Parameter estimation and optimization of multi-objective capacitated stochastic transportation problem for gamma distribution

The transportation problem in real life is an uncertain problem with multi-objective decision-making. In particular, by considering the conflicting objectives/criteria such as transportation costs, transportation time, discount costs, labour costs, damage costs, decision maker searches for the best...

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Vydáno v:Complex & intelligent systems Ročník 6; číslo 3; s. 651 - 667
Hlavní autoři: Gupta, Srikant, Garg, Harish, Chaudhary, Sachin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.10.2020
Springer Nature B.V
Témata:
Complexity
Computational Intelligence
Constraints
Data Structures and Information Theory
Decision making
Engineering
Maximum likelihood estimation
Multiple objective analysis
Operating costs
Optimization
Original Article
Parameter estimation
Probability distribution functions
Statistical analysis
Transportation problem
Gamma distribution
Maximum likelihood estimation
Interval type-2 fuzzy number
Multi-objective optimization
Capacitated transportation problem
Stochastic programming
ISSN:2199-4536, 2198-6053
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Shrnutí:The transportation problem in real life is an uncertain problem with multi-objective decision-making. In particular, by considering the conflicting objectives/criteria such as transportation costs, transportation time, discount costs, labour costs, damage costs, decision maker searches for the best transportation set-up to find out the optimum shipment quantity subject to certain capacity restrictions on each route. In this paper, capacitated stochastic transportation problem is formulated as a multi-objective optimization model along with some capacitated restrictions on the route. In the formulated problem, we assume that parameters of the supply and demand constraints’ follow gamma distribution, which is handled by the chance constrained programming approach and the maximum likelihood estimation approach has been used to assess the probabilistic distributions of the unknown parameters with a specified probability level. Furthermore, some of the objective function’s coefficients are consider as ambiguous in nature. The ambiguity in the formulated problem has been presented by interval type 2 fuzzy parameter and converted into the deterministic form using an expected value function approach. A case study on transportation illustrates the computational procedure.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-020-00156-1