Flow shop scheduling decisions through Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS)
Author(s) -
Arun Gupta,
Shailendra Kumar
Publication year - 2016
Publication title -
international journal of production management and engineering
Language(s) - English
Resource type - Journals
eISSN - 2340-4876
pISSN - 2340-5317
DOI - 10.4995/ijpme.2016.4102
Subject(s) - topsis , ideal solution , flow shop scheduling , job shop scheduling , mathematical optimization , weighting , computer science , scheduling (production processes) , heuristic , benchmark (surveying) , similarity (geometry) , operations research , mathematics , artificial intelligence , medicine , schedule , physics , geodesy , radiology , image (mathematics) , thermodynamics , geography , operating system
The flow-shop scheduling problem (FSP) has been widely studied in the literature and having a very active research area. Over the last few decades, a number of heuristic/meta-heuristic solution techniques have been developed. Some of these techniques offer excellent effectiveness and efficiency at the expense of substantial implementation efforts and being extremely complicated. This paper brings out the application of a Multi-Criteria Decision Making (MCDM) method known as techniques for order preference by similarity to an ideal solution (TOPSIS) using different weighting schemes in flow-shop environment. The objective function is identification of a job sequence which in turn would have minimum makespan (total job completion time). The application of the proposed method to flow shop scheduling is presented and explained with a numerical example. The results of the proposed TOPSIS based technique of FSP are also compared on the basis of some benchmark problems and found compatible with the results obtained from other standard procedures
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