Sumanta Basu

A route efficiency analysis using Shannon entropy-based modified DEA method and route characteristics investigation for urban bus transport in India

ABSTRACT In the context of urban bus transport, the efficiency study of the bus routes, the impact of the contextual factors on the efficiency, and environmental performance measurement have not been paid enough attention. For performance evaluation, scholars mostly apply conventional data envelopment analysis (DEA) model whose limitation regarding handling the undesirable parameter limits its usefulness in the presence of undesirable outputs. Here, we propose a Shannon entropy-based output-oriented consolidated DEA model for the holistic performance measurements of the bus routes. Apart from operational and financial performance measurement, we introduce a novel ‘emission metric’ for environmental performance assessment of the bus routes. We show the context-specific implementation of several modified DEA models for measuring the efficiencies of the bus routes and integrate the efficiencies from different models into a single score using Shannon entropy approach. We apply the proposed method on 21 bus routes of Kolkata, an urban city in India. Our method facilitates consistent and more improved score in case of profit-making and loss-making routes, respectively. According to our analysis, route length, route overlapping, number of intermediate stoppages, and cost structure play a crucial role in the performance. We provide several route-specific as well as policy-level recommendations.

Management of Mathematics or Mathematics of Management: Quantitative Methods in Management

This chapter attempts to initiate a discussion on relevance and utility of quantitative subjects in management given the conflicting background of perceived quantitative superiority of management graduates from premier institutions and partial failure of Indian business to adopt quantitative practices effectively to justify its utility in management education. This chapter introduces the readers to the standard format of quantitative courses in a management programme by segregating it into three broad areas: statistics, operations research and operations management. We elaborate on the typical courses offered by each of these areas to showcase their relevance to current management programmes and practice. We also compare the quantitative course offerings in management programmes with those in specific technical programmes in terms of their objectives, pedagogy and content. This comparison helps us in identifying the application focus in quantitative courses essential for managers in analytical domain, i.e. financial sector, data analytics, etc. We extend this discussion by providing an unbiased view about the current status, industry expectation and objective of management graduates while going through quantitative courses. We have highlighted the positives of quantitative orientation on management courses and its influence on current success of management education in India. To identify the ways of improvement in future, we focus on critical yet unaddressed areas by involving multiple stakeholders in the discussion: students, faculty members and industry. Although premier management institutes carry the repute of having students with excellent quantitative ability, unfortunately the industrial scenario in India is not able to recognize full potential of quantitative methodologies and hence fails to exploit the potential. This industry practice motivates management graduates to focus on jobs with a general management or consulting focus leaving the quantitative-focused roles for specific disciplines. We have suggested some initiatives, required from both industry and academia, to bridge this gap and make the quantitative part of management education relevant and useful. We also present our views on extending this effort in the field of management research by creating several industry-focused research groups that may help in bridging the industry academia gap to realize full potential of quantitative methods, tools and techniques.

Efficient preprocessing methods for tabu search: an application on asymmetric travelling salesman problem

Abstract This paper presents efficient methods of combining preprocessing methods and tabu search metaheuristic for solving large instances of the asymmetric travelling salesman problem (ATSP) with a focus on applications which require one to solve repeatedly different instances of ATSP and where for each instance one needs a reasonably good-quality solution quickly. For such applications, we present two hybrid metaheuristics, namely GA-SAG and RGC-SAG that, respectively, use genetic algorithm (GA) and randomized greedy contract (RGC) algorithm as preprocessing mechanisms, to sparsify a dense graph and apply an implementation of tabu search specifically designed for sparse asymmetric graphs (SAG) to further improve the solution quality. Our computational experience shows that both GA-SAG and RGC-SAG clearly outperform the conventional implementation of pure tabu search. Moreover, for benchmark instances, RGC-SAG reaches a solution within 1%–5% of the optimal solution much faster than the best known heuristics on benchmark problem instances. RGC-SAG provides tour values better than those obtained by PATCH or KP heuristic on 50% and 75% of the benchmark instances, respectively. Although the quality of the solutions obtained in Helsgaun or in the paper by doubly rooted stem and cycle ejection chain algorithm is marginally better than RGC-SAG on most of the benchmark instances, RGC-SAG establishes its potential with a significant reduction in computational time.