Publication Type : Journal Article
Publisher : International Journal of Engineering and Technology,
Source : International Journal of Engineering and Technology, Volume 2, Issue 4, p.220-224 (2010)
Url : https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.440.6271&rep=rep1&type=pdf
Keywords : Cellular manufacturing systems; Metaheuristics; Simulated Annealing
Campus : Bengaluru
School : School of Engineering
Department : Mechanical Engineering
Year : 2010
Abstract : Manufacturing industries are under intense pressure from the increasingly competitive global marketplace. Shorter product lifecycle, time to market and diverse customer needs have challenged manufacturers to improve the efficiency and productivity of their production activities. Manufacturing systems should be able to adjust or respond quickly to adopt necessary changes in product design and product demand without major investment. Traditional manufacturing systems are not capable of satisfying such requirements. Although a cellular manufacturing system (CMS) provides great benefits, the design of CMS is complex for real life problems. The design of such a kind of manufacturing system under dynamic production environment, with variety and demand varying between each planning horizon, requires pervasive use of a Metaheuristics such as Genetic Algorithm (GA), Simulated Annealing algorithm (SA), and Tabu Search (TS). The dynamic cell formation (CF) problem (involving the formation of a mathematical model depicting the variable product mix and demand across the planning horizons) is known to be one of the NP-hard combinational problems. Although some optimization algorithms can find the optimal solution for small- and medium-sized problems, they have a disadvantage in that the memory and computational time requirements are extremely high, and increase exponentially, as the problem size increases. In such situations, meta-heuristics are used for exploring and exploiting the search space to obtain good solutions. In contrast to other stochastic searches, SAs in particular have the following unique features: it does not get trapped in local minimum. Allow uphill moves controlled by parameter called temperature. Final result not dependent on initial state.These features often makes them a preferable choice over traditional heuristics. The objective of this paper is to review how the SA has been applied so far for the Design of Cellular Manufacturing System application. In this paper we present a comprehensive review of the works that applied SA for CMS deign and suggest some directions for future research.
Cite this Research Publication : Jayakumar V. and Raju, R., “Investigation of Applications of SA in the Design of Dynamic Cellular Manufacturing Systems”, International Journal of Engineering and Technology, vol. 2, no. 4, pp. 220-224, 2010.