Abu S.M. Masud

A Model for the Multiperiod Multiobjective Power Generation Expansion Problem

A long-term multiobjective model for the power generation expansion planning of electric systems is described and evaluated in this paper. The model optimizes simultaneously multiple objectives (i.e., minimizes costs, environmental impact, imported fuel and fuel price risks) and decides the location of the planned generation units in a multiperiod planning horizon. Among the attributes considered in the model are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approach to solve this problem is based on multiobjective linear programming and the analytical hierarchy process. A case study from the Mexican Electric Power System is used to illustrate the proposed framework

An aggregate production planning model and application of three multiple objective decision methods

This paper presents a multiple objective formulation of the multi-product, multi-period aggregate production planning problem. The proposed model provides for individual consideration of the conflicting multiple objectives without resorting to a priori trade-off decisions through subjective cost estimation. A numerical example is solved using three Multiple Objective Decision Making Methods.

A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

Circularity error evaluation: Theory and algorithm

Many procedures for the evaluation of circularity error based on different criteria have been developed. The procedures that are based on the minimum radial separation criterion are either too complex or lack an algorithmic approach to find optimal solution. This paper presents an optimization-based technique to find the value of circularity error based on the minimum radial separation criterion. The problem is formulated as a nonlinear optimization problem. Based on the developed necessary and sufficient conditions a generalized nonlinear optimization procedure is presented. The performance of the developed procedure is analyzed for different size problems generated using a simulation program. Results indicate that the procedure is accurate and very efficient in solving large size real life problems.

A knowledge-based advisory system for statistical quality control

Implementation of an effective quality management system requires the ready availability of expert statistical quality control (SQC) practitioners. However, this expertise may be unavailable to many small- and medium-size manufacturing organizations. Knowledge-based systems (KBS) can be used to make SQC expertise easily available to these organizations. This paper describes a KBS that has been developed for providing assistance in: (1) the selection and design of appropriate quality control charts; (2) the process monitoring analyses; and (3) providing corrective advice based on the monitoring analyses results. The KBS runs on a microcomputer and has been developed using a commercially available development shell.

A knowledge-based model management system for aircraft survivability analysis

For the survivability analysis of a weapon system, an analyst has to consider and choose from a large number of available models the set of models that best fits the study requirements. This paper presents a knowledge-based approach for this Model Management System (MMS) problem. A hierarchical structure of the models is used in the MMS, where a lower level models output must match the input requirements of a higher level model. The search procedure for the appropriate model set is driven by the user defined weapon analysis requirements. This type of search procedure ensures that the final output of the selected models would provide the required information to the user and the set of selected models would be properly matched. A prototype of the knowledge system has been developed using a commercially available PC-based shell.

A computer program for time series forecasting using single and double exponential smoothing technique

Abstract This paper presents a microcomputer program for time series forecasting. The program has been developed in GW-BASIC for Zenith 150 microcomputers which are IBM PC compatible. It utilizes Single exponential smoothing, Adaptive-response-rate single exponential smoothing, and Browns double exponential smoothing methods to forecast the future values of a given time series. The program produces plots of the original time series and forecasted series as well as forecasting errors. It computes 90% and 95% confidence intervals for forecasted values and calculates the following statistics: Mean squared error, Mean absolute percentage error, Mean absolute error, Durbin-Watson statistic, and Theils U statistic.

Sphericity error evaluation: theoretical derivation and algorithm development

Several methods for the evaluation of sphericity error exist. The Minimum Radial Separation (MRS) spheres method is a method that has been studied by several researchers. In the MRS criterion, two concentric spheres at minimum radial separation must be found such that they contain all points on the actual spherical surface. The existing procedures for finding MRS spheres are either too complex and time consuming or do not provide an optimal solution to the sphericity error evaluation problem. In this paper, mathematical optimization concepts are utilized to develop a theory and an algorithm for the evaluation of sphericity error based on MRS criterion. Results indicate that the algorithm is fast and accurate in providing optimal solution to the sphericity error evaluation problem.

SENSES: a knowledge-based sensor selection system

Abstract A key component in any measurement and control system is the sensor. To operate reliably a sensor must meet all of the critical application specifications. Due to the myriad of factors that must be taken into consideration in a sensor selection process, it is very difficult for any user, especially those who are not familiar with sensors, to select an acceptable, cost effective sensor. This paper presents a knowledge-based system (KBS) developed to assist in the sensor selection process. The KBS runs on a micro-computer and uses a group technology coding scheme for sensor classification and fast data retrieval from an external database. The system output includes the operational and dimensional parameters of the recommended sensor as well as the price and the vendor information.

Forecasting plant labor productivity with a time series model

Abstract Plant labor productivity index, defined as the ratio of the standard labor hours and actual labor hours, is an important factor in the determination of the future manpower needs at Cessna Aircraft Co. (Pawnee Division). In this paper, a time series model based on the Box-Jenkins modelling approach is developed. A greater accuracy in forecasting future labor productivity index has been achieved by using this model.