Integrationof real-timeoptimization and controlwithhigher level decision-making (scheduling andplanning) is an essential goal for profitable operation in a highly competitive environment. While integrated large-scale optimization models have been formulated for this task, their size and complexity remains a challenge to many available optimization solvers. On the other hand, recent development ...

abstract it is the purpose of this article to introduce a linear approximation technique for solving a fractional chance constrained programming (cc) problem. for this purpose, a fuzzy goal programming model of the equivalent deterministic form of the fractional chance constrained programming is provided and then the process of defuzzification and linearization of the problem is started. a samp...

Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive at satisfact...

Integration of real-time optimization and control with higher level decision making (scheduling and planning) is an essential goal for profitable operation in a highly competitive environment. While integrated large-scale optimization models have been formulated for this task, their size and complexity remains a challenge to many available optimization solvers. On the other hand, recent develop...

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. existing scalable D-OPF algorithms either use approximations that are valid an system, or apply relaxation techniques the equations do guarantee a feasible solution. In this paper, we propose simultaneously achieve s...

We develop a trust region filter strategy for simultaneous optimal design of heat exchanger networks that includes detailed shell-and-tube exchangers. The first solves mixed-integer nonlinear programming (MINLP) formulation with shortcut models to generate candidate network topologies, which are then used in non-isothermal mixing (NLP) suboptimization embedded using modified (TRF) algorithm. An...

The fuel cost minimization problem on steady-state natural gas pipeline networks system is addressed. From the optimization perspective, this problem is modeled as a (non-convex) nonlinear program (NLP), where we consider two types of decision continuous variables: mass flow rate through each arc and pressure value at each node. The proposed method consists of two phases. In phase 1, a set of f...

This paper is an application of ontology theory, conceptual graphs and programming language theories to develop the theoretical foundations of natural language programming (NLP) that has in recent years been used to produce natural language documents for intelligent agents and human readers. The analysis given reveals three benefits of NLP. First, it is “conceptualized” programming that enables...

Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. Howeve...

abstract background: neurolinguistic programming (nlp) refers to the science and art of reaching success and perfection. it is a collection of the skills based on human beings’ psychological characteristics through which the individuals obtain the ability to use their personal capabilities as much as possible. this study aimed to investigate the efficacy of nlp training on mental health in nurs...