## 2 Literature Review  
MOJSSP is increasingly attracting scholars and researchers. Most methods are based on a multi-objective evolutionary algorithm (MOEA) [13], which first generates an initial population of production planning and then iterates continuously to obtain the best results. [PERSON] [14] proposed a hybrid genetic algorithm to solve the scheduling problem while considering transportation time. [PERSON] et al. [15] used NSGA-II for real-time scheduling under uncertainty and reliability constraints. [PERSON] et al. [16] used NSGA-II and non-dominated ranking genetic algorithm (NRGA) to optimize the makespan and stability under the disturbance of machine breakdown. [PERSON] et al. [17] presented a cooperative coevolution genetic programming with two sub-populations to generate scheduling policies. [PERSON] et al. [18] utilized a genetic algorithm combined with enhanced local search to solve the energy-efficiency job shop scheduling problems. A high-dimensional multi-objective optimization method was proposed by [PERSON] et al. [19]. To improve the robustness of scheduling, constrained nondominated sorting differential evolution based on decision variable classification is developed to acquire the ideal set. In addition to the MOEA method, there are many other approaches to solve MOJSSP. [PERSON] et al. [20] studied the multi-objective particle swarm optimization method. In their paper, makespan, human error, and machine availability are considered. [PERSON] et al. [21] employed a cellular grey wolf optimizer (GWO) for the multi-objective scheduling problem with the objectives of noise and energy, while [PERSON] et al. [22] tried to overcome the premature convergence of the GWO in solving MOJSSP by combining an improved tabu search algorithm. [PERSON] et al. [23] proposed three agent-based hyper-heuristics to solve scheduling policies with constrains of dynamic events. [PERSON] et al. [24] designed a multi-objective brain storm optimization algorithm to minimize the total tardiness and energy consumption. Their goal is to minimize multiple objectives through MOEA or other metaheuristic algorithms, without exploiting a data mining method to improve the performance of scheduling. With the development of industrial intelligence, data mining could be applied to management analysis and knowledge extraction. For MOJSSP, it could play a key role to solve the scheduling problem in the future. Recently, some researchers are trying to apply data mining to overcome the difficulties of job shop scheduling problems. [PERSON] and [PERSON] [25] used imitation learning to discover dispatching rules. [PERSON] and [PERSON] [26] introduced a method for generating scheduling rules using the decision tree. In their research, data mining extracts the rules that the attributes of jobs determine which one is better to be processed first. Similarly, [PERSON] and [PERSON] [27] applied a decision tree to learn directly from scheduling data where the selected instances are identified as preferred training data. [PERSON] et al. [28] proposed a random-forest-based approach to extract dispatching rules from the best schedules. This approach includes schedule generation, rule learning, and discretization such that they could minimize the average total weighted tardiness for job shop scheduling. [PERSON] and [PERSON] [29] applied data mining to extract patterns in data generated by the ant colony algorithm, which generate a rule set scheduler that approximates the ant colony algorithm's scheduler. [PERSON] et al. [30] gained a rule-based initial population via GES/TS method, and then employed PSO and GA to verify the advantage of this method, but lacked the consideration of operations sequence. Most of them considered data mining as a tool to extract dispatching rules, while only a few of them utilized a data mining approach to generate the initial population of metaheuristic methods, not to mention multi-objective job shop scheduling problem. This paper uses data mining to generate the rule-based initial population for MOJSSP.