## 2 Description of the Papers
Their proposed approach is based on using the combination of the univariate search method and the harmony search algorithm. The efficacy of the proposed method is demonstrated by showing 24.9% of cost savings while making overrunning clutch assembly in comparison with the existing method. Based on the obtained results in this work, the contribution of the proposed novel methodology is legitimate in solving selective assembly problems. The originality of the following paper written by [PERSON] and [PERSON]. [11] lies in an application of the hybrid bat optimization algorithm, which is based on the variable neighborhood structure, and two learning strategies to solve a three-stage distributed assembly permutation flow shop scheduling problem (DAPFSP) with the aim to minimize the makespan. The proposed algorithm is firstly designed to increase the population diversity by classifying the populations which solves the difficult trade-off between convergence and diversity of the bat algorithm. Secondly, a selection mechanism is used to update the bat's velocity and location, solving the difficulty of the algorithm trade-off of exploration and mining capacity. For this purpose, the Gaussian learning strategy and elite learning strategy assist the whole population in jumping out of the local optimal frontier. Based on the obtained simulation results, this algorithm can solve the DAPFSP problem well. In comparison with other metaheuristic algorithms, it provides better performance than the compared ones; thus, it is suitable to find the optimal solution(s). The brief overview of the further paper titled \"A multi-criteria assessment of manufacturing cell performance using the AHP method\" [12] is as follows. It introduces the solution for how to find the optimal manufacturing cell design from alternative designs by using a multi-criteria evaluation. Alternative design solutions are mutually comparedby using the selected performance criteria, namely operational complexity, production line balancing rate, and makespan. Then, multi-criteria decision analysis based on the analytic hierarchy process method is used to show that two more-cell solutions better satisfy the determined criteria of manufacturing cell design performance than three less-cell solutions. The main benefit of this approach lies in the exactly enumerated values of the selected criteria, according to which the points from the mentioned scale are assigned to the alternatives. The first paper of the second group of articles is titled \"Meta-heuristic technique-based parametric optimization for electrochemical machining of Monel 400 alloys to investigate the material removal rate and the sludge\" [13]. The authors investigate in this work the predominant electrochemical machining process parameters, namely applied voltage, flow rate, and electrolyte concentration, and their effects on the performance measures, i.e., the material removal rate and the nickel presence. For this purpose, authors used a metaheuristic algorithm named as the grey wolf optimizer for the multi-objective optimization of the process parameters for electrochemical machining. The obtained results were compared with the moth-flame optimization and particle swarm optimization algorithms. Based on the obtained results, it was observed that all the process variables significantly influenced the objectives. Then, it is confirmed that these metaheuristic algorithms--the moth-flame optimization algorithm and the grey wolf optimizer--are suitable for finding the optimum process parameters for machining Monel 400 alloys with electrochemical machining. The second paper of this group is named \"Optimization of process parameters for turning Hastelloy X under different machining environments using evolutionary algorithms: A comparative study\" [14]. In their research, the authors investigated the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments.