Service-oriented cloud manufacturing systems: Balancing profit, customer satisfaction, and resource fairness

The rapid growth of customized demand and geographically distributed manufacturing resources has increased the need for integrated decision-making in cloud manufacturing systems. In such environments, scheduling, logistics, pricing, and quality decisions are highly interrelated and significantly affect both system profitability and customer satisfaction. However, most existing studies address these decisions separately and pay limited attention to customer satisfaction and fairness. To address this gap, this paper proposes a multi-objective mixed-integer programming model that simultaneously integrates scheduling, logistics, pricing, and quality decisions while explicitly considering customer satisfaction and fairness among customers. The model pursues three objectives: maximizing cloud manufacturing system profit, maximizing customer satisfaction as a function of price and product quality, and minimizing unfairness among customers. An LP-metric approach is employed to aggregate the objectives, and the model is solved using CPLEX in GAMS. Computational experiments on small-, medium-, and large-scale instances demonstrate the effectiveness of the proposed framework. The results show that ignoring logistics decisions, customer satisfaction, or earliness/tardiness penalties leads to inferior solutions. Furthermore, a Genetic Algorithm is developed for large-scale instances, where the exact approach becomes computationally demanding. Comparative results indicate that the proposed heuristic provides high-quality solutions with significantly lower computational times for large-scale problems. The findings confirm the effectiveness and scalability of the proposed framework for integrated decision-making in cloud manufacturing systems.