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117 results on '"Amit, R K"'

9. Dependent Demand Forecasting Models in Airline Revenue Management: Parametric Estimation Using Simulation

Author

Balaiyan, Kavitha, Amit, R. K., Agarwal, Amit, Krishna Mohan, T. V., Tolk, Andreas, Series Editor, Castro, Rodrigo, Advisory Editor, Lehmann, Axel, Advisory Editor, Robinson, Stewart, Advisory Editor, Szabo, Claudia, Advisory Editor, Traoré, Mamadou Kaba, Advisory Editor, Zeigler, Bernard P., Advisory Editor, Zhang, Lin, Advisory Editor, Karlsruhe Institute of Technology, Sanja, Advisory Editor, Fakhimi, Masoud, editor, and Mustafee, Navonil, editor

Published
2024
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10. Introduction

Author

Amit, R. K., Pawar, Kulwant S., Sundarraj, R. P., Ratchev, Svetan, Amit, R. K., editor, Pawar, Kulwant S., editor, Sundarraj, R. P., editor, and Ratchev, Svetan, editor

Published
2023
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14. Factors affecting the market dynamics of lithium-ion battery for electric mobility: a system dynamics perspective.

Author

Bhanu, Pratap, Krishna Mohan, T V, Amit, R K, and Shankar, Venugopal

Abstract

As an electrical vehicle (EV) power source, a lithium-ion battery (LIB) is essential in enabling electric mobility growth. However, the high LIB cost, evolving LIB electrode chemistry, and EV range anxiety limit LIBs' market growth. In this paper, we present a system dynamics model to analyse the interrelationship between battery capacity (Battery OEMs), battery electrode composition, range anxiety (EV owners), subsidy (Government), and their effect on LIB cost (per kWh) and market demand. Our study shows that range anxiety's impact on EV sales diminishes with average battery capacity increment. Higher LIB subsidy and low raw materials costs (based on battery capacity and LIB electrode composition) will result in higher LIB demand. We observe that LIB demand increases even when no subsidy exists. Our study will help the government and the industry to contextualise the subsidy policy and marketing strategies for LIBs for different consumer segments. [ABSTRACT FROM AUTHOR]

Published
2024
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20. An experimental investigation of newsvendor decisions under ambiguity.

Author

Shinde, Abhishek, Mehta, Peeyush, and Amit, R. K.

Subjects
AMBIGUITY, NEWSVENDOR model, DISTRIBUTION (Probability theory), OPERATIONS management
Abstract

The literature on decision biases in the newsvendor model assumes classical version of the problem where the distribution of random demand is known. This context is decision-making under risk. In many real-life settings, firms are not able to elicit complete and exact information about the demand distribution. This results in decision-making under ambiguity. We examine the newsvendor ordering preferences under ambiguity. Our study is the first attempt in behavioural operations management research to examine the biases in newsvendor decisions under ambiguity. We design experiments to understand the ordering preferences under ambiguity and risk. The experimental results show that subjects deviate from the normative benchmarks. We observe 'pull-to-center' bias in newsvendor decisions under ambiguity. We also observe that subjects exhibit 'asymmetry in ordering'. Both these biases have significant implications for both theory and practice. Our research is a building block for research in a variety of normative models in operations management literature where ambiguity in demand is a highly relevant context for decision-making. [ABSTRACT FROM AUTHOR]

Published
2021
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28. Capital rationing under perfect information

Author

Arya Kumar Srustidhar Chand and Amit R K

Subjects
TheoryofComputation_MISCELLANEOUS, jel:L2, Financial contracts,capital rationing,perfect information, jel:L0
Abstract

In this paper, we discuss a scenario in capital structure where two divisional managers compete for capital from a firm for their projects in a perfect information setting. We consider verifiable profits and study take-it-or-leave-it contracts where the managers ask for capital from the firm privately or sequentially in public and offer a part of the profit to the firm. Under capital constraint, we demonstrate that in private meeting, there is no sub game perfect Nash equilibrium (SPNE) in pure strategies; but in sequential public meeting, SPNE exists in pure strategies and, for the firm it is better to operate under capital constraint to increase the competition among managers.

Published
2015

30. Aumann-Shapley Pricing for Airline Baggage Service.

Author

Bharadwaj, Prabhupad and Amit, R. K.

Subjects
AIRLINE industry, LUGGAGE, GAME theory, COST allocation, COST functions
Abstract

The airline industry derives a substantial portion of its ancillary revenue from baggage fees. However, it has not utilized a controlled and structured approach to baggage pricing. Airlines have been using different pricing strategies, such as flat rate pricing or weight-based pricing, without considering the actual cost allocation for providing those services, resulting in overcharging or undercharging scenarios. This study discusses the application of non-atomic game theory in pricing strategy for a joint cost setting in passenger baggage transportation. Cost allocation-based pricing can provide a "fair" approach to pricing these services and will be widely accepted by the passengers. Aumann-Shapley price is an application of non-atomic game theory that involves finding fair prices for divisible goods where each infinitesimally divisible good is considered a separate player in the game. We demonstrate that this pricing approach for baggage services will be helpful in generating prices based on the weight and volume consumption of a passenger, which are the players in the continuum for this game. The concept of "Chargeable-capacity" is adopted in our research to postulate axioms that will generate prices that are cost-covering, positive, and aggregation invariant. Although the prices are calculated only on a portion of the total capacity, they satisfy the budget-balance requirement by charging a penalty to the capacity consumptions beyond the free limit. Pricing functions can be considered linear functionals and satisfy the absolute continuity property to represent an integrable function. This idea is used to prove that the pricing mechanism proposed by us satisfies all the pricing axioms. To differentiate between the prices of pre-booked and on-site baggage, we extend the axioms further to provide an Advance-purchase discount and ensure the pre-booked excess baggage is charged lower as compared to the on-spot bookings. Various cost functions are examined to support our claims and establish the robustness of the pricing mechanism. We also compare the weighted value prices with the Ramsey-Boeitux rule to establish the relationship between the weights and the price elasticity of demand for pre-booked and on-spot baggage services. It is justifiable to assume that prebooked baggage is more price elastic than spot-booked baggage, as pre-booked baggage is usually purchased well in advance, giving customers more time to compare prices and make informed decisions. This gives us the idea that the weight for pre-booked baggage is less than the spot bookings. Any differentiable joint-cost function satisfies the rules stated in our research, and findings imply that prices will rely on consumption patterns and the proportion of chargeable capacity selected by the airline. The pricing mechanism encompasses all aspects of baggage price and permits airlines to offer variable weight and volume allowances. The proposed method is extendable to comparable cases such as railway and road freight services. [ABSTRACT FROM AUTHOR]

Published
2023

34. Newsvendor Models and Biases under Ambiguity.

Author

Mehta, Peeyush and Amit, R. K.

Subjects
NEWSVENDOR model, STATISTICAL bias, DECISION making, PROBABILITY theory, ECONOMIC demand
Abstract

In this study, we model the classical newsvendor ordering preferences under ambiguity. The extant literature on normative models in the newsvendor setting assumes decision-making under risk, where decision-maker has exact knowledge of the probabilities associated with the outcomes. In several business situations, the demand distribution is often incomplete or unknown. This results in decisionmaking under ambiguous situations. Decision theory recognizes the difference between exact probabilities and more realistic ambiguous probabilities. In his seminal paper, Scarf (1958) develops a max-min approach for the newsvendor with incomplete demand information. In the Scarf model, the newsvendor is assumed to be risk-neutral and ambiguity averse. In the recent experimental literature, it has been observed that the newsvendor behavior is not consistent with the Scarf model, and exhibits pull-to- center bias and other biases. This motivates our research to develop quantitative models under ambiguity to describe the observed biases in the literature. [ABSTRACT FROM AUTHOR]

Published
2019

41. Digital Manufacturing

Author

Singh, Amandeep, Ramkumar, Janakarajan, Vipin, B., Amit, R. K., editor, Pawar, Kulwant S., editor, Sundarraj, R. P., editor, and Ratchev, Svetan, editor

Published
2023
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46. Synthesis and Fatigue Life Optimization of a Spring-Less Compliant Robot Leg Design

Author

Khatavkar, Rohan Vijay, Bhattu, Ajay Pandit, Cavas-Martínez, Francisco, Editorial Board Member, Chaari, Fakher, Series Editor, di Mare, Francesca, Editorial Board Member, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Editorial Board Member, Ivanov, Vitalii, Series Editor, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Pradeep Pratapa, P., editor, Saravana Kumar, G., editor, Ramu, Palaniappan, editor, and Amit, R. K., editor

Published
2023
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47. Differential Evolutionary (DE) Method for Strut Performance Design and Optimization Using Machine Learning-Based Meta-Models Prediction

Author

Shanmugam, Balasubramanian, Bhaire, Arun Shanthkumar, Gopal, Sritharan, Li, Fan, Cavas-Martínez, Francisco, Editorial Board Member, Chaari, Fakher, Series Editor, di Mare, Francesca, Editorial Board Member, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Editorial Board Member, Ivanov, Vitalii, Series Editor, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Pradeep Pratapa, P., editor, Saravana Kumar, G., editor, Ramu, Palaniappan, editor, and Amit, R. K., editor

Published
2023
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48. Parametric Modelling, Analysis and Design of Compact Diffuser for Heat Exchanger

Author

Mittal, Kumud, Saravana Kumar, G., Arul Prakash, K., Jayanti, Sreenivas, Vengadesan, S., Cavas-Martínez, Francisco, Editorial Board Member, Chaari, Fakher, Series Editor, di Mare, Francesca, Editorial Board Member, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Editorial Board Member, Ivanov, Vitalii, Series Editor, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Pradeep Pratapa, P., editor, Saravana Kumar, G., editor, Ramu, Palaniappan, editor, and Amit, R. K., editor

Published
2023
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49. A State Estimation, Kalman Filter Auto-tuning and Uncertainty Quantification Framework with Application to Industrial Storage Tank-farms

Author

Potu, Shirish Nanda, Razak, Rihab Abdul, Vadivelu, Senthil K., Cavas-Martínez, Francisco, Editorial Board Member, Chaari, Fakher, Series Editor, di Mare, Francesca, Editorial Board Member, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Editorial Board Member, Ivanov, Vitalii, Series Editor, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Pradeep Pratapa, P., editor, Saravana Kumar, G., editor, Ramu, Palaniappan, editor, and Amit, R. K., editor

Published
2023
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50. DMLS Support Structure Optimization Using Homogenized Model Distortion Prediction

Author

Varun, A., Saravana Kumar, G., Cavas-Martínez, Francisco, Editorial Board Member, Chaari, Fakher, Series Editor, di Mare, Francesca, Editorial Board Member, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Editorial Board Member, Ivanov, Vitalii, Series Editor, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Pradeep Pratapa, P., editor, Saravana Kumar, G., editor, Ramu, Palaniappan, editor, and Amit, R. K., editor

Published
2023
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