A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands

Moslemipour, Ghorbanali

doi:10.1007/s40092-017-0222-x

Cited by 13 publications

(4 citation statements)

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“…Most often, the methodology has been selected based on the nature and number of solutions of the search space. And also, the algorithms are combined to bring forth added advantages due to hybridization [44,45]. On these considerations of the simplicity of SAA and the exhaustive search capability of GA, this paper proposes hybrid heuristic (HH) based on SAA-GA for finding an optimal/near optimal solution.…”

Section: Solution Methodology: Hybrid Heuristic Saa Embedded With Gamentioning

confidence: 99%

An improved design for cellular manufacturing system associating scheduling decisions

Subhaa

Jawahar²,

Ponnambalam

2019

Sādhanā

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This paper presents a model for the design of Cellular Manufacturing System (CMS) to evolve simultaneously structural design decisions of Cell Formation (CF) and operational issue decisions of optimal schedule. This integrated decision approach is important for designing a better performing cell. The model allows machine duplication and incorporates cross-flow for scheduling flexibility. The cross-flow is the term introduced to mean the inter-cell movement of parts from parent cell to identical machines in other cells though machines are available in the parent cell. This cross-flow facilitates routing flexibility and paves way for reduced schedule length thereby optimizing resources leading to minimized operational cost. A non-linear integer mathematical programming model is formulated with the objective function of minimizing operating cost which is the sum of Machine Utility Cost (MUC) and inter-cell costs. The MUC is a new cost parameter based on machine utility and it integrates CF, scheduling, and machine duplication decisions. The proposed model belongs to the class of NP-hard problems. A hybrid heuristic (HH) that has ''Simulated Annealing Algorithm (SAA) embedded with Genetic Algorithm (GA)'' is proposed. A comparison with the mathematical solution reveals that the proposed HH is capable of providing solutions closer to optimal in a computationally efficient manner. The model is validated by studying the effect of integrated decisions, machine duplications, and association of scheduling and cross-flow. The model validation reveals that the proposed CMS model evolves CF, scheduling, and machine duplication decisions with minimum operating cost. Thus, it can be inferred that the proposed model gives optimal integrated decisions for designing an effectively and efficiently performing cells and thus evolves improved CMS design decisions.

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Section: Solution Methodology: Hybrid Heuristic Saa Embedded With Gamentioning

confidence: 99%

An improved design for cellular manufacturing system associating scheduling decisions

Subhaa

Jawahar²,

Ponnambalam

2019

Sādhanā

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“…Unlike other metaheuristics, SA is simple to implement, and is computationally more efficient one, but it gives a single solution for each run. Due to the simplicity in implementation of SA, many researchers applied SA to facility layout problems (Tam, 1992;Baykasoglu and Gindy, 2001;McKendall et al, 2006;Pillai et al, 2011;Kulturel-Konak and Konak, 2015;Hunagund et al, 2018;Moslemipour, 2018). In the present study also, SA heuristic is applied to solve the proposed robust approach UA-DFLP with FBS formulation.…”

Section: Dynamic Unequal Area Facility Layout Problem In Continuous Spacementioning

confidence: 97%

“…Rosenblatt (1986) was the first researcher to deal with the DFLPs. Then onwards, many researchers have solved the equal area DFLPs with various meta-heuristics by adaptive approach (Baykasoglu and Gindy, 2001;McKendall et al, 2006;Lacksonen and Enscore, 1993;Balakrishnan et al, 2000;Balakrishnan et al, 2003;Chan et al, 2004;Pourvaziri and Naderi, 2014;Moslemipour, 2018). A few researchers considered the UA-DFLPs with fixed dimension for facilities (Dunker et al, 2005;McKendall and Hakobyan, 2010;Derakhshan Asl and Wong, 2017;Liu et al, 2017), and flexible dimensions for facilities (Lacksonen, 1994(Lacksonen, , 1997Kulturel-Konak and Konak, 2015;Kulturel-Konak, 2017) without FBS in the layout.…”

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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“…8 Particle swarm optimization (PSO) has shown strong performance compared with other competing algorithms 9 owing to the simple implementation, few parameters setting, and effective global search. 10 However, PSO is prone to get stuck in local optimum when applied to the complex and combinatorial optimization problem. The exploration (global search across the entire search domain) and exploitation (local search) of PSO should be enhanced and balanced to obtain a better solution.…”

Section: Introductionmentioning

confidence: 99%

A hybrid optimization approach for unequal-sized dynamic facility layout problems under fuzzy random demands

Zha

Guo

Huang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Dynamic facility layout problems involve devising the optimal layout for each different production period. This article studies unequal-area dynamic facility layout problems under fuzzy random environment to minimize the sum of the material handling costs and rearrangement costs. For a more general situation, a novel model of unequal-area dynamic facility layout problems is proposed on the basis of fuzzy random theory, in which uncertain demands are characterized by fuzzy random variables. Unequal-area dynamic facility layout problems are one of the non-deterministic polynomial-time hard problems. Therefore, a hybrid particle swarm optimization and simulated annealing algorithm is innovated to solve the proposed unequal-area dynamic facility layout problems model under fuzzy random environment, in which the shapes and areas of facilities are changed dynamically. Two facility-swapping methods and two local search methods help hybrid algorithm escape from local optima, allowing a more reliable solution. Moreover, a new shifting method is developed to prevent the spatial overlapping between adjacent facilities and save material handling costs. The performance of the hybrid algorithm is confirmed by some test problems available. Finally, the proposed method is extended to a facility layout planning of a new aircraft assembly shop floor. Computational results show that the efficiency and effectiveness of the proposed method, in sharp comparison with other approaches.

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A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands

Cited by 13 publications

References 51 publications

An improved design for cellular manufacturing system associating scheduling decisions

An improved design for cellular manufacturing system associating scheduling decisions

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

A hybrid optimization approach for unequal-sized dynamic facility layout problems under fuzzy random demands

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