Project scheduling problem is to determine the schedule of allocating the loans to a project in order to balance the total cost and the completion time of the project under some constraints. This paper considers project scheduling problem in an uncertain environment, of which the duration time of each activity is an uncertain variable. Then uncertain chance-constrained programming model is cons...

A procedure is provided to detect multiple change-points in the mean of very large Gaussian signals. From an algorithmical point of view, visiting all possible con gurations of change-points cannot be performed on large samples. The proposed procedure runs CART rst in order to reduce the number of con gurations of change-points by keeping the relevant ones, and then runs an exhaustive search on...

A large class of separable quadratic programming problems is presented The problems in the class can be solved in linear time The class in cludes the separable convex quadratic transportation problem with a xed number of sources and separable convex quadratic programming with nonnegativity con straints and a xed number of linear equality constraints

Uncertainty issues associated with a multi-site, multi-product supply chain planning problem has been analyzed in this paper using the chance constraint programming approach. In literature, such problems have been addressed using the two stage stochastic programming approach. While this approach has merits in terms of decomposition, computational complexity even for small size planning problem ...

In this paper, “chance optimization” problems are introduced, where one aims at maximizing the probability of a set defined by polynomial inequalities. These problems are, in general, nonconvex and computationally hard. With the objective of developing systematic numerical procedures to solve such problems, a sequence of convex relaxations based on the theory of measures and moments is provided...

Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging task for the academic research currently is to address large-scale, complex optimization problems under various uncertainties. Therefore, investigations on the development of stochastic optimization approaches are necessi...

In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been utilized to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes ar...

In this study, a fuzzy bi-level chance constraint programming (FBCP) model is developed for urban ecological management in Xiamen, China. FBCP has advantages balancing trade-offs between multiple decision makers and can address stochastic uncertainty ecosystem management. It also reflect the impact of different violation risk levels emission reduction measures on system benefit, service value, ...