Production coordination of local and cloud orders in shared manufacturing: a bi-objective pre-scheduling approach

This paper presents a bi-objective solution approach to address the production scheduling challenge encountered by manufacturers in a shared manufacturing environment. In such scenarios, manufacturers are required to manage orders received through a cloud platform (referred to as cloud orders) while simultaneously fulfilling orders from their long-term and regular clients (local orders). The problem is to efficiently coordinate the production of both types of orders within shared manufacturing facilities. We formulate the problem into a bi-objective mixed integer programming model aimed at simultaneously minimizing the delivery time of cloud orders and mitigating the disruptions to local order production caused by cloud orders. This solution approach comprises three key components: computation of cloud orders’ starting times, construction of available time intervals of manufacturing facilities, and a bi-objective heuristic. This heuristic combines an enhanced hybrid discrete differential evolution with a modified forward–backward earliest starting time algorithm. We introduce an advanced population initialization technique, a novel individual update strategy, and an adaptive local search mechanism based on Pareto-dominance principles to improve the search capabilities of the algorithm towards discovering Pareto non-dominated solutions. Computational results show that the proposed approach outperforms the existing algorithm in most test instances in terms of five common metrics. Insights are discussed, highlighting the practical implications and potential benefits of the proposed approach for shared manufacturing scheduling.