Sustainable flexible flow shop scheduling optimization in flexible packaging industry using genetic algorithm

Abstract

Abstract Flexible flow shop (FFS) scheduling optimization process has a high level of complexity and categorized as an NP-hard problem. X Co is a flexible packaging manufacturer that scheduled FFS manually with a low sustainability level. The objective of this research is to optimize FFS scheduling in X Co using Genetic Algorithm (GA) method to increase the sustainability level and measure its performance. The sustainability parameters contain three main aspects of sustainability. In this optimization process, makespan minimization used as the objective function, and elitism is applied in the GA method to make sure the makespan value is getting smaller in each generation. The result shows an increment of sustainability level in all parameters. Makespan value reduction by 135 hours, zero lateness, idle time reduction by 1755 (hour*machine), machine utility improvement by 5.58%, electricity consumption reduction by 23664 kWh which converted into a reduction of 16731 KgCO2e greenhouse gas, and also increase employee productivity as much as 0.066 jobs/shift. This research shows an optimal result with a higher level of sustainability in the implementation of optimization on FFS scheduling at X Co.