Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

Abstract

Cross-trained worker assignment has become increasingly important for manufacturing efficiency and flexibility in cellular manufacturing system because of the recent increase in labor cost. Researchers mainly focused on assigning skilled workers to tasks for favorable capacity or cost. However, few of them have recognized the need for skill level enhancement through cross-training to avoid excessive training, especially for workload balance across multiple cells. This study presents a new mathematical programming model aimed at minimum training and maximum workload balance with economical labor utilization, to address the worker assignment problem with a cross-training plan spanning multiple cells. The model considers the trade-off between training expenditure and workload balance to achieve a more flexible solution based on decision-maker’s preference. Considering the computational complexity of the problem, the classical swarm intelligence optimizers, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC), are implemented to search the problem landscape. To improve the optimization performance, a superior tracking ABC with an augmented information sharing strategy is designed to address the problem. Ten benchmark problems are employed for numerical experiments. The results indicate the efficiency and effectiveness of the proposed models as well as the developed algorithms.

Consideration of workers’ differences in production systems modelling and design: State of the art and directions for future research

“…Based on the proposed model, it can be concluded that each worker requires a specified amount of time to be trained in the next skill depth level related to the given task, which incurs a training cost. Wu et al (2018a) presented a novel mathematical programming model for addressing the worker assignment problem. In the proposed model, the training cost for each worker for each task depends on the task type and the worker's skill level.…”

“…Cross Training costChu et al (2019); Pistolesi (2018a, 2018b);Wu et al (2018a);Niakan et al (2016);Liu et al (2013);Aryanezhad, Deljoo, and Mirzapour Al-e-Hashem (2009a);McDonald et al (2009);Norman et al (2002) Labor costHashemoghli, Mahdavi, and Tajdin (2019);Salehi, Maleki, and Niroomand (2018);Niakan et al (2016);Sungur and Yavuz (2015);Xin et al (2015);Costa, Fichera, and Cappadonna (2013b); Zhang and Gen (2011); Moon, Logendran, and Lee (2009); Kuo and Yang (2007); Wirojanagud et al (2007) Throughput / Productivity / output/ line efficiency Méndez-Vázquez and Nembhard (2019); Digiesi et al (2018); Wu et al (2018b); Öner-Közen, Minner, and Steinthaler (2017); Fattahi, Samoei, and Zandieh (2016); Mossa et al (2016); Bentefouet and Nembhard (2013); Asensio-Cuesta et al (2012); Chen et al (2012); Nanthavanij, Yaoyuenyong, and Jeenanunta (2010); McDonald et al (2009); Nembhard and Shafer (2008); Wirojanagud et al (2007); Song et al (2006); Allwood and Lee (2004); Buzacott (2002); Norman et al (2002); Shafer, Nembhard, and Uzumeri (2001) Workload balancing / smoothing Lian et al (2018); Sadeghi, Rebelo, and Ferreira (2018); Dalle Mura and Dini (2017); Zacharia and Nearchou (2016); Xin et al (2015); Manavizadeh et al (2013) Others: Energy expenditure, job safety, job satisfaction and workers' health Dalle Mura and Dini (2019b); Asensio-Cuesta et al (2012); Aryanezhad et al (2009b) Age Cycle time; task processing time Digiesi et al (2018); Efe, Kremer, and Kurt (2018); Baines et al (2004) Experience Boenzi et al (2015) Throughput / Productivity / output/ line efficiency Digiesi et al (2018); Efe, Kremer, and Kurt (2018); Börsch-Supan and Weiss (2016); Baines et al (2004);Quintana, Leung, and Chen (2008) …”

Workforce reconfiguration strategies in manufacturing systems: a state of the art

“…DRC system is a manufacturing system, which is not only constrained by machine capacity, but also by workforce capacity. Cross-trained workforce is also largely used in CMS (Slomp et al, 2005;Kaku et al, 2008;Aryanezhad et al, 2009;Wu et al, 2018;Chu et al, 2019).…”

Impact of work-sharing on the performance of production line with heterogeneous workers

“…In recent years, several authors have investigated the impacts of cross-training and workforce skill levels on economic and human issues. Wu et al [9] explored the trade-off between skill training cost and workload balance by proposing a swarm intelligence optimizer. Their proposed algorithm generates a better solution than the candidate algorithm.…”