A metaheuristic for energy adaptive production scheduling with multiple energy carriers and its implementation in a real production system

Abstract

Abstract Due to climate change and the resulting introduction of sustainability goals by the UN and federal governments, there is growing pressure on manufacturers to increase the sustainability of production systems. In this paper a new, sustainable production scheduling model for job-shop scheduling is developed. The model is optimized using an adjusted genetic algorithm (GA) to minimize energy-related cost (ERC). The proposed model includes multiple energy sources and incorporates a time-of-use (TOU) demand response (DR) scheme for all energy sources. Furthermore, it considers five machine operating modes to reflect different energy states of machines. This means that underutilized machines can be powered down to use less energy, thus reducing ERC. The model and algorithm are evaluated within the Energy-Technology and Application (ETA) research factory environment using a Python application that interfaces with other components to get information about the production system.