The asymmetric vehicle routing problem with simultaneous pickup and deliveries is considered. This paper develops four new classes of valid inequalities for the problem. We generalize the idea of a no-good cut. Together, these help us solve 45-node randomly generated problem instances more efficiently. We report results on a set of benchmark instances in literature. In this set, we are able to show an order of magnitude improvement in computational times over currently published results in literature. © 2019 Elsevier B.V.

In multi attribute decision making (MADM), the choice of a decision-maker (DM) is often the result of an interplay between the DM's unique attitude, the utilities (specific to the DM) for the attributes, and the degree of positive (or negative) interaction among the attributes. Based on this conglomeration of information, we introduce a few choice models to give the probabilities for different possible choices of a DM. More specifically, the conventional multinomial logit (MNL) model is extended to consider the interaction among the attributes, and also the DM's unique attitudinal character. The usefulness of the proposed models is illustrated in a real world application. © 2019

Alarmed by declining enrolment in government schools and potentially adverse academic, administrative and fiscal consequences associated with it, policy makers in India have initiated experimenting with closure of government schools with low enrolments (‘small’ schools), an exercise commonly referred to as ‘school rationalisation’. However, the impact of this policy on access to schooling and learning remains empirically unexplored. Utilising ASER 2014 data, this paper asks three key questions: (a) what are the characteristics of villages in which ‘small’ schools are located?, (b) what options would students have if ‘small schools’ were to be closed, and finally (c) what are the differences in characteristics of ‘small’ and non-‘small’ schools? Results indicate that the villages which have ‘small’ schools are more disadvantaged in terms of essential public services such as all-weather roads leading to village, availability of government health facilities or banks and post offices. Additionally, these villages are less likely to have an alternative to the ‘small’ school, either government or private. Results also show that ‘small schools’ are much more likely to have multi-grade teaching. They are less likely to have basic infrastructural facilities. Interestingly, learning levels are unlikely to be different in ‘small’ schools than non- ‘small’ schools even after controlling for child, household and village attributes. Thus, the analysis suggests that school rationalisation can potentially have severe consequences on children's access to schools without any meaningful impact on learning levels in a ‘business as usual’ scenario. © 2019 Elsevier Ltd

While quota-based and other affirmative actions remain on the policy radar of nations faced with social inequalities, there is limited evidence informing policy choices at the national level. This paper estimates the mid-term impact of quota-based affirmative action in higher education (HE) in India implemented from 2008, which mandates that 27 per cent of seats are to be reserved for the Other Backward Classes (OBC) in public funded institutions of HE. Exploiting the differences in participation across social groups, age cohort,s and geographies with varied histories of affirmative action, our triple difference method estimates the impact of the Act by the year 2011–2012. Our results indicate that southern and northcentral states that already had quotas in place for a fairly long period of time, do not contribute much in further expansion of enrolment of OBCs; instead, the eastern region, where such a policy did not exist for long has about 0.12 points improvement in enrolment. Our estimates are robust to different specifications and the impact seems to be non-existent amongst the richest. It suggests that future policy initiatives need to be more nuanced considering regional differences in policy histories, supply of institutions, and extant rates of HE participation of the disadvantaged sections. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Group testing has been widely used in epidemiology and related fields to estimate prevalence of rare diseases. Parametric binary regression models are used in group testing to estimate the covariate adjusted prevalence. Unlike the standard binary regression model (viz., logistic, complementary log–log, etc.), the regression model for group testing data connects the maximum of a group of independent binary responses to its covariate values. Recently, in group testing literature, it has been extensively used for estimating covariate adjusted prevalence of a disease making the tacit assumptions that (i) the regression model is identifiable, and (ii) the standard asymptotic theory is valid for the maximum likelihood estimators of the regression parameters. Verifying these assumptions is found to be non-trivial theoretical issues. In this paper, we give theoretical proofs of these assumptions under a set of simple sufficient conditions, thus, providing a theoretical justification for likelihood based inference on the regression parameters. We also provide an outline of the proof extending the asymptotic theory to the data obtained by Dorfman retesting, where all subjects in a positive group are retested. © 2019 Elsevier B.V.

Although healthcare is one of the fastest growing sectors in the world, it faces crucial shortages in human resource (HR) availability and retention. This challenge is worsened in social enterprises. In this research, we build on a multimethod and a multistudy approach. In the first study, using an exploratory qualitative study, we identify HR practices that influence HR acquisition and retention. Utilizing an institutional logics lens, we propose that market logic and community logic-driven HR practices influence a firm's ability to acquire and retain HR. In the second study, we test our hypotheses using primary data from 182 faith-based hospitals in India and a robust empirical model accounting for endogeneity. We find that while market logic-driven HR practices help with HR acquisition, community logic-driven HR practices help with HR retention. In the third study, through a simple field experiment, we showcase that, indeed, market and community logic-based HR practices are responsible for HR acquisition and retention. © 2019 Elsevier Inc.

Purpose: Extant literature suggests that post-entry internationalization strategies and performance implications of born globals are an under-researched area. This paper aims to examine the internationalization strategies of born global service firms (BGSFs) and performance implications thereof through strategic group analysis of hand-collected data from Indian information technology (IT) firms. Design/methodology/approach: Strategic group analysis has been used to examine if there are any differences in the strategic decisions across firms and if there are any implications of these strategies on the performance of firms. The strategic group approach helps understand not only the antecedents but also the strategic trade-offs that different groups of BGSFs face after internationalization. The study uses Indian IT industry as the context for analysis of BGSFs, as studies have found that the Indian IT industry to have significant presence of born globals. Findings: Four distinct strategic paths were found to be followed by BGSFs. In addition, the nature of services and ways of mobilization or deployment of resources explain the different internationalization paths and consequently performance. The performance differences primarily stem from the choice of degree of commitment and scope of internationalization. Practical implications: This study provides distinct insights to practice by charting internationalization paths for an emerging born global. Originality/value: This paper contributes to the theory on born globals by using strategic lens to explain the internationalization paths and their performance implications. © 2019, Emerald Publishing Limited.

A cutting-plane approach is developed for the problem of optimally scheduling jobs with arbitrary precedence constraints on unrelated parallel machines to minimize weighted completion time. While the single machine version of this problem has attracted much research efforts, enabling solving problems with up to 100 jobs, not much has been done on the multiple machines case. A novel mixed-integer programming model is presented for the problem with multiple machines. For this model, many classes of valid inequalities that cut off fractional linear programming solutions are developed. This leads to an increase of the linear programming lower bound from 89.3 to 94.6% of the corresponding optimal solution, and a substantial reduction in the computational time of an optimal branch-and-bound algorithm for this problem. This enables us to report optimal solutions for problem instances with up to 25 jobs and 5 machines, which is more than twice the size of problems for which optimal solutions have been reported in the literature thus far. For a special case of the problem—that of minimizing makespan—application of our model helps solve 18 of 27 previously unsolved problem instances to optimality. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Planning and scheduling transportation operations is considered computationally hard and time demanding. We consider a complex network of interconnected links across a set of locations and a set of vehicle movements on these links to fulfil some traffic demands. In such a set-up, basically two types of computational solutions are sought: one is a decision making, if a link (and/or a vehicle) is to be included in a route and second, is computation of a specific time duration during which a link and a vehicle are allocated to fulfil a traffic demand. Such planning efforts are undertaken at a strategic level. At the operational level, the planned schedules are executed. For any unforeseen reasons, if a planned schedule gets disrupted, while en-route, the entire planning efforts turn futile. The situation turns an emergency, as the available time to restore the original schedule is very small and it also knocks down other transport schedules that follow. Using an available approach to reschedule disrupted transportation services, we developed a learning architecture that is capable of learning and creating a knowledge base from the solved instances of disruption resolution. When a new disruption happens, the learner deduces the applicability of any solved instances in the knowledge base to quickly resolve the disruption. If not, the disruption is resolved completely and the resolution is added to the existing knowledge base. The combined abilities of the agents based architecture include monitoring, identification, deduction, decision making, and resolution execution, by learning, storing solution seeds and facilitating quick resolutions. The architecture and the application were developed using JADE Giovanni et al. (2007) toolkit on Ubuntu OS; we verified the deployability of the system on a Windows machine. The agent architecture and empirical results are presented in this paper. Our proposed architecture is faster than traditional methods and novel as an evidence of agent capabilities in solving real-life complex problems and safety-critical domains. © 2018 IEEE.

Sharpe ratio is one of the widely used measures in the financial literature to compare two or more investment strategies. Since it is a ratio of the excess expected return of a portfolio to its standard deviation of returns, it is not robust against the presence of outliers. In this paper we propose a modification of the Sharpe ratio which is based on robust measures of location and scale. We investigate the properties of this proposed ratio under six alternative return distributions. It is seen that the modified Sharpe ratio performs better than the original Sharpe ratio in the presence of outliers. A real life stock market return data set is analyzed and the comparative performances of the two ratios are studied. The results indicate that modified Sharpe ratio may be a better measure for comparing different investment strategies. When downside risk is the only concern of the investors a modification of the Sharpe ratio known as Sortino ratio is often used. It is shown that the Sortino ratio is not robust and we propose a modified version of the same which is robust. © 2019, Indian Statistical Institute.

Achieving fast and inclusive economic growth concurrently with greenhouse gases (GHG) emission control could have wide-ranging implications for the Indian economy, predominantly fuelled by fossil energies. India faces high income inequality with the bottom 50% of its population owning only 2% of total national wealth. Other developmental challenges include 304 million people living in poverty, 269 million without access to electricity, 92 million without access to safe drinking water, and around 2 million homeless. Despite such challenges, India has committed to reduce the GHG emission intensity of its GDP 33–35% below its 2005 level by 2030, including via turning 40% of its power-generation capacity away from fossil sources. To explore the macroeconomic consequences of achieving development along low-carbon pathways, we use a hybrid modelling architecture that combines the strengths of the AIM/Enduse bottom-up model of Indian energy systems and the IMACLIM top-down economy-wide model of India. This hybrid architecture stands upon an original dataset that reconciles national accounting, energy balance and energy price statistics. With this tool, we demonstrate that low-carbon scenarios can accommodate yearly economic growth of 5.8% from 2013 to 2050 i.e. perform close to if not slightly higher than our business-as-usual scenario, despite high investment costs. This result partly stems from improvement of the Indian trade balance via substantial reduction of large fossil fuel imports. Additionally, it is the consequence of significant shifts of sectoral activity and household consumption towards low-carbon products and services of higher value-added. These transitions would require policies to reconcile the conflicting interests of entrenched businesses in retreating sectors like coal and oil, and the emerging low-carbon sectors and technologies such as renewables, smart grids, electric vehicles, modern biomass energy, solar cooking, carbon capture and storage, etc. © 2019 Elsevier Ltd

The Women's Empowerment in Agriculture Index (WEAI) is a direct, multi-dimensional measure of women's access to resources and decision-making in various domains of agriculture. However, several challenges characterize its use: adaptation of questionnaires to local agricultural contexts, modifications to index construction once underlying activities and adequacy thresholds are modified, and sensitivity analysis. In this paper, we address such challenges based on our experience of adapting and using the WEAI across 3600 households in India. In doing so we contribute to the methodological and technical base underlying the index, expand the WEAI evidence base for South Asia, and highlight the importance of tailoring the index to specific agricultural contexts in order to impact public policies in a meaningful way. © 2019 The Authors

Over half of all women of reproductive age are affected by anaemia in India. In this paper we study the role that both household market integration and women’s empowerment in agriculture can play in determining women’s dietary diversity. Our analysis is based on primary data from 3600 households across India on agriculture, nutrition and anthropometric outcomes. We account for market integration by way of per capita household purchases (quantity) of cereals and non- cereal food groups, such as pulses, meat/ fish/ poultry, fruits and vegetables, eggs and dairy. We construct an adapted version of the Abbreviated Women’s Empowerment in Agriculture Index (A-WEAI) that is context- specific and agriculture- oriented. After controlling for individual, household and village- level explanatory factors, we find that – for a given level of per capita market purchases – women who are empowered in their agricultural decisions have significantly higher dietary diversity scores relative to women who are disempowered of such decisions. More specifically it is women’s empowerment in two areas: input in production decisions and membership in self- help groups that supports this result. Women’s empowerment also enhances dietary diversity in the presence of disaggregated per capita purchases of non-cereals such as pulses, meat, dairy and eggs. This highlights the importance of reorienting India’s agricultural price and procurement policies beyond staple grains to ensure better dietary diversity. © 2019, The Author(s).

This paper examines the determinants and impacts of the adoption of five mutually exclusive practices System of Rice Intensification (SRI) on yields and household incomes using a multinomial endogenous treatment effects model. Farm household survey data is collected from selected districts of three States of India. Results suggest that the decision to adopt SRI is a function of experience in terms of age, farm assets, irrigation facility and information about SRI. The analysis showed that small and marginal farmers are more likely to adopt SRI as compared to large farmers. The National Food Security Mission (NFSM) came out to be significant and positive only in the case of few practices in some States. The welfare outcome results showed that the adoption increased the yield and income of three out of four practices-plant plus water, plant plus soil and plant plus water plus soil management. Briefly, the results show that the adoption of SRI especially full adoption of SRI has greater impact on the yield and income. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Ms. Swathi Bhargava, welcome and thank you for agreeing to join us for your internship. As you know, we are a small-scale auto parts manufacturing company but have quite a reputation in the local market. We just secured an order from a well-known automobile equipment manufacturer in the Detroit area. Good times are ahead, and I am quite excited. However, I am also nervous about meeting the new surge in demand. How much more can I get out of my present workforce without overexerting them? Should I be on the lookout for new employees? I seek your help in making sure that we have production plans to ensure deliveries on time. Copyright: © 2019 The Author(s)

SYNOPSIS: This study examines fee premiums earned by Big 4 auditors in India and identifies the primary reason for such fee premiums. There are three primary drivers of Big 4 fee premiums. Big 4 auditors charge a fee premium for their reputation, for providing a superior quality of audit, and for indemnifying losses for a company’s stakeholders. Since the risk of auditor litigation in India is relatively low, Big 4 premiums in India would not be driven by the need for auditors to indemnify losses. The results indicate that Big 4 auditors earn significantly higher fees in India and also that their clients enjoy significantly higher earnings response coefficients compared to non-Big 4 clients. However, there is no difference in the quality of audit provided by Big 4 and non-Big 4 auditors as measured by the magnitude of reported discretionary accruals. © 2019, American Accounting Association. All rights reserved.

Order release policies such as a card-controlled CONWIP policy aim to improve system responsiveness and minimize system-wide inventory levels. It is not clear if order release policies (without card control) can be equally effective under certain settings of the production system design parameters. In this research, we study the performance of alternate order release (material control policies) such as CONWIP and scheduled start time policy under a variety of design parameter settings such as number of stations, station service time characteristics, availability of raw material, and the location of the bottleneck station using queuing network models. Using an exhaustive numerical study, we observe that throughput times in CONWIP systems are about 2–50% less compared to open control systems; however, for a balanced system, the Deterministic scheduled start time policy performs equally well for a large set of production system designs. We further identify that the number of stations and the location of the bottleneck station affect the choice of the order release policy. Additionally, we also analyze the system for a variety of demand inter-arrival times and check its effect on the expected number of backlogs and system-wide expected waiting times. © 2018

In this paper, we explore a popular flexible work arrangement (FWA), home-based telework, in the Indian IT industry. We show how IT managers used the dominant meanings of telework to portray telework as an employee benefit that outweighed the attendant cost—intensified work. While using their discretion to grant telework, the managers drew on this portrayal to orchestrate a negotiated exchange with their subordinates. Consequently, the employees consented to accomplish the intensified work at home in exchange of telework despite their opposition to the intensified work in the office. Thus, whereas the extant studies consider work intensification as an unanticipated outcome of using FWAs, we show how firms may use FWAs strategically to get office-based intensified work accomplished at home. While the dominant argument is that employees reciprocate the opportunity to telework with intensified work, we show a discursively orchestrated negotiation that favors management. A corrective policy measure is to frame telework as an employee right. © 2017, Springer Science+Business Media Dordrecht.

The growth of networks is prevalent in almost every field due to the digital transformation of consumers, business and society at large. The unfolding of community structure in such real-world complex networks is crucial since it aids in gaining strategic insights leading to informed decisions. Moreover, the co-occurrence of disjoint, overlapping and nested community patterns in such networks demands methodologically rigorous community detection algorithms so as to foster cumulative tradition in data and knowledge engineering. In this paper, we introduce an algorithm for overlapping community detection based on granular information of links and concepts of rough set theory. First, neighborhood links around each pair of nodes are utilized to form initial link subsets. Subsequently, constrained linkage upper approximation of the link subsets is computed iteratively until convergence. The upper approximation subsets obtained during each iteration are constrained and merged using the notion of mutual link reciprocity. The experimental results on ten real-world networks and comparative evaluation with state-of-the-art community detection algorithms demonstrate the effectiveness of the proposed algorithm. © 2019 Elsevier B.V.

Cross-border mergers and acquisitions (M&As) allow economic integration across countries and impact industries’ productivity and structure, which has implications for managers, customers, suppliers, and policy-makers. Therefore, it is crucial to examine what drives inbound and outbound M&As more in certain industries compared to other industries. Prior research examined firm-level and country-level factors as antecedents of cross-border M&As, with the firm as the main unit of analysis and subsumed industry effects within country effects. However, not all industry effects—an industry’s capital intensity, for example—can be subsumed under country effects; consequently, the extant empirical findings do not present a holistic view. This study addresses this research gap by segregating industry effects from country effects and examining the impact of an industry’s domestic growth rate, imports growth rate, and capital intensity on the industry’s inbound and outbound M&A activity. Using a dataset of 87 industries from India that spans over a period of 11 years, we find that low capital-intensive industries have higher outbound and inbound M&As in the service sector but low inbound M&As and high outbound M&As in the manufacturing sector. Capital-intensive industries attract inbound M&A only when domestic industry growth and/or imports growth rate is low. These results indicate that foreign multinational-companies prefer to enter India via exports rather than via M&As in capital-intensive industries. This trend conforms to the traditional theories of international business, which do not seem to be completely applicable to outbound M&As from India. Further, an industry’s level of outbound M&A is positively related to the industry’s profitability; however, inbound M&A has no impact on the profitability. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.