Purchase and Production Optimization in a Meat Processing Plant

TL;DR

This paper presents an optimization approach for purchase and processing in a meat plant, focusing on production constraints and demonstrating efficiency with real data using an iterative ILP method.

Contribution

It introduces a novel optimization model considering processing alternatives, expiration dates, and constraints like minimum order and percentage, proving NP-hardness and providing an effective solution method.

Findings

The proposed algorithm finds optimal solutions in seconds for real cases.

Inclusion of new constraints increases problem complexity to NP-hard.

The iterative ILP approach effectively handles large, real-world data sets.

Abstract

The food production industry, especially the meat production sector, faces many challenges that have even escalated due to the recent outbreak of the energy crisis in the European Union. Therefore, efficient use of input materials is an essential aspect affecting the profit of such companies. This paper addresses an optimization problem concerning the purchase and subsequent material processing we solved for a meat processing company. Unlike the majority of existing papers, we do not concentrate on how this problem concerns supply chain management, but we focus purely on the production stage. The problem involves the concept of alternative ways of material processing, stock of material with different expiration dates, and extra constraints widely neglected in the current literature, namely, the minimum order quantity and the minimum percentage in alternatives. We prove that each of these two constraints makes the problem \mbox{$\mathcal{NP}$-hard}, and hence we design a simple iterative approach based on integer linear programming that allows us to solve real-life instances even using an open-source integer linear programming solver. Another advantage of this approach is that it mitigates numerical issues, caused by the extensive range of data values, we experienced with a commercial solver. The results obtained using real data from the meat processing company showed that our algorithm can find the optimum solution in a few seconds for all considered use cases.