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

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  • Ezra Wari
  • Weihang Zhu

Abstract

This paper presents a Constraint Programming (CP) scheduling model for an ice cream processing facility. CP is a mathematical optimisation tool for solving problems either for optimality (for small-size problems) or good quality solutions (for large-size problems). For practical scheduling problems, a single CP solution model can be used to optimise daily production or production horizon extending for months. The proposed model minimises a makespan objective and consists of various processing interval and sequence variables and a number of production constraints for a case from a food processing industry. Its performance was compared to a Mixed Integer Linear Programming (MILP) model from the literature for optimality, speed, and competence using the partial capacity of the production facility of the case study. Furthermore, the model was tested using different product demand sizes for the full capacity of the facility. The results demonstrate both the effectiveness, flexibility, and speed of the CP models, especially for large-scale models. As an alternative to MILP, CP models can provide a reasonable balance between optimality and computation speed for large problems.

Suggested Citation

  • Ezra Wari & Weihang Zhu, 2019. "A Constraint Programming model for food processing industry: a case for an ice cream processing facility," International Journal of Production Research, Taylor & Francis Journals, vol. 57(21), pages 6648-6664, November.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:21:p:6648-6664
    DOI: 10.1080/00207543.2019.1571250
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    Cited by:

    1. Maheshwari, Pratik & Kamble, Sachin & Belhadi, Amine & Venkatesh, Mani & Abedin, Mohammad Zoynul, 2023. "Digital twin-driven real-time planning, monitoring, and controlling in food supply chains," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    2. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.

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