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An economic order quantity model for an imperfect production process with entropy cost

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  • Jaber, M.Y.
  • Bonney, M.
  • Moualek, I.

Abstract

Among the assumptions of the classical economic order quantity (EOQ) model is that all units that are purchased (or produced) are of perfect quality. However, this is frequently unrealistic since production processes deteriorate resulting in the production of defective products requiring rework. Some recent studies suggest that production systems performance might be improved by applying the first and second laws of thermodynamics to reduce system entropy (or disorder). This paper applies the concept of entropy cost to extend the classical EOQ model under the assumptions of perfect and imperfect quality. Mathematical models are developed and numerical examples illustrating the solution procedure are provided. Accounting for entropy cost suggests that order quantities should be larger than the figures derived from the classical EOQ model.

Suggested Citation

  • Jaber, M.Y. & Bonney, M. & Moualek, I., 2009. "An economic order quantity model for an imperfect production process with entropy cost," International Journal of Production Economics, Elsevier, vol. 118(1), pages 26-33, March.
    • Handle: RePEc:eee:proeco:v:118:y:2009:i:1:p:26-33
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    References listed on IDEAS

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    Cited by:

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    2. Yoo, Seung Ho & Kim, DaeSoo & Park, Myung-Sub, 2009. "Economic production quantity model with imperfect-quality items, two-way imperfect inspection and sales return," International Journal of Production Economics, Elsevier, vol. 121(1), pages 255-265, September.
    3. Ata Allah Taleizadeh, 2018. "A constrained integrated imperfect manufacturing-inventory system with preventive maintenance and partial backordering," Annals of Operations Research, Springer, vol. 261(1), pages 303-337, February.
    4. Björk, Kaj-Mikael, 2012. "A multi-item fuzzy economic production quantity problem with a finite production rate," International Journal of Production Economics, Elsevier, vol. 135(2), pages 702-707.
    5. Mitali Sarkar & Biswajit Sarkar, 2019. "Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management," Energies, MDPI, vol. 12(11), pages 1-16, May.
    6. Paul, Sanjoy Kumar & Sarker, Ruhul & Essam, Daryl, 2014. "Managing real-time demand fluctuation under a supplier–retailer coordinated system," International Journal of Production Economics, Elsevier, vol. 158(C), pages 231-243.
    7. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.

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