Computer Engineering Commons^™

Stochastic Optimization Of Supply Chain Risk Measures –A Methodology For Improving Supply Security Of Subsidized Fuel Oil In Indonesia, Adinda Yuanita, Andi Noorsaman Sommeng, Anondho Wijonarko

Makara Journal of Technology

Monte Carlo simulation-based methods for stochastic optimization of risk measures is required to solve complex problems in supply security of subsidized fuel oil in Indonesia. In order to overcome constraints in distribution of subsidized fuel in Indonesia, which has the fourth largest population in the world—more than 250,000,000 people with 66.5% of productive population, and has more than 17,000 islands with its population centered around the nation's capital only—it is necessary to have a measurable and integrated risk analysis with monitoring system for the purpose of supply security of subsidized fuel. In consideration of this complex issue, uncertainty and probability …

Second-Order Uncertainty As A Bridge Between Probabilistic And Fuzzy Approaches, Hung T. Nguyen, Vladik Kreinovich, Luc Longpre

Departmental Technical Reports (CS)

On the example of physics, we show that the traditional one-level description is not completely adequate. For a more adequate structure, a hierarchical description of uncertainty is necessary, which supplements the more traditional first-order uncertainty with second-order, third-order and more sophisticated models. In particular, the second-order approach seems to provide a bridge between probabilistic and fuzzy approaches to uncertainty.

Reduction To Independent Variables: From Normal Distribution To General Statistical Case To Fuzzy, Mourad Oussalah, Hung T. Nguyen, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical problems, we must combine ("fuse") data represented in different formats, e.g., statistical, fuzzy, etc. The simpler the data, the easier to combine them. Therefore, to combine complex data, it is desirable to "decompose" this complex data into simpler (easy-to-combine) data chunks.

It is well known that when we have n random variables x1, ..., xn with a joint Gaussian distribution, then we can reduce them to n independent variables by an appropriate linear transformation x1, ..., xn --> y1 = f1(x1,...,xn), ..., yn = fn(x1,...,xn). It is not so well known but also true that when we …