Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Theory and practice of uncertain programming

This paper presents a genetic algorithm (GA) with specialized encoding, initialization, and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the fitness function involves calculating the all-terminal reliability of the network, which is a computationally expensive calculation. Therefore, it is imperative that the search balances the need to thoroughly explore the boundary between feasible and infeasible networks, along with calculating fitness on only the most promising candidate networks. The algorithm results are compared to optimum results found by branch and bound and also to GA results without local search operators on a suite of 79 test problems. This strategy of employing bounds, simple heuristic checks, and problem-specific repair and local search operators can be used on other highly constrained combinatorial applications where numerous fitness calculations are prohibitive.

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Local search genetic algorithm for optimal design of reliable networks

Usually engineers try to achieve the required reliability level with minimal cost. The problem of total investment cost minimization, subject to reliability constraints, is well known as the reliability optimization problem. When applied to multi-state systems (MSS), the system has many performance levels, and reliability is considered as a measure of the ability of the system to meet the demand (required performance). In this case, the outage effect will be essentially different for units with different performance rate. Therefore, the performance of system components, as well as the demand, should be taken into account. In this paper, we present a technique for solving a family of MSS reliability optimization problems, such as structure optimization, optimal expansion, maintenance optimization and optimal multistage modernization. This technique combines a universal generating function (UGF) method used for fast reliability estimation of MSS and a genetic algorithm (GA) used as an optimization engine. The UGF method provides the ability to estimate relatively quickly different MSS reliability indices for series-parallel and bridge structures. It can be applied to MSS with different physical nature of system performance measure. The GA is a robust, universal optimization tool that uses only estimates of solution quality to determine the direction of search. Copyright © 2001 John Wiley & Sons, Ltd.

A new approach to solving problems of multi-state system reliability optimization

Task allocation for maximizing reliability of distributed systems: a simulated annealing approach

The use of computer communication networks has been rapidly increasing in order to: (1) share expensive hardware and software resources, and (2) provide access to main system from distant locations. The reliability and cost of these systems are important and are largely determined by network topology. Network topology consists of nodes and the links between nodes. The selection of optimal network topology is an NP-hard combinatorial problem so that the classical enumeration-based methods grow exponentially with network size. In this study, a heuristic search algorithm inspired by evolutionary methods is presented to solve the all-terminal network design problem when considering cost and reliability. The genetic algorithm heuristic is considerably enhanced over conventional implementations to improve effectiveness and efficiency. This general optimization approach is computationally efficient and highly effective on a large suite of test problems with search spaces up to 2/spl middot/10/sup 90/.

Efficient optimization of all-terminal reliable networks, using an evolutionary approach

This paper explains the development and implementation of a new methodology for expanding existing computer networks. Expansion is achieved by adding new communication links and computer nodes such that reliability measures of the network are optimized within specified constraints. A genetic algorithm-based computer network expansion methodology (GANE) is developed to optimize a specified objective function (reliability measure) under a given set of network constraints. This technique is very powerful because the same approach can be extended to solve different types of problems; the only modification required is the objective function evaluation module. The versatility of the genetic algorithm is illustrated by applying it to solve various network expansion problems (optimize diameter, average distance and computer network reliability for network expansion). The results are compared with the optimal solutions computed using an exhaustive search of complete solution space. The results demonstrate that GANE is very effective (in both accuracy and computation time) and applies to a wide range of problems, but it does not guarantee the optimal results for every problem. >

Genetic-algorithm-based reliability optimization for computer network expansion

A genetic algorithm for distributed system topology design

Genetic algorithm based approach for file allocation on distributed systems

One of the important features of computer networks is the potential for high reliability. The reliability of a network depends on many parameters such as connectivity, degree of each node, and average distance between any pair of nodes. The main focus of the problem considered in this paper is to design reliable computer network topologies. A generalized framework based on Genetic Algorithm is developed which is applicable to wide range of network design problems. Several topology design problems are solved to demonstrate the generality of this solution approach. The results obtained from genetic algorithm based solution approach are compared with the optimal solutions to illustrate the effectiveness of the proposed approach.

Genetic algorithm based approach for designing computer network topology

One of the important features of distributed computing systems (DCSs) is the potential of high reliability. When the hardware configuration of a DCS is fixed, the system reliability mainly depends on the allocation of various resources. One of the important resources used in a DCS are various files. The authors have developed a reliability oriented file allocation scheme for distributed systems. In this scheme various files are allocated to different nodes of a DCS such that the reliability of executing a program which requires files from remote nodes(s) is maximized. Several variations of this problem are solved to illustrate the genetic algorithm based solution approach. The paper also provides the relation between degree of redundancy of files and the maximum achievable reliability of executing a program. >

Rakesh M. Pathak

Papers

Genetic-algorithm-based reliability optimization for computer network expansion

A genetic algorithm for distributed system topology design

Genetic algorithm based approach for file allocation on distributed systems

Genetic algorithm based approach for designing computer network topology

Reliability oriented allocation of files on distributed systems