A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality

Abstract

Design of an appropriate Cellular Manufacturing System (CMS) leads to system exibility and production e ciency by using the similarities in the manufacturing process of products. One of the main issues in these systems is to consider product quality level and worker's skill level in the production process. This study proposes a comprehensive bi-objective possibilistic nonlinear mixed-integer programming model under uncertain environment to design a suitable CMS with the aim of minimizing the total costs and total inaction of workers and machines, simultaneously. In this respect, the demand for each product with a speci c quality level and linguistic parameters such as product quality level, worker's skill level, and job hardness level on machines are considered under fuzzy environment. To this end, the robust possibilistic programming approach is tailored to cope with fuzzy impute parameters. Finally, a real case study is provided to show the e ciency and applicability of the proposed model. In this respect, the proposed approach could reduce the total costs by 23.6% and the total inaction of workers and machines by 11.7% in comparison with real practice. In addition, the performance of the presented model is demonstrated by comparing the results obtained from the proposed model and actual practice.

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

“…The labour cost and a worker's wage, salary or employment cost can vary depending on the worker's skill and capability levels, which can impact the total cost of the production system (Ramezanian and Ezzatpanah 2015;Hashemoghli, Mahdavi, and Tajdin 2019;Samouei and Ashayeri 2019). For example, Kuo and Yang (2007) presented a mixed-integer programming formulation for optimising mixed-skill multi-line operator allocation problems, where the salary of each operator depends on his or her skill level.…”

“…Idle time Osawa and Ida (2007); Wong, Mok, and Leung (2006) 2 Cost Dalle Mura and Dini (2019a); Hashemoghli, Mahdavi, and Tajdin (2019); Samouei and Ashayeri (2019); Hochdörffer, Hedler, and Lanza (2018); Martignago, Battaïa, and Battini (2017); Rabbani, Akbari, and Dolatkhah (2017);Fattahi, Samoei, and Zandieh (2016); Ramezanian and Ezzatpanah (2015); Denkena, Charlin, and Merwart (2013); Othman, Bhuiyan, and Gouw (2012a); Othman, Gouw, and Bhuiyan (2012b); McDonald et al (2009); Moon, Logendran, and Lee (2009); Doerr, Klastorin, and Magazine (2000) 13…”

“…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) …”

Retract-Complete Similarity Measure Mathematical Model For Cell Layout Design

“…Mahdavi et al [16] proposed a model which considered reducing the cost of to and fro movements between cells and within the cells and the investment cost of machines. The literature given by Hashemoghli et al [17] is proposed a bi-objective possibilistic nonlinear mixedinteger programming model in uncertain situations to have a suitable CMS with the aim of minimizing the total costs and total inaction of workers and machines, simultaneously. In this context, the demand for each product with a specific quality level and linguistic parameters such as product quality level, worker's skill level, and job hardness level on machines were considered with fuzzy logics.…”

A multi-objective vibration damping optimization algorithm for solving a cellular manufacturing system with manpower and tool allocation

“…Forghani and Fatemi Ghomi [24] formulated a mixed-integer nonlinear mathematical model to minimize the production, subcontract, material handling, machine idleness, and handling costs. To solve the model, a heuristic method is suggested.Hashemoghli, et al [25] presented a non-linear mixed-integer programming model under an uncertain environment to minimize the total costs and total inaction workers and machines, simultaneously. They used GAMS to solve their model after linearizing this model.…”