацій вихідних значень. В даному випадку певні значення амплітуди та фази напруги ВСП можуть бути сформовані різним сполученням характеристик матеріалу та радіусу ОК, а також частоти струму збудження. Стандартно цей етап виконується за допомогою оптимізаційних методів. Це передбачає багатократний розв'язок прямої задачі, що є ресурсо-та часозатратним процесом. В даному дослідженні пропонується при реалізації інверсії використати «data-driven» метод [3], що базується на застосуванні НМ з «тандем»-архітектурою. Такий підхід дозволяє створити компактний у сенсі мінімізації витрат часу, максимізації надійності та точності обчислень алгоритм розв'язку вимірювальної ОЗ. Висновки. В роботі запропоновано розв'язок ОЗ вихрострумової структуроскопії з використанням НМ «тандем»-архітектури, що дозволяє ефективно створити обернений НМ-оператор. Література 1. Гальченко В.Я. Відновлення приповерхневих радіальних профілів електрофізичних характеристик циліндричних об'єктів при вихрострумових вимірюваннях із наявністю апріорних даних. Формування вибірки для побудови сурогатної моделі. / Гальченко В.Я., Тичков В.В., Сторчак А.В., Трембовецька Р.В. -Український метрологічний журнал.

Penjadwalan unit pembangkit agar biaya operasi pembangkit minimum Metode Penjadwalan Pembangkit (Unit Commitment) qUnit Commitment qUnit Decommitment qModified Unit Decommitment • Penelitian ini mengaplikasikan Dynamic Progrmming pada metode Unit Commitment, Unit Decommitment, dan Modified Unit Decommitment untuk penjadwalan unit pembangkit sistem kelistrikan Jawa-Bali.

At present, natural fiber reinforced polymer matrix composites have received wide attention of the researchers from all over the world because of their outstanding advantages of environment friendliness, biodegradability, recyclability, cost-effectiveness and comparable physico-mechanical properties. Among various natural fibers easily available to human beings, Water Hyacinth (Eichhornea crassipes) is one of the cheapest fibers which have not been systematically explored so far. Water Hyacinth can be used as filler in composites materials in various polymer matrices. In this study, an attempt has been made to fabricate Water Hyacinth fiber based polymer composite and evaluation its impact behavior. For this composite preparation, Water Hyacinth fibers were used as reinforcement and the epoxy resin (ADR 246 TX) was used as the matrix. The fabrication of the composite is done by using hand layup techniques. The composite thus made was tested for its mechanical properties like hardnes...

I am grateful to my Amath 507 students for their enthusiasm and hard work and for uncovering interesting applications of the calculus of variations. I owe special thanks to William K. Smith for supervising my undergraduate thesis in the calculus of variations (35 years ago) and to Hanno Rund for teaching a fine series of courses on the calculus of variations during my graduate years. Sadly, these two wonderful teachers are now both deceased. I thank the fine editors and reviewers of the American Mathematical Society for their helpful comments. Finally, I thank my family for their encouragement and for putting up with the writing of another book.

This study aims at identifying the impact factors of social media marketing on the buying behaviors' of superstore customers in Bangladesh. Altogether, 291 buyers were surveyed from five superstores of Bangladesh such as, Agora, Meena Bazar, Shwapno, Almas Super Shop, and Nandan. A structured questionnaire with five points Likert scale was used to survey the customers. Descriptive statistics were used to present the profiles of the respondent customers while inferential statistics like Factor Analysis was used to identify the impact factors and Multiple Regression Analysis was used o identify the relationships between the impact factors and the overall impact on purchase decision of the customers. Study identified four significant impact factors such as, quick searching and verification of authenticity of product information, easy to order and saving time, customers addiction to media buying, and awareness of innovative and new products that induce the superstore customers to make their purchase decisions. This study suggests that the policymakers should focus on authenticity of product information, easy to order system that saves time, publicity of innovative and variety of products, and customer addicted towards media marketing for making the customers inclined towards buying the products and services from superstores of Bangladesh.

An agent based control system that manages the charging of electric vehicle batteries according to distribution network constraints and electricity market price signals, is described. The system is adapted to the EV-ON platform and Microgrid equipment of Tecnalia-Lab. A case study is defined to evaluate the behaviour of the control system and the results are discussed. Uncontrolled Electric Vehicle (EV) battery charging is anticipated to modify voltage profiles of distribution feeders, overload transformers and cables, and increase power losses . The Multi Agent System (MAS) technology has emerged as a potential solution to manage dispersed energy resources in distribution networks. In this research, an MAS is proposed to manage the charging of EVs. This MAS comprises a hierarchical structure based on the European Union's (EU) project Mobile Energy Resources in Grids of Electricity (MERGE) . A description of the MAS and its adaptation to the EV-ON platform and Microgrid of Tecnalia-Lab is provided. A case study of the MAS operation is defined and the results are analysed. The MAS considers two entities: (i) the EV Supplier/Aggregator (EVS/A), which facilitates the participation of the EVs into the electricity markets and (ii) the Distribution System Operator (DSO), responsible for the technical operation of the distribution system. The aggregator's hierarchy consists of: the Regional Aggregation Unit (RAU) agent, located at the primary substation; the Microgrid Aggregation Unit (MGAU) and the Clusters of Vehicle Controllers (CVC) agents, located at the secondary substation; the Vehicle Controller (VC) agent. The DSO is represented by the Central Autonomous Management Controller (CAMC) agent located at the primary substation.

This paper presents a performance study of a one-dimensional search algorithm for solving general high-dimensional optimization problems. The proposed approach is a hybrid between a line search algorithm of Glover (The 3-2-3, stratified split and nested interval line search algorithms. Research report, OptTek Systems, Boulder, CO, 2010) and an improved variant of a global method of Gardeux et al. (Unidimensional search for solving continuous high-dimensional optimization problems. In: ISDA '09: Proceedings of the 2009 ninth international conference on intelligent systems design and applications, IEEE Computer Society, Washington, DC, USA, pp 1096-1101, 2009) that uses line search algorithms as subroutines. The resulting algorithm, called EM323, was tested on 19 scalable benchmark functions, with a view to observing how optimization techniques for continuous optimization problems respond with increasing dimension. To this end, we report the algorithm's performance on the 50, 100, 200, 500 and 1,000-dimension versions of each function. Computational results are given comparing our method with three leading evolutionary algorithms. Statistical analysis discloses that our method outperforms the other methods by a significant margin.

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This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.

This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.

Developing models and algorithms to generate robust project schedules that are less sensitive to disturbances are essential in today's highly competitive uncertain project environments. This paper addresses robust scheduling in project environments; specifically, we address the discrete time/cost trade-off problem (DTCTP). We formulate the robust DTCTP with three alternative optimization models in which interval uncertainty is assumed for the unknown cost parameters. We develop exact and heuristic algorithms to solve these robust optimization models. Furthermore, we compare the schedules that have been generated with these models on the basis of schedule robustness.

Projects are often subject to various sources of uncertainties that have a negative impact on activity durations and costs. Therefore, it is crucial to develop effective approaches to generate robust project schedules that are less vulnerable to disruptions caused by uncontrollable factors. In this paper, we investigate the robust discrete time/cost trade-off problem, which is a multi-mode project scheduling problem with important practical relevance. We introduce surrogate measures that aim at providing an accurate estimate of the schedule robustness. The pertinence of each proposed measure is assessed through computational experiments. Using the insights revealed by the computational study, we propose a two-stage robust scheduling algorithm. Finally, we provide evidence that the proposed approach can be extended to solve a complex robust problem with tardiness penalties and earliness revenues.

This paper investigates the budget variant of the discrete time/cost trade-off problem (DTCTP). This multi-mode project scheduling problem requires assigning modes to the activities of a project so that the total completion time is minimized and the budget and the precedence constraints are satisfied. This problem is often encountered in practice as timely completion of the projects without exceeding the budget is crucial. The contribution of this paper to the literatures is to describe an effective Benders Decomposition-based exact algorithm to solve the DTCTP instances of realistic sizes. Although Benders Decomposition often exhibits a very slow convergence, we have included several algorithmic features to enhance the performance of the proposed tailored approach. Computational results attest to the efficacy of the proposed algorithm, which can solve large-scale instances to optimality.

We address line balancing under uncertainty and propose two robust optimization models. In these models, we assume interval uncertainty for operation times and generate line designs that are protected against disruptions. In order to solve the models exactly, a decomposition based algorithm is proposed and enhancement strategies are integrated to solve large scale instances. The efficiency of the algorithm is tested and the experimental results are presented. The contribution of this research lies in the novel models proposed and the decomposition based exact solution algorithm. To the best of our knowledge, this research is a pioneer work on robust assembly line balancing and it will serve as a basis to develop decision support systems on this crucial subject.

The Home Health Care (HHC) sector continues to grow at an increasing rate across the globe. As such, developing proper health care management systems and operations strategies is essential for survivability, growth and competitiveness of healthcare systems. As labor is the most critical asset in the home care organizations, it is crucial to obtain an in-depth understanding of HHC operations, from an operations management view point, with specific emphasis on staff planning and scheduling. The aim of this paper is to provide a taxonomic analysis of HHC staff planning and scheduling decisions, from an operations management perspective. We identify from various cases in the literature, the core activities characterizing staff planning and scheduling. We then present a taxonomic analysis of these activities, and propose an integrated approach to decision making. A conceptual modeling framework is then proposed to assist the decision maker in solving problems in HHC staff planning and scheduling.

Firmalar bünyesinde gerçekleştirilen yüksek katma değerli mal ve hizmet üretiminin merkezinde yer alan inovasyonlar, modern ekonomilerde sürdürülebilir büyüme ve kalkınmanın temelini oluşturmaktadır. Bu bağlamda, inovasyonları ortaya çıkaran Ar-Ge faaliyetlerinin finansmanı teknolojik ilerleme ve rekabet gücünün sağlanmasında hayati önemdedir. Bu çalışmada kamu ve yükseköğretim sektörleri tarafından gerçekleştirilen Ar-Ge performansının özel sektör Ar-Ge harcamaları üzerindeki uzun dönem etkisi incelenmektedir. 1997-2021 yılları arası dönemi kapsayan panel veri setine ilişkin bulgular, kamu Ar-Ge harcamalarının özel sektör Ar-Ge faaliyetleri üzerinde anlamlı bir etkisine işaret etmezken, kamu tarafından finanse edilen Ar-Ge'nin özel sektör Ar-Ge harcamalarını önemli şekilde artırdığını göstermektedir. Bu kapsamda kamu Ar-Ge'sinin firmaların kendi Ar-Ge faaliyetleri kapsamında kaldıraç işlevi gördüğü ifade edilebilir. Bu işlev finansman engelleri ve stratejik sektörlerdeki Ar-Ge maliyetlerinin indirgenmesinin yanı sıra Ar-Ge'yi teşvik eden hibe, sübvansiyon ve vergi uygulamaları ile ilişkilendirilebilmektedir. İkinci olarak, yükseköğretim Ar-Ge harcamalarının özel sektör Ar-Ge harcamaları kapsamında tamamlayıcı bir etki yarattığı sonucuna varılmaktadır. Bu etkinin özellikle bilgi taşmaları, uzman işgücü ve iş birliği kanallarıyla ortaya çıktığı söylenebilir. Üçüncü olarak, ülke düzeyinde beşerî sermayenin niteliğine ilişkin ortaya çıkan farklılıkların Ar-Ge faaliyetlerinde insan faktörünün katkısını sınırladığı anlaşılmaktadır. Bu kapsamda, çoğunlukla eğitim sistemindeki yetersizliklere bağlı bilgi ve beceri eksiklikleri, vasıflı işçilerin sektörel dağılımındaki dengesizlik ve işgücüne ilişkin yüksek maliyetlerin beşerî sermayenin Ar-Ge faaliyetlerindeki rolünde ülkeler arası eşitsizliklere yol açabilmektedir. Son olarak, ekonomik gelişmişliğin özel sektör Ar-Ge'sinde kritik bir rol oynadığı görülmektedir. Bu durum özellikle ekonomik gelişmeye paralel olarak güçlü kurumsal yapılara bağlı kanunlar, düzenlemeler ve destekler, gelişmiş araştırma altyapıları, çeşitlenmiş piyasa talebi ve küresel iş birliği ile finansman ağlarının önemine işaret etmektedir.

In the present research note, we define a new extension of beta function by making use of the multi-index Mittag-Leffler function. Here, first we derive its fundamental properties and then we present a new type of beta distribution as an application of our proposed beta function. Moreover, we present a new extension of Gauss and confluent hypergeometric functions in terms of our newly introduced beta function. Some interesting properties of our extended hypergeometric functions (like integral representations, differential formulae, transformations and summation formulae and a generating relation) are also indicated in the last section.

For a large homogeneous population, where individuals rely on the availability of a resource for survival, we introduce a continuous time model for the availability of the resource in time and for each individual. In this framework, cooperation is defined as a mutual insurance mechanism, aimed at covering shortages for group members. We explore essential questions regarding the importance of cooperation: what are the characteristics of populations where cooperation is valuable, versus individualist populations, where cooperation destroys value; how large should be the groups of cooperating entities? In order to answer the latter question, we first characterise the optimal cooperation, which maximises the expected lifetime of entities in the population. Secondly, we explore the same question using a non cooperative stochastic game. This allows to understand to what extent strategic cooperation leads to inefficiencies as compared with optimal cooperation. We complete the theoretical results by suggestive numerical approaches.

The problem of searching for randomly moving targets such as children and submarines is known to be fundamentally difficult, but finding efficient methods for generating optimal or near optimal solutions is nonetheless an important practical problem. This paper investigates the efficiency of Branch and Bound methods, with emphasis on the tradeoff between the accuracy of the bound employed and the time required to compute it. A variety of bounds are investigated, some of which are new. In most cases the best bounds turn out to be imprecise, but very easy to compute.

Flexibility can be an attractive property to add the infrastructure systems during the design phase to reduce the impact of uncertainty. The management of this flexibility, however, is a complicated sequential decision problem that must be solved to extract all the benefits that flexibility may offer. In this paper, a multistage stochastic model is proposed to simulate the flexibility management process. The model combines a novel mathematical representation of flexibility with a set of policies and a scenario-based representation of uncertainty to determine the optimal initial configuration and the optimal flexibility for the system. The results showed that, under specific conditions, flexibility can be a desirable property to add to the system. In addition, the model provides valuable insight about the interaction between system design properties and management policies.

In this article, we have introduced a new probability distribution having three parameters using half Cauchy family of distribution named half Cauchy extended exponential distribution. The statistical properties and characteristics of the proposed distribution like the hazard rate function (HRF), cumulative hazard function, and the probability density function (PDF), and the cumulative distribution function (CDF), quantile function and the skewness, kurtosis are provided. The parameters of the proposed distribution are estimated using the Cramer-Von-Mises (CVM) least-square estimation (LSE), and maximum likelihood estimators (MLE) methods. A real data set is analyzed to test the goodness-of-fit of the proposed distribution. It is found that the half Cauchy extended exponential distribution performed well as compared to some competing distributions.

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the return to scale (RS) problem in the presence of uncertain data and undesirable outputs. A combined cycle power plant is considered a decision making unit (DMU) which consumes fuels to produce electricity and emissions. The uncertainty of the inputs and outputs are modeled with interval data and the emissions are assumed to be undesirable outputs. The proposed DEA method determines the interval efficiency scores of the DMUs and offers a practical benchmark for enhancing the efficiency scores. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a six-year study of 17 combined cycle power plants in Iran. The main contributions of this paper are six fold: we (1) model the uncertainties in the input and output data using interval data; (2) consider undesirable outputs; (3) determine the efficiency scores of the DMUs as interval values; (4) develop a group of indices to distinguish between the efficient and inefficient DMUs; (5) determine the most economic scale size for the efficient DMUs; and (6) determine practical benchmarks for the inefficient DMUs.

Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the return to scale (RS) problem in the presence of uncertain data and undesirable outputs. A combined cycle power plant is considered a decision making unit (DMU) which consumes fuels to produce electricity and emissions. The uncertainty of the inputs and outputs are modeled with interval data and the emissions are assumed to be undesirable outputs. The proposed DEA method determines the interval efficiency scores of the DMUs and offers a practical benchmark for enhancing the efficiency scores. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a six-year study of 17 combined cycle power plants in Iran. The main contributions of this paper are six fold: we (1) model the uncertainties in the input and output data using interval data; (2) consider undesirable outputs; (3) determine the efficiency scores of the DMUs as interval values; (4) develop a group of indices to distinguish between the efficient and inefficient DMUs; (5) determine the most economic scale size for the efficient DMUs; and (6) determine practical benchmarks for the inefficient DMUs.

In 21th centaury energy has become a prime need of the civilization. One of the most important types of energy is the electrical energy, which is very much essential for both domestic & industrial purposes. In present century, more than 60% of the total electric energy is producing from coals, 14-17% of energy is producing from Renewable sources & the remaining part is from natural gases. So, it is clear that maximum sources (more than 80%) of energy are causes pollution. Pollution & Global warming are the main reason of rapid environmental misbalancing. In case of our state (Tripura) this energy generation figures or records are not impressive, approximately 1% of total energy is producing from hydro-power & the rest percentages are from gas-thermal plants. It is observed that the year round insufficiencies of river water & irregular rainfall in hilly river sub-basin areas are the main reasons of decrement in hydro-power generation. So, in present scenario, the concept of Pump-Storage Hydro-Power station is very much appropriate, because this type of plant can recycle fix storage of water. That's why insufficiency of river water will not affect the generation like conventional hydro power station. In this work Multi Criteria Decision Making Algorithm (MCDMA) is used to find the best alternative with respect to the design consideration to select a best location in this state to set pump storage plant, the suitable location has been selected by both MCDM (AHP) & cost effective analysis. This work tried to give some key point regarding the problems, mitigations & future scope of Gomati Hydro Power Project by integrated studies on Dumbur Lake, river sub-basin, average annual rainfall & annual mean depth of the river. At the end the project implies a probability of more than 11 MW Hydropower generations by installing Mini-Hydro Pump storage plant in this state Tripura using the water of River Gomati. Finally plant network has been optimized by Flex-Sim software, to check the efficiencies of different component under the assigned/ proposed conditions.

Authentication is one among a set of services that constitute a security sub-system in a modern computing or communications infrastructure. End User Authentication flexibility model would proposed in this paper allow the user to have multiple authentication mechanisms with varying levels of guarantee, and for suppliers to request and rely on mechanisms appropriate to the service requested. Authentications to enduser in a simple three level ticket request model algorithms solution on open distributed environment. This paper describes the ticket used by clients, servers, and Kerberos to achieve authentication toward prevention of unauthorized access to in sourced data on applications level. However we explore an approach to end user authentication that generalizes the notion of a textual password that, in many cases, improves the security. Our approach is based on the use of Kerberos authentication technique and Diffie-Hellman Key exchange.

This study assesses the performance of hydropower production by reservoirs with and without climate change impacts on river discharge. The case study of this research includes the Khersan 1, Karoon 4, and Karoon 3 reservoirs in Iran. The HADCM3 climate model with A2 greenhouse gas emission scenario is coupled with proportional downscaling to assess the impact of climate change on river discharge and reservoir hydropower production. The IHACRES rainfall-runoff model is implemented for calculating river discharge under climate-change conditions. Reservoir simulation and optimization models are implemented to calculate hydropower production in the base period (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000), future period 1 (2025-2039), future period 2 (2055-2069), and future period 3 (2085-2099). The power production and performance criteria of the reservoirs are calculated using simulation (standard operating policy) and optimization models in the considered periods. Our results show that the largest reductions of reservoir discharge correspond, in decreasing order, to the future periods 3, 1, and 2, respectively. Moreover, the hydropower production obtained with the optimization model is found to be larger than that obtained with the simulation model. The calculated increase in power production in the base period and future periods 1, 2, and 3 is equal to 6, 19, 10, and 22 %, respectively. These results demonstrate the benefit of applying optimization modeling for hydropower production in the Khersan -Karoon reservoir system to mitigate and adapt to climate-change impacts on river discharge.

This paper proposes a procedure for analysing tourism productivity. The procedure is based on the Luenberger productivity indicator for estimating and decomposing productivity change into efficiency change and technological change. The authors expand the procedure and further decompose the process of technological change to study the sources of bias within it. Therefore, a clearer and more enlightening view emerges of the productivity of travel agencies. The Portuguese travel agency sector is used as an application. Some managerial implications are developed.

This paper proposes a framework for benchmarking French and Italian seaports and the rationalisation of their operational activities. The analysis is based on the Luenberger productivity indicator. The main benefit of our method is to allow for both input contraction and output expansion, in determining relative efficiencies and productivity changes. Benchmarks are provided for improving the operations of those seaports, which perform worse than others. Several interesting and useful managerial insights and implications arise from the study. Following this approach, the benchmarking of multi-country seaports gives a different picture than the one which is obtained from a single country.

Energy consumption is known as the catalyst for economic growth. However, the association of CO 2 emissions with energy consumption has prompted policymakers to pursue types of energy that ensure sustainable development. Thus, this paper analyses the marginal impact of Biomass energy consumption and foreign direct investment (FDI) on CO 2 emissions in West Africa by employing spatial econometric models. The robust LM lag and the robust LM error test result implied that the spatial lag model was the best model to explain the spatial effects of the exogenous variables. From the result, the spatial parameter (ρ) revealed that CO 2 emissions have endogenous geographical impacts. Thus, indicating that a rise of CO 2 emissions in a neighboring country results in an increase in a focal country by 0.302%. The results also revealed that biomass energy consumption significantly lessens environmental pollution in a country by 0.048% and that of its neighboring countries by 0.012%. An increase in FDI in a focal country increases CO 2 emissions in its environment and its surrounding countries by 0.076% and 0.042% respectively.Thus, the investment in biomass energy projects in the West African region must increase significantly. This could help fight the environmental problems and these projects could be done by attracting foreign investors through FDI in funding. Overall the study spotlighted some policies suggestions for the West African energy market in achieving a carbon-neutral environment.

Ensuring a stable and sustainable electricity supply requires effective planning that balances cost efficiency and system reliability. This study explores the optimization of Basic Generation Cost (BPP) in Electricity Companies in Indonesia Electricity Supply Business Plan (RUPTL) 2025- 2034 while considering the reliability of the Sumatra power system. Using DIgSILENT PowerFactory, simulations incorporating Unit Commitment and Economic Dispatch methodologies were conducted to achieve cost reductions without compromising system stability. The significant result of this theses is optimization with economic dispatch reduces BPP up to 41,4% compared to conventional methods, enhancing power system cost efficiency. Increasing voltage reliability from 0.99 pu in 2025 to 1.01 pu in 2034. Higher renewable energy integration in 2034 reduces fuelcosts but increases challenges in maintaining frequency and voltage stability. Strategic recommendations include increasing transmission capacity, implementing energy storage systems, and optimizing unit commitment to balance cost and reliability. This research offers valuable insights for power system planning, addressing energy transition challenges and facilitating the integration of renewable energy sources in Sumatra.

Keywords: Basic Generation Cost, System Reliability, Economic Dispatch, DIgSILENT PowerFactory

This study aims to equip students with a foundational understanding of the business data analysis process. By utilizing Python and widely used libraries such as pandas, scikit-learn, matplotlib, and seaborn, it demonstrates key steps including data preprocessing, exploration, feature transformation, and the development and evaluation of machine learning models. Th e dataset, obtained from Kaggle, comprises 200,000 records related to marketing campaigns with variables such as campaign type, customer segment, cost, and impressions. Through the implementation of regression models-Linear Regression, Decision Tree, and Random Forest-the study helps learners grasp practical data analysis workflows and strengthens their ability to apply Python to real-world business challenges.

This chapter reports a hybrid optimization technique, a combination of stochastic methods – particle swarm optimization (PSO) and ant colony optimization (ACO), which is applied to find economic dispatch schedule and minimum generation cost for convex and non-convex power generation system simulated in MATLAB. A 40-generator system is considered here with combinations of valve point loading, ramp rate limit, and prohibited operating zone. The output is also noted when transmission loss is taken into consideration. The results are found better than those of many other hybrid methods. Considering the quality of the solution obtained and nature of convergence, PSO-ACO may be accepted as a good alternative for solving ELD problems of varying complexity. Though PSO has been extensively used in ELD problems for its flexibility, robustness, and fast convergence, it often produces suboptimal solution due to its premature convergence. ACO, on the other hand, known for its good global explora...

The growth in demand for air transport has generated new challenges for capacity and safety. In response, manufacturers develop new types of aircraft while airlines open new routes and adapt their fleet. This excessive demand for air transport also leads to the need for further investments in airport expansion and ATM modernization. The current work was focused on the ATM problem with respect to new procedures, such as free flight, for addressing the air capacity issues in an environmental approach. The study was triggered by and aligned with the following performance objectives set by EUROCONTROL and the European Commission: (1) to improve ATM safety whilst accommodating air traffic growth; (2) to increase the ATM network efficiency; (3) to strengthen ATM's contribution to aviation security and to environmental objectives; (4) to match capacity and air transport growth. The proposed mathematical model covers the aforementioned objectives by focusing on energy losses and costs of flights under the scenario of a controlled free flight and a unified airspace. The factors enhanced in the model were chosen based on their impact on the ATM energy efficiency, such as the airborne delays and flight duration, the delays due to ground holding, the flight cancellation, the flight speed deviations and the flight level alterations. Therefore, the presented mathematical model minimizes the energy costs due to the above terms under certain assumptions and constraints. Finally, simulation case studies, used as proof tests, have been conducted under different ATM scenarios to examine the complexity and the efficiency of the developed model.