Linear programming model for optimum mixing the flour from sieve passages in flour mill industry

Avunduk, Hüseyin

doi:10.18844/prosoc.v4i10.3102

Cited by 2 publications

(3 citation statements)

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“…This study demonstrated how a well‐established optimization technique, such as linear programming, can be utilized to efficiently blend flour streams with differing quality, to achieve specific production targets that meet specifications. Others had proposed this technique for flour blending (Avunduk, 2018; Elevli et al., 2017); however, this study demonstrated the advantages of these techniques over conventional blending techniques, such as the use of the sequential cumulative ash curve.…”

Section: Discussionmentioning

confidence: 63%

“…However, most commercial flour mills have more than 20 flour streams. For example, the L.P. modeling conducted by Avunduk (2017) described a flour mill with 64 flour streams. The approach described in this study can be adjusted to suit the number of flour streams by adjusting the number of variables in the model and adjusting the constraints as shown in Equations .

0.20 . Q_{t 1} = {false\sum}_{i = 1}^{S} Q_{i 1}

0.50 . Q_{t 2} = {false\sum}_{i = 1}^{S} Q_{i 2}

0.30 . Q_{t 3} = {false\sum}_{i = 1}^{S} Q_{i 3}

…”

Section: Resultsmentioning

confidence: 99%

“…They presented a Visual Basic interface in Microsoft Excel, which utilizes the Solver add‐in, without demonstrating the solution's effectiveness. Avunduk (2018) demonstrated the use of L.P. in blending flour streams to produce specific flour types meeting maximum ash content specifications. This study demonstrated that linear programming could develop a blending solution; however, quantifiable benefits over conventional methods were not demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of flour mill stream blending approaches: Linear programming versus ash curve

et al. 2022

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Background and objectives Flour millers often produce several flour types from a single wheat grist. Consequently, different specifications characterize each flour. For example, French standards specify six different flour types, each classified by ash content. The proportional blending of different flour streams from a single wheat grist achieves the target flour specifications. This study explores the opportunity to improve flour blending using linear programming and compares it to sequential ash curve blending. Findings Linear programming and ash curve approaches were used to meet specifications for French flour types from a wheat grist milled to produce 10 flour streams, each stream having different flour quality attributes. The first simulation set quantity targets for Types 45, 55, and 65 flour. The balance of the flour went to the lower value Types 80, 110, and 150. The flour type targets were met using Linear Programming. By utilizing the ash curve method, Type 65 flour was under‐delivered. The second simulation aimed to maximize income using the two methods with no constraints on the amount of each flour type. The linear programming approach resulted in a 0.13% increase in revenue compared to the ash curve technique. Conclusion In the first simulation, the linear programming technique reduced the lower‐value high ash flour types, generating an additional $6.16/ton of flour. In the second simulation, linear programming increased income by $0.84/ton of flour. Thus, a milling plant operating for 8,000 hr/year and processing 20 tons of wheat/hr translates to $779,000 and $107,000 per annum, respectively. Significance and novelty This study showed that linear programming could significantly improve flour blending outcomes, resulting in increased profitability and resource utilization in the milling industry.

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Section: Discussionmentioning

confidence: 63%

0.20 . Q_{t 1} = {false\sum}_{i = 1}^{S} Q_{i 1}

0.50 . Q_{t 2} = {false\sum}_{i = 1}^{S} Q_{i 2}

0.30 . Q_{t 3} = {false\sum}_{i = 1}^{S} Q_{i 3}

…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of flour mill stream blending approaches: Linear programming versus ash curve

et al. 2022

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Assoc. Prof. Hüseyi̇n Avunduk, Phd: Contributions to Academic Life With His Character and Academic Identity

Sürgevil

Yilmaz

2023

JOBDA

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Bu çalışma bir akademisyen ve bilim insanı olarak Doç. Dr. Hüseyin Avunduk’un yazına katkılarını ortaya koymak, karakteri ve akademik kimliğiyle ilgili temel özelliklerini çalışma arkadaşlarının gözüyle derlemek amacıyla gerçekleştirilmektedir. Bu amaçla Doç. Dr. Hüseyin Avunduk tarafından 2017 ve 2021 yılları arasında yayınlanmış (tek veya çok yazarlı) 13 uluslararası makale, 1 uluslararası kitap bölümü ve 1 uluslararası bildiri derlenmiş ve çalışmalar (başlıkları, özetleri, anahtar kelimeleri kapsamında) içerik analizine tabi tutulmuştur. Çalışmada ayrıca Doç. Dr. Hüseyin Avunduk’un karakteri ve akademik kimliğiyle ilgili çalışma arkadaşlarından veri toplanmıştır. Bu veriler aracılığıyla, Doç. Dr. Hüseyin Avunduk’un kişilik özellikleri, karakteri ve akademisyenliğiyle gerek çalışma arkadaşlarının yaşamında gerekse akademik yaşamda bıraktığı izler görünür kılınmaktadır.

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Linear programming model for optimum mixing the flour from sieve passages in flour mill industry

Cited by 2 publications

References 0 publications

Comparison of flour mill stream blending approaches: Linear programming versus ash curve

Comparison of flour mill stream blending approaches: Linear programming versus ash curve

Assoc. Prof. Hüseyi̇n Avunduk, Phd: Contributions to Academic Life With His Character and Academic Identity

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