Praveenkumar Reddy

Bio: Praveenkumar Reddy is an academic researcher from VIT University. The author has contributed to research in topics: Approximation algorithm & Neighbourhood (graph theory). The author has an hindex of 2, co-authored 2 publications receiving 17 citations.

Improvement in Hungarian Algorithm for Assignment Problem

Abstract: Hungarian method for assignment problem is generally used in parallel environment for the assignment of job to a processor. If the number of processors and number of jobs are same, then we can assign each processor 1 job with less cost using Hungarian method. If the number of jobs is larger compared to number of processors, then this method does not work (another approach is using dummy processors, but it is not implementable). In this paper, we proposed an alternate approach same as Hungarian method for assignment of more jobs to lesser processors.

Vertex Cover Problem—Revised Approximation Algorithm

Abstract: This paper is aimed to present the solution to vertex cover problem by means of an approximation solution As it is NP complete problem, we can have an approximate time algorithm to solve the vertex cover problem We will modify the algorithm to have an algorithm which can be solved in polynomial time and which will give near to optimum solution It is a simple algorithm which will be based on articulation point Articulation point can be found using the Depth First Search algorithm

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Personality driven task allocation for emotional robot team

Abstract: The task allocation of emotional robots is a new and valuable issue. There are many allocation algorithms in the rational robot but only a few in the emotional robot. Emotional robots are neoteric and meaningful although it is complex. In this paper, we reference to the previous research, propose emotional robot pursuit problem, build a mathematical model of emotional stimulation base on personality in task allocation and use this model propose an emotional robot pursuit task allocation algorithm. Different from other algorithms, our algorithm allocate different personality pursuers through emotional change after stimulation by allocation. The experiments reflect the influence and the positive role of personality in allocation, also show the algorithm reduces the total pursuit time and avoids the worst case scenario. This algorithm not only solves the emotional robot pursuit problem base on personality, but also shorten and stabilize the total pursuit time

A hybrid SCA–VNS meta-heuristic based on Iterated Hungarian algorithm for physicians and medical staff scheduling problem in outpatient department of large hospitals with multiple branches

Abstract: This paper investigates the scheduling problem of physicians and medical staff in outpatient department of large hospitals with multi-branch. The large hospital has several branches and each branch has its own medical staff, while the physicians need to serve in all the branches affiliated to the hospital. In order to improve the working efficiency of physicians, each physician would be equipped with a medical staff during his working hours. The working time of physicians and medical staff have several requirements considering the satisfaction of them. The paper takes into account the demand and the available resources of the hospital, the workload of physicians and medical staff, etc. as the constraints, and the purpose is to minimize the dissatisfaction of physicians, the cost of physicians and the deviation of the frequency of physicians at work in different clinics. Then, a hybrid meta-heuristic algorithm SCA–VNS combining a Sine Cosine Algorithm (SCA) and variable neighborhood search (VNS) based on Iterated Hungarian algorithm, which is incorporated to solve the physicians and medical staff assignment, is proposed to solve this problem. Through computational experiments that available physicians and medical staff scheduling have been generated and perform better than other compared algorithms.

Efficient Algorithms for Social Network Coverage and Reach

Abstract: Social networks, though started as a software tool enabling people to connect with each other, have emerged in recent times as platforms for businesses, individuals and government agencies to conduct a number of activities ranging from marketing to emergency situation management. As a result, a large number of social network analytics tools have been developed for a variety of applications. A snapshot of social networks at any particular time, called a social graph, represents the connectivity of nodes and potentially the flow of information amongst the nodes (or vertices) in the graph. Understanding the flow of information in a social graph plays an important role in social network applications. Two specific problems related to information flow have implications in many social network applications: (a) finding a minimum set of nodes one has to know to recover the whole graph (also known as the vertex cover problem) and (b) determining the minimum set of nodes one required to reach all nodes in the graph within a specific number of hops (we refer this as the vertex reach problem). Finding an optimal solution to these problems is NP-Hard. In this paper, we propose approximation based approaches and show that our approaches outperform existing approaches using both a theoretical analysis and experimental results.