Formulations and heuristics for scheduling in a buffer-constrained flowshop and flowline-based manufacturing cell with different buffer-space requirements for jobs: Part 2

Formulations and heuristics for scheduling in a buffer-constrained flowshop and flowline-based manufacturing cell with different buffer-space requirements for jobs: Part 2

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Article ID: iaor1998736
Country: United Kingdom
Volume: 35
Issue: 1
Start Page Number: 101
End Page Number: 122
Publication Date: Jan 1997
Journal: International Journal of Production Research
Authors: ,
Keywords: heuristics
Abstract:

This paper is the second of two papers (see IAOR 62601) that deal with the problem of scheduling in the permutation flowshop and flowline-based manufacturing cell with the consideration of different buffer-space requirements for 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 idle-time 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 two 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 perturbation technique called ‘multiple random insertion technique’. An extensive computational experimentation has been carried out to compare the performance of the proposed heuristic and its variant with those of the existing heuristics for scheduling in the buffer-constrained and flowline-based manufacturing systems under study. The results of the experimentation reveal that the proposed heuristic and its variant emerge to be better than the existing heuristics.

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