A hybrid particle swarm optimisation for dynamic facility layout problem

Hosseini-Nasab, Hasan; Emami, Leila

doi:10.1080/00207543.2013.774486

Cited by 39 publications

(16 citation statements)

References 26 publications

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“…Chen [27] introduced a new data structure for solution representation in hybrid ant colony optimization for large problems. Hosseini-Nasab and Emami [28] proposed a hybrid particle swarm optimisation algorithm to find near-optimal solutions. Emami and Nookabadi [29] introduced a DFLP objective function where each of the cost terms can be given a different importance by the decision makers.…”

Section: Dynamic Facility Layout Problemmentioning

confidence: 99%

Dynamic facility layout problem in footwear industry

Ulutas

Islier

2015

Journal of Manufacturing Systems

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Section: Dynamic Facility Layout Problemmentioning

confidence: 99%

Dynamic facility layout problem in footwear industry

Ulutas

Islier

2015

Journal of Manufacturing Systems

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“…Hosseini-Nasab and Emami [10] have presented a hybrid PSO for DFLP. Before extending this algorithm for our bilevel studied model, it is necessary to define a solution representation for each level.…”

Section: A Proposed Bilevel Pso Algorithm For Bldflpmentioning

confidence: 99%

Dynamic Facility Layout Problem: A New Bilevel Formulation and Some Metaheuristic Solution Methods

Kheirkhah

Navidi

Bidgoli

2015

IEEE Trans. Eng. Manage.

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The dynamic facility layout problem considers flow over multiple time periods in an environment where the material flow between departments changes over time. If this complex problem is combined with the other system problems, such as material handling system designing, this results in a more complicated problem. In most recently proposed approaches for these two synchronized problems, the integrated formulations have been used where the objective function is a weighted sum of some conflicting objectives that may belong to either one or different decision makers. In the former case, the poor practicability of such an approach is due to the difficulty of normalizing these functions and of quantifying the weights. In addition to these problems, in multiagent systems with a strong cooperation, there is another important problem in incorporating the conflicting objectives of conflicting decision makers that is not considered in the existing researches. By these descriptions, developing some other solution strategies, such as iterative or hierarchical methods, are necessary for these cases. In this paper, a bilevel model is developed. The experimental results reveal significant enhancements with respect to more classical approaches (i.e., integrated formulation and Pareto optimal solutions) based on the proposed bilevel model and algorithms.Index Terms-Bilevel programming, bi-particle swarm optimization (PSO) algorithm, coevolutionary algorithm, dynamic facility layout problem (DFLP), material handling system (MHS) design. 0018-9391

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“…In DFLPs, there are several periods for the material flow among departments (periods can be weeks, months or years) and there are several layouts (one layout for each period). The material flow is constant in each period, but it can change among different periods (Hosseini-Nasab and Emami 2013). In STFLPs, the product demand is uncertain and there is only one period (Norman and Smith 2006).…”

Section: Introductionmentioning

confidence: 99%

Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm

Asl

Wong

Tiwari

2015

International Journal of Production Research

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Determining the locations of departments or machines in a shop floor is classified as a facility layout problem. This article studies unequal-area stochastic facility layout problems where the shapes of departments are fixed during the iteration of an algorithm and the product demands are stochastic with a known variance and expected value. These problems are non-deterministic polynomial-time hard and very complex, thus meta-heuristic algorithms and evolution strategies are needed to solve them. In this paper, an improved covariance matrix adaptation evolution strategy (CMA ES) was developed and its results were compared with those of two improved meta-heuristic algorithms (i.e. improved particle swarm optimisation [PSO] and genetic algorithm [GA]). In the three proposed algorithms, the swapping method and two local search techniques which altered the positions of departments were used to avoid local optima and to improve the quality of solutions for the problems. A real case and two problem instances were introduced to test the proposed algorithms. The results showed that the proposed CMA ES has found better layouts in contrast to the proposed PSO and GA.

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A hybrid particle swarm optimisation for dynamic facility layout problem

Cited by 39 publications

References 26 publications

Dynamic facility layout problem in footwear industry

Dynamic facility layout problem in footwear industry

Dynamic Facility Layout Problem: A New Bilevel Formulation and Some Metaheuristic Solution Methods

Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm

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