OPSEARCH is the official journal of the Operational Research Society of India (ORSI) publishing the research papers in the field of operations research and related fields. It is a quarterly publication (March, June, September and December).

  • a) official publication of the prestigious Operational Research Society of India
  • b) premier Indian journal in the field of Operational Research

The journal OPSEARCH published by the Operational Research Society of India (ORSI) is a national forum set up with the objective of promoting the education and applications of Operational Research (OR) in day-to-day environment in business, industry and other organizations.

Related subjects » Business & Management - Mathematics - Operations Research & Decision Theory


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  1. Abstract

    Vogel’s Approximation Method (VAM) is known as the best algorithm for generating an efficient initial feasible solution to the transportation problem. We demonstrate that VAM has some limitations and computational blunders. To overcome these limitations we develop an Improved Vogel’s Approximation Method (IVAM) by correcting these blunders. It is compared with VAM on obtained initial feasible solutions to a numerical example problem. Reduction in the total transportation cost over VAM by IVAM is found to be 2.27%. Besides, we have compared IVAM with each of twelve previously developed methods including VAM on solutions to numerical problems. IVAM leads to the minimal total cost solutions to seven, better solutions to four and the same better solution to the remaining one. Finally, a statistical analysis has been performed over the results of 1500 randomly generated transportation problems with fifteen distinct dimensions, where each of them has 100 problems instances. This analysis has demonstrated better performance of IVAM over VAM by reducing the total transportation cost in 71.8% of solved problems, especially for large size problems. Thus IVAM outperforms VAM by providing better initial feasible to the transportation problem.

  2. Abstract

    This paper deals with the inverse and reverse balanced facility location problems with considering the variable edge lengths. The aim of the inverse problem is modifying the length of edges with minimum cost, such that the difference between the maximum and minimum weights of clients, allocated to the given facilities is minimized. On the other hand, the reverse case of the balanced facility location problem considers the modifying the lengths of edges with a given budget constraint, such that the difference between the maximum and minimum weights of vertices, allocated to the given facilities is reduced as much as possible. Two algorithms with time complexity O(nlogn) are presented for the inverse and reverse balanced 2-facility location problems.

  3. 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.

  4. 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.

  5. Abstract

    We propose an interior point method to compute solution of linear complementarity problem LCP (qA) 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 (qA) in polynomial time under some assumptions. Two numerical examples are illustrated to support our result.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Abstract

    The distance measure based on hesitant fuzzy sets is an effective tool in the field of treating similar objects where it distinguishes the difference between two objects. Several distance measures have been proposed so far by different researchers. In this paper, we have proposed modifications in the existing distance measure so that some situations in real life conditions can be handled easily with the proposed distance measure whereas the existing one can not. Finally, the validity and applicability of the proposed distance measure is discussed with some existing examples.

  11. Abstract

    The present study analyzes a production-inventory system with hybrid carbon regulation policy. This hybrid carbon policy is a combination of carbon tax and cap-and-trade policies. It considers a single item that can be produced in different qualities. Production cost, setup cost, amount of emissions and the demand rate depend on the quality. The demand rate for each quality is price sensitive. Emissions occur from three sources—setup, production process and stock holding. The firm can invest on green technologies in each emission source separately to reduce emissions. This model considers profit maximization policy. The managerial problem is to select the profit-maximizing quality for production, and to find the optimum values of the production run time, green investments and the selling price. An algorithm is provided to solve the model. The model is illustrated by a numerical example. Sensitivity analysis is also performed.

  12. Abstract

    The production of sugar in plant consists of procedural steps, each step consisting of various sub-systems. Milling plant is the most important sub-system of a sugar manufacturing plant. The equipment availability in milling plant is a big issue as it has direct bearing on total production cost. Intelligent and effective maintenance planning can decrease total production cost and contribute in achieving strategic goals of sugar manufacturing plant. The objective of this paper is to generate and maintain reliable and exhaustive database of agile maintenance attribute for selection of effective maintenance strategy in milling plant of sugar industry. The database of eighty-eight (88) agile maintenance attributes were formed, out of which twenty-six (26) pertinent attributes relevant to the system under study. PM, PDM, CM, CBM and RCM strategies are selected for prioritization respectively. The proposed framework prioritizes and selects maintenance strategy by fuzzy integrated MADM approach. The fuzzy TOPSIS, fuzzy graphical and fuzzy digraph and matrix approach were used for decision making. The results from analysis were compared for better understanding and effective maintenance strategy selection. Proactive maintenance approaches of PDM RCM and CBM were identified as best alternative by different methods. Further, limitations due to uncertainty and vague expert judgement were eliminated in analysis by integrating fuzzy methodology. The novelty of study is selection of optimum maintenance policy based on agile maintenance attributes using proposed framework. The proposed framework will act as decision support system for efficient planning of maintenance activities using exhaustive database of agile maintenance attributes and selecting effective maintenance strategy.

  13. Abstract

    This work begins with the understanding of the fundamentals of blood banking by analyzing various aspects of its supply chain and then examines the current scenario of blood shortage in India. A mathematical model is proposed to curb the mismatch between surplus and shortage of blood units at blood banks. This proposed model has three main echelons: forecast the demand of blood units at the blood bank; determine the optimal allocation of units from blood banks with surplus to a blood bank with shortage; select the optimal route for the delivery of the allocations. Further, it has been shown empirically with the previous years’ data that SARIMA model is a very efficient forecasting methodology in blood supply management.

  14. 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.

  15. 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.

  16. Abstract

    Opportunistic maintenance approaches deal with performing group preventive maintenance (PM) on the other units in a series system due to the intervention of any scheduled PM of a component. Simultaneous maintenance actions show better economic performance due to the direct reduction of downtime costs and production losses. However, it is uneconomical to perform maintenance on all units simultaneously. To address this issue, various simulation and optimization approaches including Markov chains, genetic algorithm etc. have been applied in order to achieve optimum solutions in group maintenance models. However, most of these strategies suffer from intractability as the problem size increases. In the present paper, we develop an efficient opportunistic grouping methodology for the multi-unit series system while considering imperfect preventive maintenance. The aim is to obtain an optimum PM interval and grouping of units to minimize the expected total system maintenance cost per unit time during the mission. A recently developed meta-heuristic named ‘Jaya algorithm’ is applied to optimize the objective function. The effectiveness of the proposed approach is examined with three maintenance models: single unit model, mono-group model and the proposed opportunistic group model. Results reveal that the proposed group maintenance model results in 19% cost savings as compared to the mono-group model and 71% compared to the single component maintenance model.

  17. Abstract

    The objective of this study is to evaluate the effect of self and cross channel’s buyback price on demand, optimum selling price and collection rate for different manufacturer-led close loop dual supply chain structures using Stackelberg game approach. This study considers a dual channel made up of an e-tail as well as a conventional retail channel, and both of these channel members are responsible for forward selling and return collecting practices. A linear model is developed for profit maximization under the aim of optimum selling price and collection rate. A backward induction method is used to find out the optimum values of selling price and collection rate as a function of buyback price. Further, a numerical analysis is done to evaluate the effect of self and cross channel’s buyback price on decision parameters (each channel’s demand, optimum selling price and collection rate). The result shows that, the decision parameters are correlated with the buyback price. If a channel member increase the offered buyback price then it will lead to; an increase in demand at self-channel, decline the demand at the cross channel, raise in the optimum selling price at self-channel, and decline in the collection rate at the cross channel. Further, cross channel’s optimum selling price and self-channel’s collection rate is dependent on self-channel’s buyback price, but the change is based on channel structure. In addition, the results help to gauge the effect of buyback on optimum selling price, demand and collection rate. The study assists channel partners to vary the data set values for the prediction of the results and to compare the results without implementation.

  18. Abstract

    We study the sensitivity of some optimality criteria based on progressively type-II right censored order statistics scheme changes and explain how the sensitivity analysis helps to find the optimal censoring schemes. We find that determining an optimal censoring plan among a class of one-step censoring schemes is not always recommended. We consider optimality criteria as the model output of a sensitivity analysis problem and quantify how this model depends on its input factor and censoring scheme, using local and global sensitivity methods. Finally, we propose a simple method to find the optimal scheme among all possible censoring schemes.

  19. Abstract

    In a multi-pass turning process, determination of the optimal values for different machining process parameters has already been identified as a complex optimization problem due to the involvement of numerous real time constraints. In this paper, six metaheuristics, such as artificial bee colony algorithm, ant colony optimization, particle swarm optimization, differential evolution algorithm, firefly algorithm and teaching–learning-based optimization algorithm are implemented to estimate the minimum unit production costs for two different part configurations while fulfilling a given set of machining constraints. It is observed that for both the cases, teaching–learning-based optimization algorithm supersedes the remaining optimization techniques with respect to various predetermined performance measures. Two statistical tests, i.e. paired t test and Wilcoxson signed rank test, also prove the uniqueness of this algorithm as compared to the others.