Showing papers by "Chandrasekharan Rajendran published in 1996"
TL;DR: The proposed genetic algorithm is compared with the existing multi-criterion heuristic, and results of the computational evaluation are presented, and it is found to perform well for scheduling in a flowline-based CMS.
Abstract: The problem of scheduling in flowshop and flowline-based cellular manufacturing systems (CMS) is considered with the objectives of minimizing makespan, total flowtime and machine idletime. We first discuss the formulation of time-tabling in a flowline-based CMS. A genetic algorithm is then presented for scheduling in a flowshop. The proposed genetic algorithm is compared with the existing multi-criterion heuristic, and results of the computational evaluation are presented. We introduce some modifications in the heuristic seed sequences, while using them to initialize subpopulations in the algorithm for scheduling in a flowline-based CMS. The proposed algorithm is also found to perform well for scheduling in a flowline-based CMS.
112 citations
TL;DR: In this article, the problem of scheduling in the permutation flowshop and flowline-based manufacturing cell with the consideration of different buffer-space requirements (or different in-process inventory levels) for jobs and the constraints on buffer-storage capacity between machines is addressed.
Abstract: This paper is the second of two papers that deal with the problem of scheduling in the permutation flowshop and flowline-based manufacturing cell with the consideration of different buffer-space requirements (or different in-process inventory levels) for jobs and the constraints on buffer-storage capacity between machines. We consider two sets of twin objectives of scheduling: one set consists of minimizing the idle-time of machines and waiting-time of jobs, and another set consists of minimizing idletime of machines and weighted waiting-time of jobs. We present a bicriterion heuristic, with a variant, to obtain a sequence that minimizes the twin objectives under consideration. The heuristic works in two phases. The first phase, i.e., 'initial seed-sequence generation phase', deals with the development of a good seed sequence with respect to twin objectives under consideration. The second phase is the 'improvement phase' in which the seed sequence, obtained from the first phase, is improved by using a new...
10 citations