The environment of loan in bank is very complex, there are not only random factors but also fuzzy factors, so the return rates of loan often have fuzzy random characteristic. Mean chance is a measure of fuzzy random variable. This paper proposes two fuzzy random dependentchance programming models of loan portfolio, one is minimize the mean chance of a bad outcome under the certain expected retu...

* Correspondence: [email protected] Department of Medical Education, Faculty of Medicine, 06490, Bahçelievler, Ankara, Turkey Full list of author information is available at the end of the article Abstract In this article, a method is developed to transform the chance-constrained programming problem into a deterministic problem. We have considered a chance-constrained programming problem u...

Aircraft con ict detection and resolution is currently attracting the interest of many air transportation service providers and is concerned with the following question: Given a set of airborne aircraft and their intended trajecto-ries, what control strategy should be followed by the pilots and the air traf c service provider to prevent the aircraft from coming too close to each other? This pap...

Maternal undernutrition results in elevated blood pressure (BP) and endothelial dysfunction in adult offspring. However, few studies have investigated interventions during early life to ameliorate the programming of hypertension and vascular disorders. We have utilised a model of maternal undernutrition to examine the effects of pre-weaning growth hormone (GH) treatment on BP and vascular funct...

Resistance to autonomous systems comes in part from the perceived unreliability of the systems. Concerns can be addressed by guarantees of the probability of success. This is achieved in chanceconstrained constraint programming (CC-CP) by imposing constraints required for success, and providing upper-bounds on the probability of violating constraints. This extended abstract reports on novel unc...

The mixing set with a knapsack constraint arises in deterministic equivalent of chance-constrained programming problems with finite discrete distributions. We first consider the case that the chance-constrained program has equal probabilities for each scenario. We study the resulting mixing set with a cardinality constraint and propose facet-defining inequalities that subsume known explicit ine...

Various applications in reliability and risk management give rise to optimization problems with constraints involving random parameters, which are required to be satisfied with a pre-specified probability threshold. There are two main difficulties with such chance-constrained problems. First, checking feasibility of a given candidate solution exactly is, in general, impossible since this requir...

Probability and statistics are the deductive and inductive theories, respectively, of chance and uncertainty. However, they typically do not have a role in traditional logic and theorem proving. This is unfortunate, because chance is an important concept in computer science. The interaction between computer science and uncertainty typically takes three forms. First, algorithms may make random d...

Due to a wide variety of traffic requirements, the design, dimensioning and traffic management of Broadband Integrated Services Digital Networks (B-ISDN) involves the application of diverse methods and theories. In this paper we formulate a model which aims to combine results on the probabilistic analysis of congestion in the network with optimisation of traffic routing decisions. The optimizat...

The paper deals with two methods of solving optimization programs whereuncertainties occur: stochastic (in particular chance-constrained) programming androbust programming. We review briefly how these two methods deal with uncertaintyand what approximations are commonly used. Furthermore, we are concentrated onapproximations based on sample sets where some type of weak dependenc...