MIP approaches for a lot sizing and scheduling problem on multiple production lines with scarce resources, temporary workstations, and perishable products

This paper addresses a lot sizing and scheduling problem inspired from a real-world production environment apparent in food industry. Due to the scarcity of resources, only a subset of production lines can operate simultaneously, and those lines need to be assembled in each production period. In addition, the products are perishable, and there are often significant sequence-dependent setup times and costs. We first propose a standard mixed integer programming model for the problem, and then a reformulation of the standard model in order to allow us to define a branching rule to accelerate the performance of the branch-and-bound algorithm. We also propose an efficient relax-and-fix procedure that can provide high-quality feasible solutions and competitive dual bounds for the problem. Computational experiments indicate that our approaches provide superior results when benchmarked with a commercial solver and an established relax-and-fix heuristic from the literature.

Automated summary

This paper addresses a lot sizing and scheduling problem in the food industry, proposing a mixed integer programming model and a branching rule, as well as an efficient relax-and-fix procedure. Computational experiments show that the proposed approaches outperform a commercial solver and an established relax-and-fix heuristic in the literature.