A Constraint Programming model for food processing industry: a case for an ice cream processing facility

Wari, Ezra; Zhu, Weihang

doi:10.1080/00207543.2019.1571250

Cited by 12 publications

(2 citation statements)

References 42 publications

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“…Though the models are simple (for empirical kinetics) ( Sarkar et al, 2021 ) or maybe complex (for processing stages coupled with heat and mass transfer phenomena in a dynamic system, and for processing stages with complex chemical reactions) ( Abakarov and Nuñez, 2013 ; Vilas et al, 2004 ). The challenges that exist in food process optimization may be listed as follows: (1) Processing related constraint-the existence of non-linearity in the dynamic models along with the mode of the process (continuous, semi-batch or batch) ( Georgiadis et al, 2019 ), (2) Transportation and distribution-related constraint-the large numbers of process variables with complex interconnections ( Wari and Zhu, 2019 ), (3) Food safety and quality-related constraint-nonlinear, complicated and dependent on various external factors ( Enitan and Adeyemo, 2011 ; Roy et al, 2020 ).…”

Section: Complexities In Food Process Optimizationmentioning

confidence: 99%

Application of bio-inspired optimization algorithms in food processing

Sarkar

Salauddin

Mukherjee³

et al. 2022

Current Research in Food Science

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Section: Complexities In Food Process Optimizationmentioning

confidence: 99%

Application of bio-inspired optimization algorithms in food processing

Sarkar

Salauddin

Mukherjee³

et al. 2022

Current Research in Food Science

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“…There are several examples of the application of L.P. to optimize food operations. Examples include production scheduling of yoghurt production (Doganis & Sarimveis, 2007), the least cost production of surimi blend (Park et al., 2019), streamlining the formulation of ready‐to‐use therapeutic foods (Callaghan‐Gillespie & Mui, 2018), and scheduling of ice cream production (Wari & Zhu, 2019).…”

Section: Introductionmentioning

confidence: 99%

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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