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The mixed-model production can assemble a wide variety of products with similar characteristics in one production line, which makes it an effective way to deal with the challenge of a highly diversified product portfolio and is applied in numerous industries. The mixed-model line sequencing problem (MMSP) is important to improve the efficiency of mixed model production, and has already received considerable attention. Various operational characteristics of a workstation, features of an assembly line, and the optimization objectives have been investigated in the literatures (Boysen & Fliedner, 2009).
The standard work time is usually used as work time data in the MMSP math model traditionally. However, production scheduling is a process integrating data optimization and human operation, the actual operation time of the workers at each workstation is affected by behavior factors (Wezel, McKay, & Wafler, 2015). Although scheduling results determine the start and completion time of each operation activity, the actual action times are often different from the scheduled ones in the optimized production sequences. It is necessary to consider more worker related factors in MMSP so as to make the optimized production plan more realistic and accurate.
The workers’ behavior factors (WBF) have already been considered in manufacturing by many researches; for example, the dynamic interaction between the performance of the workers and production system (Korytkowski, 2017), stressful condition (Azizi, Zolfaghari, & Liang, 2010), and safety issues (Vasudevan and Son, 2011). Among them, the learning and forgetting (L-F) effect was examined in various researches on manufacturing issues, as in Smunt, 1986; Koulamas and Kyparisis, 2007; Lai and Lee, 2011; Wu, Lee, Lai, and Wang 2016; et al. What’s more, Jaber and Bonny’s research (Jaber & Bonney, 2007) found that the production time in a mixed model line is sensitive to the L-F effect. The learning effect improves the efficiency of the workers, whereas the forgetting effect decreases it (McCreery & Krajewski, 1999). So, L-F effect can be regarded as a significant WBF in MMSP.
The mixed model production process provides flexibility by manufacturing homogeneous product models, and workers need to be multiple skilled to operate on various product types at workstations along the production line. The requirement of skills and the production time gap caused by product model switching would affect the efficiency and performance of the workers. During the production process, the learning effect arises from the repetition of the same or similar operation tasks, whereas the tendency of forgetting is caused by breaks among these tasks. The learning effect and the forgetting effect work together in the mixed model production process making the real operation time can be faster or slower than its predetermined standard work time.
Although a few researches have addressed the L-F effect in group scheduling problem, as in Yang and Chand, 2008; Pan, Wang, Xi, Chen, and Han, 2014; Lai and Lee, 2013; et al. This research furtherly focuses on the L-F effect in the mixed model production by investigating its effect on the MMSP. This is also the first time that the effect has been considered in the MMSP. Herein, original mathematical models of the Learning effect and forgetting effect are modified according to the characteristics of the mixed-model production line, then the improved L-F equation is used into the new objective function of the MMSP. An improved genetic algorithm (GA) is employed in numerical experiment to obtain the optimal solutions, and the numerical results are used to analyze the impact of the L-F effect on the mixed model production and MMSP.