Editor’s column

Optimization of software development life cycle process to minimize the delivered defect density Abstract Many organizations utilize information technology to gain competitive advantage. As the need for software increased, the number of software companies and the competition among them also increased. The software organizations in countries like India can no longer survive based on cost advantage alone. The companies need to deliver defect-free software on time within the budgeted cost. This paper is a case study on minimizing the delivered defect density by optimally executing the various phases in software development life cycle process. The implementation of the study on four projects has shown that the delivered defect density can be minimized by executing the software development process with optimum settings suggested by the methodology. The project managers can also utilize the approach to achieve the goals set on other important output characteristics like productivity, schedule, etc.

A hybrid regression model for water quality prediction Abstract In this work, we propose a hybrid regression model to solve a specific problem faced by a modern paper manufacturing company. Boiler inlet water quality is a major concern for the paper machine. If water treatment plant can not produce water of desired quality, then it results in poor health of the boiler water tube and consequently affects the quality of the paper. This variation is due to several crucial process parameters. We build a hybrid regression model based on regression tree and support vector regression for boiler water quality prediction and show its excellent performance as compared to other state-of-the-art.

On hidden Z -matrix and interior point algorithm Abstract We propose an interior point method to compute solution of linear complementarity problem LCP (q, A) given that A is a real square hidden Z-matrix (generalization of Z-matrix) and q is a real vector. The class of hidden Z-matrix is important in the context of mathematical programming and game theory. We study the solution aspects of linear complementarity problem with \(A \in\) hidden Z-matrix. We observe that our proposed algorithm can process LCP (q, A) in polynomial time under some assumptions. Two numerical examples are illustrated to support our result.

Portfolio optimization using Laplacian biogeography based optimization Abstract Portfolio optimization is defined as the most appropriate allocation of assets so as to maximize returns subject to minimum risk. This constrained nonlinear optimization problem is highly complex due to the presence of a number of local optimas. The objective of this paper is to illustrate the effectiveness of a well-tested and effective Laplacian biogeography based optimization and another variant called blended biogeography based optimization. As an illustration the model and solution methodology is implemented on data taken from Indian National Stock Exchange, Mumbai from 1st April, 2015 to 31st March, 2016. From the analysis of results, it is concluded that as compared to blended BBO, the recently proposed LX-BBO algorithm is an effective tool to solve this complex problem of portfolio optimization with better accuracy and reliability.

Modelling and analysis of healthcare inventory management systems Abstract The core competency of the healthcare system is to provide treatment and care to the patient. The prime focus has always been towards appointing specialized physicians, well-trained nurses and medical staffs, well-established infrastructure with advanced medical equipment, and good quality pharmacy items. But, of late, the focus is driven towards management side of healthcare systems which include proper capacity planning, optimal resource allocation, and utilization, effective and efficient inventory management, accurate demand forecasting, proper scheduling, etc. and may be dealt with a number of operations research tools and techniques. In this paper, a Markov decision process inventory model is developed for a hospital pharmacy considering the information of bed occupancy in the hospital. One of the major findings of this research is the significant reduction in the inventory level and total inventory cost of pharmacy items when the demand for the items is considered to be correlated with the number of beds of each type occupied by the patients in the healthcare system. It is observed that around 53.8% of inventory cost is reduced when the bed occupancy state is acute care, 63.9% when it is rehabilitative care, and 55.4% when long-term care. This may help and support the healthcare managers in better functioning of the overall healthcare system.

Generalization of extent analysis method for solving multicriteria decision making problems involving intuitionistic fuzzy numbers Abstract Analytic hierarchy process (AHP) is a widely used multicriteria decision making method. Chang’s extent analysis method (EAM) is appeared as a very popular fuzzy AHP approach. The aim of this paper is to generalize the EAM in intuitionistic fuzzy settings for effective modeling of imprecision and uncertainty inherent in nature. In this paper, special triangular intuitionistic fuzzy degree of possibility is defined for comparing two or more triangular intuitionistic fuzzy numbers (TIFNs) and some relevant theorems are introduced generating intuitionistic fuzzy numbers as weights of criteria or performance scores of alternatives. Based on TIFNs, a conversion scale for linguistic variables is proposed for generating a triangular intuitionistic fuzzy preference relation. The EAM is then generalized in intuitionistic fuzzy settings by proposing generalized intuitionistic fuzzy EAM using TIFNs and its arithmetic for deriving crisp priority vector from the triangular intuitionistic fuzzy preference relation. The advanced approach is validated through two numerical examples.

A comparative study on performance measurement of Indian public sector banks using AHP-TOPSIS and AHP-grey relational analysis Abstract Banks are the financial intermediaries and important means for the advancement of economies. In the cutthroat competitions, the increase in market shares is a matter of concern for all. Banks are expected to increase their efficiency to boost competitive capacity, which also helps the Decision-maker to know about grey areas for development. Therefore, performance measurements of efficiency calculation, by using different methods are the concern for research across the world. This paper tries to use the combination of AHP, TOPSIS, and Grey Relational Analysis for efficiency calculation of different public sector banks in India and finally, results were compared. AHP is used to determine the weight criteria and Grey Relational Analysis and TOPSIS are used to rank the bank performances. The proposed method of this study used various inputs and outputs criteria which were taken from various banks annual reports. Descriptive statistics and correlation matrix were used to test the validity of the criteria. The findings reveal that banks which are considered as efficient are close to relative closeness to the ideal solution, expose an alternative ranking of the banks, present research also provides better insight to focus on the area of improvement in comparison to others banks. The Comparative result shows both models have the almost same interpretation. Little deviation in their ranks is due to methodological differences. The proposed research will provide a framework for further applications and both approaches will help decision maker of Indian Public sector banks to find optimal solutions to the complex problems by assessing various alternatives.

Retraction Note to: Transient analysis of an M/M/1 queue with variant impatient behavior and working vacations The editor has retracted this article [1] because it has significant overlap with a work published by Sudhesh and Azhagappan [2] and is therefore redundant. The authors do not agree to this retraction.

Scenario generation in stochastic programming using principal component analysis based on moment-matching approach Abstract In optimization models based on stochastic programming, we often face the problem of representing expectations in proper form known as scenario generation. With advances in computational power, a number of methods starting from simple Monte-Carlo to dedicated approaches such as method of moment-matching and scenario reduction are being used for multistage scenario generation. Recently, various variations of moment-matching approach have been proposed with the aim to reduce computational time for better outputs. In this paper, we describe a methodology to speed up moment-matching based multistage scenario generation by using principal component analysis. Our proposal is to pre-process the data using dimensionality reduction approaches instead of using returns as variables for moment-matching problem directly. We also propose a hybrid multistage extension of heuristic based moment-matching algorithm and compare it with other variants of moment-matching algorithm. Computational results using non-normal and correlated returns show that the proposed approach provides better approximation of returns distribution in lesser time.