Scheduling plays an important role in improving the efficiency of panel block assembly. Because of the characteristics of the assembly lines and the imprecise and vague temporal parameters in real-world production, the scheduling of panel block assembly on parallel lines is formulated as a fuzzy parallel blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date to minimize the fuzzy makespan and maximize the average agreement index. To solve this combinational optimization problem, a multiobjective memetic algorithm (MOMA) is proposed. In the MOMA, two novel heuristics are designed to generate several promising initial solutions, and a local search method is embedded to improve the exploitation capability. The performance of the MOMA is tested on the production instances of panel block assembly in shipbuilding. Computational comparisons of the MOMA with two other well-known multi-objective evolutionary algorithms demonstrate its feasibility and effectiveness in generating optimal solutions to the bi-criterion fuzzy scheduling problem of panel block assembly. 1. Introduction The development of large ships leads to an increased demand for panel blocks. Most large shipyards have established panel block assembly lines. The scheduling of panel block assembly belongs to a class of blocking flow shop scheduling problem (BFSP) because there is no buffer between consecutive stations (machines) in an assembly line. The BFSP has received increasing attention in the literature, and most studies consider minimizing the makespan. For more than a decade, various heuristics have been proposed for the scheduling problem with a makespan criterion, including the Nawaz-Enscore-Ham (NEH) heuristic (Nawaz et al. 1983), Min-Max enumeration heuristic (Ronconi 2004), and NEH-Wang-Pan-Tasgetiren (NEH-WPT) heuristic (Wang et al. 2011). Moreover, a growing number of meta-heuristics have also been developed for this type of problem, such as the genetic algorithm (GA) (Caraffa et al. 2001), tabu search approaches (TS and TS+M) (Grabowski & Pempera 2007), iterated greedy algorithm (Ribas et al. 2011), discrete particle swarm optimization algorithm (Wang & Tang 2012), memetic algorithm (MA) (Pan et al. 2013), and modified fruit fly optimization algorithm (Han et al. 2016).