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Breakable Solid Transportation Problem with Hybrid and Fuzzy Safety Factors using LINGO and Genetic Algorithm | OMICS International | Abstract
ISSN: 2168-9679

Journal of Applied & Computational Mathematics
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Research Article

Breakable Solid Transportation Problem with Hybrid and Fuzzy Safety Factors using LINGO and Genetic Algorithm

Abhijit Baidya*, Uttam Kumar Bera and Manoranjan Maiti

Department of Mathematics, National Institute of Technology, Agartala, Jirania 799055, West Tripura, India

*Corresponding Author:
Abhijit Baidya
Department of Mathematics, National Institute of Technology, Agartala
Jirania 799055, West Tripura, India
Tel: 0381 234 6360
E-mail: [email protected]

Received date: August 04, 2014; Accepted date: August 27, 2014; Published date: August 31, 2014

Citation: Baidya A, Bera UK, Maiti M (2014) Breakable Solid Transportation Problem with Hybrid and Fuzzy Safety Factors using LINGO and Genetic Algorithm. J Appl Computat Math 3: 185. doi: 10.4172/2168-9679.1000185

Copyright: © 2014 Baidya A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

nvironments. If we carrying the produce from sources to destination by the means of unlike conveyances then due to insurgency, land slide and bad road, there are some risks or difficulties to transport the items. By this motive we initiate “Safety Factors” in transportation problem. Due to this reason desired total safety factor is being introduced. Also our objective is to evaluate the solution of STP using expected value model. Here we develop six models where first three models are formulated taking crisp unit transportation cost but the remaining three models are formulated taking hybrid unit transportation cost. To build up the different models we consider breakability and safety factor which is taken as crisp, fuzzy and hybrid for assorted models. All the fuzzy and hybrid models are reduced into its crisp equivalent using expected value modeling. Finally by Generalized Reduced Gradient (GRG) method using LINGO.13 optimization software and Genetic Algorithm we solve the mathematical models and put a enlarge discussion on it.

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