A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Abstract

Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO ® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of "complex dummy sub-cells". The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.

A genetic algorithm for scheduling multimode resource-constrained project problem in the presence of preemptive resources

“…To solve the model they offered preprocessing and priority-based heuristic methods. Delgoshaei et al (2016a) explained the uncertain conditions that may cause imbalance in a system. (Ke et al, 2010) also used a combination of fuzzy and genetic algorithm for optimizing project cost respecting to completion time (see also Chen et al (2009);Hartmann et al (2010).…”

A robust optimization approach for scheduling a supply chain system considering preventive maintenance and emergency services using a hybrid ant colony optimization and simulated annealing algorithm

“…In continue, the authors also integrated fuzzy set theory and house of quality in order to provide a structural tool to capture the inherent imprecision and vagueness of decision-relevant inputs and to facilitate the analysis of decision-relevant quality function deployment (QFD) information. Delgoshaei et al (2016b) proposed a new method for increasing the productivity of a manufacturing system by decreasing the work load variations. Baykasoglu and Gocken (2010) proposed a hybrid fuzzy based ranking and Tabu search method to solve fuzzy multi-objective aggregate production planning problem.…”

A new method for minimizing cell underutilization in the process of dynamic cell forming and scheduling using artificial neural networks

“…In order to develop a model with mentioned features, a NL-MIP model which was previously presented by Delgoshaei et al (2016a) is more developed by considering other production conditions. It is noted that Delgoshaei et al (2016a) offered a method to temporarily freeze those machines that are more likely to be over-allocated. In this research their model is more developed by considering preventive maintenance and machine broken, depreciation of machines and human resources.…”

“…Afterward, Renna & Ambrico (2015) also proposed three models for designing, reconfiguring and scheduling cells in dynamic condition of product demands. Delgoshaei et al (2016a) used GA for scheduling CMS while part demands are dynamic and may be varied from one period to another. Delgoshaei et al (2016c) employed a hybrid of Tabu search and simulated annealing for evaluating impacts of market changes in scheduling CMS.…”