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743 items in total found

Journal Articles | 2018

Uncertainty Handling in Bilevel Optimization for Robust and Reliable Solutions

Zhichao Lu, Kalyanmoy Deb, and Ankur Sinha

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Uncertainties in variables and parameters cause optimization problems to move away from globally-optimal and uncertain solutions. Practitioners resort to finding robust and reliable solutions in such situations. Bilevel optimization problems involving a hierarchy of two nested optimization problems have received a growing attention in the recent past due to their relevance in practice. While a number of studies on bilevel solution methodologies and applications are available for a deterministic setup, but studies on uncertainties in bilevel optimization are rare. In this paper, we suggest methodologies for handling uncertainty in both lower and upper level variables that may occur from different practicalities. For the first time, we perform a systematic study demonstrating the effect of uncertainties in each level along with the definition of robustness and reliability in the context of bilevel optimization. The issues and complexities introduced due to such uncertainties are then studied through a number of test cases, for brevity, we only show results on three test cases. Finally, two real-world bilevel problems involving uncertainties in their variables are solved. The study provides foundations and demon- strates viable directions for further research in uncertainty-based bilevel optimization problems.

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Journal Articles | 2018

Does plant size matter? Differential effects of FDI on wages and employment?

Shruti Sharma

Asian Development Review: Studies of Asian and Pacific Economic issues

This paper examines the differential effects, based on the size of the plant, of industry-level foreign direct investment (FDI) on plant-level employment and the wages of skilled and unskilled workers in India's manufacturing sector. On average, there are strong positive differential effects of increased inward-level FDI for large plants relative to small and average-sized plants in terms of employment and the average wages of both skilled and unskilled workers. Small plants experience negative effects from inward FDI, which can be explained by intra-industry reallocation of output from smaller to larger plants. After conducting a regional analysis, I find positive spillovers to small plants in Indian states that receive large and persistent flows of FDI. This suggests that a critical mass of FDI is necessary for small plants to experience positive spillover effects.

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Journal Articles | 2018

Transition Experiences in MD–PhD Programs

Devasmita Chakraverty, Donna B. Jeffe, and Robert H. Tai

MD–PhD training takes, on average, 8 years to complete and involves two transitions, an MD-preclinical to PhD-research phase and a PhD-research to MD-clinical phase. There is a paucity of research about MD–PhD students’ experiences during each transition. This study examined transition experiences reported by 48 MD–PhD students who had experienced at least one of these transitions during their training. We purposefully sampled medical schools across the United States to recruit participants. Semistructured interviews were audio-recorded and transcribed for analysis; items focused on academic and social experiences within and outside their programs. Using a phenomenological approach and analytic induction, we examined students’ transition experiences during their MD–PhD programs. Five broad themes emerged centering on multiple needs: mentoring, facilitating integration with students in each phase, integrating the curriculum to foster mastery of skills needed for each phase, awareness of cultural differences between MD and PhD training, and support. None of the respondents attributed their transition experiences to gender or race/ethnicity. Students emphasized the need for mentoring by MD–PhD faculty and better institutional and program supports to mitigate feelings of isolation and help students relearn knowledge for clinical clerkships and ease re-entry into the hospital culture, which differs substantially from the research culture.

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Journal Articles | 2018

Food value chain investments and the small farmer linkage: Indian experience, potential, and policy

Sukhpal Singh

World Food Policy

The agri-food value chains in the developing world are evolving fast due to many changes in policy and practice. In India, modern domestic food supermarkets have been present for more than 15 years now. Furthermore, in late 2012, foreign direct investment in multi-brand retail trade, including food, was permitted up to 51% of equity with other conditions of investment and operations. This paper tries to understand the role of investment (both domestic and foreign) in food/fibre value chains in improving the farmer/producer linkage. It uses empirical evidence from the experience of Indian domestic food retail supermarkets, and (mostly) foreign investment-based wholesale supermarkets in India, to examine the role such investments can play. It specifically examines the role and implications of investments in supermarkets for farmer income improvement, from a value chain perspective. It also explores various mechanisms which could be used to leverage the presence of such investments in food supermarkets and analyses the role of policy and regulation to promote/protect the small producer interests in food markets.

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Journal Articles | 2018

Mining top-k high utility itemsets with effective threshold raising strategies

Srikumar Krishnamoorthy

Expert Systems With Applications

Top-K High Utility Itemset (HUI) mining problem offers greater flexibility to a decision maker in specifying her/his notion of item utility and the desired number of patterns. It obviates the need for a decision maker to determine an appropriate minimum utility threshold value using a trial-and-error process. The top-k HUI mining problem, however, is more challenging and requires use of effective threshold raising strategies. Several threshold raising strategies have been proposed in the literature to improve the overall efficiency of mining top-k HUIs. This paper advances the state-of-the-art and presents a new Top-K HUI method (THUI). A novel Leaf Itemset Utility (LIU) structure and a threshold raising strategy is proposed to significantly improve the efficiency of mining top-k HUIs. A new utility lower bound estimation method is also introduced to quickly raise the minimum utility threshold value. The proposed THUI method is experimentally evaluated on several benchmark datasets and compared against two state-of-the-art methods. Our experimental results reveal that the proposed THUI method offers one to three orders of magnitude runtime performance improvement over other related methods in the literature, especially on large, dense and long average transaction length datasets. In addition, the memory requirements of the proposed method are found to be lower.

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Journal Articles | 2018

Efficiently mining high utility itemsets with negative unit profits

Srikumar Krishnamoorthy

Knowledge-Based Systems

A High Utility Itemset (HUI) mining is an important problem in the data mining literature that considers utilities of items (such as profits and margins) to discover interesting patterns from transactional databases. Several data structures, pruning strategies and algorithms have been proposed in the literature to efficiently mine high utility itemsets. Most of these works, however, do not consider itemsets with negative unit profits that provide greater flexibility to a decision maker to determine profitable itemsets. This paper aims to advance the state-of-the-art and presents a generalized high utility mining (GHUM) method that considers both positive and negative unit profits. The proposed method uses a simplified utility-list data structure for storing itemset information during the mining process. The paper also introduces a novel utility based anti-monotonic property to improve the performance of HUI mining. Furthermore, GHUM adapts key pruning strategies from the basic HUI mining literature and presents new pruning strategies to significantly improve the performance of mining. The proposed method is evaluated on a set of benchmark sparse and dense datasets and compared against a state-of-the-art method. Rigorous experimental evaluation is performed and implications of the key findings are also presented. In general, GHUM was found to deliver more than an order of magnitude improvement at a fraction of the memory over the state-of-the-art FHN method.

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Journal Articles | 2018

Efficient mining of high utility itemsets with multiple minimum utility thresholds

Srikumar Krishnamoorthy

Engineering Applications of Artificial Intelligence

Mining high utility itemsets is considered to be one of the important and challenging problems in the data mining literature. The problem offers greater flexibility to a decision maker in using item utilities such as profits and margins to mine interesting and actionable patterns from databases. Most of the current works in the literature, however, apply a single minimum utility threshold value and fail to consider disparities in item characteristics. This paper proposes an efficient method (MHUI) to mine high utility itemsets with multiple minimum utility threshold values. The presented method generates high utility itemsets in a single phase without an expensive intermediate candidate generation process. It introduces the concept of suffix minimum utility and presents generalized pruning strategies for efficiently mining high utility itemsets. The performance of the algorithm is evaluated against the state-of-the-art methods (HUI-MMU-TE and HIMU-EUCP) on eight benchmark datasets. The experimental results show that the proposed method delivers two to three orders of magnitude execution time improvement over the HUI-MMU-TE method. In addition, MHUI delivers one to two orders of magnitude execution time improvement over the HIMU-EUCP method, especially on moderately long and dense benchmark datasets. The memory requirements of the proposed algorithm was also found to be significantly lower.

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Journal Articles | 2018

Informed trading around earnings announcements Spot, futures, or options?

Sobhesh Kumar Agarwalla, Jayanth R. Varma, and Ajay Pandey

Journal of Futures Markets

Recent literature reports higher single stock options (SSO) volume before earnings announcements (EA). There are no studies that explore single stock futures (SSF) in this context because of illiquid SSF markets in developed countries. Similar to SSO, SSF provide embedded leverage and facilitate short selling although at a lower cost, but do not provide downside-risk protection. India’s liquid SSO and SSF provide a unique setting to study the preference of informed traders. We observe an increase in both SSO and SSF volume before EA. Further, SSF dominate SSO possibly due to SSO becoming expensive before EA and higher information leakage in India.

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Journal Articles | 2018

Qasab: Kutch Craftswomen's Producer Co. Ltd.

Shweta Mittal, Vishal Gupta, and Manoj Motiani

Asian Case Research Journal

This case was prepared by Assistant Professor Shweta Mittal of Institute of Management & Research, Ghaziabad, India, Associate Professor Vishal Gupta of Indian Institute of Management Ahmedabad, India and Assistant Professor Manoj Motiani of Indian Institute of Management Indore, India, as a basis for classroom discussion rather than to illustrate either effective or ineffective handling of an administrative or business situation.

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