Scatter search heuristic for least-cost design of water distribution networks

The optimization problems of water distribution networks are complex, multi-modal and discrete-variable problems that cannot be easily solved with conventional optimization algorithms. Heuristic algorithms such as genetic algorithms, simulated annealing, tabu search and ant colony optimization have been extensively employed over the last decade. This article proposed an optimization procedure based on the scatter search (SS) framework, which is also a heuristic algorithm, to obtain the least-cost designs of three well-known looped water distribution networks (two-loop, Hanoi and New York networks). The computational results obtained with the three benchmark instances indicate that SS is able to find solutions comparable to those provided by some of the most competitive algorithms published in the literature.     

A scatter search algorithm for stacking sequence optimisation of laminate composites

This paper explores the metaheuristic approach called scatter search for lay-up sequence optimisation of laminate composite panels. Scatter search is an evolutionary method that has recently been found to be promising for solving combinatorial optimisation problems. The scatter search framework is flexible and allows the development of alternative implementations with varying degree of sophistication. The main objective of this paper is to demonstrate the effectiveness of the proposed scatter search algorithm for the combinatorial problem like stacking sequence optimisation of laminate composite panels. Preliminary investigations have been carried out to compare the optimal stacking sequences obtained using scatter search algorithm for buckling load maximisation with the best known published results. Studies indicate that the optimal buckling load factors obtained using the proposed scatter search algorithm found to be either superior or comparable to the best known published results.

Later, two case studies have been considered in this paper. Thermal buckling optimisation of laminated composite plates subjected to temperature rise is considered as the first case study. The results obtained are compared with an exact enumerative study conducted on the problem to demonstrate the effectiveness and performance of the proposed scatter search algorithm. The second case study is optimisation of hybrid laminate composite panels for weight and cost with frequency and buckling constraints. The two objectives are considered individually and also collectively to solve as multi-objective optimisation problem. Finally the computational efficiency of the proposed scatter search algorithm has been investigated by comparing the results with various implementations of genetic algorithm customised for laminate composites. It was shown in this paper through numerical experiments that the scatter search is capable of finding practical solutions for optimal lay-up sequence optimisation of composite laminates and results are comparable and sometimes even superior to genetic algorithms.     

Simulated annealing based parallel genetic algorithm for facility layout problem

The facility layout problem (FLP), a typical combinational optimisation problem, is addressed in this paper by implementing parallel simulated annealing (SA) and genetic algorithms (GAs) based on a coarse-grained model to derive solutions for solving the static FLP with rectangle shape areas. Based on the consideration of minimising the material flow factor cost (MFFC), shape ratio factor (SRF) and area utilisation factor (AUF), a total layout cost (TLC) function is derived by conducting a weighted summation of MFFC, SRF and AUF. The evolution operations (including crossover, mutation, and selection) of GA provide a population-based global search in the space of possible solutions, and the SA algorithm can lead to an efficient local search near the optimal solution. By combing the characteristics of GA and SA, better solutions will be obtained. Moreover, the parallel implementation of simulated annealing based genetic algorithm (SAGA) enables a quick search for the optimal solution. The proposed method is tested by performing a case study simulation and the results confirm its feasibility and superiority to other approaches for solving FLP.     

Genetically assisted optimization of cell layout and material flow path skeleton

A continuous plane manufacturing cell layout and intercell flow path skeleton problem formulation involving rectilinear distances between cell input/output stations is mapped to a genetic search space. Certain properties of such a search space are exploited to design a very efficient method for reduction of a mixed-integer programming problem formulation to an iterative sequence of linear programming problems. This paper reports theoretical and computational insights for efficiently finding good solutions for the above problem formulation, taking advantage of the solution structure and the search stage. The scores of the objective function on a set of test cases indicate better solutions than those previously reported in the literature. The empirical results based on multiple runs also suggest that the method generates final results that are not dependent on the quality of the initial solution; hence the solution search seems to be more global than many of the previous approaches.     

An optimal and a heuristic algorithm for the single-item retrieval problem in puzzle-based storage systems with multiple escorts

Puzzle-based storage systems consist of densely stored unit loads on a square grid. The problem addressed in this paper is to retrieve a stored unit load from a puzzle-based storage using the minimum number of item moves. While previous research contributed optimal algorithms for only up to two empty locations (escorts), our approach solves configurations where multiple empty locations are arbitrarily positioned in the grid. The problem is formulated as a state space problem and solved to optimality using an exact search algorithm. To reduce the search space, we derive bounds on the number of eligible empty locations and develop several search-guiding estimate functions. Furthermore, we present a heuristic variant of the search algorithm to solve larger problem instances. We evaluate both solution algorithms on a large set of problem instances. Our computational results show that the algorithms clearly outperform existing approaches where they are applicate and solve more general configurations, which could not be solved to optimality before. The heuristic variant efficiently yields high-quality solutions for significantly larger instances of practically relevant size.     

Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms and simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.     

Structural aspects of α/β transformation in hot deformed CuZn-39Pb3 alloy

Hot compression tests were performed on commercial CuZn39Pb3 alloy deformed at strain rate of 0.0007 s^–1and 0.02 s^–1within temperature range of 923 K–1023 K. Flow stress vs. deformation temperature and strain rate dependence was found to follow the relation: . Both dynamic recovery and dynamic recrystallization have operated in the presence of fine lead particles being responsible for effective flow stress reduction with increasing deformation temperature. Transformation of - to phase above the temperature approx. 1000 K and the following fast cooling of hot deformed samples produced fine, plate-like structure of -grains, within retained -matrix. Moreover, coherent fine Fe-particles were observed within -grains being not detected at any area of -matrix. The last effect was supposed to result from lower solubility of iron in -phase than that for -phase.     

Heuristics for the generalised assignment problem: simulated annealing and tabu search approaches

The generalised assignment problem (GAP) is the problem of finding a minimum cost assignment of a set of jobs to a set of agents. Each job is assigned to exactly one agent. The total demands of all jobs assigned to any agent can not exceed the total resources available to that agent. A review of exact and heuristic methods is presented. A-generation mechanism is introduced. Different search strategies and parameter settings are investigated for the-generation descent, hybrid simulated annealing/tabu search and tabu search heuristic methods. The developed methods incorporate a number of features that have proven useful for obtaining optimal and near optimal solutions. The effectiveness of our approaches is established by comparing their performance in terms of solution quality and computional requirement to other specialized branch-and-bound tree search, simulated annealing and set partitioning heuristics on a set of standard problems from the literature.     

Electron transport properties of palladium-ruthenium alloys from 50 mK to 4.2 K

The thermopowerS, resistivity , thermal conductivity , and thermoelectric ratioG of alloys nominally of 0.1, 0.5, 1.0, and 5 at % Ru in Pd have been measured from 50 mK to 4 K. The thermopowerS varies essentially linearly withT, and is only a slowly varying function of concentration up to 1 at % Ru. The characteristic thermopower of Ru in Pd for the 0.1 at % alloy is (0.23±0.01) T µ V/K. At temperatures below 0.15 K there are departures from linearity, presumably caused by magnetic impurities. In the same temperature rangeG shows large departures from the expected constant value. The Lorenz ratioL obtained from two independent sets of data,S/GT and /T, are in good agreement. Departures ofL from the Sommerfeld valueL ₀ are discussed.Work supported by the National Science Foundation under Grant number DMR-75-01584.     

Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company

Mixed-model assembly line sequencing is significant in reducing the production time and overall cost of production. To improve production efficiency, a mathematical model aiming simultaneously to minimize overtime, idle time and total set-up costs is developed. To obtain high-quality and stable solutions, an advanced scatter search approach is proposed. In the proposed algorithm, a new diversification generation method based on a genetic algorithm is presented to generate a set of potentially diverse and high-quality initial solutions. Many methods, including reference set update, subset generation, solution combination and improvement methods, are designed to maintain the diversification of populations and to obtain high-quality ideal solutions. The proposed model and algorithm are applied and validated in a case company. The results indicate that the proposed advanced scatter search approach is significant for mixed-model assembly line sequencing in this company.     

Characteristic times of oxygen mass transfer at the liquid metal–vapour interface

The interactions of liquid metals and alloys with the environment mostly depends on the thermodynamic properties of the liquid surface. In fact, the surface tension is strongly influenced by the presence in the surrounding atmosphere of reactive gases through solution, adsorption mechanisms and/or surface reactions. In particular, oxygen, which shows a high surface activity towards a large number of metallic systems, is the most important contaminant of liquid metals and alloys.Theoretical approaches for estimating the oxygen mass transfer at the liquid–vapour interface under inert atmosphere and vacuum have been developed already in order to relate the observed physical properties to the real surface composition data.In the present work a model of the interfacial transport of a liquid metal–oxygen system under Knudsen conditions that foresees the temporal evolution of the interfacial composition is presented. The diffusion characteristic times for reaching steady-state conditions are evaluated in order to define two system sizes depending on the different oxygen transport mechanisms in the liquid phase.An experimental study of the interface evolution is at present under way and preliminary results show a satisfactory agreement with theoretical studies.     

HYBRID MULTIMODAL OPTIMIZATION WITH CLUSTERING GENETIC STRATEGIES

Abstract

Two-stage hybrid multimodal optimization approaches that combine cluster identification techniques in genetic algorithms with sharing and gradient-based local search methods are proposed. The multimodal optimization comprises the use of a sharing function implementation in genetic searches to pursue multiple local optima and subsequent executions of local searches to locate each local optimum when an extreme-containing region is identified. A new cluster identification technique is proposed for automatic and adaptive identification of the locations and sizes of design clusters in genetic algorithms with sharing. The first stage of the hybrid multimodal optimization is to use sharing-enhanced genetic algorithms for the identification of the near-optimum designs inside extreme-containing regions. The second stage simply involves consecutive employment of efficient gradient-based local searches by using the near-optimum designs as initial designs. Two strategies defining the coupling of the genetic search and local searches are proposed. The proposed hybrid optimization strategies are tested in a number of illustrative multimodal optimization problems.     

Use of genetic algorithms to solve production and operations management problems: A review

Operations managers and scholars in their search for fast and good solutions to real-world problems have applied genetic algorithms to many problems. While genetic algorithms are promising tools for problem solving, future research will benefit from a review of the problems that have been solved and the designs of the genetic algorithms used to solve them. This paper provides a review of the use of genetic algorithms to solve operations problems. Reviewed papers are classified according to the problems they solve. The basic design of each genetic algorithm is described, the shortcomings of the current research are discussed and directions for future research are suggested.     

GA BASED SIMULATION OF IMMUNE NETWORKS APPLICATIONS IN STRUCTURAL OPTIMIZATION

Genetic algorithms have received considerable recent attention in the optimal design of structural systems. These algorithms derive a computational leverage from an intrinsic pattern recognition capability, whereby patterns or schemata associated with a high level of fitness are identified and evolved at a near-exponential growth rate through generations of simulated evolution. This highly exploitative search process has been shown to be extremely effective in searching for schema that represent an optimum, requiring only that an appropriate measure of fitness be defined. This exploitative pattern recognition process is also at work in another biological system - the immune system responsible for recognizing antigens foreign to the system and generating antibodies to combat the growth of these antigens. The paper describes key elements of how the functioning of the immune system can be modelled in the context of genetic search. It then provides an overview of the implications of this model in improving the convergence characteristics of genetic search, in particular, in the context of handling design constraints.     

Microstructural development in cast and aged Ni–Al–Cr-based alloys derived from the B2 type β-NiAl structure

The formation of intermetallic compounds consisting of nickel-rich B2-type NiAl (-phase) ductilized by two-phase A1 ()/L12 () regions provides the possibility of combining ductility and high-temperature performance. Similar microstructures can also form the basis of high-temperature shape memory alloys, due to martensitic transformation of the -phase to an L10-type product. One route by which –/ microstructures can be produced involves the use of chromium as a -stabilizer. However, microstructural development in such a case is complicated by the formation of -Cr precipitates.This paper examines microstructural development and stability in cast Ni-25 at % Al-14 at % Cr, Ni-29 at % Al-22 at % Cr and Ni-27 at % Al-8 at % Cr alloys, together with a more complex material, namely, Ni-20 at % Al-13 at % Co-9 at % Cr-4 at % Ti-1 at % Mo-1 at % V. Both the as-cast condition and samples aged at 850 and 1100 °C for 140 h are examined using transmission electron microscopy. The paper discusses the formation of L10 martensite, intradendritic ', interdendritic /' and -Cr precipitation.     

Maximum weight matching and genetic algorithm for fixed-shape facility layout problem

To achieve efficient facility design, the problem of finding an initial slicing tree of a complete graph may influence the layout solutions in many ways. We introduce a maximum weight-matching algorithm to generate the slicing tree, which is used as an initial solution. This initial solution produces modifications to generate alternative layouts for further selection. The system employs a genetic algorithm (GA) as the search engine with a relationship weight function to obtain good solutions. The model is proposed to solve a fixed-shape layout problem. The research has made contributions to two areas. First, it defines a quality function for the clustering technique to generate an initial slicing tree. Second, it designs a process for generating layout alternatives that can take predetermined location constraints and weights based on relationships among facilities into consideration. We compare our initial slicing tree GA with other approaches in the literature. In addition, computational results to demonstrate the performance characteristics of our algorithm are also evaluated.     

基于复合形算子的基础支护桩优化设计智能算法研究

本文通过遗传算法和传统复合形搜索法相结合,基于对遗传算法算子计算结构的调整,并将遗传算法与神经网络相结合,提出并研究了一种新的优化设计方法,协同求解复杂工程中的优化问题。并针对悬臂式支护桩的优化设计的数学模型,采用该算法进行了优化设计分析;计算结果表明,该算法可克服遗传算法最终进化至最优解较慢和人工神经网络易陷入局部解的缺陷,具有较好的全局性和收敛速度。     

Tensile failure mechanisms in carbon fibre reinforced plastics

Tensile failure mechanisms in type I carbon fibre-reinforced epoxy resin have been studied by examining the modes of failure of cured and semi-cured CFRP and of fibre bundle specimens. The rigid matrix in the cured material modified the appearance of the fractured specimen but by detecting the acoustic emission generated during loading the basic fibre bundle behaviour was found to exert a major influence on fracture. Microscopic examination of fractured CFRP specimens has revealed that consecutive fibre failure may be restricted to sub-bundles as a result of shearing between these sub-bundles, and that the material is weakened by a number of internal failures that are not necessarily connected. Ultimate failure seems to be statistically determined and it is a characteristic of the material that some scatter in the strength of CFRP must be expected.     

Particle-swarm harmony search for water network design

The optimal design of water distribution networks is a non-linear, multi-modal, and constrained problem classified as an NP-hard combinatorial problem. Because of the drawbacks of calculus-based algorithms, the problem has been tackled by assorted stochastic algorithms, such as the genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping algorithm, ant colony optimization algorithm, harmony search, cross entropy, and scatter search. This study proposes a modified harmony search algorithm incorporating particle swarm concept. This algorithm was applied to the design of four bench-mark networks (two-loop, Hanoi, Balerma, and New York City networks), with good results.     

A genetic scheduling methodology for virtual cellular manufacturing systems: an industrial application

Effective solutions to the cell formation and the production scheduling problems are vital in the design of virtual cellular manufacturing systems (VCMSs). This paper presents a new mathematical model and a scheduling algorithm based on the techniques of genetic algorithms for solving such problems. The objectives are: (1) to minimize the total materials and components travelling distance incurred in manufacturing the products, and (2) to minimize the sum of the tardiness of all products. The proposed algorithm differs from the canonical genetic algorithms in that the populations of candidate solutions consist of individuals of different age groups, and that each individual's birth and survival rates are governed by predefined aging patterns. The condition governing the birth and survival rates is developed to ensure a stable search process. In addition, Markov Chain analysis is used to investigate the convergence properties of the genetic search process theoretically. The results obtained indicate that if the individual representing the best candidate solution obtained is maintained throughout the search process, the genetic search process converges to the global optimal solution exponentially.

The proposed methodology is applied to design the manufacturing system of a company in China producing component parts for internal combustion engines. The performance of the proposed age-based genetic algorithm is compared with that of the conventional genetic algorithm based on this industrial case. The results show that the methodology proposed in this paper provides a simple, effective and efficient method for solving the manufacturing cell formation and production scheduling problems for VCMSs.