A GRASPxELS for Scheduling of Job-Shop Like Manufacturing Systems and CO2 Emission Reduction

Part 1: Knowledge-Based SustainabilityInternational audienceThe issue of reducing CO2 emission and associated carbon footprint consumption for manufacturing scheduling is addressed. We focus our attention on a job-shop environment where machines can work at different speeds and therefore different energies consumed, i.e. CO2 emissions. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds, problem which has been introduced by [1]. Energy-efficient scheduling of such type of manufacturing systems demands an optimization approach whose dual objectives are to minimize both the CO2 emissions and the makespan. To solve this new problem, a GRASPxELS is developed. New instances benchmark based on well know Laurence’s instances are introduced and numerical experiments are proposed trying to evaluate the method convergence. The performance is evaluated using the optimal solutions found after a strongly time consuming resolution based on a linear formulation of the problem