197 research outputs found
FQSPM-SWOT for strategic alliance planning and partner selection; case study in a holding car manufacturer company
Full text linkIn today's competitive environment, holding companies are usually unable to successfully compete in production of goods and services due to technological sophistication. Therefore, for success of holding companies, selecting appropriate strategic alliance partner is a critical factor. Accordingly, the aim of the paper is to propose a systematic approach for an effective partner selection. Firstly, the underlying motivation and reasons for a strategic alliance building are presented using a SWOT analysis. Criteria of partners’ evaluation are attained on the basis of combining strengths, weaknesses, opportunities and threats. Due to uncertainty of criteria, they are weighted using fuzzy quantitative strategic planning matrix (FQSPM). Because of a large number of criteria obtained from the SWOT-FQSPM analysis, criteria are diminished based on their weights using the Gap analysis with fuzzy data ranking. In the next step, it is proposed to apply four ranking algorithms including the Fuzzy Additive Ratio Assessment (ARAS-F), the Fuzzy Complex Proportional Assessment (COPRAS-F), the Fuzzy Multi-Objective Optimization by Ratio Analysis (Fuzzy MOORA), and the Fuzzy Technique for Order Preference by Similarity to Ideal solution (Fuzzy TOPSIS) to evaluate strategic partners. Finally, the results are combined with the help of the Borda method to choose the best alternative. To illustrate the efficiency of the proposed approach, a real partner selection problem at a holding industries factory in Iran is presented
An investigation of the effect of extroverted and introverted personalities on knowledge acquisition techniques
No full textPurpose This paper aims to explore the relationship between personality traits (introversion versus extroversion) and knowledge acquisition (KA) techniques. Design/methodology/approach The major methodology of the current study is survey. Results are based on 152 usable responses provided by experts in different industries including electronic, communication, information technology, computer and biology. The major analytical technique used is Pearson correlation analysis. Findings According to the results, there are significant relationships between personality traits (i.e. introversion versus extroversion) and KA techniques. Research limitations/implications This study was conducted on data from 152 Iranian experts which limits the generalizability of the results. This limitation can be addressed by future studies conducting similar studies on cross-country samples. Further, due to the analytical technique adopted in this study, causality implications cannot be drawn from the results. Originality/value This study reveals linkages between personality traits (i.e. introversion versus extroversion) and KA techniques. Results shed light on the KA process for both scholars and practitioners involved in KA programs in the organizations.http://media.proquest.com/media/hms/PFT/1/zDFl1?cit%3Aauth=Akhavan%2C+Peyman%3BDehghani%2C+Maryam%3BRajabpour%2C+Amir%3BPezeshkan%2C+Amir&cit%3Atitle=An+investigation+of+the+effect+of+extroverted+and+introverted+...&cit%3Apub=VINE+Journal+of+Information+and+Knowledge+Management+Systems&cit%3Avol=46&cit%3Aiss=2&cit%3Apg=194&cit%3Adate=2016&ic=true&cit%3Aprod=ABI%2FINFORM+Global&_a=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%3D%3D&_s=4bL2cITM627XLOaPacNfkURsKbA%3
Testing in learning conjunctive invariants
Full text linkWe show a new approach in learning conjunctive invariants using dynamic testing of the program. Coming up with correct set of loop invariant is the most challenging part of any verification methods. Although new methods tend to generate a large number of possible invariants hoping this set contains all required invariants needed to verify the program, this large number will cause a significant delay in verification which often ends up to a time out. Our approach introduce a new method in which we can solve this problem by reducing the number of generated candidate invariants.
We apply our method in a verification engine that uses natural proofs for heap verification. We implement our method by running tests for linked list data structures and evaluate it by comparing the results to the original approach without testing. We also use an existing GPU verification tool, called GPUVerify, and apply our method to it. Finally, we show that our approach can significantly improve the verification time and in some cases prove programs that were initially timed out.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-08-01The student, Peyman Mahdian, accepted the attached license on 2017-07-14 at 16:52.The student, Peyman Mahdian, submitted this Thesis for approval on 2017-07-14 at 17:02.This Thesis was approved for publication on 2017-07-17 at 10:52.DSpace SAF Submission Ingestion Package generated from Vireo submission #11479 on 2017-09-29 at 11:19:28Made available in DSpace on 2017-09-29T17:52:24Z (GMT). No. of bitstreams: 2
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Reply to Heinschke, S.; Schneider, J.J. Comment on “Pashchanka, M. Conceptual Progress for Explaining and Predicting Self-Organization on Anodized Aluminum Surfaces. Nanomaterials 2021, 11, 2271”
No full textTeam Peyman Taher
Robust Linear Quadratic Regulator: Exact Tractable Reformulation
No full textWe consider the problem of controlling an unknown stochastic linear dynamical system subject to an infinitehorizon discounted quadratic cost. Existing approaches for handling the corresponding robust optimal control problem resort to either conservative uncertainty sets or various approximations schemes, and to our best knowledge, the current literature lacks an exact, yet tractable, solution. We propose a class of novel uncertainty sets for the system matrices of the linear system. We show that the resulting robust linear quadratic regulator problem enjoys a closed-form solution described through a generalized algebraic Riccati equation arising from dynamic game theory.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Peyman Mohajerin EsfahaniTeam Bart De Schutte
Simplistic correlations between molecular electronic properties and inhibition efficiencies: Do they really exist?
Full text linkThe often used simplistic correlations between molecular electronic parameters and experimentally determined corrosion inhibition efficiencies are critically evaluated for a set of 24 heterocyclic organic compounds, tested as corrosion inhibitors for copper in 3 wt.% NaCl aqueous solution. Twelve different molecular electronic descriptors—such as ionization potential, electron affinity, HOMO–LUMO gap, dipole moment—are tested and it is shown that none of them displays any noticeable correlation with the inhibition efficiency. Our results, therefore, cast serious doubt on reported correlations between such parameters and inhibition efficiency, obtained for only a few inhibitors, which are abundant in the literature. We also discuss some pros and cons of inhibition efficiency as a metric for evaluating the performance of corrosion inhibitors, and introduce a new metric termed inhibition power that uses the universal logarithmic scale and dimensionless decibel (dB) units.Team Peyman TaheriTeam Arjan Mo
Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach
No full textWe propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define "Approximation Usefulness" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].References[1] Mohammed Ghaith Altarabichi, Yuantao Fan, Sepideh Pashami, Peyman Sheikholharam Mashhadi, and Sławomir Nowaczyk. 2021. Extracting invariant features for predicting state of health of batteries in hybrid energy buses. In 2021 ieee 8th international conference on data science and advanced analytics (dsaa). IEEE, 1–6.[2] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2021. Surrogate-assisted genetic algorithm for wrapper feature selection. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 776–785.[3] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2023. Fast Genetic Algorithm for feature selection—A qualitative approximation approach. Expert systems with applications 211 (2023), 118528.© 2023 Copyright held by the owner/author(s).</p
What to bid and when to stop
No full textNegotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators.Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent.There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies.To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted.The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios.We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions.Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies.Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance.Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other.Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies.We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model.Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent
Designing an expert fuzzy system to select the appropriate knowledge management strategy in accordance with APO model and Bloodgood KM strategies
No full textPurpose
Selection of knowledge management strategies (KMS) is one of the most important and effective factors in acquiring the competitive advantage and elevating the knowledge level of the organizations. Those organizations that have taken steps toward knowledge management necessarily need to pay utmost attention to the matter of KMS before taking any further steps in their activities. One of the effective ways in adopting the proper KMS is evaluating the knowledge management maturity level in the organization. The purpose of this paper is to design an expert fuzzy system to adopt the KMS based on Bloodgood model in accordance with the maturity level of the organization.
Design/methodology/approach
In this method, with the help of expert fuzzy system, a model has been designed, by using MATLAB software, to adopt the KMS. The KM maturity level, tacit knowledge and explicit knowledge are chosen as inputs, and each one of Bloodgood’s KMS (production, transfer and protecting the knowledge) are chosen as outputs. To perform the system, the maturity level of knowledge management of an industrial organization that has been evaluated by the standard Asian Productivity Organization questionnaire is used as the input, which has been given to expert fuzzy system. Then, considering the output of the system, KMS for the organization have been recommended.
Findings
Knowledge management maturity level of the organization is on Level 4; considering the expert fuzzy system that has been designed, “knowledge production” strategy is recommended for the organization under study.
Originality/value
An expert fuzzy system has been designed regarding the maturity of knowledge management and Bloodgood model that can be used as a guide for organizations and academic people as an appropriate practical model for selecting knowledge management strategies.
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Movement of Iranian Academic Research Centers towards Knowledge Management: An Exploration of KM Critical Factors
No full text- …
