Welcome to the InfoSci Platform
Reference Hub12
Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand

Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand

Sankar Kumar Roy, Deshabrata Roy Mahapatra
Copyright: © 2014 |Volume: 5 |Issue: 3 |Pages: 26
ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781466656451|DOI: 10.4018/ijsds.2014070101
Cite Article Cite Article

MLA

Roy, Sankar Kumar, and Deshabrata Roy Mahapatra. "Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand." IJSDS vol.5, no.3 2014: pp.1-26. http://doi.org/10.4018/ijsds.2014070101

APA

Roy, S. K. & Mahapatra, D. R. (2014). Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand. International Journal of Strategic Decision Sciences (IJSDS), 5(3), 1-26. http://doi.org/10.4018/ijsds.2014070101

Chicago

Roy, Sankar Kumar, and Deshabrata Roy Mahapatra. "Solving Solid Transportation Problem with Multi-Choice Cost and Stochastic Supply and Demand," International Journal of Strategic Decision Sciences (IJSDS) 5, no.3: 1-26. http://doi.org/10.4018/ijsds.2014070101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This paper proposes a new approach to analyze the solid transportation problem (STP). This new approach considers the multi-choice programming into the cost coefficients of objective function and stochastic programming which is incorporated in three constraints namely sources, destinations and capacities constraints followed by Cauchy's distribution for solid transportation problem. The multi-choice programming and stochastic programming are combined into solid transportation problem and this new problem is called multi-choice stochastic solid transportation problem (MCSSTP). The solution concepts behind the MCSSTP are based on a new transformation technique which will select an appropriate choice from a set of multi-choice which optimizes the objective function. The stochastic constraints of STP convert into deterministic constraints by stochastic programming approach. Finally, the authors have constructed a non-linear programming problem for MCSSTP and have derived an optimal solution of the specified problem. A realistic example on STP is considered to illustrate the methodology.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.