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Showing papers in "Opsearch in 2018"


Journal ArticleDOI
04 Apr 2018-Opsearch
TL;DR: In this paper, a review of the literature on sustainable freight transportation for perishable food products is presented using a structured approach termed as literature review analytics, which involves bibliometric and network analytics.
Abstract: Sustainable freight transportation (SFT) and cold-chain for perishable food products are the two most prominent areas where research is tremendously growing with increasing number of publications in various reputed journals. The area of SFT for perishable products is still in the nascent stage and not much explored. There are a number of published articles in the cold chain, but only a few have addressed the issue of sustainability. Perishable products require careful handling throughout their supply chain, which often requires reefer vehicles and cold storage facilities. They need to move fast in the supply chain as their longer stay would cause more energy consumption and higher perishability losses leading to increased cost and carbon footprints. The paper analyzes existing literature on this vital problem area and attempts to derive valuable insights using a structured approach termed as literature review analytics, which involves bibliometric and network analytics. This review analyzes the articles published during 1985–2017 using a set of keywords. Some of the key findings of this study unveil (1) research on SFT of perishable items is growing rapidly; (2) among all, Italy followed by the United States is the most contributing country in this research area. Further, network analytics is used to analyze the co-occurrence network for authors and keywords.

37 citations


Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: In this paper, the authors have tried to present a bench-marking framework for ranking the drivers for implementation of green supply chain management (GSCM) in Indian construction industry.
Abstract: Construction industries play a major role in Indian economy and are also a major cause for degradation of the environment. India has felt the need for adopting environmental management practices in the construction industry. Green supply chain management (GSCM) is a tremendous concept which incorporates environmental issues in traditional supply chain management. Many industries have started to use GSCM and it will be appropriate if the Indian construction industry also falls into place. The objective of the present study is to analyze and prioritize the most important drivers for the implementation of GSCM in the Indian construction industry. From the literature review and discussion with experts 27 drivers were identified under 7 categories. This study was carried out among employees of different contractors/developers in the National Capital Region, consisting of New Delhi, Gurgaon and Noida, which is the hub of the construction industry. Most important drivers for the GSCM adoption are ranked with the help of Analytic Hierarchy Process approach through expert judgments. The Government category was identified as the most important category followed by market, supplier, customer, internal drivers and finally environment. This paper has tried to present a bench-marking framework for ranking the drivers for implementation of GSCM. This has been done to make it easier for the Indian construction industries to make decisions on implementing GSCM.

34 citations


Journal ArticleDOI
03 Feb 2018-Opsearch
TL;DR: In this article, the authors present a systematic literature review in the context of bank efficiency and productivity, focusing on the recent developments related to empirical methodological advances and new dimensions added to the evergrowing field of bank performance analysis.
Abstract: The objective of this study is to present a systematic literature review in the context of bank efficiency and productivity. It focuses on the recent developments related to empirical methodological advances and new dimensions added to the ever-growing field of bank performance analysis. Selected research papers were coded in terms of their key objectives and were segregated into 11 themes—Branch, Comparison, Consolidation and Expansion, Deregulation and Regulation, Environment, Input–output, Methodological advances, Non-traditional activities, Risk, Stock performance and Others. The 103 selected studies were further analysed based on efficiency measures, input–output approaches and methodology. While summarising the extant literature on bank efficiency and productivity, the ongoing debate regarding the optimal input output approaches and ideal frontier techniques for bank performance analysis has also been dealt with. The current study also highlights the possible future research avenues in this area.

28 citations


Journal ArticleDOI
11 Jan 2018-Opsearch
TL;DR: This article uses supervised learning techniques to model and select the optimal academic characteristics of students to enhance their placement probability and shows that the proposed hybrid CT–ANN model achieves greater accuracy in predicting students’ placement than conventional supervised learning models.
Abstract: In recent years, business schools face a common problem of selecting quality students for their Master of Business Administration (MBA) programs so that the target placement percentage is achieved. Selecting a wrong student may increase the number of unplaced students. Also, more the number of unplaced students more is the negative impact on the institute’s reputation. Business school authorities would therefore always want to ensure that they admit the right set of students to their MBA program. In this article, we used supervised learning techniques to model and select the optimal academic characteristics of students to enhance their placement probability. We propose a novel hybrid model based on classification tree (CT) and artificial neural network (ANN) which we call as hybrid CT–ANN model, to analyse business school data. A comparative study of various supervised models with our proposed model using different performance measures is also presented. Our finding shows that the proposed hybrid CT–ANN model achieves greater accuracy in predicting students’ placement than conventional supervised learning models.

28 citations


Journal ArticleDOI
13 Jan 2018-Opsearch
TL;DR: A new model of multi-objective capacitated transportation problem (MOCTP) with mixed constraints is formulated and fuzzy goal programming approach has been applied for solving this resultant MOCTP model for obtaining the optimum order quantity.
Abstract: In this paper, we have formulated a new model of multi-objective capacitated transportation problem (MOCTP) with mixed constraints. In this model, some objective functions are linear and some are fractional and are of conflicting in nature with each other. The main objective of this paper is to decide the optimum order of the product quantity which is to be shipped from source to the destination subject to the capacitated restriction on each route. Here the two situations have been discussed for the MOCTP model. In the first situation, we have considered that all the input information of the MOCTP model is exactly known and therefore a fuzzy goal programming approach have been directly used for obtaining the optimum order quantity of the product. While in the second situation the input information of the MOCTP model are uncertain in nature and this uncertainty have been studied and handled by the suitable approaches like trapezoidal fuzzy numbers, multi-choices, and probabilistic random variables respectively. Due to the presence of all these uncertainties and conflicting natures of objectives functions, we cannot solve this MOCTP directly. Therefore firstly we converted all these uncertainties into deterministic forms by using the appropriate transformation techniques. For this, the vagueness in MOCTP defined by trapezoidal fuzzy numbers has been converted into its crisp form by using the ranking function approach. Multichoices in input information parameters have been converted into its exact form by the binary variable transformation technique. Randomness in input information is defined by the Pareto probability distribution, and for conversion into deterministic form chance constrained programming has been used. After doing all these transformations, we have applied fuzzy goal programming approach for solving this resultant MOCTP model for obtaining the optimum order quantity. A case study has been done to illustrate the computational procedure.

24 citations


Journal ArticleDOI
22 Jan 2018-Opsearch
TL;DR: The analytic hierarchy process is applied in fuzzy environment to help the management of any project based company to prioritize in terms of investment and clearly prioritize the projects among the suggested proposals.
Abstract: Strategies in project assortment and prioritization directly influence organizational production and cost-effectiveness. Due to dearth of funding and nominal technology with improper judgments of expert’s, selection of optimal project portfolio can be viewed as a risk based decision making problem considering various risk factors. Recently, fuzzy set theory is applied for modeling real world problems due to failure of traditional models in uncertain environments. This paper deals with project proposal prioritization from a set of project portfolio satisfying a set of criteria evaluated by decision makers. The analytic hierarchy process is applied in fuzzy environment to help the management of any project based company to prioritize in terms of investment. In the decision approach, expert team decides whether to accept or reject a project as per set of criteria and sub-criteria based on diverse project risk levels. A hierarchy among the different projects based on ratings clearly prioritize the projects among the suggested proposals.

23 citations


Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: In this article, the authors investigated and ranked the environmental criteria for the green supplier selection based on expert's judgment in an Indian automobile sector perspective, and the analytical hierarchy process was used to rank the criteria.
Abstract: Adoption of environmental management practices is essential in every industry to satisfy the customers, and environmental regulation policies along with improvement of environmental performance. Environmental practices are not only based on the industry alone, supplier is also one of the partners to improve industries environmental image through supplying environmental friendly products. Selection of a suitable supplier based on environmental criteria is a challenging and difficult task for industries because there are many criteria available for selection of suitable environment friendly suppliers to improve industry environmental image. Industries should know which criterion is the most important for the selection of green supplier. This study investigates and ranks the environmental criteria for the green supplier selection based on expert’s judgment in an Indian automobile sector perspective. The analytical hierarchy process is used to rank the criteria.

19 citations


Journal ArticleDOI
13 Mar 2018-Opsearch
TL;DR: In this paper, the authors presented a production, remanufacture and waste disposal EPQ model under different carbon regulatory mechanisms of a firm that sells new and repaired versions of its product at two different markets and it is assumed that remanufactured items are of poorer quality; i.e., not "as-good-as new".
Abstract: Industries across the world today are looking for cost effective solutions to reduce waste and curb the carbon emission, due to heightened awareness about ecological responsibility coupled with stringent government regulation and legislations, through operational adjustments in manufacturing/remanufacturing and collection (used products) policy. This paper presents a production, remanufacture and waste disposal EPQ model under different carbon regulatory mechanisms of a firm that sells new and repaired versions of its product at two different markets and it is assumed that remanufactured items are of poorer quality; i.e., not ’as-good-as new’. In reality, the return rates of used product from the end customers of both markets are influenced by the purchasing price, and therefore this study suggests that the return flow of used product as an increasing function of purchasing price. We studied the optimization model under two scenarios: (i) no carbon emission policy (ii) carbon emission norms (a. carbon tax; b. strict carbon cap; c. carbon cap and trade). Mathematical modeling followed by numerical analysis is carried out to examine the impact of regulatory policies on optimal decisions and overall emissions. The results indicate that remanufacturing is an effective strategy to decrease carbon emission compared to manufacturing process.

17 citations


Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: A new hybrid approach to evaluate and select the partners (suppliers and 3PRL providers) in sustainable supply chain network is developed by combining Fuzzy Analytic Hierarchy Process (F-AHP), F-PROMETHEE and F-TOPSIS, which is used to rank the partners comparatively.
Abstract: Partner selection is a crucial problem in supply chain management in which it is essential today to integrate sustainability criteria due to regulation, stakeholder pressers and economic interests. Thus, a sustainability-focused evaluation model for partner selection is required in order to improve the overall performance of the supply chain. This paper develops a new hybrid approach to evaluate and select the partners (suppliers and 3PRL providers) in sustainable supply chain network by combining Fuzzy Analytic Hierarchy Process (F-AHP) with Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE), and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS). A set of sustainability criteria for both supplier and 3PRL provider selection is proposed based on extensive literature review and experts’ opinions. F-AHP is used to calculate the priority weight of each criterion. Then, F-PROMETHEE and F-TOPSIS are both used to rank the partners comparatively. The validity and efficacy of the proposed approach is demonstrated through an application for selecting partners in the case of light bulbs recycling which has been strengthened by sensitivity analysis.

16 citations


Journal ArticleDOI
02 Mar 2018-Opsearch
TL;DR: Two display orientations for the items are considered and each orientation can have a different effect on selling the product and the total inventory and inventory of each product in the store can be obtained by considering the third dimension.
Abstract: In this paper, a shelf retail decision making model is proposed and examined the effect of different factors such as vertical and horizontal location, product cross elasticity, the number of items in eye-level, etc. The success of a retailer depends on his/her ability to adapt environmental changes through continuous decision making about how much and which product, should be placed from which horizontal level of which shelf, and with what display orientation. In this paper, shelves are considered 3-dimensionally, and in addition to the length and width of the shelves, the depth of the shelves is also effective in decision making. The height of the shelves has been considered as a variable, and its optimal value would be obtained by solving the proposed mathematical model. Considering the third dimension of shelf makes it possible to use the back space of shelves for the purpose of holding the inventory. In this paper, two display orientations for the items are considered and each orientation can have a different effect on selling the product. Items can be stacked on the shelves, but the note is that it is not possible to stack some of the item. By considering the third dimension, the total inventory and inventory of each product in the store can be obtained. For solving the model, genetic algorithm (GA) is proposed and the Taguchi method is applied for tuning parameter of the GA.

14 citations


Journal ArticleDOI
01 Nov 2018-Opsearch
TL;DR: This paper addressed supply chain network (SCN) as bi-level programming problem in which the primary objective is to determine optimal order allocation of products where the customer’s demands and supply for the products are fuzzy.
Abstract: In this paper we addressed supply chain network (SCN) as bi-level programming problem in which the primary objective is to determine optimal order allocation of products where the customer’s demands and supply for the products are fuzzy. In the proposed SCN model, we suppose that the first level (leader) and second level (follower) operate two separate groups of SCN. The leader, who moves first, determines quantities shipped to retailers, and then, the follower decides his quantities rationally. The leader’s objective is to minimize the total transportation costs, and similarly, the follower’s objective is to minimize the total delivery time of the SCN and at the same time balancing the optimal order allocation from each source, plant, retailer and warehouse respectively. The fuzzy goal programming approach has been used to achieve the highest degree of the membership goals by minimizing the deviational variables so that most satisfactory or the preferred solution for both the levels to be obtained. A numerical example is given to demonstrate the proposed methodology.

Journal ArticleDOI
01 Jun 2018-Opsearch
TL;DR: The steady-state solution of the model is obtained and different measures of effectiveness are derived and the impact of balking probability and retention probability on the total expected profit of the system is studied.
Abstract: This paper presents an analysis of heterogeneous two-server queueing system with feedback, reverse balking, reneging and retention of reneged customers. The steady-state solution of the model is obtained and different measures of effectiveness are derived. Economic analysis of the model is performed studying the impact of balking probability and retention probability on the total expected profit of the system.

Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: This study facilitates a more reasonable investment decisions with four objective decision criteria including Burg’s entropy in the multi objective non linear models with focus is the generation of well diversified portfolios within the optimal allocation.
Abstract: A new non-Shannon fuzzy Mean–Variance–Skewness-entropy model is proposed with stock returns are considered as triangular fuzzy numbers. The fuzzy stock portfolio selection models are presented with credibility theory that maximizes mean and skewness and minimizes portfolio variance and cross-entropy in terms of Burg. With addition of Burg’s entropy in the multi objective non linear models, focus is the generation of well diversified portfolios within the optimal allocation. For an imprecise capital market, this study facilitates a more reasonable investment decisions with four objective decision criteria including Burg’s entropy. Numerical examples with case studies are used to illustrate the entire method which can be efficiently used in practical purposes like national stock exchanges.

Journal ArticleDOI
01 Nov 2018-Opsearch
TL;DR: The aim of this paper is to solve the problem of selecting the best Advanced Manufacturing Technology by Fuzzy TOPSIS method by taking into account the Objective Factor Measurement to rank the alternatives.
Abstract: Modern world is a competitive world. To survive in this world, every industry must achieve competitiveness. So, it has become the most important task for them to select the best Advanced Manufacturing Technology (AMT). The process involves both quantitative and qualitative factors. The aim of this paper is to solve the problem by Fuzzy TOPSIS method. According to the method of TOPSIS, a closeness co-efficient is determined by calculating the distances to both the Fuzzy positive ideal solution (FPIS) and Fuzzy negative ideal solution (FNIS). Then, a Suitability Index (SI) is calculated by taking into account the Objective Factor Measurement (OFM) to rank the alternatives. Finally, a numerical example using triangular fuzzy numbers is shown to highlight the proposed method.

Journal ArticleDOI
01 Dec 2018-Opsearch
TL;DR: In this paper, the effect of the credit period offered by the manufacturer which is functionally depended on the warranty period of the product was analyzed and solution algorithms were developed to solve the derived model.
Abstract: This article considers an integrated inventory model that deals with one manufacturer and one retailer. The manufacturer provides warranty period for the finished product to ensure product reliability. Also, the manufacturer gives the opportunity to take the facility of delay payment to the retailer. The main objective of this article is to analyze the effect of the credit period offered by the manufacturer which is functionally depended on warranty period of the product. Again, the retailer also provides trade credit to the end customers. Thus, because of presence of product warranty and customers’ credit, demand rate of customers has been assumed depending on warranty period and customers’ credit period. In this paper, two policies have been discussed according to the manufacturer’s policy on warranty period such as (1) free repairing for the defective products within the warranty period and (2) no warranty period to be offered. The purpose of this article is to maximize the integrated model optimizing product warranty, customers’ credit period and cycle length. Also, solution algorithms have been developed to solve the derived model. Finally, numerical examples have been carried out to illustrate the feasibility of the model.

Journal ArticleDOI
28 Nov 2018-Opsearch
TL;DR: In this paper, the notion of geodesic E-preinvex function and semi-E-privex functions are introduced on Riemannian manifold and several properties, results and relations are studied within the aforesaid functions.
Abstract: In the present paper, the notion of geodesic E-preinvex function and geodesic semi E-preinvex function are introduced on Riemannian manifold. Moreover, several properties, results and relations are studied within aforesaid functions. An example is also constructed to illustrate the definition of geodesic E-preinvex function. In addition, we have studied the optimality results with the help of geodesic E-preinvex and geodesic semi E-preinvex functions.

Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: In this paper, an inventory model with positive service time and retrial of customers is analyzed and a suitable cost function for the expected total cost is constructed and analyzed numerically and graphically.
Abstract: This paper analyses an inventory model with positive service time and retrial of customers. The customers arrive according to a Markovian Arrival Process with representation ( $$D_{0} ,D_{1}$$ ). The service times are assumed to be of Phase type distribution with representation $$\left( {\eta ,U} \right)$$ . When the inventory level depletes to the reorder point s, the service is given at a reduced rate and the service time distribution has the representation $$\left( {\eta ,\alpha U} \right)$$ , where $$0 < \alpha < 1$$ . An arriving customer, who finds the inventory level zero or the server busy, enters into an orbit of infinite capacity and will retry for service from there. The lead time follows an exponential distribution with rate β. We analyze the system using Matrix Analytic Method. Some important performance measures in the steady state are obtained. A suitable cost function for the expected total cost is constructed and analyzed numerically and graphically.

Journal ArticleDOI
01 Nov 2018-Opsearch
TL;DR: In this paper, the maximum likelihood and consistent estimators of arrival and service parameters are obtained by observing interarrival and service times in the system by considering an M|Er|1 queueing model.
Abstract: By considering an M|Er|1 queueing model, the maximum likelihood and consistent estimators of arrival and service parameters are obtained by observing interarrival and service times in the system. Bayes estimators of the parameters both under squared error loss function (SELF) and entropy loss function along with minimum posterior risk and minimum Bayes risk associated with the estimators under SELF are derived. Further, Bayes estimator, minimum posterior risk and minimum Bayes risk of the expected number of entity arrivals in the system under SELF are obtained. An expression for the Bayes estimator of the expected number of entity arrivals in the system under LINEX loss function is also derived. Simulation study to illustrate the performance of the proposed estimators is carried out.

Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: A heuristic to find the travelling salesman tour (TST) in a connected network based on the minimum spanning tree reduces the complexity of the TST.
Abstract: This paper presents a heuristic to find the travelling salesman tour (TST) in a connected network. The approach first identifies a node and two associated arcs that are desirable for inclusion in the required TST. If we let this node be denoted by $$p$$ and two selected arcs emanating from this node be denoted by $$\left( {p,q} \right)\,{\text{and}}\,\left( {p,k} \right),$$ then we find a path joining the two nodes $$q\,{\text{and}}\, k$$ passing through all the remaining nodes of the given network. A sum of these lengths, i.e. length of the links $$\left( {p,q} \right)\,{\text{and}}\,\left( {p,k} \right)$$ along with the length of the path that joins the nodes $$q\,{\text{and}}\, k$$ passing through all the remaining nodes will result in a feasible TST, hence gives an upper bound on the TST. A simple procedure is outlined to identify: (1) the node $$p$$ , (2) the two corresponding links $$\left( {p,q} \right)\,{\text{and}}\,\left( {p,k} \right),$$ and (3) the path joining the nodes $$q\,{\text{and}}\, k$$ passing through all the remaining nodes. The approach is based on the minimum spanning tree; hence the complexity of the TST is reduced. The network in the present context has been assumed to be a connected network with at least two arcs emanating from each node.

Journal ArticleDOI
01 Jun 2018-Opsearch
TL;DR: The state of the art in sentiment analysis is reviewed, some of the important recent applications of sentiment analysis are summarized, and some more recently proposed techniques to solve a set of problems in specific management domains are discussed.
Abstract: In the past decade, the explosive growth of social media has led to the emergence of a wide variety of information sources that can significantly impact individual level decision making processes. This has resulted in an increasing availability of unstructured textual data and automated evaluation of opinions, attitudes, and emotions has been accepted as an indispensable analytical tool in diverse domains. Consequently, there is a strong need to understand the underlying technical aspects of this emerging new field of analysis. In the current paper we address this need by reviewing the state of the art in sentiment analysis, summarize some of the important recent applications of sentiment analysis and offer future directions for further research. This paper differs from earlier reviews in a number of ways: first, it offers preliminary technical exposition of various techniques following a simple classification scheme so as to help potential future users to develop overall understanding of this rapidly developing field; second, it discusses in greater detail some of the more recently proposed techniques to solve a set of problems in specific management domains; third, it also presents some examples to elucidate how combining sentiment analysis techniques with conventional econometric approaches can help us solve business specific problems. The main goal of this paper is to generate more interest about this interesting new domain among management researchers.

Journal ArticleDOI
16 Mar 2018-Opsearch
TL;DR: The proposed gradient-based neural network approach is affirmed to be stable in the sense of Lyapunov and it is capable for obtaining the optimal solution in solving both NLPPs and BOOPs tasks.
Abstract: A new gradient-based neural network approach is proposed for solving nonlinear programming problems (NLPPs) and bi-objective optimization problems (BOOPs). The most prominent feature of the proposed approach is that it can converge rapidly to the equilibrium point (optimal solution), for an arbitrary initial point. The proposed approach is affirmed to be stable in the sense of Lyapunov and it is capable for obtaining the optimal solution in solving both NLPPs and BOOPs tasks. Further, BOOP is converted into an equivalent optimization problem by the mean of the weighted sum method, where the Pareto optimal solutions are obtained by using different weights. Also the decomposition of parametric space for BOOP is analyzed in details based on the stability set of the first kind. The experiments results also affirmed that the proposed approach is a promising approach and has an effective performance.

Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: In this paper, the authors investigate the transportation problem where the unit cost of transportation, supplies, demands are initially taken as rough variables based on subjective estimation of experts and the model is then transformed to a deterministic linear programming model by minimizing the expected value of the uncertain objective function under the constraints at a certain confidence level.
Abstract: In a transportation problem, the parameters like unit cost of transportation of goods or services from source to destination, supplies from the sources and demands at destinations depend on many factors which may not be deterministic in nature. To deal with the uncertainty of the parameters, random variables and fuzzy variables were used previously. Sometimes, in absence of sufficient sample observations, the uncertain parameters are estimated by the belief degree of the experts. In this paper, the aim is to investigate the transportation problem where the unit cost of transportation, supplies, demands are initially taken as rough variables based on subjective estimation of experts. Further, these rough estimates are suitably approximated as uncertain normal variables and the conceptual uncertain programming model has been developed. The model is then transformed to a deterministic linear programming model by minimizing the expected value of the uncertain objective function under the constraints at certain confidence level.

Journal ArticleDOI
01 Nov 2018-Opsearch
TL;DR: In this paper, an M/M/1 queue with single, multiple working vacations and customers' variant impatient behavior was studied and the transient system size probabilities of this model were derived explicitly in the closed form using continued fraction.
Abstract: This paper studies an M/M/1 queue with single, multiple working vacations and customers’ variant impatient behavior. During working vacations, the arriving customers are served with slower service rate than the service rate of non-vacation period. An arriving customer, during working vacation, finds the system empty and gets his service immediately, does not become impatient. The only customers who are waiting for service, during working vacation, become impatient. The transient system size probabilities of this model are derived explicitly in the closed form using continued fraction. The time-dependent mean and variance are also computed. Numerical examples are provided to visualize the analytical results.

Journal ArticleDOI
28 Nov 2018-Opsearch
TL;DR: In this article, the authors constructed a data envelopment analysis model to analyze countries' performance in the 2016 Summer Olympic games in Rio 2016, and the Olympic success is measured regarding medal ranking of each country.
Abstract: Summer Olympic games in Rio 2016 were the biggest and the most important sport event in 2016. Athletes’ performance at Olympics is always of a high interest and serve as a basis for various parametric and non-parametric analyses. In this article, we construct data envelopment analysis model to analyze countries’ performance in Summer Olympic games in Rio 2016. The traditional model structure is based on GDP-population theory. In this article, we go beyond this traditional model structure and introduce economic active population and corruption factors into the model. Similarly, the Olympic success is measured regarding medal ranking of each country. Nevertheless, we enlarge traditional golden, silver and bronze medals output structure, including medal ranking up to 8th position. This model structure enables us to also measure performance of lower performed countries that are traditionally not ranked in the medal rankings. As a complement to the achieved results, we decompose the results regarding World Bank’s income classification to be able to make conclusion of countries’ performance.

Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: An approach is proposed which finds marketing decision of the fuzzy and rough models using credibility measure of a fuzzy event and trust measure of an rough event correspondingly, without transferring fuzzy/rough objective to any crisp equivalent.
Abstract: In this research work for first time a model has been proposed to find an appropriate business strategy for a seasonal sale/sale item. Here, an EOQ model for an item has been developed with selling price and time dependent demand under retailer promotional effort, where a wholesaler offers a conditional credit period to his/her retailer to boost the demand of the item to clear the end season stock. Here, it is assumed that, wholesaler offers a credit period to his/her retailer depending upon the order quantity. But retailer does not offer any credit to his/her customers. In this paper, the base demand decreases with increase of time and also no credit is available for the customers, so the base demand goes down. On the other hand, to maintain the base demand, the retailer introduced a promotional effort to boost the demand and also the less selling price increases the base demand. Due to the uncertainty of the different costs, related to the inventory control system, the proposed model also developed in imprecise environments i.e., in fuzzy, rough environments. The main purpose of this paper is to find the optimal order quantity for retailer in such a way that the profit is maximized. Under these considerations a particle swarm optimization algorithm is implemented to find the most appropriate business strategy. Here an approach is proposed which finds marketing decision of the fuzzy and rough models using credibility measure of a fuzzy event and trust measure of a rough event correspondingly, without transferring fuzzy/rough objective to any crisp equivalent. The models are illustrated with different numerical examples and some managerial insights are outlined.

Journal ArticleDOI
21 Nov 2018-Opsearch
TL;DR: A non-radial measure of efficiency of DMUs is presented by establishing a multi-period additive DEA model taking into consideration both desirable and undesirable inputs and outputs with positive and non-positive values.
Abstract: The conventional data envelopment analysis (DEA) models make an assumption of non-negativity in the inputs and outputs data of the decision making units (DMUs) under analysis. In this paper, we present a non-radial measure of efficiency of DMUs by establishing a multi-period additive DEA model taking into consideration both desirable and undesirable inputs and outputs with positive and non-positive values. The proposed efficiency model possesses the requisite features of translation invariance and unit independence, obligatory when dealing with negative values in the data set. Furthermore, this paper contributes with the proposal of a multi-period additive super-efficiency DEA model to discriminate the performance of efficient decision making units. Finally, we present an efficiency evaluation 37 countries worldwide on economic parameters to demonstrate the applicability and efficacy of the proposed models.

Journal ArticleDOI
01 Jun 2018-Opsearch
TL;DR: How queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals is explained, also understanding cost factor for getting optimum profit is understood.
Abstract: In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit.

Journal ArticleDOI
01 Jun 2018-Opsearch
TL;DR: The capabilities of Harmony Search (HS) algorithm, a music inspired meta heuristic for solving maximum clique problem, are investigated and it is concluded that former performs better than latter by testing them on all the instances of DIMACS benchmark graphs.
Abstract: The maximum clique problem (MCP) is to determine a complete subgraph (clique) of maximum cardinality in a given graph. MCP is conspicuous for having real world applications and for its potentiality of modeling other combinatorial problems and is one of the most studied NP-hard problems. This paper investigates the capabilities of Harmony Search (HS) algorithm, a music inspired meta heuristic for solving maximum clique problem. We propose and compare two different instantiations of a generic HS algorithm namely Harmony Search for MCP (HS_MCP) and Harmony Search with idiosyncratic harmonies for MCP (HSI_MCP) for this problem. HS_MCP has better exploitation and inferior exploration capabilities than HSI_MCP whereas HSI_MCP has better exploration and inferior exploitation capabilities than HSI_MCP, it has been concluded that former performs better than latter by testing them on all the instances of DIMACS benchmark graphs. HS_MCP has been compared with a recently proposed Harmony search based algorithm for MCP called Binary Harmony search (BHS) and the simulation results show that HS_MCP significantly outperforms BHS in terms of solution quality. The asymptotic time complexity of HS_MCP is \(O(G \times N^3)\) where G is the number of generations and N is the number of nodes in the graph. A glimpse of effectiveness of some state-of-the-art exact algorithms on MCP has also been provided.

Journal ArticleDOI
03 Mar 2018-Opsearch
TL;DR: This problem is transformed to a parametric quadratic programming problem without any non-convex constraint and then by solving the parametric problem via an iterative scheme and updating the parameter in each iteration, the solution of the problem is achieved.
Abstract: In this paper we consider a quadratically constrained quadratic programming problem with convex objective function and many constraints in which only one of them is non-convex. This problem is transformed to a parametric quadratic programming problem without any non-convex constraint and then by solving the parametric problem via an iterative scheme and updating the parameter in each iteration, the solution of the problem is achieved. The convergence of the proposed method is investigated. Numerical examples are given to show the applicability of the new method.

Journal ArticleDOI
01 Mar 2018-Opsearch
TL;DR: This study includes process restoration cost in the inspection/production model to study its effects on the optimal production lot size and the economic inspection lot under the consideration of minimizing the approximated total cost per week.
Abstract: Economic production lot size models with a process inspection delay have been studied over several decades in the literature. When an imperfect process is assumed to have a given probability to run from an in-control state to an out-of-control state each time an item is produced, an inspection schedule is arranged to monitor the process reliability; however, a time delay to obtain the inspection results exists. This study further includes process restoration cost in the inspection/production model to study its effects on the optimal production lot size and the economic inspection lot under the consideration of minimizing the approximated total cost per week. For the case of multiple inspections, when the restoration cost is considered, the resulting the economic inspection lot may depend on the production lot size, especially when the fixed part of the restoration cost is large.