Workforce planning and production scheduling in a reconfigurable manufacturing system facing the COVID-19 pandemic. (April 2022)

• Record Type: Journal Article
• Authors: Vahedi-Nouri, Behdin; Tavakkoli-Moghaddam, Reza; Hanzálek, Zdeněk; Dolgui, Alexandre
• Is Part Of: Journal of manufacturing systems. Volume 63(2022)
• Page Start: 563
• Page End: 574
• Publication Date: 2022-04
• Subjects: Scheduling -- Workforce planning -- COVID-19 pandemic -- Reconfigurable manufacturing system -- Reconfigurable machine tool -- Constraint programming; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.04.018
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Due to the outbreak of the COVID-19 pandemic, the manufacturing sector has been experiencing unprecedented issues, including severe fluctuation in demand, restrictions on the availability and utilization of the workforce, and governmental regulations. Adopting conventional manufacturing practices and planning approaches under such circumstances cannot be effective and may jeopardize workers' health and satisfaction, as well as the continuity of businesses. Reconfigurable Manufacturing System (RMS) as a new manufacturing paradigm has demonstrated a promising performance when facing abrupt market or system changes. This paper investigates a joint workforce planning and production scheduling problem during the COVID-19 pandemic by leveraging the adaptability and flexibility of an RMS. In this regard, workers' COVID-19 health risk arising from their allocation, and workers' preferences for flexible working hours are incorporated into the problem. Accordingly, first, novel Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models are developed to formulate the problem. Next, exploiting the problem's intrinsic characteristics, two properties of an optimal solution are identified. By incorporating these properties, the initial MILP and CP models are considerably improved. Afterward, to benefit from the strengths of both improved models, a novel hybrid MILP-CP solution approach is devised. Finally, comprehensive computational experiments are conducted to evaluate the performance of the proposed models and extract useful managerial insights on the system flexibility. Graphical Abstract: ga1 Highlights: Studying an integrated workforce planning and production scheduling in an RMS facing COVID-19 crisis. Devising novel MILP and CP models to formulate the problem. Employing two properties of an optimal solution of the problem to enhance the proposed models. Developing a hybrid MILP-CP model to take advantage of both models' strengths. Conducting computational experiments to evaluate the performance of the proposed solution methods.