An Effective Optimization Approach to Minimize Waste in a Complex Industrial System

Abstract

Industrial waste is a major environmental concern. This paper proposes an optimization approach to better manage the operations of a food production line, with the aim of minimizing material waste. The configuration considered is inspired by a real situation. It is a two-stage flow shop with dedicated machines at stage 1 and identical parallel machines at stage 2. A mathematical model is first proposed that considers the minimization of the number of idle times exceeding an acceptable threshold as an objective. A positive time lag for the product and machine setup is considered as a constraint. To solve large problem instances, a hybrid genetic algorithm (HGA) is proposed. In addition, we developed a two-phase procedure based on a decision tree to select the parameters of the HGA. The results show that we could achieve the minimization of material waste without altering too much, another industrial goal, which is the total production time.

A Nonlinear Subspace Predictive Control Approach Based on Locally Weighted Projection Regression

“…The significance of industrial process control extends beyond mere operational efficiency. It plays a pivotal role in ensuring consistent product quality, minimizing waste, and mitigating environmental impact [6][7][8]. Furthermore, effective control strategies empower industries to adapt swiftly to changing market demands and regulatory requirements, fostering competitiveness and sustainability [9].…”