A stochastic chance-constraint framework for poultry planning and egg inventory management
Dariush Zamani Dadaneh,
Sajad Moradi () and
Behrooz Alizadeh
Additional contact information
Dariush Zamani Dadaneh: Sahand University of Technology
Sajad Moradi: Sahand University of Technology
Behrooz Alizadeh: Sahand University of Technology
Operations Management Research, 2024, vol. 17, issue 4, No 6, 1328-1344
Abstract:
Abstract This study addresses the capacitated lot-sizing problem in the poultry industry for egg production planning, aiming to minimize production, transportation, and inventory costs. This problem has already been investigated with data certainty and formulated as a mathematical model and a heuristic algorithm has been applied to solve it due to high complexity. In this study, we reformulate the same problem as a new mixed integer linear programming model to achieve optimal solution in a relatively short time without the need for heuristic algorithms. To evaluate the model performance, it is executed using the available data, and its efficiency is validated by comparing the obtained results. Subsequently, the uncertainty of weekly demand is considered, leading to potential shortage or surplus in storage. To address this uncertainty, the chance-constraints method is employed with various attitudes, and several production plans are proposed accordingly. The performance of these plans is compared using random data, and the most suitable programs are identified. The presented decision-making tool can provide production planning that meets customer demand with high reliability while also minimizing surplus inventory in the warehouse.
Keywords: Lot-sizing; Poultry industry; Egg production planning; Demand uncertainty; Chance-constraint (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s12063-024-00507-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:opmare:v:17:y:2024:i:4:d:10.1007_s12063-024-00507-y
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12063
DOI: 10.1007/s12063-024-00507-y
Access Statistics for this article
Operations Management Research is currently edited by Jan Olhager and Scott Shafer
More articles in Operations Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().