Search

A new encoding method is described for using neural network models to estimate the reliability of telecommunications networks with identical link reliabilities. The precision of the neural network estimate of reliability and the ability of the neural network model are shown to generalize to a variety of network sizes, including the application to three actual large scale communications networks.

This paper introduces a tractable methodology for finding practical aisle structures for a facility for a given flexible bay block layout. The proposed methodology begins with a heuristic to identify candidate horizontal and vertical aisles using user guidance for the general form of the aisles. There then follows an enumeration algorithm that determines the final aisle structure. Using the calculated aisle structure, a non-linear programming model adjusts departmental areas and shapes to accommodate straight aisles. Finally, input/output points are sited using a genetic algorithm. Together, these algorithms specify a reasonable aisle structure and define the material flow through the facility. Two variations of the problem are solved--one with a limit on the total aisle distance and one with a cost per unit aisle length. The effectiveness of the proposed methodology is demonstrated on test problems with 20 and 50 departments. Keywords: Aisle construction, facility layout design, genetic algorithms, aisle, heuristic, 1. Introduction Facilities design has three interrelated components; structure, layout and the determination of a (network) system to support material flow interaction, e.g., material handling systems (Chhajed et al., 1992). [...]

In this paper, the unequal area facility expansion and relayout problem is studied. The facility relayout problem is important since both manufacturing and service entities must modify their layouts over time when their operational characteristics change. A bi-objective approach is proposed to solve the relayout problem for cases of both a fixed facility area and an expanded facility area. Material handling costs and relayout costs are minimized using a tabu search meta-heuristic optimizer. This heuristic randomly alternates the objective function between the two objectives of the problem in each step and, by doing so, eliminates the difficulty of weighting and scaling the two objectives. The approach is flexible in handling various aspects of the problem such as stationary portions of departments (i.e., monuments), addition of new departments, and changes in existing department and facility areas. Computational experiments show that the bi-objective tabu search approach is effective and tractable. The use of the Pareto front of designs is demonstrated by showing a few approaches to analyzing the trade-offs between initial costs (relayout cost) and ongoing expenses (material handling costs). Keywords: Bi-objective unequal-area facility relayout problem, facility expansion, monuments, fixed departments, Pareto front, tabu search, 1. Introduction Most of the literature on facility layout focuses on greenfield design, which is the design of a new facility without the influence or constraint of an existing facility. [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.11.020 Byline: Haluk Yapicioglu (a), Alice E. Smith (a), Gerry Dozier (b) Keywords: Multiple criteria analysis; Semi-obnoxious; Location; Evolutionary computation; Bi-objective PSO Abstract: In this paper, a new model for the semi-obnoxious facility location problem is introduced. The new model is composed of a weighted minisum function to represent the transportation costs and a distance-based piecewise function to represent the obnoxious effects of the facility. A single-objective particle swarm optimizer (PSO) and a bi-objective PSO are devised to solve the problem. Results are compared on a suite of test problems and show that the bi-objective PSO produces a diverse set of non-dominated solutions more efficiently than the single-objective PSO and is competitive with the best results from the literature. Computational complexity analysis estimates only a linear increase in effort with problem size. Author Affiliation: (a) Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA (b) Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA Article History: Received 4 April 2005; Accepted 21 November 2005

A single-objective particle swarm optimization method for solving semi-obnoxious facility location problem is presented.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.08.026 Byline: Sadan Kulturel-Konak (a), Alice E. Smith (b), Bryan A. Norman (c) Keywords: Multi-objective combinatorial optimization; Tabu search Abstract: A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible. Author Affiliation: (a) Management Information Systems, Penn State Berks-Lehigh Valley College, Reading, PA 19610, USA (b) Department of Industrial and Systems Engineering, 207 Dunstan Hall, Auburn University, Auburn, AL 36849, USA (c) Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA Article History: Accepted 13 August 2004

A tabu-search method for solving multi-objective combinatorial optimization problems is presented.

Sensitivity analysis of capital investments can be effectively carried out by employing a metamodel approach and experimental designs. Although polynomial regression metamodels are popular and straightforward, they do not consider spatial relationships among the data. Dual kriging is an estimation technique that allows the incorporation of spatial correlation into the interpolation or estimation process and has been used primarily in geophysical statistical analysis. This article investigates the dual kriging approach as an alternative technique to polynomial regression and artificial neural networks for metamodel analysis of capital investments. It is observed that dual kriging shows potential in performing sensitivity analysis as its accuracy is as good as, or better than, other techniques, and, while model building and interpretation is more complicated than that of polynomial regression, it is significantly more straightforward than that of neural networks. Furthermore, this is the first known work of using kriging in the field of engineering economics; there may be other useful applications such as in cost estimation., INTRODUCTION Sensitivity analysis is crucial in deterministic capital investment evaluation since the estimates of input factors such as the interest rate and amount and timing of cash flows used in [...]

An ant colony meta-heuristic optimization method is used to solve the redundancy allocation problem (RAP). An ant colony optimization algorithm for the RAP is devised and tested on the well-known suite of problems from the literature and it is shown that the ant colony method performs with little variability over problem instance or random number seed.

A tabu search meta-heuristic has been developed and successfully demonstrated to provide solutions to the system reliability optimization problem of redundancy allocation. Tabu search is particularly well-suited to this problem and it offers distinct advantages compared to alternative optimization methods. While there are many forms of the problem, the redundancy allocation problem generally involves the selection of components and redundancy levels to maximize system reliability given various system-level constraints. This is a common and extensively studied problem involving system design, reliability engineering and operations research. It is becoming increasingly important to develop efficient solutions to this reliability optimization problem because many telecommunications (and other) systems are becoming more complex, yet with short development schedules and very stringent reliability requirements. Tabu search can be applied to a more diverse problem domain compared to mathematical programming methods, yet offers the potential of greater efficiency compared to population-based search methodologies, such as genetic algorithms. The tabu search is demonstrated on numerous variations of three different problems and compared to integer programming and genetic algorithm solutions. The results demonstrate the benefits of tabu search for solving this type of problem., 1. Introduction A well-known and complex reliability design problem is the Redundancy Allocation Problem (RAP). Realistic formulations of the RAP are characterized by a large combinatorial search space with multiple [...]

The redundancy-allocation problem associated with maximizing a lower percentile of the system time-to-failure distribution can be solved by an algorithm that uses a genetic algorithm and a bisection search. The genetic algorithm was used to compute the prospective solution-space, while the bisection search was used as a function evaluator. The algorithmic results indicate that risk should be explicitly considered in engineering-design decisions and that measuring product integrity using only the mean time-to-failure is insufficient.

A method is presented for using statistical experiment design and multiple regression in the sensitivity analysis of engineering economy or capital budgeting studies where multiple factors are subject to uncertainty. Rather than an enumeration of all combinations of possible changes, a metamodel of the behavior of the project is constructed. Application of this methodology can lead to more specific and useful understanding of the effects of various factors on the project value than is practical with Monte Carlo simulation. In addition, the metamodel can be combined with simulation to provide a distribution of outcomes upon which the project's overall risk can be assessed. The method is illustrated using two actual case studies - a municipal building project and an industrial equipment project., INTRODUCTION Sensitivity analysis is an important aspect of any engineering economy study. Simply computing traditional project valuation metrics such as net present value (NPV), equivalent annuity (EA), or internal rate [...]

A genetic heuristic search algorithm based on evolutionary methods is proposed for all-terminal network design cost and reliability problems. The proposed algorithm significantly improved effectiveness and efficiency as compared to conventional implementations. The approach was also computationally accurate and highly effective on test problems with search spaces of up to 2.10(super 90).

Cost estimation generally involves predicting labor, material, utilities or other costs over time given a small subset of factual data on 'cost drivers.' Statistical models, usually of the regression form, have assisted with this projection. Artificial neural networks are non-parametric statistical estimators, and thus have potential for use in cost estimation modeling. This research examined the performance, stability and ease of cost estimation modeling using regression versus neural networks to develop cost estimating relationships (CERs). Results show that neural networks have advantages when dealing with data that does not adhere to the generally chosen low order polynomial forms, or data for which there is little a priori knowledge of the appropriate CER to select for regression modeling. However, in cases where an appropriate CER can be identified, regression models have significant advantages in terms of accuracy, variability, model creation and model examination. Both simulated and actual data sets are used for comparison., INTRODUCTION Cost estimation is a fundamental activity of many engineering and business decisions, and normally involves estimating the quantity of labor, materials, utilities, floor space, sales, overhead, time and other [...]

A genetic algorithm (GA) that solves the k-out-of-n:G subsystem redundancy problems for series-parallel systems, and maximizes reliability under the given cost and weight constraints has been developed. The GA is more reliable and effective than previous dynamic, integer, mixed-integer, and nonlinear programming methods. It easily adapts to various diverse design requirements including those with mixing of components and more complex forms of redundancy.

This paper applies genetic optimization with an adaptive penalty function to the shape-constrained unequal-area facility layout problem. We implement a genetic search for unequal-area facility layout, and show how optimal solutions are affected by constraints on permitted department shapes, as specified by a maximum allowable aspect ratio for each department. We show how an adaptive penalty function can be used to find good feasible solutions to even the most highly constrained problems. We describe our genetic encoding, reproduction and mutation operators, and penalty evolution strategy. We provide results from several test problems that demonstrate the robustness of this approach across different problems and parameter settings., 1. Introduction to the unequal-area facility layout problem Facility layout problems (FLP) are a family of optimization problems involving the partition of a planar region of known dimensions (usually rectangular) [...]

This paper describes the formulation, building and validation of an artificial neural network model of the dynamic coefficient of friction (DCOF) as measured by a slip resistance testing device. The model predicts the DCOF as a function of six independent variables over a wide range of conditions. A grouped cross validation method is used to show the consistent performance of the model in predicting the DCOF for new values of the independent variables., 1. Introduction 1.1. Slip resistance and DCOF Slips and falls continue to be a major ergonomic and safety concern in the workplace, accounting for between 10 and 20% of all [...]

Equivalent annuities are often used to compare mutually exclusive alternatives with unequal lives. This comparison assumes identical repetitions until a horizon equal to the least common multiple of the lives. Our paper examines the robustness of the equivalent annual cost (EAC) measure as the identical repetition assumption is violated, considering the following: premature project terminations, subsequent cost changes, differing discount rates, projected salvage values, and the amount and profile of annual costs. The paper concludes with a relative sensitivity analysis that highlights the impact these variables have on potential decisions., INTRODUCTION Analyzing mutually exclusive alternatives with differing lives typically requires (1) estimating the salvage value at a selected horizon or (2) calculating an equivalent annuity [1, 5, 6, 7, 9, [...]

Mitsuo Gen and Runwei Cheng Wiley, 2000, hardcover, $99.00 Genetic Algorithms and Engineering Optimization is a follow on to the authors' successful Genetic Algorithms and Engineering Design, and is published [...]

BRYAN A. NORMAN [1] RIFAT AYKUT ARAPOGLU [1] ALICE E. SMITH [2] The unequal area facility design problem has been studied in the literature generally by considering block layout separate [...]

ALICE E. SMITH [*] This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A [...]

This paper describes a general approach to the optimal design of communications networks when considering both economics and reliability. The approach uses a genetic algorithm to identify the best topology of network arcs to collectively meet cost and network reliability considerations. This approach is distinct because it is highly flexible and can readily solve many versions of the network design problem, including formulations not previously seen in the literature that more closely reflect actual design scenarios. The method is shown to be effective, computationally efficient and flexible on a suite of diverse test problems., 1. Introduction The problem of how to cost effectively design a network so that certain constraints are met and an objective is optimized is relevant in many real world applications [...]

This book contains all the basics of production and operations management in a cleverly constructed narrative. Rather than assuming a normal textbook style or that of a third person author, [...]