Faculty & Research

Research Productive

Show result

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

Journal Articles | 2018

Hesitant information sets and application in group decision making

Manish Aggarwal

Applied Soft Computing

The recent information set theory provides a useful mechanism to represent an agent’s perceived information values. However, often a decision-maker (DM) considers multiple evaluations for the same information source value. To this end, we extend the recent information set as hesitant information set (HIS). It gives the multiple perceived information values, corresponding to an information source value. In the context of multi-attribute decision making, HIS represents a set of different possible subjective utilities that an agent may perceive as an evaluation of an alternative-attribute pair. The basic operations, and properties of HIS are investigated. A few information measures based on HIS are presented. Besides many illustrative examples, a real application in group multi attribute decision making problem is included.

Read More

Journal Articles | 2018

Learning attitudinal decision model through pair-wise preferences

Manish Aggarwal

Kybernetes

Purpose

This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets.

Design/methodology/approach

The study includes emerging preference learning algorithms.

Findings

Learning of a DM’s attitudinal choice model.

Originality/value

Preferences-based learning of a DM’s attitudinal decision model.

Read More

Journal Articles | 2018

Learning of a decision-maker's preference zone with an evolutionary approach

Manish Aggarwal

IEEE Transactions on Neural Networks and Learning Systems

A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)'s best solution on a conflicting multiobjective space. Given the exemplary pairwise comparisons of solutions by a DM, we learn an ideal point (for the DM) that is used to evolve toward a better set of solutions. The process is repeated to get the DM's best solution. The comparison of solutions in pairs facilitates the process of eliciting training information for the proposed learning model. Experimental study on standard multiobjective data sets shows that the proposed method accurately identifies a DM's preferred zone in relatively a few generations and with a small number of preferences. Besides, it is found to be robust to inconsistencies in the preference statements. The results obtained are validated through a variant of the established NSGA-2 algorithm.

Read More

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.

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.

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.

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.

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.

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.

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.

Read More
IIMA