Indian Institute of Management Calcutta

About: Indian Institute of Management Calcutta is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Supply chain & Context (language use). The organization has 415 authors who have published 1354 publications receiving 21725 citations. The organization is also known as: IIMC & IIM Calcutta.

Mother or motherland: Can a government have an impact on educational attainment of the population? Preliminary evidence from India

Abstract: In this paper, using data from the 61st round of the (Indian) National Sample Survey, we examine the relative impacts of personal-household and state-level characteristics (including government policy) on the likelihood of transition from one educational level to the next. Our analysis suggests that the most important factors driving these transition likelihoods are personal and household characteristics like gender and education of household heads. However, state-level characteristics and government policies have a significant impact on these transition likelihoods as well, especially for transitions from the lowest levels of education to somewhat higher levels. The odds of making the transition to higher education, especially tertiary education, are systematically lower for women than for men, for individuals in rural areas than those in urban areas, and for Muslims than for Hindus. An important conclusion of our analysis is that there is significant scope for government policy to address educational gaps between various demographic and other groups in the country.

Governance of Marwari capital: Daily living as a decolonial ‘matrix-of-praxis’ intermeshing commercial, religious and familial spheres:

Abstract: This paper is a narration on practices of unlimited liability Marwari businesses of a textile town in Western India. Although depicted as an ‘outdated’ form of incorporation, these businesses were ...

Stochastic Greedy Algorithms: A Learning-Based Approach to Combinatorial Optimization

Abstract: Research in combinatorial optimization initially focused on finding optimal solutions to various problems. Researchers realized the importance of alternative approaches when faced with large practical problems that took too long to solve optimally and this led to approaches like simulated annealing and genetic algorithms which could not guarantee optimality, but yielded good solutions within a reasonable amount of computing time. In this paper we report on our experiments with stochastic greedy algorithms (SGA) – perturbed versions of standard greedy algorithms. SGA incorporates the novel idea of learning from optimal solutions, inspired by data-mining and other learning approaches. SGA learns some characteristics of optimal solutions and then applies them while generating its solutions. We report results based on applying this approach to three different problems – knapsack, combinatorial auctions and single-machine job sequencing. Overall, the method consistently produces solutions significantly closer to optimal than standard greedy approaches. SGA can be seen in the space of approximate algorithms as falling between the very quick greedy approaches and the relatively slower soft computing approaches like genetic algorithms and simulated annealing. SGA is easy to understand and implement -once a greedy solution approach is known for a problem, it becomes possible to very quickly rig up a SGA for the problem. SGA has explored only one aspect of learning from optimal solutions. We believe that there is a lot of scope for variations on the theme, and the broad idea of learning from optimal solutions opens up possibilities for new streams of research. Keywordsgreedy algorithms; stochastic approaches; approximate solutions; knapsack problem; combinatorial auctions; single-machine scheduling; machine learning

Post deployment planning of 3G cellular networks through dual homing of NodeBs

Abstract: 3G Cellular networks typically consist of a group of NodeBs connected to a Radio Network Controller (RNC), and a group of RNCs to a Serving GPRS Support Node (SGSNs) as well as to a Mobile Switching Centre (MSCs). Post deployment planning of such network is to re-plan the connectivity among the above mentioned network elements with an objective to minimize the cost of operation of the network. This planning problem is traditionally solved under single homing consideration (i.e., one NodeB is homed with only one RNC). However, a single homing solution becomes ineffective when the subscriber distribution changes over time and groups of subscribers begin to show a specific diurnal pattern of their inter-MSC/SGSN mobility. One of the solutions for this problem is dual-homing where some selected NodeBs are connected to two RNCs to reduce the complex handoffs involving two different RNCs as well as two different MSCs/SGSNs. In this paper, we have mapped the dual-homing problem into a search problem and used Simulated Annealing (SA) and Tabu Search (TS) techniques to optimally select the NodeBs and RNCs to be connected. A comparison of the performances of the two meta-heuristic techniques reveals that, though both are efficient enough to produce good solutions, TS is found to be better than SA throughout.