A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem

Şahin, Ramazan; Türkbey, Orhan

doi:10.1007/s00170-008-1530-5

Cited by 42 publications

(19 citation statements)

References 23 publications

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“…Three proposed methods -ACO + Sahu (ACO-DDML), ACO + Urban (ACO-UDML), and ACO + Pareto (ACO-PDML) -were set to a single period only to solve the static multi-objective problem. The results in Table 3 match up with those in the existing literature (Şahin and Türkbey, 2009), and the multi-objective aspect of the proposed methods has been validated.…”

Section: Results From Solving the (Static) Multi-objective Facility Lsupporting

confidence: 81%

“…In this study, both weighted material handling costs and closeness rating scores multiplied by distance are minimized, similar to Khare et al (1988) and Şahin and Türkbey (2009). Additionally, the closeness rating scores between departments are distributed based on Fortenberry and Cox (1985).…”

Section: Results From Solving the (Static) Multi-objective Facility Lmentioning

confidence: 99%

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Dynamic Facility Layout With Multi-Objectives

Chen

2014

Asia Pac. J. Oper. Res.

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A problem in multi-objective dynamic facility layout is achieving distance-and adjacency-based objectives for arranging facility layouts across multiple time periods. As a non-deterministic polynomial time-hard problem, it resembles the quadratic assignment problem (QAP), which can be solved through meta-heuristics such as ant colony optimization (ACO). This study investigates three multi-objective approaches coupled with ACO to solve this problem. As the experimental design, we apply the proposed methods to solve the dynamic facility layout problem (DFLP), multi-objective facility layout problem, and multi-objective DFLP based on data sets from the literature to test the quality of the solution. The results show that the proposed methods are effective for solving the problem.

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Section: Results From Solving the (Static) Multi-objective Facility Lsupporting

confidence: 81%

Section: Results From Solving the (Static) Multi-objective Facility Lmentioning

confidence: 99%

Dynamic Facility Layout With Multi-Objectives

Chen

2014

Asia Pac. J. Oper. Res.

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“…Sahin and Turkbey (2009) and Ripon et al (2011) applied Pareto efficiency to solve the static multi-objective FLP. Sahin and Turkbey (2009) proposed a basic SA algorithm combined with the Pareto optimal concept while Ripon et al (2011) based their solution method on non-dominated sorting genetic algorithm 2 (NSGA 2) However, layout decision making is always a multiple-attribute decision making problem. The evaluation of FLP alternatives is always difficult and time consuming because of its inherent multiple characteristic features.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated SA-DEA-TOPSIS-based solution approach for multi objective stochastic dynamic facility layout problem

Tayal

Singh

2017

IJBSR

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Abstract:Layout design often has a significant impact on the performance of a manufacturing industry. In the real world, a facility layout is subjected to various quantitative and qualitative conditions or criteria such as, total material handling cost, flow distance, closeness factor, maintenance etc. Researchers have devised various algorithmic and procedural methodologies for finding a layout that is applicable for practical scenarios. The paper proposes an integrated methodology of combining algorithmic (meta-heuristic) and procedural (decision-making) techniques to identify a layout that has the highest degree of satisfaction for all the criteria. A computer program was developed for simulated annealing. It was used to generate a set of layouts based on qualitative and quantitative aspect of facility layout problem for multiple time periods and uncertain product demand. DEA was applied to identify a subset of efficient layouts. TOPSIS was used to rank these layouts based on criteria and their weights as per expert's opinion.Keywords: simulated annealing; data envelopment analysis; DEA; TOPSIS; facility layout problem; FLP; multi objective facility layout problem; stochastic dynamic facility layout problem.Reference to this paper should be made as follows: Tayal, A. and Singh, S.P. (2017) 'Integrated SA-DEA-TOPSIS-based solution approach for multi objective stochastic dynamic facility layout problem', Int.

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“…Compared with nodes and arcs of the flow chart, they convert this problem into a shortest path problem by studying its cost function and machine adding/removing heuristic rules [26]. Sahin and Turkbey (2009) considered the facility layout problem which combines the objective of minimization of the total material handling cost and the maximization of total closeness rating scores [27]. Sahin et al (2010) proposed an effective simulated annealing-based heuristic approach for DFLP with budget constraints [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Tabu search heuristic for efficiency of dynamic facility layout problem

Bozorgi

Abedzadeh

Zeinali

2014

Int J Adv Manuf Technol

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The facility layout problem (FLP) is a wellresearched problem of finding positions of departments on a plant floor such that departments do not overlap and some specific critical criteria involved in FLP are optimized. In addition, the facility layout dramatically influences the efficiency of material handling within a manufacturing system. In order to ensure optimal performance within a manufacturing system, the facility layout should reflect changes throughout time. However, the static facility layout problem with constant material flows between departments may not be a realistic scenario because a manufacturing facility is a dynamic system that constantly evolves. On the other hand, layout problem has multi-objective nature, and it is necessary to apply various criteria and goals for determining a good facility layout. As a result, this research is about determining a solution for dynamic facility layout problem (DFLP) with equal departments by applying data envelopment analysis (DEA) with consideration of some specific criteria which are cost, adjacency, and distance requested. Cost as a negative criterion is minimized and used as an input, whereas adjacency and distance requested are considered positive criteria and used as output of DEA model. In the proposed algorithm, at first, the initial layout is generated, and then its neighborhood is created by the pair exchange and reverse strategy. By applying tabu search heuristic using diversification strategy which includes "frequency-based memory," "penalty function," and "dynamic tabu list size" to the DEA model, the most desired efficient layout is obtained. Two data sets taken from the relevant literature are used to test and evaluate the performance of the proposed heuristic. Computational experiments show that the proposed heuristic outperforms other heuristics presented in relevant literature, and the most efficient layout is achieved.

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A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem

Cited by 42 publications

References 23 publications

Dynamic Facility Layout With Multi-Objectives

Dynamic Facility Layout With Multi-Objectives

Integrated SA-DEA-TOPSIS-based solution approach for multi objective stochastic dynamic facility layout problem

Tabu search heuristic for efficiency of dynamic facility layout problem

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