Selection of an industrial boiler for a soda-ash production plant using analytical hierarchy process and TOPSIS approaches

Hasnain, Saqib; Ali, Muhammad Khurram; Akhter, J.I.; Ahmed, Bilal; Abbas, Naseem

doi:10.1016/j.csite.2020.100636

Cited by 31 publications

(17 citation statements)

References 35 publications

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“…Metode AHP-TOPSIS sudah banyak digunakan untuk menyelesaikan banyak permasalahan pengambilan keputusan dan masih menjadi salah satu alat penelitian penting bagi peneliti saat ini. Kebaruan dari metode AHP-TOPSIS ada pada aplikasi inovatif dan menarik seperti aplikasi pada penelitian sebelumnya (Hasnain et al, 2020). Penelitian ini mengaplikasikan metode AHP-TOPSIS untuk pemilihan mesin filler pada produksi farmasi.…”

Section: Pendahuluanunclassified

Pengambilan Keputusan Dalam Pemilihan Mesin Filler Syrup Dengan Metode AHP-TOPSIS Pada PT X

Willyandi

2022

JRSI

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PT X is a pharmaceutical company and one of its products is cough syrup. The company wants to install a new filler machine to support their business development. There are 4 machine brands will be chosen by the company which are ITM, 3CR, IMA, MCS. This research focuses on filler machine selection for the production line. Research objectives are to determine the priority criteria and the best alternative filler machine. Using AHP-TOPSIS method where AHP method is used for weighting the assessment criteria while TOPSIS method is to sort alternative machines. The company decides there are 5 assessment criteria which are machine price, filling accuracy, filling speed, lead time availability and maintenance team; where the highest weight on maintenance team with 0,34. Based on TOPSIS method, the best alternative machine comes from the IMA brand with preference value of 0,9084. Sensitivity analysis shows machine price and filling speed could affect the selected machine. This research conclusion is AHP-TOPSIS method can be used as a decision-making framework in machine selection and the IMA brand is decided to be installed on the production line.

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Section: Pendahuluanunclassified

Pengambilan Keputusan Dalam Pemilihan Mesin Filler Syrup Dengan Metode AHP-TOPSIS Pada PT X

Willyandi

2022

JRSI

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“…TOPSIS analysis method first normalizes the original data matrix and then determines the optimal and inferior solutions among a finite number of solutions through the optimal vector and inferior vector. en, it determines the closeness of each study factor of the object of investigation relative to the optimal solution by comparing the object of investigation with the optimal and inferior solutions and then evaluates the superiority and inferiority of the object of investigation [34,35]. e specific steps are as follows.…”

Section: E Comprehensive Performance Evaluation Of Dry-mentioning

confidence: 99%

Design and Performance Evaluation of Dry-Mixed Cement Recycled Aggregate Pile

Shang¹,

Feng²,

Rong³

et al. 2022

Advances in Materials Science and Engineering

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In this paper, dry-mixed recycled cement concrete gravel (CCG) piles and dry-mixed cement macadam gravel (CMG) piles were analyzed. CCG and CMG were prepared by using recycled concrete aggregate (RCA) with different substitution rates, recycled macadam aggregate (RMA) from waste bricks and tiles, and fly ash, respectively. Mechanical properties, hydration heat release performance, and frost resistance were used as evaluation factors. The compressive strength, hydration heat, relative dynamic elastic modulus, and mass loss rate were tested. Based on the requirements of subgrade reinforcement on the material strength, hydration heat release, and frost resistance of dry-mixed CRA piles, TOPSIS analysis method was used to conduct a comprehensive evaluation. The results show that the mechanical properties and frost resistance of CCG and CMG gradually deteriorate with the increase of RCA and RMA replacement rates. At the same time, the hydration heat release of CCG and CMG increases with the increase of the replacement rate. The fly ash instead of cement can improve the compressive strength and frost resistance of CCG and CMG and reduce the hydration heat and material cost. When the CCG replacement rate reaches 20%, the CCG composite score of 20% fly ash is higher than that of traditional dry-mixed cement gravel pile and has better comprehensive performance.

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“…The maintenance supplier performance evaluation model proposed in Tong et al ( 2020 ) uses a fuzzy PROMETHEE-II technique for evaluating four service providers based on nine different criteria. The authors (Hasnain et al 2020 ) employed AHP-TOPSIS for evaluating different boilers using various factors to select most suitable industrial boiler for a production plant. In a more recent work, the authors (Hezer et al 2021 ) evaluated 100 regions based on six health safety criteria for assessing the safety of individual regions during COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

Samal

Dash

2022

Granul. Comput.

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It has always been the goal of many researchers to gain a thorough understanding of the patterns in the stock market and forecast the trends it will follow. The use of an advanced forecasting model can assist with accurately forecasting the future price of stocks, their fluctuations in the markets, as well as make profits in trading. With this motivation, in this study, a novel stock index trend predictor model is designed by integrating Multiple Criteria Decision-Making (MCDM) with an optimized Online Sequential Extreme Learning Machine (OSELM). Forecasting the future stock index prices and analyzing the upward or downward trends of these price forecasts are the two objectives of the proposed model. As the performance of OSELM is heavily dependent on the activation functions used in it, suitable selection of the activation function for OSELM is addressed as a MCDM problem. According to this approach, the trend prediction performance of six popular activation functions is assessed based on five regression-based and five classification-based criteria. In this investigation, three MCDM approaches are used to assess the performance matrix and determine which activation function is the best for OSELM based on six alternative models and ten criteria. To further optimize OSELM's performance, a hybrid crow search algorithm (hCSA) is incorporated in its training phase. By introducing the chaotic map and mutation operator in position update scheme and catfish behavior in the search process of original CSA, the proposed hCSA is able to achieve the right balance between exploration and exploitation improving the convergence. The proposed trend predictor model is empirically evaluated over historical data of three stock indices such as BSE SENSEX, S&P 500 and DJIA collected during pre-COVID and COVID time frame. In most of the test cases, the hCSA-OSELM model outperforms the state-of-the-art baseline models in terms of all evaluation criteria. When compared to the second-best baseline model, the suggested model is able to achieve the MSE improvements of 4–6%, 25–31%, and accuracy improvements of 0.4–0.8%, 0.9–1.3% over the pre-COVID and COVID time-frames, respectively. The statistical test also reveals the better performance of the proposed model. The robust and reliable MCDM-based model selection, superior prediction and classification outcomes clearly reveal that the proposed model can be used for financial time-series forecasting amid daily volatility as well as highly volatile markets.

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Selection of an industrial boiler for a soda-ash production plant using analytical hierarchy process and TOPSIS approaches

Cited by 31 publications

References 35 publications

Pengambilan Keputusan Dalam Pemilihan Mesin Filler Syrup Dengan Metode AHP-TOPSIS Pada PT X

Pengambilan Keputusan Dalam Pemilihan Mesin Filler Syrup Dengan Metode AHP-TOPSIS Pada PT X

Design and Performance Evaluation of Dry-Mixed Cement Recycled Aggregate Pile

Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

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