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3721 items in total found

Journal Articles | 2018

Convex preference cone-based approach for many objective optimization problems

Ankur Sarin, Pekka Malo, and Markku Kallio

Computers and operations Research

Many objective optimization problems have turned out to be a considerable challenge for evolutionary algorithms due to the difficulty of finding and visualizing high-dimensional Pareto frontiers. Fortunately, however, the task can be simplified whenever an interaction with a human decision maker is possible. Instead of finding the entire Pareto frontier, the evolutionary search can be guided to the parts of the space that are most relevant for the decision maker. In this paper, we propose an interactive method for solving many objective optimization problems. Drawing on the recent developments in multiple criteria decision making, we introduce an effective strategy for leveraging polyhedral preference cones within an evolutionary algorithm. The approach is mathematically motivated and is applicable to situations, where the user’s preferences can be assumed to follow an unknown quasi-concave and increasing utility function. In addition to considering the preference cones as a tool for eliminating non-preferred solution candidates, we also present how the the cones can be leveraged in approximating the directions of steepest ascent to guide the subsequent search done by the evolutionary algorithm through a proposed merit function. To evaluate the performance of the algorithm, we consider well known test problems as well as a practical facility location problem.

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Journal Articles | 2018

Using Karush-Kuhn-Tucker proximity measure for solving bilevel optimization problems

Ankur Sarin, Tharo Soun, and Kalyanmoy Deb

Swarm and Evolutionary Computation

A common technique to solve bilevel optimization problems is by reducing the problem to a single level and then solving it as a standard optimization problem. A number of single level reduction formulations exist, but one of the most common ways is to replace the lower level optimization problem with its Karush-Kuhn-Tucker (KKT) conditions. Such a reduction strategy has been widely used in the classical optimization as well as the evolutionary computation literature. However, KKT conditions contain a set of non-linear equality constraints that are often found hard to satisfy. In this paper, we discuss a single level reduction of a bilevel problem using recently proposed relaxed KKT conditions. The conditions are relaxed; therefore, approximate, but the error in terms of distance from the true lower level KKT point is bounded. There is a proximity measure associated to the new KKT conditions, which gives an idea of the KKT error and distance from the optimum. We utilize this reduction method within an evolutionary algorithm to solve bilevel optimization problems. The proposed algorithm is compared against a number of recently proposed approaches. The idea is found to lead to significant computational savings, especially, in the lower level function evaluations. The idea is promising and might be useful for further developments on bilevel optimization both in the domain of classical as well as evolutionary optimization research.

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Journal Articles | 2018

Stakeholder orientation and market impact: Evidence from India

Arzi Adbi, Ajay Bhaskarabhatla, and Chirantan Chatterjee

Journal of Business Ethics

This study integrates insights from stakeholder theory and the literature on competitive dynamics and incumbent responses to entry. While research in economics and strategy has examined how market incumbents respond to new entrants, little is known about the heterogeneity in these responses to the entry of a stakeholder-oriented firm; our study addresses this research gap. Findings from a novel, longitudinal dataset of 206 granularly defined pharmaceutical markets in India suggest that stakeholder-oriented firm entry in these markets is associated with an impact on prices and product differentiation with heterogeneous responses from high-end and low-end incumbents. Specifically, entry by a stakeholder-oriented firm results in a reduction in prices and dosage sizes from high-end incumbents, whereas low-end incumbents respond in the opposite direction.

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Journal Articles | 2018

When the big one came: A natural experiment on demand shock and market structure in India's Influenza Vaccine markets

Arzi Adbi, Chirantan Chatterjee, Matej Drev, and Anant Mishra

Production and Operations Management

This study examines the relationship between exogenous demand shock and market structure in India's influenza vaccine markets. Using a novel dataset of detailed purchasing information for vaccines in India, and exploiting the 2009–10 global H1N1 pandemic as an exogenous demand shock, we provide evidence of heterogeneous responses to the shock by domestic and multinational vaccine manufacturers in the influenza vaccine market relative to our control group of all other vaccine markets. We find that such a shock results in a reversal of the market structure for influenza vaccines in India, with a decline in the market share of multinational vaccine manufacturers and significant gains in the market share of domestic vaccine manufacturers. This reversal of the market structure is driven by increased efforts at new product introduction among domestic vaccine manufacturers, the effects of which persist even after the pandemic has ended. Our results remain robust to the use of alternative controls, synthetic control method, coarsened exact matching method, and other relevant estimation methodologies. These results provide new evidence on the role of a pandemic-induced demand shock in the context of an emerging economy by creating differential incentives for domestic and multinational vaccine manufacturers to bring new products to market. We also conduct additional analysis to explore the impact of targeted policy instruments on the new product introduction efforts of domestic vaccine manufacturers. Finally, we discuss the implications of our findings and offer insights into the role of policy on pandemic preparedness in emerging markets facing adverse welfare effects from pandemics.

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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.

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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.

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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.

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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.

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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.

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Journal Articles | 2018

Generalized attitudinal Choquet Integral

Manish Aggarwal

International Journal of Intelligent Systems

Attitudinal Choquet integral (ACI) extends Choquet integral (CI) through a consideration of a decision-maker's (DM's) attitudinal character. In this paper, we generalize ACI, and the resulting operator is termed as generalized ACI (GACI). GACI adds to the efficacy of the ACI in the representation of a DM's unique and complex attitudinal character. It also generates a vast range of exponential ACI operators, such as harmonic ACI, ACI, quadratic ACI, to name a few. We further present induced GACI to consider additional information that may be associated with the arguments of aggregation. The special cases of the proposed operators are investigated. The usefulness of the proposed operators in modelling human decision behavior is shown through a case study.

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