Energy-efficient Flow-shop Scheduling in the Printing Industry using Memetic Algorithm

Abstract

Facing the climate change and the energy crisis, plenty of challenges remain to achieving the carbon neutrality in the energy intensive industry. This work investigates a flowshop production scheduling problem minimizing the energy cost without delaying any jobs. The research problem comes from a Belgium printing company with solar panels as its own energy source. Two sub-problems, the job sequence determination and the job-machine allocation, are tackled using a memetic algorithm. An energy-efficient local search heuristic is also designed to make best use of the self-generated electricity. Validated using realistic production data of 12 jobs in a 2 day planning horizon, the proposed method outperforms a standard binary-encoded genetic algorithm in both solution quality (having 27.17% lower energy cost) and the calculation time (saving 69.89%).