Balancing Revenue and Deviation of Demand Fulfilment in Duck Meat Processing by MIP, Heuristic and Hybrid Models

Abstract

The poultry processing industry often struggles with demand-supply imbalances due to inherent mismatches in product segmentation. Unlike chicken processing, which benefits from standardized cutting patterns, duck meat processing requires a more complex approach due to variations in carcass weights, consumer preferences, and diverse market demands. This discrepancy results in deviation of demand fulfillment, where some products are overproduced leading to excess inventory and price reductions, while others are underproduced causing shortages and lost revenues. The perishable nature of meat further amplifies this challenge as excess inventory cannot be stored for long periods without spoilage. Keeping a balance between revenue and deviation of demand fulfillment is essential for duck meat processing. Despite its importance, research on this issue in duck meat processing is limited. This paper evaluates three models: Mixed-Integer Programming (MIP), Heuristic, and Hybrid. Each model is tested on structured, real-world datasets to assess its effectiveness in balancing revenue and deviation of demand fulfillment. A comparative analysis highlights the strengths and limitations of each approach and offers insights into their applicability in duck meat processing and broader poultry industries facing similar challenges.