Search results for: portfolio optimization

Authors: Mohammadhadi Mirmohammadi, Reza Safian, Hossein Haddad

Abstract:

This paper presents an innovative solution for complex multi-objective optimization problem which is a part of efforts toward maximizing rolling mill throughput and minimizing processing costs in tandem cold rolling. This computational intelligence based optimization has been applied to the rolling schedules of tandem cold rolling mill. This method involves the combination of two derivative-free optimization procedures in the form of nested loops. The first optimization loop is based on Improving Hit and Run method which focus on balance of power, force and reduction distribution in rolling schedules. The second loop is a real-coded genetic algorithm based optimization procedure which optimizes energy consumption and productivity. An experimental result of application to five stand tandem cold rolling mill is presented.

Keywords: derivative-free optimization, Improving Hit and Run method, real-coded genetic algorithm, rolling schedules of tandem cold rolling mill

Procedia PDF Downloads 663

Authors: Mohammad Q. M. Momani

Abstract:

The objective of this study is to examine whether the value relevance of ownership structure changed as the Amman Stock Exchange market conditions changed. Using data from 2005 to 2014, the study finds that the performance of portfolios that contain firms with concentrated ownership structure declines significantly during the post-crisis period. These portfolios exhibit poor performance relative to portfolios that contain firms with dispersed ownership structure during the post-crisis period. The results argue that uninspired performance of the Amman Stock Exchange during the post-crisis period, increased the incentives for controlling shareholders to expropriate. Investors recognized these incentives and discounted firms that were more likely to expropriate.

Keywords: value relevance, ownership structure, portfolio performance, Jordan, ASE

Procedia PDF Downloads 92

Authors: Vinayak Bassi, Rajpreet Singh

Abstract:

Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

Procedia PDF Downloads 128

Authors: Abdelmounaim Lahrech, Hajar Bousfiha

Abstract:

This paper examines equity market integration between Morocco and France and its consequent implications on international portfolio diversification. In the absence of stock market linkages, Morocco can act as a diversification destination to European investors, allowing higher returns at a comparable level of risk in developed markets. In contrast, this attractiveness is limited if both financial markets show significant linkage. The research empirically measures financial market’s integration in by capturing the conditional correlation between the two markets using the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. Then, the research uses the Dynamic Conditional Correlation (DCC) model of Engle (2002) to track the correlations. The research findings show that there is no important increase over the years in the correlation between the Moroccan and the French equity markets, even though France is considered Morocco’s first trading partner. Failing to prove evidence of the stock index linkage between the two countries, the volatility series of each market were assumed to change over time separately. Yet, the study reveals that despite the important historical and economic linkages between Morocco and France, there is no evidence that equity markets follow. The small correlations and their stationarity over time show that over the 10 years studied, correlations were fluctuating around a stable mean with no significant change at their level. Different explanations can be attributed to the absence of market linkage between the two equity markets.

Keywords: equity market linkage, DCC GARCH, international portfolio diversification, Morocco, France

Procedia PDF Downloads 408

Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

Abstract:

This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

Procedia PDF Downloads 341

Authors: David Lindström

Abstract:

We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cycling parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface.

Keywords: expensive function, infill sampling criterion, kriging, global optimization, response surface, Runge phenomenon

Procedia PDF Downloads 546

Authors: Romisaa Ali

Abstract:

The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

Procedia PDF Downloads 131

Authors: Jong-Yeon Kim, Kwang-Bok Shin, Tae-Hwan Ko

Abstract:

The purpose of this paper is to suggest a weight-reduction design method for the aluminum extrusion carbody structure of a double deck high-speed train using size optimization method. The size optimization method was used to optimize thicknesses of skin and rib of the aluminum extrusion for the carbody structure. Thicknesses of 1st underframe, 2nd underframe, solebar and roof frame were selected by design variables in order to conduct size optimization. The results of the size optimization analysis showed that the weight of the aluminum extrusion could be reduced by 0.61 tons (5.60%) compared to the weight of the original carbody structure.

Keywords: double deck high-speed train, size optimization, weigh-reduction, aluminum extrusion

Procedia PDF Downloads 258

Authors: Syed Auon Raza, Tahir Mahmood, Syed Basit Ali Bukhari

Abstract:

In power system properly coordinated protection scheme is designed to make sure that only the faulty part of the system will be isolated when abnormal operating condition of the system will reach. The complexity of the system as well as the increased user demand and the deregulated environment enforce the utilities to improve system reliability by using a properly coordinated protection scheme. This paper presents overview of over current relay coordination techniques. Different techniques such as Deterministic Techniques, Meta Heuristic Optimization techniques, Hybrid Optimization Techniques, and Trial and Error Optimization Techniques have been reviewed in terms of method of their implementation, operation modes, nature of distribution system, and finally their advantages as well as the disadvantages.

Keywords: distribution system, relay coordination, optimization, Plug Setting Multiplier (PSM)

Procedia PDF Downloads 351

Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

Abstract:

Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

Procedia PDF Downloads 252

Authors: Ali Rabah

Abstract:

This research received no grant or financial support from any public, commercial, or none governmental agency. The author conducted this work as part of his normal research activities as a professor of Chemical Engineering at the University of Khartoum, Sudan. Abstract While fossil oil reserves have been receding, the demand for diesel and gasoline has been growing. In recent years, syngas of biomass origin has been emerging as a viable feedstock for Fischer-Tropsch (FT) synthesis, a process for manufacturing synthetic gasoline and diesel. This paper reports the optimization of syngas quality to match FT synthesis requirements. The optimization model maximizes the thermal efficiency under the constraint of H2/CO≥2.0 and operating conditions of equivalent ratio (0 ≤ ER ≤ 1.0), steam to biomass ratio (0 ≤ SB ≤ 5), and gasification temperature (500 °C ≤ Tg ≤ 1300 °C). The optimization model is executed using the optimization section of the Model Analysis Tools of the Aspen Plus simulator. The model is tested using eleven (11) types of MSW. The optimum operating conditions under which the objective function and the constraint are satisfied are ER=0, SB=0.66-1.22, and Tg=679 - 763°C. Under the optimum operating conditions, the syngas quality is H2=52.38 - 58.67-mole percent, LHV=12.55 - 17.15 MJ/kg, N2=0.38 - 2.33-mole percent, and H2/CO≥2.15. The generalized optimization model reported could be extended to any other type of biomass and coal. Keywords: MSW, Syngas, Optimization, Fischer-Tropsch.

Keywords: syngas, MSW, optimization, Fisher-Tropsh

Procedia PDF Downloads 41

Authors: Viviana Ventre, Giacomo Di Tollo, Roberta Martino

Abstract:

The portfolio selection problem includes the evaluation of many criteria that are difficult to compare directly and is characterized by uncertain elements. The portfolio selection problem can be modeled as a group decision problem in which several experts are invited to present their assessment. In this context, it is important to study and analyze the process of reaching a consensus among group members. Indeed, due to the various diversities among experts, reaching consensus is not necessarily always simple and easily achievable. Moreover, the concept of consensus is accompanied by the concept of false consensus, which is particularly interesting in the dynamics of group decision-making processes. False consensus can alter the evaluation and selection phase of the alternative and is the consequence of the decision maker's inability to recognize that his preferences are conditioned by subjective structures. The present work aims to investigate the dynamics of consensus attainment in a group decision problem in which equivalent portfolios are proposed. In particular, the study aims to analyze the impact of the subjective structure of the decision-maker during the evaluation and selection phase of the alternatives. Therefore, the experimental framework is divided into three phases. In the first phase, experts are sent to evaluate the characteristics of all portfolios individually, without peer comparison, arriving independently at the selection of the preferred portfolio. The experts' evaluations are used to obtain individual Analytical Hierarchical Processes that define the weight that each expert gives to all criteria with respect to the proposed alternatives. This step provides insight into how the decision maker's decision process develops, step by step, from goal analysis to alternative selection. The second phase includes the description of the decision maker's state through Markov chains. In fact, the individual weights obtained in the first phase can be reviewed and described as transition weights from one state to another. Thus, with the construction of the individual transition matrices, the possible next state of the expert is determined from the individual weights at the end of the first phase. Finally, the experts meet, and the process of reaching consensus is analyzed by considering the single individual state obtained at the previous stage and the false consensus bias. The work contributes to the study of the impact of subjective structures, quantified through the Analytical Hierarchical Process, and how they combine with the false consensus bias in group decision-making dynamics and the consensus reaching process in problems involving the selection of equivalent portfolios.

Keywords: analytical hierarchical process, consensus building, false consensus effect, markov chains, portfolio selection problem

Procedia PDF Downloads 64

Authors: Abdelouahab Errida, Bouchra Lotfi, Elalami Semma

Abstract:

In this paper, we try to address the topic of multiple change management by adopting an engineered research methodology, conducted within a Moroccan company during its implementation of several change projects that aim at improving its supply chain management performance. Firstly, we present the key concepts related to our research, namely change management, multiproject management and supply chain management. Then, we try to assess how the change management and multi-project management are applied in this company. Finally, we try to propose an approach that will help managers in dealing with multiple change projects. This approach proposes to integrate change management, project management and multi-project management for managing change projects according to three organizational levels: executive level, project portfolio level and change project level.

Keywords: change management, multi-project management, project management, change portfolio, supply chain management,

Procedia PDF Downloads 186

Authors: Inayat Ur Rehman, Georgia Sakellari

Abstract:

Cloud computing has evolved into a vital technology for businesses, offering scalability, flexibility, and cost-effectiveness. However, maintaining high availability and optimal performance in the cloud is crucial for reliable services. This paper explores the significance of cloud monitoring and performance optimization in sustaining the high availability of cloud-based systems. It discusses diverse monitoring tools, techniques, and best practices for continually assessing the health and performance of cloud resources. The paper also delves into performance optimization strategies, including resource allocation, load balancing, and auto-scaling, to ensure efficient resource utilization and responsiveness. Addressing potential challenges in cloud monitoring and optimization, the paper offers insights into data security and privacy considerations. Through this thorough analysis, the paper aims to underscore the importance of cloud monitoring and performance optimization for ensuring a seamless and highly available cloud computing environment.

Keywords: cloud computing, cloud monitoring, performance optimization, high availability

Procedia PDF Downloads 21

Authors: Walid Chkili

Abstract:

This paper investigates the dynamic relationship between gold and stock markets using data for BRICS counties. For this purpose, we estimate three multivariate GARCH models (namely CCC, DCC and BEKK) for weekly stock and gold data. Our main objective is to examine time variations in conditional correlations between the two assets and to check the effectiveness use of gold as a hedge for equity markets. Empirical results reveal that dynamic conditional correlations switch between positive and negative values over the period under study. This correlation is negative during the major financial crises suggesting that gold can act as a safe haven during the major stress period of stock markets. We also evaluate the implications for portfolio diversification and hedging effectiveness for the pair gold/stock. Our findings suggest that adding gold in the stock portfolio enhance its risk-adjusted return.

Keywords: gold, financial markets, hedge, multivariate GARCH

Procedia PDF Downloads 436

Authors: Mehrdad Rezaei, Reza Haghmaram, Nima Amjadi

Abstract:

This paper proposes an operating and cost optimization model for micro-grid (MG). This model takes into account emission costs of NOx, SO2, and CO2, together with the operation and maintenance costs. Wind turbines (WT), photovoltaic (PV) arrays, micro turbines (MT), fuel cells (FC), diesel engine generators (DEG) with different capacities are considered in this model. The aim of the optimization is minimizing operation cost according to constraints, supply demand and safety of the system. The proposed genetic algorithm (GA), with the ability to fine-tune its own settings, is used to optimize the micro-grid operation.

Keywords: micro-grid, optimization, genetic algorithm, MG

Procedia PDF Downloads 467

Authors: Sofia Ayouche, Rachid Ellaia, Rajae Aboulaich

Abstract:

Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve.

Keywords: optimization, zero-coupon curve, Nelson-Siegel-Svensson, particle swarm optimization, Nelder-Mead algorithm

Procedia PDF Downloads 403

Authors: Rahul Paul, Kedar Nath Das

Abstract:

The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models.

Keywords: machine learning, reinforcement learning, loss function, evolutionary optimization techniques

Procedia PDF Downloads 41

Authors: Arslan Ellahi, Syed Amjad Hussain, Fawaz Saleem Bokhari

Abstract:

Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them.

Keywords: complexity, rapid evolution, swarm intelligence, dimensions

Procedia PDF Downloads 226

Authors: Saeed Kamarian, Mahmoud Shakeri

Abstract:

Sandwich structures are used in a variety of engineering applications including aircraft, construction and transportation where strong, stiff and light structures are required. In this paper, frequency maximization of Functionally Graded Sandwich (FGS) beams resting on Pasternak foundations is investigated. A generalized power-law distribution with four parameters is considered for material distribution through the thicknesses of face layers. Since the search space is large, the optimization processes becomes so complicated and too much time consuming. Thus a novel meta–heuristic called Imperialist Competitive Algorithm (ICA) which is a socio-politically motivated global search strategy is implemented to improve the speed of optimization process. Results show the success of applying ICA for engineering problems especially for design optimization of FGM sandwich beams.

Keywords: sandwich beam, functionally graded materials, optimization, imperialist competitive algorithm

Procedia PDF Downloads 540

Authors: Li Ni, Pen ManMan, Li KenLi

Abstract:

Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: chemical reaction optimization, expection maimization, initia, objective function clustering

Procedia PDF Downloads 678

Authors: Tomasz Robert Kuczerski

Abstract:

The high complexity of the algorithm of the autonomous tandem detonator system creates an optimization problem due to the parallel operation of several machine states of the system. Many years of experience and classic analyses have led to a partially optimized model. Limitations on the energy resources of this class of autonomous systems make it necessary to search for more effective methods of optimisation. The use of the Quantum Approximate Optimization Algorithm (QAOA) in these studies shows the most promising results. With the help of multiple evaluations of several qubit quantum circuits, proper results of variable parameter optimization were obtained. In addition, it was observed that the increase in the number of assessments does not result in further efficient growth due to the increasing complexity of optimising variables. The tests confirmed the effectiveness of the QAOA optimization method.

Keywords: algorithm analysis, autonomous system, quantum optimization, tandem detonator

Procedia PDF Downloads 57

Authors: M. Farhaoui, M. Derraz

Abstract:

In the drinking water treatment processes, the optimization of the treatment is an issue of particular concern. In general, the process consists of many units as settling, coagulation, flocculation, sedimentation, filtration and disinfection. The optimization of the process consists of some measures to decrease the managing and monitoring expenses and improve the quality of the produced water. The objective of this study is to provide water treatment operators with methods and practices that enable to attain the most effective use of the facility and, in consequence, optimize the of the cubic meter price of the treated water. This paper proposes a review on optimization of drinking water treatment process by analyzing all of the water treatment units and gives some solutions in order to maximize the water treatment performances without compromising the water quality standards. Some solutions and methods are performed in the water treatment plant located in the middle of Morocco (Meknes).

Keywords: coagulation process, optimization, turbidity removal, water treatment

Procedia PDF Downloads 389

Authors: Surayyo Shaamirova, Anwar Hasan Abdullah Othman

Abstract:

This study investigates the co-integration association between the top 10 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Bitcoin Cash, EOS, Cardano, Litecoin, Stellar, IOTA, and NEO. The study applies Johansen Juselius co-integration test to examine the long-run co-integration and utilize the Engle and Granger casualty test to examine the short-run relationship. The findings of the study show that there is a strong co-integration relationship among the cryptocurrencies; however, in the short run, there is no causal relationship among the crypto currencies. These results, therefore, suggest that there are portfolio diversification opportunities in the cryptocurrencies industry when it comes to long run investment decisions, on the other hand, the cryptocurrencies industry shows the characteristics of efficiency in the short-run. This is an indication of a non-speculation investment in the cryptocurrencies industry in the short term investment.

Keywords: cryptocurrencies, Johansen-Juselius co-integration test, Engle and Granger casualty test, portfolio diversification

Procedia PDF Downloads 108

Authors: K. Y. Tseng, C. Y. Wu

Abstract:

This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.

Keywords: differential evolution, Truss structure optimization, optimal chiller loading, modified binary differential evolution

Procedia PDF Downloads 131

Authors: Aman Sharma, Sonali Sengupta

Abstract:

The study explores the utility of Bayesian optimization in tuning the physical and chemical parameters of reactions in an offline experimental setup. A comparative analysis of the influence of the acquisition function on the optimization performance is also studied. For proxy first and second-order reactions, the results are indifferent to the acquisition function used, whereas, while studying the parameters for oxidative desulphurization of thiophene in an offline setup, upper confidence bound (UCB) provides faster convergence along with a marginal trade-off in the maximum conversion achieved. The work also demarcates the critical number of independent parameters and input observations required for both sequential and offline reaction setups to yield tangible results.

Keywords: acquisition function, Bayesian optimization, desulfurization, kinetics, thiophene

Procedia PDF Downloads 146

Authors: A. Lefebre, M. Willekens, K. Debackere

Abstract:

Since the 1990s, patent applications have been booming, especially in the field of telecommunications. However, this increase in patent filings has been associated with an (alleged) decrease in patent quality. The plethora of low-quality patents devalues the high-quality ones, thus weakening the incentives for inventors to patent inventions. Despite the rich literature on strategic patenting, previous research has neglected to emphasize the importance of patent portfolio management and its impact on patent quality. In this paper, we compare related patent portfolios vs. nonrelated patents and investigate whether the patent quality and innovativeness differ between the two types. In the analyses, patent quality is proxied by five individual proxies (number of inventors, claims, renewal years, designated states, and grant lag), and these proxies are then aggregated into a quality index. Innovativeness is proxied by two measures: the originality and radicalness index. Results suggest that related patent portfolios have, on average, a lower patent quality compared to nonrelated patents, thus suggesting that firms use them for strategic purposes rather than for the extended protection they could offer. Even upon testing the individual proxies as a dependent variable, we find evidence that related patent portfolios are of lower quality compared to nonrelated patents, although not all results show significant coefficients. Furthermore, these proxies provide evidence of the importance of adding fixed effects to the model. Since prior research has found that these proxies are inherently flawed and never fully capture the concept of patent quality, we have chosen to run the analyses with individual proxies as supplementary analyses; however, we stick with the comprehensive index as our main model. This ensures that the results are not dependent upon one certain proxy but allows for multiple views of the concept. The presence of divisional applications might be linked to the level of innovativeness of the underlying invention. It could be the case that the parent application is so important that firms are going through the administrative burden of filing for divisional applications to ensure the protection of the invention and the preemption of competition. However, it could also be the case that the preempting is a result of divisional applications being used strategically as a backup plan and prolonging strategy, thus negatively impacting the innovation in the portfolio. Upon testing the level of novelty and innovation in the related patent portfolios by means of the originality and radicalness index, we find evidence for a significant negative association with related patent portfolios. The minimum innovation that has been brought on by the patents in the related patent portfolio is lower compared to the minimum innovation that can be found in nonrelated portfolios, providing evidence for the second argument.

Keywords: patent portfolio management, patent quality, related patent portfolios, strategic patenting

Procedia PDF Downloads 57

Authors: Dávid Csercsik, Péter Kádár

Abstract:

In the case of the proposed method, the problem is parallelized by considering multiple possible mode of operation profiles, which determine the range in which the generators operate in each period. For each of these profiles, the optimization is carried out independently, and the best resulting dispatch is chosen. For each such profile, the resulting problem is a quadratic programming (QP) problem with a potentially negative definite Q quadratic term, and constraints depending on the actual operation profile. In this paper we analyze the performance of available MATLAB optimization methods and solvers for the corresponding QP.

Keywords: optimization, MATLAB, quadratic programming, economic dispatch

Procedia PDF Downloads 517

Authors: Till Gramberg

Abstract:

In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

Procedia PDF Downloads 28

Authors: Mandeep Kaur, Fatehgarh Sahib

Abstract:

The purpose of economic load dispatch is to allocate the required load demand between the available generation units such that the cost of operation is minimized. It is an optimization problem to find the most economical schedule of the generating units while satisfying load demand and operational constraints. The multiobjective optimization problem in which the engineer’s goal is to maximize or minimize not a single objective function but several objective functions simultaneously. The purpose of multiobjective problems in the mathematical programming framework is to optimize the different objective functions. Many approaches and methods have been proposed in recent years to solve multiobjective optimization problems. Weighting method has been applied to convert multiobjective optimization problems into scalar optimization. MATLAB 7.10 has been used to write the code for the complete algorithm with the help of genetic algorithm (GA). The validity of the proposed method has been demonstrated on a three-unit power system.

Keywords: economic load dispatch, genetic algorithm, generating units, multiobjective optimization, weighting method

Procedia PDF Downloads 117