## 1 Introduction
However, considering that the number of workers is limited in some manufacturing industries, scheduling without considering workers may be significantly different from the shop floor [6]. Studies have been conducted to solve the problem of co-considering the workers and machines as the dual resource constrained (DRC) scheduling problem. In particular, the dual resource constraints are investigated in the job shop and flexible job shop environments. The DRCFJSP has been studied using metaheuristic algorithms, such as GA [12; 13], variable neighborhood search (VNS) algorithm [6; 8], artificial bee colony algorithm [14], and memetic algorithm [15], because DRCFJSP adds another subproblem of worker assignment, which makes the problem complex, in addition to the machine assignment and operation sequence determination [16]. For example, [PERSON] et al. [12] proposed a GA algorithm to solve the DRCFJSP in an environment with equal skill level of the workers. The allocation of the operations, machines, and workers was expressed as three chromosomes, and the superiority of the algorithm was proved by comparing it with the dispatching rules. [PERSON] et al. [15] proposed a memetic algorithm for the DRCFJSP with multiobjective functions. The aim of the proposed algorithm was to minimize the makespan and workload of the machine and worker under the consideration of different worker skill levels. [PERSON] et al. [8] proposed an efficient algorithm for solving the DRCFJSP by applying VNS. They demonstrated the performance of their algorithms in comparison with the GA. [PERSON] et al. [6] proposed a hybrid algorithm combining the VNS and GA to solve the DRCFJSP considering worker's learning ability. The VNS was applied to the populations, exhibiting a good performance. They demonstrated that the GA-VNS exhibits a better performance and lower relative percentage deviation than those of the GA and hybrid discrete particle swarm optimization. The aforementioned metaheuristic algorithms are at risk of being isolated to the local optima due to the characteristics of the algorithm. Therefore, most metaheuristic algorithms prevent the solution from being isolated by introducing a wide variety of diversification, rather than concentrating on the intensification, to compensate for the weaknesses of the regional search algorithm. However, it is difficult to determine a feasible solution with many complicated constraints, and even intuitive improvements focused on intensification within a limited time are difficult due to the problem complexity. Therefore, exploring the solution space with a poor performance within the given computation time weakens the algorithm's performance. Previous studies on the DRCFJSP were conducted by mitigating some constraints, such as worker skill, batch size, and complex product structure. In particular, the majority of the studies have developed algorithms for a simple product structure, and the metaheuristic algorithms demonstrated good performances. In this study, to deal with more complex problems with additional constraints, we propose a new local search algorithm that identifies a good search space and focuses on the intensification of a more likely space to compensate for the problem of diversification. In addition, the simple exchange of the operation sequence used in most scheduling algorithms in MLPS requires the process of solution repair for the feasibility. This results in heavy modification of the solution and increased computation time. Therefore, we use the priority rules to create a reliable initial solution for the time-based integrated initial solution algorithm, rather than the existing operation sequence expression, and derive an optimal schedule through the local search algorithm that limits the exploration space. To the best of our knowledge, our study is the first to propose an algorithm to solve the DRCFJSP that reflects the MLPS. The remainder of this paper is organized as follows. Section 2 describes the environment and assumptions for the problem. Section 3 explains the proposed algorithm.