Multi-floor dynamic facility layout: a simulated annealing-based solution

Ghadikolaei, Yaser Khosravian; Shahanaghi, Kamran

doi:10.1504/ijor.2013.052711

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…Izadinia et al (2014) provided a robust model to be used for the multi-floor facility layout with uncertain material flow between the departments and the storage. Khosravian Ghadikolaei and Shahanaghi (2013) studied MF-DFLP and solved it using a SA algorithm. They also compared the algorithm’s results with those obtained by the Branch and Bound solver in the GAMS software.…”

Section: Literature Reviewmentioning

confidence: 99%

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A mathematical model and a simulated annealing algorithm for unequal multi-floor dynamic facility layout problem based on flexible Bay structure with elevator consideration

Zolfi

Jouzdani

2022

JFM

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Purpose As far as the authors know, no research has already been carried out on the multi-floor dynamic facility layout problem (MF-DFLP) in the continuous form regarding the flexible bay structure, the number and the variable location of the elevator. Therefore, the present paper models the given problem and attempts to find a sub-optimal solution for it using a meta-heuristic simulated annealing (SA) algorithm. Design/methodology/approach The efficient use of resources has always been a prominent matter for decision-makers. Many reasons including land use, construction considerations and proximity of departments have led to the design of multi-floor facilities. On the other hand, their fast-evolving environment calls for dynamic planning. Therefore, in this paper, a model and the SA algorithm for MF-DFLP are presented. Findings After presenting a mathematical model, the problem was solved precisely in a small size using the GAMS software. Also, a near-optimal solution method using a SA meta-heuristic algorithm is suggested and the proposed algorithm was run in the MATLAB software. To evaluate the presented model and the proposed solution, some test cases were considered in two aspects. The first aspect was the test cases that are newly generated in small, medium and large sizes to compare the exact optimal solution with the results of the meta-heuristic algorithm. Eight test cases with small sizes were solved using the GAMS software, the optimum solutions were obtained in a reasonable time, and the cost of their solutions was equal to that of the SA algorithm. Eight test cases with medium sizes were run in the GAMS software with the time limit of 80,000 s, and the SA algorithm had performed better for these test cases. Two test cases were also considered in large size that GAMS could not solve them, whereas the SA algorithm successfully found a proper solution for each. The second aspect included the test cases from the literature. The result showed that suggested algorithm is more capable of finding best solutions than compared algorithms. Originality/value In this paper, an unequal area MF-DFLP was studied in a continuous layout form in which the location and number of the elevators were considered to be variable, and the layouts were considered with flexible bay structure. These conditions were investigated for the first time.

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Section: Literature Reviewmentioning

confidence: 99%

“…1. In some models like the Khosravian Ghadikolaei and Shahanaghi (2013) and Kochhar and Heragu (1999), the rearrangement cost is considered fixed in two consecutive periods and are applied regardless of the amount of movement. In this problem, this case is assumed for the elevator movement.…”

Section: Notementioning

confidence: 99%

A mathematical model and a simulated annealing algorithm for unequal multi-floor dynamic facility layout problem based on flexible Bay structure with elevator consideration

Zolfi

Jouzdani

2022

JFM

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“…High computation time and memory are required to solve SFLP; therefore, meta-heuristic techniques such as genetic algorithm (GA), simulated annealing (SA), modified simulated annealing (MSA), chaotic simulated annealing (CSA). Further study on meta-heuristic techniques for solving combinatorial problem can be found from Azadeh et al (2013), Fon and Wong (2010), Ghadikolaei and Shahanaghi (2013) and Jaramillo and McKendall (2010).…”

Section: Introductionmentioning

confidence: 99%

Analysis of simulated annealing cooling schemas for design of optimal flexible layout under uncertain dynamic product demand

Tayal

Singh

2019

IJOR

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Manufacturing facilities are subjected to many uncertainties such as variability in demand, queuing delays, variable task times, rejects and machine breakdown. These volatilities have a large impact on leap time, inventory cost and delivery performance of a manufacturing unit. To operate efficiently the manufacturing facilities should adapt to these variations. The paper explores the way uncertainties are addressed in designing of flexible optimal layout. Such facility layout problem is known as stochastic dynamic facility layout problem (SDFLP). SDFLP is an NP-hard combinatorial optimization problem, which means the time taken to solve increases exponentially with problem size. To solve SDFLP, the paper presents an adaptation of simulated annealing (SA) meta-heuristic. Various SA cooling schemas are discussed, computed and evaluated for generating the optimal flexible layout. An optimal layout is one that minimises the distance travelled by materials taking into account uncertain product demand (material handling cost). A computer-based tool was developed and analysis was conducted on small to large size problem set. The results showed that SA with exponential cooling schedule provides better solution in terms of layout efficiency and gave better solution as compared to literature.

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