An optimal solution of energy scheduling problem based on chance-constraint programming model using Interval-valued neutrosophic constraints

Touqeer, Muhammad; Umer, Rimsha; Ahmadian, Ali; Salahshour, Soheil; Феррара, Массимилиано

doi:10.1007/s11081-021-09622-2

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…Touqeer et al solved linear programming network problems with constraints concerning interval-valued neutrosophic numbers. They used the score function and upper and lower membership functions of interval-valued neutrosophic numbers (Touqeer et al 2021). Then, Elfarouk et al (Elfarouk et al 2021), developed a more complex model, namely a multi-objective, multi-echelon, multi-product, stochastic closed-loop supply chain network.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

Szmelter-Jarosz

Ghahremani-Nahr

Nozari

2021

JRFM

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In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.

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Section: Literature Reviewmentioning

confidence: 99%

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

Szmelter-Jarosz

Ghahremani-Nahr

Nozari

2021

JRFM

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“…Interval-valued fuzzy sets have widely been acknowledged as proficient in modeling suspicions and practical in assigning an interval of values where allotting an accurate and precise number to an expert's outlook is too restrictive. In Touqeer et al (2021) the authors establish a technique to solve linear programming network problems with constraints concerning interval-valued neutrosophic numbers. They also present an application to energy scheduling problem with constraints represented as interval-valued trapezoidal neutrosophic numbers.…”

Section: Contributionsmentioning

confidence: 99%

Optimization methods in inverse problems and applications to science and engineering

2021

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“…e distributed energy network system can be connected to a large power grid and run in a grid-connected mode and can also be disconnected and run independently when the power grid fails [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Big Data Energy Scheduling Game Management Algorithm Based on Dual Carbon Goals

Zhang

2022

Mathematical Problems in Engineering

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“Carbon peak and neutrality” are an important strategic decision to promote the transformation of China’s energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management.

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An optimal solution of energy scheduling problem based on chance-constraint programming model using Interval-valued neutrosophic constraints

Cited by 7 publications

References 43 publications

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

Optimization methods in inverse problems and applications to science and engineering

Big Data Energy Scheduling Game Management Algorithm Based on Dual Carbon Goals

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