Title

Solving machine loading problem of FMS: an artificial intelligence (AI) based random search optimization approach

RIS ID

45651

Publication Details

Prakash, A., Shukla, N., Tiwari, M. & Shankar, R. (2008). Solving machine loading problem of FMS: an artificial intelligence (AI) based random search optimization approach. In P. Mandal & D. Laha (Eds.), Handbook of Computational Intelligence in Manufacturing and Production Management. (pp. 19-43). US: IGI Global.

Abstract

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

Please refer to publisher version or contact your library.

Share

COinS
 

Link to publisher version (DOI)

http://dx.doi.org/10.4018/978-1-59904-582-5.ch002