linear programming

A method for designing suboptimal control for a class of delayed fractional systems is proposed in this paper. Despite theoretical advances in fractional mathematics and computational techniques for solving fractional optimal control (FOC) problems, as well as a lack of comprehensive analytical methods, numerical methods have been developed. For this purpose, in this study, the necessary optimal conditions for the time-delay fractional optimal control (TDFOC) problem are presented first; Then an algorithm for the numerical solution to this problem is suggested. This algorithm is based on a fractional derivative approximation and linear interpolation for delayed arguments. According to this method, the TDFOC problem is transformed into a system of algebraic equations that can be solved numerically. The proposed method's efficiency is assessed by solving several numerical examples.

The objective of this manuscript is to introduce an innovative methodology for addressing multiple attribute group decision-making (MAGDM) problems utilizing interval-valued intuitionistic fuzzy sets (IVIFS). The proposed approach solves the problem using a mathematical programming methodology. In the present investigation, a group decision-making problem characterized by IVIF multiple attributes is conceptualized as a linear programming model and resolved expeditiously. The models that are being proposed have been reformulated into two analogous linear programming (LP) models through the application of a variable transformation and the concept of aggregation operators. The obtained LP models are solvable by common approaches. The principal benefit of the suggested methodology is its facilitation of decision-makers (DM) in identifying an alternative that exhibits optimal performance, and the decision-making process does not rely on DM knowledge. Application of the proposed method is represented in a decision-making problem, and the results are compared with similar methods, proving the compatibility of the proposed method with previous ones. The solid and understandable logic with computational easiness are the main advantages of the proposed method.

Regression is a statistical technique used in finance, investment, and several other domains to assess the magnitude and precision of the association between a dependent variable (often represented as Y) and a set of other factors (referred to as independent variables). This work introduces a linear programming approach for constructing regression models for Neutrosophic data. To achieve this objective, we use the least absolute deviation approach to transform the regression issue into a linear programming problem. Ultimately, the efficacy of the suggested approach in resolving such problems has been shown via the presentation of a concrete illustration.

In this study, in addition to assessing the conditions in the coastal region of Bandar Abbas, the feasibility of utilizing Archimedes torsional turbines for renewable energy production in this area was investigated through a combination of field measurements and numerical simulations. Field studies included the measurement of environmental conditions, depth, and vessel traffic. The determination of a safe depth was based on these measurements. Additionally, the current patterns were assessed in the field, measuring key parameters like salinity, electrical conductivity, and density. To further develop the results, a numerical simulation was conducted using the ROMS numerical model to establish the hydrodynamic current patterns in the target area. Upon reviewing the outcomes with the SOLVER program and employing linear programming methods, effective constraints derived from field monitoring were created. The study explored the optimal energy efficiency of Archimedes torsional turbines under different inclinations relative to the seabed and angular velocities. The research and simulations revealed that varying the tilt of the vertical axis of the turbine within the range of 5 to 15 degrees significantly impacted the turbine's efficiency. The highest efficiency, at 75 %, was achieved at a 15-degree angle with a turbine rotation speed of 150 rpm. This result is particularly notable, considering the low slope of the studied area.

The paper presents a scheme to supply energy consumers by using a multicarrier energy system (MES). Each MES unit consists of electrical vehicles (EVs), and combined heat and power (CHP) units, which are called energy hubs (EHs) hereinafter. The objective function minimizes the cost of energy of the whole system while considering power flow equations in electricity, heat, and gas grids, where constraints include technical index limits of MESs, EVs, and CHPs. The model has been formed as a non-linear problem (NLP), in the following, the present study proposes a linear programming (LP) model as a substitute for equations of the NLP method so that the global optimal solution is found with a low computation error. Furthermore, the demand parameters, electricity price, and characteristics of EVs are uncertain. To model these uncertainties, we adopt the point estimate approach. The case study of this research considers electricity, gas, and heating grids simultaneously. The energy hubs relate all three grids to each other. The method is tested on a system through simulation using GAMS software. According to obtained numerical results, the suggested LP approach reaches an optimal point with reduced computation time and low error compared to the original formulations. As a result, the indices of different networks are improved using power management of the energy hubs.