Instances for the Dual-Resource-Constrained Re-entrant Flexible Flow shop scheduling problem
The dual-resource-constrained re-entrant flexible flow shop scheduling problem is a special version of the flow shop scheduling problem, inspired by real-world scenarios in screen printing industries. In addition to the classical flow shop, identical parallel machines are grouped in stages and operations may revisit the same stage one or more times before completion. Moreover, each machine must be operated by a skilled worker, making it a dual-resource-constrained problem according to the existing literature. The objective is to minimize the maximum completion time. To address this problem, our study employs two methods: a constraint programming model and a hybrid genetic algorithm (HGA) with a single-level solution representation and an efficient decoding heuristic. To evaluate the performance of our methods, we conducted a computational study using different problem instances. Our findings demonstrate that the proposed HGA consistently delivers high-quality solutions, particularly for large instances, while also maintaining a short computational time. Additionally, our methods improve existing benchmark results for instances from the literature for a subclass of the problem. Furthermore, we provide managerial insights into how dual-resource constraints affect the solution quality and the efficiency associated with different workforce configurations in the described production setting.