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Human errors (HEs) are common problems in manual assembly processes, impacting product quality and resulting in additional costs. Based on expert judgments, this study aims to identify the most significant factors affecting HEs in manual assembly processes and explore the cause-and-effect relationships among those factors. In order to achieve this objective, a proposed model is constructed using two types of Multi-Criteria Decision-Making (MCDM) techniques. Firstly, using two rounds of the fuzzy Delphi method (FDM), twenty-seven factors with an influence score of 0.7 or higher were found to have a major impact on HEs during manual assembly processes, with at least a 75% consensus among experts. After that, the twenty-seven factors affecting HEs were given to experts in a third round to analyze the cause-and-effect relationships among those factors using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. In MCDM techniques, symmetry refers to an important property that can be used to find relationships between variables. It is based on the principle that the relative importance or preference between two variables should remain the same regardless of their positions or roles. Therefore, symmetry is a factor that MCDM approaches take into account to ensure that the relationships between variables are accurately represented, leading to more reliable decision-making outcomes. The reliability and normality of the surveying data were examined using the SPSS 22.0 software program. The study results revealed that training level, poor workplace layout, a lack of necessary tools, and experience were the major factors affecting HEs as root causes. Moreover, a failure to address the error-causing problem, unintentional unsafe acts, fatigue, and poor error visual perception were found to be effect (dependent) factors. The findings of this study can help organizations make better-informed decisions on how to reduce worker errors and interest in the factors that contribute to assembly errors and provide a good basis for reaching the quality of final assembled parts.

This paper proposes two efficient algorithms, which are tabu search and particle swarm optimization, for scheduling two identical parallel machines with a single server. The server has to set up the relevant machine before starting job processing. The objective is to minimize the makespan. This problem is considered unary NP-hard problem. The performance of the two proposed algorithms is evaluated using previously solved instances from the literature. The instances were solved using three different algorithms, which are genetic algorithm (GA), simulated annealing algorithm (SA) and I-L algorithm. We used the results of these three algorithms as a benchmark to compare with the two new introduced algorithms, which are tabu search (TS) and geometric particle swarm optimization (GPSO). The obtained results show that the proposed algorithms have a great performance for large instances. Moreover, the obtained results are very close to a lower bound, and in some instances, an optimal solution is achieved. In addition, TS performs better than SA and GA in term of average makespan for large instances and outperforms all algorithms in term of reaching the lower bound for all instances greater than 200 jobs, while GPSO comes second.