Making an integrated decision in a three-stage supply chain along with cellular manufacturing under uncertain environments: A queueing-based analysis

Abstract

Today’s complicated business environment has underscored the importance of integrated decision-making in supply chains. In this paper, a novel mixed-integer nonlinear mathematical model is proposed to integrate cellular manufacturing systems into a three-stage supply chain to deal with customers' changing demands, which has been little explored in the literature. This model determines the types of vehicles to transport raw materials and final parts, the suppliers to procure, the priorities of parts to be processed, and the cell formation to configure work centers. In addition, queueing theory is used to formulate the uncertainties in demands, processing times, and transportation times in the model more realistically. A linearization method is employed to facilitate the tractability of the model. A genetic algorithm is also developed to deal with the NP-hardness of the problem. Numerous instances are used to validate the effectiveness of the modeling and the efficiency of solution procedures. Finally, a sensitivity analysis and a real case study are discussed to provide important management insights and evaluate the applicability of the proposed model.

A novel mathematical model to minimize the total cost of the hospital and COVID-19 outbreak concerning waiting time of patients using Jackson queueing networks, a case study

“…food delivery [18], the production-inventory problem [19,20], cellular manufacturing [21,22] telecommunications [23,24], etc. Recently, researchers have applied the queuing theory in the health care system.…”

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Effects of green improvement and pricing policies in a double dual-channel competitive supply chain under decision-making power strategies

“…Also, they investigate the best profitable policy. Esmailnezhad and Saidi-mehrabad (2021) propose a mixinteger non-linear model to study the manufacturing systems in a three-stage SCN to act with the customers' demand functions. Sensitivity and a real case are analyzed to provide important insights.…”