Multi-objective production scheduling and workforce planning in sustainable reconfigurable manufacturing system

The emergence of the reconfigurable manufacturing paradigm presents promising solutions to effectively navigate market fluctuations and system changes. This paper delves into the integration of production scheduling and workforce planning within the reconfigurable manufacturing system (RMS) framework. The problem considers workplace risk hazards stemming from workforce assignment, as well as workforce preferences for flexible working hours. Initially, a multi-objective mixed-integer linear programming model is developed to capture the complexities of the problem. Three objectives namely, the makespan, the total production cost, and the social sustainability metric are minimized. Subsequently, two meta-heuristic algorithms, including non-dominated sorting genetic algorithm II (NSGA-II) and archived multi-objective simulated annealing (AMOSA), along with the robust improved \(\epsilon \) $ϵ$ -constraint (AUGMECON-R) algorithm, are employed to address the problem. We implement parameter tuning using the Taguchi method to enhance the performance of NSGA-II and AMOSA. Problem instances in different sizes are then generated to assess the performance of the solution approach, with seven metrics utilized for comparison. Finally, comprehensive computational experiments and sensitivity analyses are conducted to evaluate the MILP model and offer valuable managerial insights into RMS flexibility for decision-makers.