Rakesh Gupta

Diffusion tensor fractional anisotropy of the normal-appearing seven segments of the corpus callosum in healthy adults and relapsing-remitting multiple sclerosis patients.

To investigate the utility of whole‐brain diffusion tensor imaging (DTI) in elucidating the pathogenesis of multiple sclerosis (MS) using the normal‐appearing white matter (NAWM) of the corpus callosum (CC) as a marker of occult disease activity.

Assigning cells to switches in cellular networks by incorporating a pricing mechanism into Simulated annealing

Handoff and cabling costs play key roles in the design of cellular telecommunications networks. Efficient homing of cells to switches can have a significant impact on each of these costs. In the presence of capacity constraints at the switches, the problem of assigning cells to switches becomes a difficult one to solve, with all effective solution approaches being based on heuristic techniques. This paper presents a hybrid heuristic which integrates ideas from linear programming into a simulated annealing framework. Extensive computational results are presented comparing the performance of the heuristic with the lower bound obtained from the linear programming relaxation. These results indicate that this price influenced simulated annealing (PISA) procedure is extremely efficient, usually providing solutions with gaps around 1% in less than 1 s.

Development of hybrid genetic algorithms for product line designs

In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operators process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairly large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options.

Survivable Network Design: The State of the Art

In the past few years the telecommunications industry has undergone significant changes. We are rapidly evolving to a state where audio/visual and data traffic is all provided on the same networks. Telecommunication companies are investing billions of dollars in the design and maintenance of telecommunication networks to provide the users with the better quality of service that they have begun to expect. Due to their high capacity, fiber optic cables have become the medium of choice in the deployment of such new networks worldwide. Such high capacities encourage telecommunication providers to create networks that are substantially more sparse than previous copper based networks. Unfortunately, with sparsity comes vulnerability to failure. Given the dependence on the varied services offered by the modern networks, the magnitudes of the investments involved and the costs of disasters it is only logical that researchers look at problems in survivable network design as an interesting research question. There have been a number of papers that have addressed these and other related issues. In this paper we try to classify the area of survivable network design and provide a classification scheme for the same.

Improving Linkage of Web Pages

Organizations maintain informational websites for wired devices. The information content of such websites tends to change slowly with time, so a steady pattern of usage is soon established. User preferences, both at the individual and at the aggregate level, can then be gauged from user access log files. We propose a heuristic scheme based on simulated annealing that makes use of the aggregate user preference data to re-link the pages to improve navigability. This scheme is also applicable to the initial design of websites for wireless devices. Using the aggregate user preference data obtained from a parallel wired website, and given an upper bound on the number of links per page, our methodology links the pages in the wireless website in a manner that is likely to enable the “typical” wireless user to navigate the site efficiently. Later, when a log file for the wireless website becomes available, the same approach can be used to refine the design further.

VisOpt: a visual interactive optimization tool for P-median problems

Abstract In this paper, we describe a visual interactive decision support tool `VisOpt which is designed to solve P-median problems with capacity constraints. Various design features incorporated in VisOpt are also presented and analyzed. We also present a demonstration of the use of VisOpt by a number of human subjects for various problem instances. The quality of solutions obtained by subjects using VisOpt is compared with that obtained from a standard stand-alone heuristic. The visual interactive tool provides encouraging results in this specific problem context.

An investigation of mating and population maintenance strategies in hybrid genetic heuristics for product line designs

This research builds on prior work on developing near optimal solutions to the product line design problems within the conjoint analysis framework. In this research, we investigate and compare different genetic algorithm operators; in particular, we examine systematically the impact of employing alternative population maintenance strategies and mutation techniques within our problem context. Two alternative population maintenance methods, that we term Emigration and Malthusian strategies, are deployed to govern how individual product lines in one generation are carried over to the next generation. We also allow for two different types of reproduction methods termed Equal Opportunity in which the parents to be paired for mating are selected with equal opportunity and a second based on always choosing the best string in the current generation as one of the parents which is referred to as the Queen bee, while the other parent is randomly selected from the set of parent strings. We also look at the impact of integrating the artificial intelligence approach with a traditional optimization approach by seeding the GA with solutions obtained from a Dynamic Programming heuristic proposed by others. A detailed statistical analysis is also carried out to determine the impact of various problem and technique aspects on multiple measures of performance through means of a Monte Carlo simulation study. Our results indicate that such proposed procedures are able to provide multiple good solutions. This provides more flexibility for the decision makers as they now have the opportunity to select from a number of very good product lines. The results obtained using our approaches are encouraging, with statistically significant improvements averaging 5% or more, when compared to the traditional benchmark of the heuristic dynamic programming technique.

A Traffic-Grooming Algorithm for Wavelength-Routed Optical Networks

We consider the problem of grooming in all-optical networks to maximize traffic. We present an integer-programming formulation while constraining the number of optical transceivers at each node, the link load, and the capacity of each lightpath. Based on the structural properties of the problem, we develop a heuristic based on a column-generation technique. The algorithm is easy to implement, requires a modest amount of CPU time, and provides high-quality solutions. To ascertain the quality of solutions obtained by our algorithm, we present an alternative formulation that allows us to develop an upper bound using a Lagrangian-relaxation technique. An extensive computational study is presented.

Topological Design of Centralized Computer Networks

Abstract Topological design of centralized computer-communication networks is an important issue that has been investigated by a large number of researchers. Such networks typically involve a large number of terminals connected by low or medium speed lines to concentrators, which are then connected to a central computing site by high speed lines. This problem is known to be a very difficult one. In general, the design problem involves determining the following: (a) the numbers and locations of concentrators that are to be opened; (b) the topology of connecting user nodes to concentrations sites; and (c) determining the links between open concentrators and the central node. Since the overall design problem belongs to the class of problems that are NP Hard, most prior research has investigated ways in which to disaggregate the problem into several simpler problems that can be solved iteratively until a satisfactory solution is reached. The problem we solve is this research involves multipoint lines between terminals and concentrators, and dedicated lines between concentrators and the central node. This problem therefore combines features of concentrator location problems as well as local access network design problems. We utilize a Lagrangian heuristic designed to efficiently solve this problem with gaps ranging from 2.7 to 10.4% of the lower bound.

Selecting hierarchical facilities in a service-operations environment

Abstract This paper presents and solves a hierarchical model for the location of service facilities and further looks at the operational issues associated with managing such facilities. By the very nature of the demand they serve, service systems require that timely service be readily available to those who need it. We argue that the location–allocation of such facilities often involves several layers of service. When all of a facility’s resources are needed to meet each demand for service, and demand cannot be queued, the need for a backup unit may be required. Effective siting decisions must address both the need for a backup response facility for each demand point and a reasonable limit on each facility’s workload. The paper develops an integer linear programming model for locating facilities offering several layers of service. A Lagrangian relaxation methodology coupled with a heuristic is employed. Results of extensive computational experiments are presented to demonstrate the viability of the approach.