Home Menu
icon-menu-close.svg

Faculty & Research

Faculty & Research

Research Productive

Show result

Clear
Search Query :
Area :
Search Query :
3892 items in total found

Journal Articles | 2018

Modelling subjective utility through entropy

Manish Aggarwal

Journal of the Operational Research Society

We introduce a novel entropy framework for the computation of utility on the basis of an agent’s subjective evaluation of the granularised information source values. A concept of evaluating agent as an information gain function of this entropy framework is presented, which takes as its arguments both an information source value and the agent’s evaluation of the same. A method to model the agent’s perceived utility values is proposed. Based on these values, several new measures are designed for the evaluation of the information source values, perceived utilities, and the evaluating agent. A real application is included.

Go to link
Read More

Journal Articles | 2018

Preferences-based learning of multinomial logit model

Manish Aggarwal

Knowledge and Information Systyems

We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to state-of-the-art preference learning methods in terms of the prediction accuracy.

Go to link
Read More

Journal Articles | 2018

Attitudinal Choquet integrals and applications in decision making

Manish Aggarwal

International Journal of Intelligent Systems

The compensation capabilities of Choquet integral are augmented to consider the complex attitudinal character of a decision maker. The resulting operator is termed as attitudinal Choquet integral (ACI). The proposed ACI is further extended as induced ACI. The special cases of ACI are investigated. The usefulness of ACI is shown through a case study.

Go to link
Read More

Journal Articles | 2018

Energy system transitions and macroeconomic assessment of the Indian building sector

Saritha S. Vishwanathan, Panagiotis Fragkos, Kostas Fragkiadakis, Leonidas Paroussos, and Amit Garg

Building Research & Information

India’s energy sector has grown rapidly in recent years with buildings playing a major role as they constitute about 40% of India’s final energy demand. This paper provides a quantitative model-based assessment of the evolution of India’s building sector in terms of both energy systems transition and its macroeconomic implications. The coupling of a bottom-up technology-rich energy system model with a macroeconomic computable general equilibrium (CGE) model provides an innovative approach for the in-depth robust analysis of the energy transition in India’s building stock and the induced macroeconomic and employment impacts on the Indian economy. Two main scenarios are explored, namely: the business-as-usual (BAU) and the advanced nationally determined contribution (Adv. NDC) scenarios. The investigation shows that efficiency improvements are vital to counteract the upward pressure on energy demand in the building sector. Energy demand in the building sector results in an increase of CO2 emissions by 27% between 2015 and 2030 due to the technology transition from inefficient solid fuels (traditional biomass) to cleaner energy (liquefied petroleum gas (LPG), piped natural gas (PNG)) before shifting to electricity. The Adv. NDC scenario also leads to a shift in employment from agriculture and towards sectors that benefit from the implementation of Adv. NDC, especially in the construction sectors, electricity and manufacturing sectors.

Go to link
Read More

Journal Articles | 2018

All aboard the Metro rail? LTMRHL's campaign for stakeholder support

Asha Kaul and Vidhi Chaudhri

Asian Case Research Journal

On March 6, 2015 the Brand Ambassador campaign by L&T Metro Rail Hyderabad Ltd. (LTMRHL) had taken place for a little over two years. Launched in 2013 to bring about awareness and dispel negativity about the Metro Rail project, this campaign had succeeded in securing visibility and garnering support. The corporate communication team was now debating the feasibility of the ongoing campaign and exploring various options. Based on the current review, a decision had to be taken to continue or abandon the campaign post commercial operations scheduled in July 2017.

The campaign was launched in Hyderabad on January 8, 2013 through a press conference. Designed with the purpose of selecting brand ambassadors for the Hyderabad Metro Rail (HMR) project, it targeted the ‘common man’ rather than a celebrity. The choice of a common man was deliberate as the project required support from stakeholders who had become hostile due to varied political and economic reasons. Reaching out to and engaging with these stakeholders in an effort to garner support were the focal points of the campaign. The launch generated excitement and in the first week itself, there was a surge to register for the campaign. However, the intensity staggered post the felicitation ceremony on November 23, 2013. Queries related to the success and sustainability of the campaign were raised by multiple stakeholders.

A two-year review in 2015 revealed that although many of the initial problems had been overcome, and negativity considerably reduced, the campaign had only achieved partial success. Will the current strategy be the gateway to success once commercial operations began, mulled Mr. Sanjay Kapoor, General Manager & Head of Corporate Communications, PR & Advertising Business, LTMRHL.

Go to link
Read More

Journal Articles | 2018

Proactive vs. reactive order-fulfillment resource allocation for sea-based logistics

Seyed Shahab Mofidi, Jennifer A. Pazour, and Debjit Roy

Transportation Research Part E: Logistics and Transportation Review

We study proactive and reactive sea-based order-fulfillment decisions for a set of SKUs. In such systems, a proactive strategy may be more costly than a reactive strategy and variable marginal costs change with respect to an activity profile. We derive the optimal sets of SKUs and their quantities to handle prior (proactive strategy) or after (reactive strategy) demand materializes. Counterintuitive results show the proactive set may not necessarily include the high-demanded SKUs. This work extends the newsvendor model by analyzing negative marginal shortage costs. The model is illustrated with historical data from a sea-based logistics military application.

Go to link
Read More

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.

Go to link
Read More

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.

Go to link
Read More

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.

Go to link
Read More

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.

Go to link
Read More
IIMA