Genetic Algorithm Based Production Schedule Optimization System for Highly Granular Energy Consumption Variance Minimization

Abstract

In the manufacturing sector, energy demand is not considered as a manufacturing process variable when devising production schedules. This presents the potential for a large variance in the energy consumption culminating from the summation of individual machine energy demands. If not controlled, this can result in damage to the local power infrastructure. Traditional methods for protecting against this involve costly enhancements to the power infrastructure or inefficient use of time and equipment. In this paper, a production schedule modification algorithm is presented. Through the utilization of a genetic algorithm and highly granular historical energy profiles, the optimizer is able to modify an existing production schedule such that it produces a minimal variance in energy consumption when executed. Testing and experimentation show that a significant reduction in energy consumption variance can be achieved while ensuring the schedule operates within the constraints specified by the manufacturer.