A redundancy allocation problem (RAP) is a well-known NP-hard problem that involves the selection of elements and redundancy levels to maximize the system reliability under various system-level constraints. In many practical design situations, reliability apportionment is complicated because of the presence of several conflicting objectives that cannot be combined into a single-objective functi...

  In oil industry, spontaneous imbibition is an important phenomenon in recovery from fractured reservoirs which can be defined as spontaneous uptake of a wetting fluid into a porous solid. Spontaneous imbibition involves both cocurrent and countercurrent flows. When a matrix block is partially covered by water, oil recovery is dominated by cocurrent imbibition i.e. the production of non wettin...

Classification problems often have a large number of features in the datasets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of max...

Research in nanotechnology in the petroleum industry is advancing rapidly and an enormous progress in the application of nanotechnology in this area is to be expected. In particular, adding nanoparticles to fluids may drastically benefit enhanced oil recovery and improve well drilling, by changing the properties of the fluid, rocks wettability alteration, advanced drag reduction, strengthen...

This paper presents an efficient Multi-objective Crazy Chaotic Particle Swarm Optimization (MCCPSO) evolutionary algorithm to solve the Multi-objective Optimal Operation Management (MOOM) considering Fuel Cell Power Plants (FCPPs) in distribution network. The objective functions of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the tota...

Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. The crucial task of choosing a single preferred solution from the obta...

Estimation of distribution algorithms are a class of evolutionary optimization algorithms based on probability distribution model. In this article, a Pareto-based multi-objective estimation of distribution algorithm with multivariate Tcopulas is proposed. The algorithm employs Pareto-based approach and multivariate T-copulas to construct probability distribution model. To estimate joint distrib...

RAO, SUNIL, MURALI. Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization (Under the direction of Dr. Ranji Ranjithan) In the operations research literature, the Tchebycheff method has been demonstrated to be a useful approach for exploring the non-dominated solutions for multiobjective optimization problems. While this method has been investigated with mathematical pr...

Nowadays, the citrus supply chain has been motivated by both industrial practitioners and researchers due to several real-world applications. This study considers a four-echelon citrus supply chain, consisting of gardeners, distribution centers, citrus storage, and fruit market. A Mixed Integer Non-Linear Programming (MINLP) model is formulated, which seeks to minimize the total cost and maximi...