Sumanta Basu

Bio: Sumanta Basu is an academic researcher from Indian Institute of Management Calcutta. The author has contributed to research in topics: Tabu search & Travelling salesman problem. The author has an hindex of 7, co-authored 24 publications receiving 203 citations. Previous affiliations of Sumanta Basu include Wipro.

Metaheuristic applications on discrete facility location problems: a survey

Abstract: This paper provides a detailed review of metaheuristic applications on discrete facility location problems. The objective of this paper is to provide a concise summary of solution approaches based on four commonly used metaheuristics: genetic algorithm, tabu search, particle swarm optimization and scatter search for different variants of the discrete facility location problem. Such a concise summary is expected to be useful for researchers interested in any of the major variants of discrete facility location problem as for each metaheuristic the paper provides a comprehensive review of different variants on which this metaheuristic has been applied, and the details of its implementation. Therefore, a research can exploit a method developed for another variant to solve the problem variant at hand. Based on our review of these papers, we also report some interesting observations, identify research gaps and highlight directions for future research.

Tabu Search Implementation on Traveling Salesman Problem and Its Variations: A Literature Survey

Abstract: The Traveling Salesman Problem (TSP) and its allied problems like Vehicle Routing Problem (VRP) are one of the most widely studied problems in combinatorial optimization. It has long been known to be NP-hard and hence research on developing algorithms for the TSP has focused on approximate methods in addition to exact methods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. In this paper, we review the tabu search literature on the TSP and its variations, point out trends in it, and bring out some interesting research gaps in this literature.

Pricing cloud services—the impact of broadband quality ☆

Abstract: In the last few years, adoption of cloud computing has shown a marked increase across the world. Moreover, the smaller markets, viz., Asia-Pacific, Latin America, Middle-East, etc., are expected to grow at more than the average rate for the next few years. While this is good news for cloud service providers, significant obstacles to cloud adoption still remain a major cause of concern, for example, the quality of broadband services. As the quality of broadband services is not uniform across the different geographies, pricing of cloud services must take this non-uniformity into account. This paper provides managerial guidelines for cloud service providers on pricing their offerings. We develop optimal pricing strategies for a typical cloud service provider by modeling the utility of a customer of cloud services as a function of two vectors. The first vector is a set of parameters which contribute positively to the utility of a customer, and the second vector is a set of parameters which have a negative effect on the utility. We explore two pricing plans: usage based and fixed fee plan; determine the conditions under which customers would select one plan over another, and discuss the significance of these conditions for cloud service providers.

A Review of the Tabu Search Literature on Traveling Salesman Problems

Abstract: The Traveling Salesman Problem (TSP) is one of the most widely studied problems in combinatorial optimization. It has long been known to be NP-hard and hence research on developing algorithms for the TSP has focused on approximate methods in addition to exact methods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. In this paper, we review the tabu search literature on the TSP, point out trends in it, and bring out some interesting research gaps in this literature.

Method and system for workflow management of a business process

Abstract: A method and system for workflow management in a business process environment is disclosed. In one embodiment, a method includes determining respective attributes of a plurality of business documents received via an input device of the computing device, and receiving a set of business rules associated with the plurality of business documents. The set of business rules are based on the attributes of the plurality of business documents. The method also includes forming a plurality of queue filters for segregating a set of transactions associated with the plurality of business documents based on the set of business rules, and forming a plurality of queues based on the plurality of queue filters and at least one activity associated with the set of transactions. Further, the method includes generating on a display of the computing device, a list of agents assigned for each of the plurality of queues.

Redesigning Benders Decomposition for Large-Scale Facility Location

Abstract: The uncapacitated facility location (UFL) problem is one of the most famous and most studied problems in the operations research literature. Given a set of potential facility locations and a set of customers, the goal is to find a subset of facility locations to open and to allocate each customer to open facilities so that the facility opening plus customer allocation costs are minimized. In our setting, for each customer the allocation cost is assumed to be a linear or separable convex quadratic function. Motivated by recent UFL applications in business analytics, we revise approaches that work on a projected decision space and hence are intrinsically more scalable for large-scale input data. Our working hypothesis is that many of the exact (decomposition) approaches that were proposed decades ago and discarded soon after need to be redesigned to take advantage of the new hardware and software technologies. To this end, we “thin out” the classical models from the literature and use (generalized) Benders ...

Measuring Eco-inefficiency: A New Frontier Approach

Abstract: Increasing social concerns over the environmental externalities associated with business activities are pushing firms to identify activities that create economic value with less environmental impact and to become more eco-efficient. Over the past two decades, researchers have increasingly used frontier efficiency models to evaluate productive efficiency in the presence of undesirable outputs, such as greenhouse gas emissions. In this paper, we identify critical flaws of existing frontier models and show that under these models eco-inefficient firms can be identified as eco-efficient. We develop a new eco-inefficiency frontier model that rectifies these problems. Our model allows us to calculate, for each firm, an eco-inefficiency score and improvements in outputs necessary to attain eco-efficiency. We demonstrate, through a Monte-Carlo experiment that our eco-inefficiency model provides a more reliable measurement of corporate eco-inefficiency than the existing frontier models. In the simulation experiment we develop a production function of multiple desirable and undesirable outputs that extends the classical Cob-Douglas function of a single output. The multi-output production function allows for greater flexibility in the simulation analysis of frontier models.

Combinatorial Optimization: Comparison of Heuristic Algorithms in Travelling Salesman Problem

Abstract: The Travelling Salesman Problem (TSP) is an NP-hard problem with high number of possible solutions. The complexity increases with the factorial of n nodes in each specific problem. Meta-heuristic algorithms are an optimization algorithm that able to solve TSP problem towards a satisfactory solution. To date, there are many meta-heuristic algorithms introduced in literatures which consist of different philosophies of intensification and diversification. This paper focuses on 6 heuristic algorithms: Nearest Neighbor, Genetic Algorithm, Simulated Annealing, Tabu Search, Ant Colony Optimization and Tree Physiology Optimization. The study in this paper includes comparison of computation, accuracy and convergence.

User Interface for Unified Data Science Platform Including Management of Models, Experiments, Data Sets, Projects, Actions and Features

Abstract: A system and method for providing various user interfaces is disclosed. In one embodiment, the various user interfaces include a series of user interfaces that guide a user through the machine learning process. In one embodiment, the various user interfaces are associated with a unified, project-based data scientist workspace to visually prepare, build, deploy, visualize and manage models, their results and datasets.