Statistical Modelling for Simulating and Interpreting an Egg Packaging Process for Giveaway Mitigation

Giveaway, the excess product being packed into orders, is one of the contributors of revenue loss that pre-packaged food manufacturers care the most. In collaboration with an egg packaging company, this study aims to discover operation rules to mitigate the giveaway in egg orders. For that, two variables have been raised as potential controllable factors of giveaway. One statistical model has been developed to better interpret the experimental results by understanding the underlying rules of the egg grading machine. The experiments have been accurately reproduced by a simulation using the estimated model parameters, which indicates the success of the model. Based on the experiments, we claim that the number of accepted downgrade grades has a significant influence on the final giveaway ratio. Limitations and further potentials of the statistical model have also been discussed

Modeling the egg packing station planning problem

This paper presents the planning problem encountered by egg packing station managers. In an egg packing station, pallets containing unsorted eggs arrive daily from chicken farms and need to be packed into order-specific boxes on packing lanes. The grader automatically identifies damaged eggs and grades the remaining eggs in several weight classes. The number of packing lanes is station specific but can reach up to 32. The orders are destined for retailers and they specify the number of required eggs and a set of allowed grades. The challenge is to assign these orders to the packing lanes such that the incoming supply (distribution of grades) is completely covered by active orders on the packing lanes. This paper positions the egg packing problem in the optimization literature and proposes a model that efficiently covers this planning problem. Furthermore, we point out several future research directions such as additional practical work floor constraints and algorithmic challenges.status: publishe