A convex optimisation framework for the unequal-areas facility layout problem

Jankovits, Ibolya; Luo, Chaomin; Anjos, Miguel F.; Vannelli, A.

doi:10.1016/j.ejor.2011.04.013

Cited by 40 publications

(52 citation statements)

References 38 publications

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“…{Zik, i=1, 2,…, n}. Then introduce every chaos variable into its corresponding decision variable by (2).…”

Section: Hybrid Pso-based Genetic Algorithm (Hpso-ga)mentioning

confidence: 99%

A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems

Zhao

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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Layout problems belong to NP(nondeterministic polynomial)-Complete problems theoretically. They are paid more and more attention in recent years and arise in a variety of application fields such as the layout design of spacecraft modules, shipping, vehicle and robots, plant equipments, platforms of marine drilling well. The algorithms based on swarm intelligence are relatively effective to solve this kind of problems. But usually there still exist two main defects of them, i.e. premature convergence and slow convergence rate. To overcome them, a novel improved hybrid PSO-based genetic algorithm (HPSO-GA) is proposed on the basis of parallel genetic algorithms (PGA). In this algorithm, chaos initialization and multi-subpopulation evolution based on improved adaptive crossover and mutation are adopted. And more importantly, in accordance with characteristics of different classes of subpopulations, different modes of PSO update operator are introduced. It aims at making full use of the fast convergence property of particle swarm optimization (PSO). The proposed adjustable arithmetic-progression rankbased selection can prevent the algorithm from premature in the early stage and benefit accelerating convergence in the late stage as well. An example of layout problems shows that HPSO-GA is feasible and effective.

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“…{Zik, i=1, 2,…, n}. Then introduce every chaos variable into its corresponding decision variable by (2).…”

Section: Hybrid Pso-based Genetic Algorithm (Hpso-ga)mentioning

confidence: 99%

A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems

Zhao

et al. 2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

View full text Add to dashboard Cite

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“…Contrastingly, many of the novel heuristics for the FLP take advantage of ideas and machinery from mathematical programming: e.g. van Camp et al (1991); Anjos and Vannelli (2006); Bernardi (2010); Bernardi and Anjos (2013); Jankovits et al (2011); Lin and Hung (2011); Luo et al (2008a,b), and Liu and Meller (2007), albeit in a way that cannot prove optimality. We note in particular the surveys of Meller and Gau (1996) and Singh and Sharma (2006), which collect pointers to much of the heuristic literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Strong mixed-integer formulations for the floor layout problem

Huchette

Dey

Vielma

2017

INFOR: Information Systems and Operational Research

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The floor layout problem (FLP) tasks a designer with positioning a collection of rectangular boxes on a fixed floor in such a way that minimizes total communication costs between the components. While several mixed integer programming (MIP) formulations for this problem have been developed, it remains extremely challenging from a computational perspective. This work takes a systematic approach to constructing MIP formulations and valid inequalities for the FLP that unifies and recovers all known formulations for it. In addition, the approach yields new formulations that can provide a significant computational advantage and can solve previously unsolved instances. While the construction approach focuses on the FLP, it also exemplifies generic formulation techniques that should prove useful for broader classes of problems.

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“…In addition to simplifying methods to reduce problem complexity, there is a continued emphasis on the development of heuristic search strategies for solving the alternative variations of the block layout problem. For example, Jankovits et al (2011) present a convex-optimisation-based approach for block layout problems with side constraints on work centre shapes. A branch and bound algorithm for the block layout problem is also applied in Solimanpur and Jafari (2008), but with retention of the quadratic assignment framework and application of intelligent search procedures.…”

Section: Introductionmentioning

confidence: 99%

On the effectiveness of Monte Carlo simulation and heuristic search for solving large-scale block layout problems

Chan¹,

Malmborg

2013

International Journal of Production Research

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The effectiveness of Monte Carlo simulation relative to intelligent search strategies for solving block layout problems is investigated. For testing purposes, 810 block layout problems are constructed to span a wide range of problem sizes, material flow variation levels, work centre space requirements distributions, and work centre shape distributions. Contrary to preliminary results reported in earlier studies, greedy search and simulated annealing consistently outperform Monte Carlo Simulation across the full range of test problems and sample sizes. This divergence is explained through a comparison based on probabilistic derivations between the proportion of good solutions sampled by the Monte Carlo method and those found by the heuristic search methods. Conditions for the superiority of either method are identified. Therefore, the current study complements earlier studies by providing analytical arguments and additional experimental evidence for the effectiveness of simple Monte Carlo method and intelligent search heuristics on solving layout problems.

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A convex optimisation framework for the unequal-areas facility layout problem

Cited by 40 publications

References 38 publications

A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems

A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems

Strong mixed-integer formulations for the floor layout problem

On the effectiveness of Monte Carlo simulation and heuristic search for solving large-scale block layout problems

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