Production Optimization in a Grain Facility through Mixed-Integer Linear Programming

Abstract

This article introduces a Mixed-Integer Linear Programming model for cost optimization in multi-product multi-line production scheduling. This model considers discrete time windows and includes realistic constraints. The NP completeness of the problem is proven. A novel scheme based on embedding bounds is applied to speed up convergence. The model is tested on 16 input configurations of a real case study from the top Uruguayan grain production facility. The numerical results show that the model significantly improves the outcome of the current ad hoc heuristic planning, reducing on average 10% the overall production costs; and that the introduction of the embedded bounds-based scheme reduces significantly the elapsed time, on average by 22%.

Development of Integrated Linear Programming Fuzzy-Rough MCDM Model for Production Optimization

“…Along with the development of computer systems, there has been an intensified application of linear programming and other related functions to solve specific economic problems. By applying linear programming, greater efficiency is achieved, and production costs can be significantly reduced, as indicated in [1] in which the authors applied the Mixed-Integer Linear Programming model. This is also confirmed by the authors Chandrawat et al [2] applying fuzzy linear programming to optimize production costs because, in order to optimize these processes, we must make changes that are allow managers to be well informed [3].…”