## 1 Introduction  
Several changes have occurred with the onset of the mass customization era following the traditional mass production era. Additionally, multipurpose machines and manufacturing processes have been adapted to flexible production systems producing a batch of small quantity [1]. In response to this trend, scheduling studies have focused on flexible manufacturing processes and have actively been conducted to solve the flexible job-shop scheduling problem (FJSP). In the FJSP research, various constraints, such as sequence dependent setup time [2; 3; 4], learning effect [5], and dual resource constraint [6; 7; 8], are considered depending on the manufacturing environment. This study deals with the dual resource constrained flexible job-shop scheduling problem (DRCFJSP) under the consideration of the worker's skill level for machines and multilevel product structures (MLPS). Research on the complexity of processes has been conducted, but there has been little research to reflect the complexity of product structures. The majority of the literature assumes a product structure composed of simple sequential operations for a job, which is very different from the bill of materials (BOM) structure that is widely used in manufacturing industries, especially for products requiring assembly processes. In several advanced planning and scheduling (APS) studies, the product complexity was reflected through the concept of MLPS. MLPS is a hierarchical tree that expresses the product structure and is essential for products requiring assembly processes. The top node of tree is a final product. Each child node of the tree represents a component or part of the product. With the MLPS, the operations for all the child nodes associated with the parent node must be performed, and then the operation for the parent node can be conducted. [PERSON] et al. [9] developed a mixed integer programming model, which reflected the MLPS as a constraint; however, they only solved small-size problems with two orders. [PERSON] et al. [10] solved a multiobjective APS problem using a genetic algorithm (GA). To solve the structural problem of an MLPS, two chromosomes were constructed for the operation priority and machine assignment, respectively. The priority was randomly assigned through the operation priority chromosome, and the solution was constructed by assigning a feasible solution with the highest priority to a machine through the machine assignment chromosome. The performance and efficiency of the algorithm have been proven by experiments in 5 orders, 50 operations, and 20 machines. [PERSON] et al. [11] focused on the determination of the operation sequence using the Bat algorithm. To solve the structural problems of the MLPS, the operation sequence was first determined and the solution feasibility was derived by correcting the misplacement of the items. In this study, unlike previous studies that separately determined the operation sequence and machine assignment, we propose a time-based integrated initial solution algorithm that allocates feasible operations and available resources (machine and worker), based on the priority rules that maintain solution feasibility and local search algorithms that narrow the search space using the earliest possible start time (EPST) and latest possible start time (LPST). In addition, previous studies determined the batch size based on the individual order quantity without considering the inventory level of the subparts and machine-dependent batch size, which is not suitable in environments where inventories exist and the batch size is required for the machine efficiency. Therefore, we combine the quantity of the subcomponents required for each order and generate the operations based on the inventory level and batch size. The majority of the literature on scheduling considers only the machine as a constrained resource and ignores the constraints of worker availability required for the operations [12].