Author
Srimantoorao S. Appadoo
Bio: Srimantoorao S. Appadoo is an academic researcher from University of Manitoba. The author has contributed to research in topics: Fuzzy logic & Fuzzy number. The author has an hindex of 14, co-authored 64 publications receiving 599 citations.
Papers published on a yearly basis
Papers
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TL;DR: An ant colony (AC) algorithm based meta-heuristics is proposed as the solution method of the EVRP and the effectiveness of the proposed algorithms is evaluated through extensive numerical experiments on the newly generated instances.
120 citations
TL;DR: This paper extends results to centered moments and finds the kurtosis for a class of FCA (Fuzzy Coefficient Autoregressive) and FCV (F fuzzy Coefficient Volatility) models and demonstrates the superiority of the fuzzy forecasts over the minimum square error forecast through a numerical example.
68 citations
TL;DR: This paper considers moment properties for a class of quadratic adaptive fuzzy numbers defined in Dubois and Prade and hinges on a characterization of imprecision by means of fuzzy set theory.
Abstract: In this paper, we consider moment properties for a class of quadratic adaptive fuzzy numbers defined in Dubois and Prade [D. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York, 1980]. The corresponding moments of Trapezoidal Fuzzy Numbers (Tr.F.N's) and Triangular Fuzzy Numbers (T.F.N's) turn out to be special cases of the adaptive fuzzy number [S. Bodjanova, Median value and median interval of a fuzzy number, Information Sciences 172 (2005) 73-89]. A numerical example is presented based on the Black-Scholes option pricing formula with quadratic adaptive fuzzy numbers for the characteristics such as volatility parameter, interest rate and stock price. Our approach hinges on a characterization of imprecision by means of fuzzy set theory.
50 citations
TL;DR: This paper proposes a class of FCV (Fuzzy Coefficient Volatility) models and studies the moment properties of these models, which incorporate fuzziness, subjectivity, arbitrariness and uncertainty observed in most financial time series.
47 citations
TL;DR: Fuzzy set theory is used to price binary options by fuzzifying the maturity value of the stock price using trapezoidal, parabolic and adaptive fuzzy numbers.
46 citations
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01 Jan 1994
TL;DR: In this Chapter, a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view is imagined.
Abstract: In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.
1,329 citations
TL;DR: A multi-depot green vehicle routing problem (MDGVRP) is developed by maximizing revenue and minimizing costs, time and emission, and an improved ant colony optimization (IACO) algorithm is applied that aims to efficiently solve the problem.
175 citations
TL;DR: A generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network for adversarial training to forecast high-frequency stock market can effectively improve stock price direction prediction accuracy and reduce forecast error.
Abstract: Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.
155 citations
TL;DR: The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models.
Abstract: In order to ensure high-quality and on-time delivery in logistic distribution processes, it is necessary to efficiently manage the delivery fleet. Nowadays, due to the new policies and regulations related to greenhouse gas emission in the transport sector, logistic companies are paying higher penalties for each emission gram of /km. With electric vehicle market penetration, many companies are evaluating the integration of electric vehicles in their fleet, as they do not have local greenhouse gas emissions, produce minimal noise, and are independent of the fluctuating oil price. The well-researched vehicle routing problem (VRP) is extended to the electric vehicle routing problem (E-VRP), which takes into account specific characteristics of electric vehicles. In this paper, a literature review on recent developments regarding the E-VRP is presented. The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models. Several variants of the E-VRP and related problems are observed. To cope with the new routing challenges in E-VRP, efficient VRP heuristics and metaheuristics had to be adapted. An overview of the state-of-the-art procedures for solving the E-VRP and related problems is presented.
137 citations