Showing papers by "Sankar Kumar Roy published in 2014"
30 Apr 2014
TL;DR: This paper proposes the approaches of revised multi-choice goal programming (RMCGP) and utility function into the MOTP and then compared the solution between them and showed the feasibility and usefulness of the paper.
Abstract: This paper explores the study of multi-choice multi-objective transportation problem (MCMTP) under the environment of utility function approach. MCMTP is converted to multi-objective transportation problems (MOTP) by transforming the multi-choice parameters like cost, demand, and supply to real-valued parameters. A general transformation procedure using binary variables is illustrated to reduce MCMTP into MOTP. Most of the MOTP are solved by goal programming (GP) approach. Using GP, the solution of MOTP may not be satisfied all the time by the decision maker (DM) when the proposed problem contains interval-valued aspiration level. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and utility function into the MOTP and then compared the solution between them. Finally, numerical examples are presented to show the feasibility and usefulness of our paper.
48 citations
TL;DR: In this paper, a multi-choice stochastic transportation problem (TP) with Weibull distribution is presented, where the cost coefficients of the objective function associated with TP are multiview type.
Abstract: This paper presents a multi choice stochastic transportation problem (TP) where the supply and demand parameters of the constraints follow Weibull distribution. The cost coefficients of the objective function associated with TP are multi-choice type. At first, all the stochastic constraints are transformed into deterministic constraints using stochastic programming approach. Multi-choice type cost coefficients are tractabled by introducing binary variables in the multi-choice programming. Secondly, the transformed problem is considered as a deterministic multi-choice transportation problem. Finally, a numerical example is presented to illustrate the methodology.
40 citations
TL;DR: In this paper, a fuzzy programming approach was used to solve the single-sink, fixed-charge, multiobjective, multi-index stochastic transportation problem (SSMISTP).
Abstract: SYNOPTIC ABSTRACTThe aim of this article is to present a fuzzy programming approach to a single-sink, fixed-charge, multiobjective, multi-index stochastic transportation problem (SSMISTP). This article focuses on the minimization of the transportation cost, deterioration rate, and underused capacity for transportation of raw materials from different sources to the ”Single-Sink” by different transportation modes. The parameters of the proposed problem are transportation cost, fixed-charge, deterioration rate, and underused capacity. These parameters are to be treated here as random variables. Because of the globalization of the market, assume that the “Sink” demand is an interval representing the inexact demand component for the SSMISTP. By considering the uncertainties of these parameters, we formulate the mathematical model of the proposed problem. Using a stochastic programming approach, all the stochastic objective functions are converted into deterministic objective functions. Again using the interval...
27 citations
TL;DR: 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.
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.
21 citations