Training ANFIS structure using simulated annealing algorithm for dynamic systems identification

Haznedar, Bülent; Kalınlı, Adem

doi:10.1016/j.neucom.2018.04.006

Cited by 80 publications

(34 citation statements)

References 15 publications

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“…Black hole algorithm (BHA) is inspired by this phenomenon and belongs to the swarm intelligence paradigm using adaptive strategies to search and optimize. Black hole algorithm can be defined as a sub-field of particle swarm optimization and they are inspired by physical laws, like gravitation search [64], intelligent water drop [65], or simulated annealing [66]. Other types of heuristic optimization algorithms are inspired by living bodies, like bacterial algorithm [67], bat algorithm [68], artificial bee colony algorithm [69], firefly algorithm [70], and ant colony algorithm [71].…”

Section: Black Hole Algorithm-based Optimizationmentioning

confidence: 99%

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Bányai

2018

Energies

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Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

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Section: Black Hole Algorithm-based Optimizationmentioning

confidence: 99%

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Bányai

2018

Energies

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“…This model can be introduced as neural network with a fuzzy parameter or a distributed learning fuzzy system. Fuzzy systems can reduce the dimensionality of the search space by distributing input information over the network and also find the optimum values of the control parameter for non-linear problems by using the back propagation behaviors of neural networks [29]. The architecture of this algorithm is generally composed of five layers as shown in Fig.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

Hassanzadeh¹,

Jafari-Bavil-Olyaei²,

Taghi-Aalami³

et al. 2019

Period. Polytech. Civil Eng.

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An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive networkbased fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods.

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“…The possibility that a change that worsens (increases) the energy is retained implies that the algorithm would hardly be trapped in local energy minima. Detailed steps of the SA algorithm can be seen in Lukovac et al (2017) and Haznedar & Kalinli (2018).…”

Section: Supervised Learningmentioning

confidence: 99%

A fuzzy model for organization structure design with human resource allocation

Pamučar

Knežević

Macura

et al. 2019

Manage: J. Sustainable Bus. Manage, Solutions Emerging Econ.

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Research Question: The paper explores the problem of defining the number of required employees and their skills, as well as optimizing the work time. Motivation: Motivation for this paper arises from the fact that an organizational structure of business system has to be conceived and proportioned so as to justify the main company jobs and goals. Almost every job set for the management must be performed properly and reliably in changeable environmental conditions. In order to maintain the efficiency of complex business systems with dynamic and stochastic changeable and unchangeable variables, it is essential that the organizational structure be flexible and constantly adjusted. Idea: The idea of the paper was to develop a model for human resource allocation (HRA) in fuzzy environment. Data: The model is tested in realistic industrial environment - the example of the logistics administrative bodies in the Petroleum Industry of Serbia. Tools: To solve the problem, the following approaches are used: Fuzzy logic, Neural network and Simulated annealing. The model for HRA is based on an adaptive neuro-fuzzy inference system (ANFIS) and fuzzy mathematical model (FMM) for treating uncertainty. ANFIS-FMM model enables the human resource strategy development based on the optimisation of the employees’ work time. In this ANFIS-FMM model the input variables are described using fuzzy sets represented by Gaussian functions. Using expert reasoning, an unique knowledge base is formed which enables the human resource strategy development based on the optimisation of the employees’ work time. Findings: Based on authors’ knowledge there is a lack of the fuzzy logic applications, adaptive neuro-fuzzy models, linear and dynamic programming, as well as heuristic and metaheuristic models related to HRA. In most of the papers, the classical methods of strategic management have been used for HRA. Contribution: In this paper the model for HRA is developed based on adaptive neuro-fuzzy technique application and FMM. The model has four main advantages over other HRA methods: the system possesses adaptability, the model is efficient in conditions of uncertainty, the neuro-fuzzy based HRA model allows dynamic decision making in HRA management through the implementation of a computer-based system, it can be IT-supported tool.

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Training ANFIS structure using simulated annealing algorithm for dynamic systems identification

Cited by 80 publications

References 15 publications

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

A fuzzy model for organization structure design with human resource allocation

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