Application of Layered Coding Genetic Algorithm in Optimization of Unequal Area Production Facilities Layout

Hou, Shiwang; Wen, Huabing; Feng, Shunshan; Wang, Hui; Li, Zhibin

doi:10.1155/2019/3650923

Cited by 18 publications

(12 citation statements)

References 31 publications

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“…2.2 Unequal area facility layout problems with slicing tree structure and flexible bay structure in continuous space The issue of facilities clustering towards the centre of the factory floor is resolved by delimiting the floor area equal to the sum of all facilities area. In this case, the arrangements of facilities are made by slicing tree structure (Scholz et al, 2009;Komarudin and Wong, 2010;Liu and Sun, 2012;Aiello et al, 2012;Kang and Chae, 2017;Hou et al, 2019) or flexible/ relaxed flexible bay structure (Tate and Smith, 1995;Konak et al, 2006;Komarudin and Wong, 2010;Kulturel-Konak and Konak, 2011;Ulutas and Kulturel-Konak, 2012;Palomo-Romero et al, 2017;Garcia-Hernandez et al, 2019). Tate and Smith (1995) first time presented the more realistic layout by representing the UA-FLPs using FBS, and they used GA as a solution approach.…”

Section: Static Unequal Area Facility Layout Problem In Continuous Spacementioning

confidence: 99%

Design of robust layout for unequal area dynamic facility layout problems with flexible bays structure

Hunagund¹,

Pillai

Kempaiah

2020

JFM

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Purpose The purpose of this paper is to develop a mathematical model for the design of robust layout for unequal area-dynamic facility layout problem with flexible bay structure (UA-DFLP with FBS) and test the suitability of generated robust layout in a dynamic environment. Design/methodology/approach This research adopts formulation of a mathematical model for generating a single layout for unequal area facility layout problems with flexible bay structure under dynamic environment. The formulated model for the robust layout formation is solved by developing a simulated annealing algorithm. The proposed robust approach model for UA-DFLP with FBS is validated by conducting numerical experiments on standard UA-DFLPs reported in the literature. The suitability of the generated robust layout in a dynamic environment is tested with total penalty cost criteria. Findings The proposed model has given a better solution for some UA-DFLPs with FBS in comparison with the adaptive approach’s solution reported in the literature. The total penalty cost is within the specified limit given in the literature, for most of the layouts generated for UA-DFLPs with FBS. In the proposed model, there is no rearrangement of facilities in various periods of planning horizon and thus no disruptions in operations. Research limitations/implications The present work has limitations that when the area and aspect ratio of the facilities are required to change from one period to another, then it is not possible to make application of the robust approach-based formulation to the dynamic environment facility layout problems. Practical implications Rearrangement of facilities in adaptive approach disrupts the operations whereas in the proposed approach no disruption of production. The FBS approach is more suitable for layout planning where proper aisle structure is required. The solution of the proposed approach helps to create a proper aisle structure in the detailed layout plan. Thus, easy interaction of the material handling equipment, men and materials is possible. Originality/value This paper proposes a mathematical formulation for the design of robust layout for UA-FLPs with FBS in a dynamic environment and an efficient simulated annealing algorithm as its solution procedure. The proposed robust approach generates a single layout for the entire planning horizon. This approach is more useful for facilities which are difficult/sensitive to relocate in various periods of the planning horizon.

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Section: Static Unequal Area Facility Layout Problem In Continuous Spacementioning

confidence: 99%

Design of robust layout for unequal area dynamic facility layout problems with flexible bays structure

Hunagund¹,

Pillai

Kempaiah

2020

JFM

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“…The GA is a stochastic search technique that mimics the mechanisms of Darwinian evolution based on the concept of the survival of the fittest (Deb [11]; Goldberg [12]). The previous application of GA indicates its favorable performance in manufacturing system optimization, such as for FMS scheduling problem in loop layout (Rifai et al [13]), layout planning in SRLP (Datta et al [14]), and unequal area production facilities planning (Hou et al [15]). GA uses a selection operator that is created by a random number and selection probability calculated by the objective values of chromosomes in the population, and a mating pool is created with the selected chromosomes from the original population.…”

Section: Genetic Algorithm Designmentioning

confidence: 99%

A Genetic Algorithm for the Double Row Layout Problem

Rifai

Mara

Kusumastuti

et al. 2020

jti

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The double row layout problem (DRLP) is an NP-hard and has many applications in the industry. The problem concerns on arranging the position of machines on the two rows so that the material handling cost is minimized. Although several mathematical programming models and local heuristics have been previously proposed, there is still a requirement to develop an approach that can solve the problem efficiently. Here, a genetic algorithm is proposed, which is aimed to solve the DRLP in a reasonable and applicable time. The performances of the proposed method, both its obtained objective values and computational time, are evaluated by comparing it with the existing mathematical programming model. The results demonstrate that the proposed GA can find relatively high-quality solutions in much shorter time than the mathematical programming model, especially in the problem with large number of machines.

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“…Safety factors were implemented by the TNT (Trinitrotoluene) explosion model [9] or domino hazard index [10]. Limitations on the area and aspect ratio of facilities were taken into account as well [11][12][13]. The adjacent arrangement of special plants was implemented by arranging plants with upstream and downstream relationships jointly [14].…”

Section: Introductionmentioning

confidence: 99%

Coupling Layout Optimization of Key Plant and Industrial Area

Zhao

et al. 2020

Processes

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Layout problems are an engineering task that heavily relies on project experience. During the design of a plant, various factors need to be considered. Most previous efforts on industrial layout design have focused on the arrangement of facilities in a plant. However, the area-wide layout was not thoroughly studied and the relationship between plant layout and area-wide layout was rarely mentioned. In this work, the key plant that has the greatest impact on the industrial area is figured out first, and then the coupling relationships between the key plant and the industrial area are studied by changing the occupied area and length-width ratio of the key plant. Both of them are achieved by changing the floor number. A hybrid algorithm involving the genetic algorithm (GA) and surplus rectangle fill algorithm (SRFA) is applied. Various constraints are considered to make the layout more reasonable and practical. In the case study, a refinery with 20 plants is studied and the catalytic cracking plant is found to be the key plant. After the retrofit, the total cost of the refinery is 1,806,100 CNY/a less than that before, which illustrates the effectiveness of the method.

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Application of Layered Coding Genetic Algorithm in Optimization of Unequal Area Production Facilities Layout

Cited by 18 publications

References 31 publications

Design of robust layout for unequal area dynamic facility layout problems with flexible bays structure

Design of robust layout for unequal area dynamic facility layout problems with flexible bays structure

A Genetic Algorithm for the Double Row Layout Problem

Coupling Layout Optimization of Key Plant and Industrial Area

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