Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains

Chen, Yizhong; Li, Jing; Lu, Hongwei; Yan, Pengdong

doi:10.1016/j.jclepro.2020.123209

Cited by 82 publications

(26 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When operating the engineering systems of the housing stock, it is important at the commissioning stage to determine the optimal method for choosing a repair strategy for engineering systems due to the constant wear and tear of equipment, which is of a probabilistic nature of occurrence, depending on various factors [20,21].…”

Section: Resultsmentioning

confidence: 99%

Repair strategy for housing stock engineering systems to improve energy efficiency

Plyusnina

Akristiniy

2021

E3S Web Conf.

View full text Add to dashboard Cite

The article discusses problems of current interest concerning the housing and utility sector associated with increasing the level of comfort of living by constantly improving measures for carrying out planned and un-scheduled repairs and surveys. This work provides a methodology for justi-fying the choice of a repair strategy for engineering systems of water sup-ply and sewerage in the process of operating the housing stock, the imple-mentation of which will contribute to the timely and rational repairs and inspections of buildings engineering systems. This will allow systematical-ly eliminating engineering systems malfunctions, predicting and planning the optimal quantity and chronology of repair and restoration activities by maintaining the technical condition of the engineering system elements within the permissible limits during the standard service life.The main goal of the research in the presented material is to substantiate the methodology for choosing a repair strategy for water supply and sewerage systems dur-ing the operation of apartment buildings.The optimal periodicity for the re-pair of engineering systems depends on the failure rate of elements and the costs associated with the execution of all operational activities.Three op-tions considered for organizing repairs of engineering equipment.Applied in actual practice the technique of choosing the repair strategy of engineer-ing systems for selecting the optimal option to organize repairs of engi-neering equipment.

show abstract

Section: Resultsmentioning

confidence: 99%

Repair strategy for housing stock engineering systems to improve energy efficiency

Plyusnina

Akristiniy

2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Sarkar [ 53 ] also developed an imperfect production inventory model, assuming a dual-stage assembly operation with backorders and variable production rate. Further, tradeoff between various objectives, and system uncertain analysis in a lifecycle of three-echelon shale gas supply chain has been studied by [ 54 , 55 , 56 ]. Decision for vendor selection [ 57 ], production and distribution [ 58 ], pricing strategy [ 59 ], and sustainable parameters for stakeholder’s satisfaction [ 60 ] are vital studies for providing a baseline of SCM.…”

Section: Related Studymentioning

confidence: 99%

A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions

Alkahtani

Omair

Khalid

et al. 2021

IJERPH

View full text Add to dashboard Cite

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

show abstract

“…It is well established that the cost and use of energy affect human lives every day. In this sense, many issues arise from the content of energy consumption such as acid rain, dependency on depleting supplies of fossil fuels, greenhouse gas emissions [8][9][10][11][12][13][14][15][16][17], climate change [18][19][20][21], as well as environmental concerns that come along with energy power supply [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In recent years, various techniques have been used for the optimal design of the HVAC system [36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

View full text Add to dashboard Cite

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

show abstract

Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains

Cited by 82 publications

References 70 publications

Repair strategy for housing stock engineering systems to improve energy efficiency

Repair strategy for housing stock engineering systems to improve energy efficiency

A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Contact Info

Product

Resources

About