The uncertainty for the continuous-time linear programming problem with time-dependent matrices is considered in this paper. In case, robust counterpart of introduced. order to solve counterpart, it will be transformed into conventional form matrices. discretization formulated sake numerically calculating ϵ-optimal solutions, and a computational procedure also designed achieve purpose.

A new nonlinear programming algorithm is proposed for stochastic programming problems. This method relies on sampling to estimate the probabilistic objective function and constraints. The computational burden of excessive model calculations for determining the search direction is bypassed through a reweighting method using Kernel Density Estimation. The improvements accomplished by this algorit...

A global optimization method is presented for computing the minimum limit load factor of trusses subjected to unknown but bounded loads. We assume that the external forces consist of a part proportional to a load factor and a part that is uncertain around its nominal value. The worst-case limit load factor is introduced as the smallest limit load factor realized with some uncertain parameters. ...

We study database application programming interfaces for uncertain and probabilistic databases and present a programming model that is independent of representation details. Conceptually, we use the possible worlds semantics, and programs are independently evaluated in each world. We study a class of programs that appear to the user as if they are running in a single world rather than on a set ...

Abstract Manufacturing systems flexibility is critical to meet the challenges arising from the uncertain demands. In this paper, a framework is proposed to evaluate the effect of utilizing flexibility in manufacturing systems which have the capability of producing multiple products at multiple plants. After transformation, a nonlinear polynomial programming model was proposed to describe the pr...

Object-oriented modelling with words seeks to extend the new field of modelling with words, itself derived form computing with words, with features of object-oriented knowledge representation and programming. We show that object-oriented modelling with words has two key benefits; firstly the uncertain class hierarchy provides a natural knowledge representation framework for real-world problems,...

This paper presents an optimization model for planning tool purchases for a semiconductor manufacturing facility under uncertain operating conditions. By modeling the uncertain parameters using a scenario tree, we develop a stochastic programming formulation for the problem. In contrast to earlier two-stage approaches for this problem, our model allows for revision of the tool purchase plan as ...