A belief-degree based multi-objective transportation problem with multi-choice demand and supply

Kakran, Vandana Y.; Dhodiya, Jayesh M.

doi:10.11121/ijocta.2022.1166

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…FLC has an advanced level of efficiency for nonlinear converters [10]. Many researchers approved FLC to become one of intelligent controller for their appliances and successfully implemented their tactics [11][12][13][14]. FLC does not require an accurate mathematical model of a circuit.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter

Demirtas,

Ahmad

2023

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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The power circuit of AC voltage controller capable of operating at a leading, lagging, and unity power factor is studied by a lot of researchers in the literature. Circuits working with high switching frequency are known as power factor correctors (PFCs). The single-phase boost converter has become the most popular topology for power factor correction (PFC) in general purpose power supplies. Power factor correction circuit provides conventional benefits to electric power systems. The benefits are the reduction of power factor penalty and utility bill and power loss. Therefore, a boost converter power factor correction scheme is presented in this paper. A PI, fuzzy logic PI and fractional order PI (FOPI) controllers are used to fix an active shaping of input current of the circuit and to improve the power factor. The controller parameters (coefficients) are optimized using the Particle Swarm Optimization (PSO) algorithm. Average current mode control (ACMC) method is used in the circuit. The converter circuit consists of a single-phase bridge rectifier, boost converter, transformer and load. A mathematical model of the plant is required to design the PI controller. A model for power factor correction circuit is formed in MATLAB/Simulink toolbox and a filter is designed to reduce THD value. The proposed model is simulated using a combination of PI, fuzzy logic and FOPI controllers. The control scheme is applied to 600 Watt PFC boost converter to get 400 Volt DC output voltage and 0.99 power factor. The input voltage is 230 VRMS with 50 Hz. The combination of FOPI and PI controller has the best solution to control the power factor according to PI and fuzzy controllers.

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Section: Fuzzy Logic Controllermentioning

confidence: 99%

Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter

Demirtas,

Ahmad

2023

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Solving bi-objective bi-item solid transportation problem with fuzzy stochastic constraints involving normal distribution

Buvaneshwari¹,

Anuradha

2023

MATH

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<abstract> <p>In today's competitive world, entrepreneurs cannot argue for transporting a single product. It does not provide much profit to the entrepreneur. Due to this reason, multiple products need to be transported from various origins to destinations through various types of conveyances. Real-world decision-making problems are typically phrased as multi-objective optimization problems because they may be effectively described with numerous competing objectives. Many real-life problems have uncertain objective functions and constraints due to incomplete or uncertain information. Such uncertainties are dealt with in fuzzy/interval/stochastic programming. This study explored a novel integrated model bi-objective bi-item solid transportation problem with fuzzy stochastic inequality constraints following a normal distribution. The entrepreneur's objectives are minimizing the transportation cost and duration of transit while maximizing the profit subject to constraints. The chance-constrained technique is applied to transform the uncertainty problem into its equivalent deterministic problem. The deterministic problem is then solved with the proposed method, namely, the global weighted sum method (GWSM), to find the optimal compromise solution. A numerical example is provided to test the efficacy of the method and then is solved using the Lingo 18.0 software. To highlight the proposed method, comparisons of the solution with the existing solution methods are performed. Finally, to understand the sensitivity of parameters in the proposed model, sensitivity analysis (SA) is conducted.</p> </abstract>

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A belief-degree based multi-objective transportation problem with multi-choice demand and supply

Cited by 2 publications

References 39 publications

Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter

Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter

Solving bi-objective bi-item solid transportation problem with fuzzy stochastic constraints involving normal distribution

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