Preface " Optimizing, Innovating, and Capitalizing on Information Systems for Section 1: Optimization Methodologies

Operations " belongs to Advances in Operations Research and Information Systems Series book project. There are four sections and 20 chapters in this book. Section one consists of 6 chapters. In Chapter 1, Payam Hanafizadeh, Abolfazl Ghaemi, and Madjid Tavana conduct a " Local Perturbation Analysis of Linear Programming with Functional Relation among Parameters. " The main purpose of classical sensitivity analysis is to examine the variations of the objective function's optimal values and the solution components as a result of the infinitesimal changes in one of the parameters while the other ones are kept fixed. One of the problems confronted in classical sensitivity analysis is when several parameters are changed simultaneously. In those cases, classical methods cannot obtain the effect of the perturbations on the objective function as well as on the optimal solution because when the simultaneous changes happen in the amplitudes range of basic variables or binding constraints, the order of basic solution changes. Although the importance of sensitivity analysis in linear programming has been widely stressed in the management science literature, the research on sensitivity analysis with functional relation has been sporadic and scattered. Usually variation occurs in the right-hand-side of the constraints and/or the objective function coefficients. In this chapter, the authors consider the sensitivity analysis for a class of linear programming problems with functional relation among the objective function parameters or those of the right-hand-side. The classical methods and standard sensitivity analysis software packages fail to function when a functional relation among the linear programming parameters prevails. In order to overcome this deficiency, they derive a series of sensitivity analysis formulae and devise corresponding algorithms for different group of homogenous linear programming parameters. The validity of the derived formulae and devised algorithms is corroborated by open literature examples having linear as well as nonlinear functional relations. In Chapter 2, Heping Liu and Yanli Chen explore " Taylor Kriging Metamodeling for Stochastic Simulation Interpolation. " Interpolation is a key problem of simulation modelling. The objective of interpolation is to significantly reduce the times and thus the expense of running a simulation model. In this chapter, the authors applied a novel Kriging model to the interpolation of stochastic simulation with high computational expense. The novel Kriging model is called Taylor Kriging, and it was developed by integrating Taylor expansion with Kriging and using Taylor expansion to construct the drift function of Kriging. To investigate the interpolation …