Elham Ghashghai

Bio: Elham Ghashghai is an academic researcher from RAND Corporation. The author has contributed to research in topics: Network planning and design & Complete graph. The author has an hindex of 2, co-authored 2 publications receiving 20 citations.

Using Genetic Algorithms to Find Good K-Tree Subgraphs

Abstract: Many combinatorial problems which are (NP) hard on general graphs yield to polynomial algorithms when restricted to k-trees which are graphs that can be reduced to the k-complete graph by repeatedly removing degree k vertices having completely connected neighbors. We present a genetic algorithm which seeks a heuristic optimum solution by generating an evolving population of k-tree subgraphs. Each is evaluated by computing an exact optimum over the subgraph, which provides a feasible solution over the original graph. Then we validate our algorithm by testing it on the task of finding a minimum total cost 3-tree in a complete graph.

Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial

Abstract: Heuristic optimization algorithms seek good feasible solutions to optimization problems in circumstances where the complexities of the problem or the limited time available for solution do not allow exact solution. Although worst case and probabilistic analysis of algorithms have produced insight on some classic models, most of the heuristics developed for large optimization problem must be evaluated empirically—by applying procedures to a collection of specific instances and comparing the observed solution quality and computational burden. This paper focuses on the methodological issues that must be confronted by researchers undertaking such experimental evaluations of heuristics, including experimental design, sources of test instances, measures of algorithmic performance, analysis of results, and presentation in papers and talks. The questions are difficult, and there are no clear right answers. We seek only to highlight the main issues, present alternative ways of addressing them under different circumstances, and caution about pitfalls to avoid.

Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach

Abstract: This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-hfsp.

Scheduling jobs on a k -stage flexible flow-shop

Abstract: We consider the problem of makespan minimization on a flexible flow shop with k stages and ms machines at any stage. We propose a heuristic algorithm based on the identification and exploitation of the bottleneck stage, which is simple to use and to understand by practitioners. A computational experiment is conducted to evaluate the performance of the proposed method. The study shows that our method is, in average, comparable with other bottleneck-based algorithms, but with smaller variance, and that it requires less computational effort.

P-hub protection models for survivable hub network design

Abstract: The design of survivable networks has been a significant issue in network-based infrastructure in transportation, electric power systems, and telecommunications. In telecommunications networks, hubs and backbones are the most critical assets to be protected from any network failure because many network flows use these facilities, resulting in an intensive concentration of flows at these facilities. This paper addresses a series of new hub and spoke network models as survivable network designs, which are termed p-hub protection models (PHPRO). The PHPRO aim to build networks that maximize the total potential interacting traffic over a set of origin–destination nodes based on different routing assumptions, including multiple assignments and back-up hub routes with distance restrictions. Empirical analyses are presented using telecommunication networks in the United States, and the vulnerabilities of networks based on possible disruption scenarios are examined. The results reveal that PROBA, the model with a back-up routing scheme, considerably enhances the network resilience and even the network performance, indicating that the model is a candidate for a strong survivable hub network design. An extension, PROBA-D, also shows that applying a distance restriction can be strategically used for designing back-up hub routes if a network can trade off between network performance and network cost, which results from the reduced length of back-up routings.