Hasanov Zakir

Uncertainty Modeling and Optimization of Supply Chain Planning

Miskolci Egyetem

Hatvany József Informatikai Tudományok Doktori Iskola

Bányai Tamás József

Veres Péter

társ

PhD

Uncertainty is a characteristic element for real world problems. Nevertheless, its accurate incorporation to optimization problems requires specific approach, especially in supply chain planning. Thus far, several approaches such as stochastic programming and fuzzy logic have been implemented to tackle uncertainty. Stochastic programming is an optimization problem where some or all parameters are uncertain but follow probability distributions. Although stochastic programming is useful, it is not always possible to use it due to existence of uncertainty in data or even cases lack of historical data. In addition, stochastic models are usually based on representation of existing uncertainty by probability concepts are, consequently limited to addressing the uncertainties captured by some probability distributions. Nevertheless, fuzzy programming based on fuzzy set theory resembles human reasoning and tackles uncertainty in terms of vagueness and ambiguity. Considering that most of the uncertainties associated with supply chain planning such as forecasted demand, production capacity and transportation time require subjective judgement, therefore implementation of fuzzy programming will be of vital importance for uncertainty modelling. The aim of this research is to develop new models and methods that enable the optimization of supply chains with uncertainties.

2025-09-01

2029-08-31

folyamatban lévő

Uncertainty Modeling and Optimization of Supply Chain Planning