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On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts  Towards Memetic Algorithms
, 1989
"... Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that ..."
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Cited by 186 (10 self)
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Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could possibly enumerate 10 9 tours per second on a computer it would thus take roughly 10 639 years of computing to establish the optimality of this tour by exhaustive enumeration." This quote shows the real difficulty of a combinatorial optimization problem. The huge number of configurations is the primary difficulty when dealing with one of these problems. The quote belongs to M.W Padberg and M. Grotschel, Chap. 9., "Polyhedral computations", from the book The Traveling Salesman Problem: A Guided tour of Combinatorial Optimization [124]. It is interesting to compare the number of configurations of realworld problems in combinatorial optimization with those large numbers arising in Cosmol...
A Genetic Approach to the Quadratic Assignment Problem
, 1995
"... The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algori ..."
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Cited by 55 (7 self)
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The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algorithm (GA) approach to QAP. Genetic algorithms are a class of randomized parallel search heuristics which emulate biological natural selection on a population of feasible solutions. We present computational results which show that this GA approach finds solutions competitive with those of the best previouslyknown heuristics, and argue that genetic algorithms provide a particularly robust method for QAP and its more complex extensions. 5 A Genetic Approach to the Quadratic Assignment Problem David M. Tate and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh Pittsburgh, PA 15261 4126249837 4126249831 (Fax) 1. Introduction The Quadrat...
An Investigation of Genetic Algorithms for Facility Layout Problems
 University of Edinburgh
, 1995
"... The Facility Layout Problem (FLP) concerns minimising total traffic cost between facilities in a particular location under given conditions including facility size and traffic between each pair of them. Because of its NPcompleteness, many suboptimal methods, which look for reasonably good solutions ..."
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Cited by 5 (0 self)
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The Facility Layout Problem (FLP) concerns minimising total traffic cost between facilities in a particular location under given conditions including facility size and traffic between each pair of them. Because of its NPcompleteness, many suboptimal methods, which look for reasonably good solutions, have been suggested. Although many papers exist which compare the performance of these methods with each other, the work is limited in the following ways: benchmark tests were done only on FLPs consisting of identical facilities; most of the algorithms being compared relied on deterministic approaches. Genetic Algorithms (GAs), which use a stochastic approach, have been used with some success for a number of NPcomplete problems, typically finding good answers but not necessarily the best. However, a range of other approaches, from traditional operations research to simulated annealing, are possible. Moreover, a GA itself can be varied in many ways. So, in this research project, not only t...
Facilities, Locations, Customers: Building Blocks of Location Models. A Survey
, 2001
"... As evidenced by the remarkable diversity of real world applications which have been modeled and solved as location problems, the field studying the optimal location of facilities is a very interdisciplinary and broad research area. The purpose of this paper is to fit the large variety of location mo ..."
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As evidenced by the remarkable diversity of real world applications which have been modeled and solved as location problems, the field studying the optimal location of facilities is a very interdisciplinary and broad research area. The purpose of this paper is to fit the large variety of location models within a general unified framework, which arises from the description of the three buildings blocks of location problems, namely: facilities, customers, and locations. We provide evidences of how a particular problem specification can be stated mathematically as an optimization problem by opportunely combining into a compact and workable model the main features that characterize and relate these three elements.
Optimal Plant Layout Design for Processfocused Systems
"... In this paper we have proposed a semiheuristic optimization algorithm for designing optimal plant layouts in processfocused manufacturing/service facilities. Our proposed algorithm marries the wellknown CRAFT (Computerized Relative Allocation of Facilities Technique) with the Hungarian assignment ..."
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In this paper we have proposed a semiheuristic optimization algorithm for designing optimal plant layouts in processfocused manufacturing/service facilities. Our proposed algorithm marries the wellknown CRAFT (Computerized Relative Allocation of Facilities Technique) with the Hungarian assignment algorithm. Being a semiheuristic search, our algorithm is likely to be more efficient in terms of computer CPU engagement time as it tends to converge on the global optimum faster than the traditional CRAFT algorithm a pure heuristic. We also present a numerical illustration of our algorithm. Key Words Facilities layout planning, load matrix, CRAFT, Hungarian assignment algorithm
[9] B. Kernighan and P. Plauger, The Elements ofProgrammingStyle. New York: McGrawHill, 1978.
"... include algorithms for data storage and retrieval, programming languages, and software engineering. Dr. Comer is a member of the Association for Computing Machinery and Sigma Xi. ..."
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include algorithms for data storage and retrieval, programming languages, and software engineering. Dr. Comer is a member of the Association for Computing Machinery and Sigma Xi.
DESIGN OF A MANUFACTURING FACILITY LAYOUT WITH A CLOSED LOOP CONVEYOR WITH SHORTCUTS USING QUEUEING THEORY AND GENETIC ALGORITHMS
"... Most current manufacturing facility layout problem solution methods aim at minimizing the total distance traveled, the material handling cost, and/or the time spent in the system (based on distance traveled at a specific speed). The methodology proposed in this paper solves the looped layout design ..."
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Most current manufacturing facility layout problem solution methods aim at minimizing the total distance traveled, the material handling cost, and/or the time spent in the system (based on distance traveled at a specific speed). The methodology proposed in this paper solves the looped layout design problem for a looped layout manufacturing facility with a looped conveyor material handling system with shortcuts by using the operational performance metric, i.e. the workinprocess on the conveyor in a manufacturing facility, as the design criterion. 1
www.elsevier.comrlocaterautcon Automated facilities layout: past, present and future
"... This paper reviews the history of automated facility layout, focusing particularly on a set of techniques which optimize a single objective function. Applications of algorithms to a variety of space allocation problems are presented and evaluated. ..."
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This paper reviews the history of automated facility layout, focusing particularly on a set of techniques which optimize a single objective function. Applications of algorithms to a variety of space allocation problems are presented and evaluated.