A Novel Hybrid ACO/SA Approach to Solve Stochastic Dynamic Facility Layout Problem (SDFLP)

Lee, T. S.; Moslemipour, Ghorbanali; Ting, T. O.; Rilling, Dirk

doi:10.1007/978-3-642-31837-5_15

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Other data including distance between machine locations, machine sequence for each part, mean value and variance of part demands in each period are not shown here. The problems are solved by using SA, ACO-SA hybrid algorithm proposed by Lee et al (2012), and the novel hybrid AC-CS-SA algorithm proposed in this paper. The results, including objective function f (X) value, and elapsed computational time for three different values of confidence levels are obtained by running the algorithms ten times and are shown in Tables 8 and 9.…”

Section: Results and Discussion In Stochastic Casementioning

confidence: 99%

“…They applied the two algorithms to the job shop scheduling (JSS) problem and the TSP and concluded that the performance of SA was improved from both computational time and solution quality standpoints. Lee et al (2012) is proposed a hybrid algorithm which contains two stages namely, construction of only a single feasible initial solution using ACO approach in which the approach used SA to improve the initial solution. The hybrid algorithm is applied in stochastic case of SDFLP and provide a better results compare to SA algorithm.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

“…Experimental studies on SA demonstrate that selecting a good initial solution for this algorithm leads to improvement in both the obtained solution quality and execution time (Ram et al, 1996). Lee et al (2012) proposed a novel hybrid AC/SA approach to solve SDFLP. In this paper, using a new QAP-based robust layout design model, AC algorithm, and CS solution approach a novel hybrid algorithm named AC-CS-SA is proposed to solve both of the SDFLP and DFLP by combining the ant colony (AC), clonal selection (CS), and SA algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Solving stochastic dynamic facility layout problems using proposed hybrid AC-CS-SA meta-heuristic algorithm

Moslemipour¹,

Loong²,

Lee³

2018

IJISE

Self Cite

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This paper proposes a novel hybrid algorithm in which simulated annealing algorithm starts with a population of good initial solutions constructed by combining ant colony, clonal selection, and robust layout design approaches. The proposed algorithm can be used to solve a dynamic (multi-period) facility layout problem in both deterministic and stochastic cases. In the stochastic environment, product demands are assumed to be normally distributed random variables with known probability density function that changes from period to period at random. In addition, a quadratic assignment-based mathematical model, which is used in the proposed hybrid algorithm, is developed to design a robust layout for the stochastic dynamic layout problem. Finally, the performance of the proposed algorithm is evaluated by solving a large number of randomly generated test problems and some test problems from the literature in stochastic and deterministic cases respectively. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view.

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Section: Results and Discussion In Stochastic Casementioning

confidence: 99%

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving stochastic dynamic facility layout problems using proposed hybrid AC-CS-SA meta-heuristic algorithm

Moslemipour¹,

Loong²,

Lee³

2018

IJISE

Self Cite

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“…The key concept is to combine the high‐level algorithms that explore search spaces using different strategies of intensification and diversification. Lee et al and Tayal and Singh applied Hybrid ACO/SA and Hybrid FA/CSA, respectively to solve SDFLP. However recent trends and application in Metaheuristic can studied from Ganesan et al, Gupta and Deep, Vasant et al and Zelinka et al…”

Section: Mathematical Formulation and The Methodologymentioning

confidence: 99%

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

Tayal

Köse

Solanki

et al. 2019

Computational Intelligence

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The facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three‐stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta‐heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.

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“…Ashtiani et al (2007) utilised SA algorithm to solve their developed model in the SDFLP. Lee et al (2012) proposed a novel hybrid Ant Clony (AC)/SA approach having outstanding performance to solve the SDFLP. reviewed intelligent approaches for solving layout problems comprehensively.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Developing and solving two-echelon inventory system for perishable items in a supply chain: case study (Mashhad Behrouz Company)

2013

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This paper aims at proposing a quadratic assignment-based mathematical model to deal with the stochastic dynamic facility layout problem. In this problem, product demands are assumed to be dependent normally distributed random variables with known probability density function and covariance that change from period to period at random. To solve the proposed model, a novel hybrid intelligent algorithm is proposed by combining the simulated annealing and clonal selection algorithms. The proposed model and the hybrid algorithm are verified and validated using design of experiment and benchmark methods. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view. Besides, the proposed model can be used in both of the stochastic and deterministic situations.

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A Novel Hybrid ACO/SA Approach to Solve Stochastic Dynamic Facility Layout Problem (SDFLP)

Cited by 6 publications

References 13 publications

Solving stochastic dynamic facility layout problems using proposed hybrid AC-CS-SA meta-heuristic algorithm

Solving stochastic dynamic facility layout problems using proposed hybrid AC-CS-SA meta-heuristic algorithm

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

Developing and solving two-echelon inventory system for perishable items in a supply chain: case study (Mashhad Behrouz Company)

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