A hybrid EOQ and fuzzy model to minimize the material inventory in ready mixed concrete plants

Ravanshadnia, Mehdi; Ghanbari, Milad

doi:10.1109/ieem.2014.7058693

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Some previous studies have been carried out to support activities to analyze sales data and the number of raw material stocks. These studies produced various techniques to identify predictions of the number of raw materials for production, among others: making the optimal inventory control model for order quantities of raw materials predicted using the Fuzzy Logic (FL) approach and Economic Order Quantity (EOQ) [2] and predict the trend stock market with the Artificial Neural Network (ANN) technique [3], predict sales stocks using the ARIMA method [4] and stock price forecasting in taking Apple Inc. as an example [5].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Verano

Husnawati

Ermatita

2020

JITeCS

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The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

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Section: Introductionmentioning

confidence: 99%

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Verano

Husnawati

Ermatita

2020

JITeCS

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“…In the two models, scholars made efforts to cope with the demand in different ways. For EOQ model, scholars always adopted a consumption rate (Yu et al, 2015;Priyan et al, 2015;Ravanshadnia and Ghanbari, 2014) which is more relevant to manufacture than construction because needs are constantly changing. As for DPM, some take the demand of materials as a fuzzy variable (Xu et al, 2013), setting parameter to denote or calculate the demand, others take it a random variable of normal distribution (Awudu and Zhang, 2013) or a Markov process (Shin and Lee, 2015;Shin and Lee, 2016).…”

Section: Introductionmentioning

confidence: 99%

Reducing construction material cost by optimizing buy-in decision that accounts the flexibility of non-critical activities

Meng

Yan

Xue

et al. 2018

ECAM

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Purpose The goal of making buy-in decisions is to purchase materials at the right time with the required quantity and a minimum material cost (MC). To help achieve this goal, the purpose of this paper is to find a way of optimizing the buy-in decision with the consideration of flexible starting date of non-critical activities which makes daily demand adjustable. Design/methodology/approach First, a specific algorithm is developed to calculate a series of demand combinations modeling daily material demand for all the possible start dates. Second, future material prices are predicted by applying artificial neural network. Third, the demand combinations and predicted prices are used to generate an optimal buy-in decision. Findings By comparing MC in situation when non-critical activities always start at the earliest date to that in situations when the starting date is flexible, it is found that making material buy-in decision with the consideration of the flexibility usually helps reduce MC. Originality/value In this paper, a material buy-in decision-making method that accounts non-critical activities’ flexible starting date is proposed. A ternary cycle algorithm is developed to calculate demand combinations. The results that making material buy-in decision considering non-critical activities’ flexible starting date can reduce MC in most times indicates that contractors may consider non-critical activities’ flexibility a part of the buy-in decision-making process, so as to achieve an MC decrease and profit increase.

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A hybrid EOQ and fuzzy model to minimize the material inventory in ready mixed concrete plants

Cited by 2 publications

References 13 publications

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

Reducing construction material cost by optimizing buy-in decision that accounts the flexibility of non-critical activities

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