A multi-constraint neural network for the pragmatic design of cellular manufacturing systems

Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

“…Malakooti and Yang (1995) developed an unsupervised learning algorithm after formulating this problem within an optimization context. The works of Rao andGu (1994, 1995) introduced a three-layered neural network, extending the two layers of the ART network, to consider additional constraints in this problem. Venugopal and Narendran (1992) tested a competitive learning algorithm, along with ART1 and Kohonen's (1984) self-organizing feature map (SOFM), and demonstrated the applicability of all three networks for this problem.…”

“…In recent years, several researchers have demonstrated the applicability of pattern recognition methods based on arti® cial neural networks (ANNs) for solving the part± machine grouping problem (Dagli and Huggahalli 1991, 1995, Kaparthi and Suresh 1991, Malave and Ramachandran 1991, Burke and Kamal 1992, 1995, Venugopal and Narendran 1992, Chu 1993, Liao and Chen 1993, Rao and Gu 1994, 1995, Suresh and Kaparthi 1994, Chen and Cheng 1995, Malakooti and Yang 1995, Kamal and Burke 1996 . In particular, ANNs have been shown to be e ective for clustering large, industry-size data sets (Dagli and Sen 1992, Kaparthi and Suresh 1992, Kaparthi et al 1993, and Suresh and Kaparthi 1994.…”

A new genetic algorithm for the machine/part grouping problem involving processing times and lot sizes

“…Ballakur & Steudel, 1987;Boctor, 1996;Logendran & Ramakrishna, 1995;Nagi, Harhalakis, & Proth, 1990;Okogbaa, Chen, Changchit, & Shell, 1992;Rao & Gu, 1995;Sule, 1991;Venugopal & Narendran, 1992;Wei & Gaither, 1990;Zolfaghari & Liang, 1998) have been reported in the literature to solve the general grouping problem. Though these studies have contributed to the understanding of the problem, each of the studies adopts a different grouping objective, mostly associated with only one grouping issue.…”

An integrated method for multiobjectives cell formation in cellular manufacturing systems

“…A variety of methods were developed to solve CM problems. These include traditional mathematical Integer Linear Programming (ILP) such as dynamic programming in Chen (1998) and Bender's decomposition in Heragu and Chen (1998), nontraditional methods such as tabu search in Lozano et al (1999), a systems approach in Singh (1996) and neural networks in Jamal (1993), Rao and Gu (1995), Malakooti andYang (1995), Moon (1990), Suresh and Kaparthi (1994) and Suresh et al (1999). Simulation method has been used mainly for CM system analysis.…”