Solving Stochastic Fuzzy Transportation Problem with Mixed Constraints Using the Weibull Distribution

Table 1

Comparison of the approach to the present models.

Authors	Problem type	Stochastic parameters	Distribution	Methodology
Gessesse [31]	Fractional TP	Demand & supply	Normal distribution	Simulation-based genetic algorithm
Jerbi [32]	TP	Demand & supply	The power law distribution	Fuzzy programming approach
Al Qahtani et al. [33]	TP	Demand & supply	Extreme value distribution	Goal programming approach
Nasseri and Bavandi [34]	TP	Demand & supply	Expectation value model	Fuzzy programming approach
Dutta et al. [35]	Fuzzy TP	Demand & supply	Fuzzy lognormal distribution and the confidence levels are treated as fuzzy numbers	Genetic algorithm approach
Mahapatra et al. [36]	TP	Demand & supply	Logistic distribution	A transformation technique is presented for manipulating cost coefficients involving multichoice for binary variables with auxiliary constraints and solved by Lingo software
Agrawal and Ganesh [15]	TP	Demand	Galton distribution	Parameters are replaced by Newton’s divided difference interpolating polynomial and solved by Lingo software
Das and Lee [37]	Solid TP	Demand, supply, and capacity	Weibull distribution	Global criterion method and fuzzy goal programming approach
This article	SFTPMC	Demand & supply	Weibull distribution	Alpha cut representation for cost function and WD for constraints and then solved by Lingo software