Mixed-Integer Linear Programming Optimization Framework for Industrial Manufacturing Processes.

Abstract

Industrial manufacturing processes often involve complex decision-making under constraints such as resource availability, production scheduling, and cost minimization, which can be effectively modeled using optimization techniques. In this research a Mixed-Integer Linear Programming (MILP) optimization framework is designed to enhance efficiency and sustainability in industrial manufacturing systems. The framework integrates key operational variables into a unified mathematical model that minimizes total costs while adhering to practical constraints. The simulation results show that the proposed approach reduces operational costs and energy usage significantly compared to conventional methods. This research provides practitioners with a scalable tool for digital transformation in manufacturing, bridging operations research and industrial engineering. Future research directions include incorporating stochastic elements and machine learning for hybrid solving strategies.