Risk-averse RMS balancing and planning under uncertain demand and processing times

In response to the challenges of uncertain market demand and production environment, this study addresses the risk-averse balancing and planning of a reconfigurable manufacturing system (RMS) under demand and processing time uncertainties. Amidst these uncertainties, the decision-maker’s risk attitude shapes the final decisions. Moreover, technological progress makes heterogeneous machines common in production to improve efficiency. However, the existing literature lacks models that address machine selection, uncertain processing times, and risk aversion comprehensively. To bridge this gap, we aim to maximize the profit by optimizing station utilization, machine rental cost, energy consumption, and product revenue. We introduce a risk-averse two-stage stochastic programming (TSSP) model that encompasses balancing and planning stages, incorporating time-of-use (TOU) electricity prices. Then, we prove that the problem is NP-hard. To solve it, we develop two algorithms: an improved genetic algorithm with stochastic variable neighborhood search (GASVNS) and a rule-based heuristic algorithm integrated with CPLEX (RBH). Using K-means clustering, the two algorithms are further enhanced, referred to as K-GASVNS and K-RBH, respectively. We conduct numerical experiments to compare the performance of the state-of-the-art algorithm and the proposed algorithms. Numerical results show that K-GASVNS outperforms the others in solution quality, while K-RBH has the best performance in terms of running time. This work further provides managerial insights.