Optimization of production system with environmental consideration: the effect of worker learning in a metallurgy factory

Over the last few years, energy consumption and carbon emissions have been the subject of a great deal of attention, due to the ever-increasing climate change and diminishing of fossil energy resources. In this context, this article aims to minimize the maximum completion time (makespan) by taking into account environmental aspects in a production system. In particular, we consider a flow shop group scheduling problem with sequence-dependent setup times, learning effect related to the ability of each operator as well as machine speeds and the various carbon emissions associated with them, inspired by a real case study of the manufacture of forged connecting rods in a metallurgy factory. The objective is to determine both an optimal scheduling of the groups and the jobs within the groups in order to minimize the makespan by restricting the carbon emissions of the system. A mixed integer linear programming model is formulated, the computational results are also presented and discussed through the different scenarios.