A fast simulation-optimization approach was able to automate scheduling in a fast paced dynamic egg sorting operation and improve daily operating profit by 8.35%
There are more than 350 million laying hens in the European Union. The laying hens produce close to 6.7 million tons of eggs each year. These eggs are collected from farms and centralized in packing stations where they are weighed and graded in weight classes Small, Medium, Large, Extra Large and packed into boxes destined for retailers.
Grading is a fully continuous with the larger machines processing > 130.000 eggs per hour. As each egg is graded into a weight class, it needs to be individually sent to a packing lane where it is packed into a retail box. In packing plants, operators manually assigned orders to one or more packing lanes. This assignment needs to find a delicate balance in order to have sufficient receiving capacity in packing lanes for all the eggs in each weight class.
Eggs that cannot be sent to a packing lane because of insufficient capacity gets sent to the breaker where the yolk is separated from the egg white and sold separately. This fetches a much lower profit margin as is to be avoided as much as possible.
Operating packing lanes is costly since packing lanes, most of the time, require human operators. Therefore, the number of required packing lanes has a large impact on daily profitability of a packing station and should be minimized.
As in many packaging systems, certainly in food, packing lanes suffer often from packaging jams and breakdowns introducing downtime. Given the fact that it is so prevalent, any schedule should account for such breakdowns and ensure sufficient spare capacity for each weight class.
Since a hen lays nearly the same egg with regards to weight everyday, incoming supply from farms is relatively easy to forecast. Consequently, the egg supply follows a predictable weight distribution. Stochastic problems are typically hard to solve, nevertheless, this information on distribution should be exploited when designing a scheduling solution.
eggs per hour to allocate in real-time
orders
improvement in operating profit
Given the highly decision-dependent processing times of packing orders, a simulation-optimization approach was developed. Careful attention was given to efficiently simulate schedules as to allow evaluating thousands of schedules in very short computation times as the high-paced shop floor requires fast reaction times in case of unforeseen machine breakdowns or other uncertainty realizations.
A GRASP based metaheuristic is used to generate a high-quality initial solution which was further improved using a Variable Neighborhood Search utilizing 5 tailored schedule improvement subroutines.
In order to evaluate schedule quality, comparisons were made with schedules based on human expert decisions rules (with benefit-of-the-doubt additional optimization in case of ties) and highly optimistic upper bound calculations of operating profit. Generated schedules were within 1% of the unrealistic upper bound and 8.35% better than a human expert.
Grounded in research but with a focus to get to actionable results fast, we iterate quickly with the client on these kinds of problems to get the use case correctly defined, data requirements and limitations scoped early and initial results evaluated for their practical implementation potential.
RESULT 1
8.35% increase in operating profit
RESULT 2
Reduction of planning stress on shop floor
RESULT 3
Higher retail order fulfillment for a given egg supply.
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