Combined computer-aided approach for the facilities design problem and estimation of the distribution parameter in the case of multigoal optimization

Khare, Vikas; Khare, Manu; Neema, Mohit

doi:10.1016/0360-8352(88)90048-4

Cited by 33 publications

(9 citation statements)

References 11 publications

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“…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%

“…The additive model provided the following benefits: Maintaining the negative rating for undesirable "X" by separating the facilities with as much distance as possible; handling facilities with no work flow between them; and considering closeness rating between non-adjacent facilities. Khare et al (1988) defined the objective function in the form of A ijkl = α 1 d jl f ik + α 2 r ik d jl where the α 1 and α 2 are the weights assigned to distance and adjacency-based objectives, respectively. Their proposition, however, differed from others' by minimizing both objectives in terms of their weighted distance-costs/ ratings.…”

Section: Multi-objective Facility Layoutmentioning

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 Lmentioning

confidence: 99%

Section: Multi-objective Facility Layoutmentioning

confidence: 99%

Dynamic Facility Layout With Multi-Objectives

Chen

2014

Asia Pac. J. Oper. Res.

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“…The objectives are combined using weight approach. Khare et al (1988) proposed a combined computer-aided approach for multi-objective FLP. They obtained the solutions by summation of objective values at different weightage.…”

Section: Multi-objective Flps Using Weight Approachmentioning

confidence: 99%

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

Singh¹,

Ingole²

2019

10.5267/j.ijiec

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Quantitative and qualitative objectives are both significant to solve any facility layout problem (FLP), which is called as multi-objective FLP. Generally, quantitative factors are considered as material handling cost, time, etc., and qualitative factors are considered as closeness rating, hazardous movement between the facilities, etc. For solving and optimizing two or more objectives, two methods are available. First is weight approach method and second is nondominated sorting method. In the former method, suitable weights are given to each objective and combined in a single objective function; while in later method, the objectives are defined separately and by making comparison of the solutions on the non-dominance criteria, best Pareto-optimal solutions are obtained. In this paper, equal area multi-objective FLPs which are formulated as quadratic assignment problem (QAP) are considered and optimized using biogeography based optimization (BBO) algorithm and non-dominated sorting BBO (NSBBO) algorithm. BBO is one of the efficient metaheuristic techniques, developed to solve complex optimization problems. Computational results of BBO algorithm using weight approach illustrate its better performance compared to other methods while solving multi-objective FLPs. Furthermore to obtain Pareto optimal solutions, NSBBO algorithm is implemented.

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“…Outras aplicações podem ser encontradas em McCormick (1970), Hubert & Schulz. (1976), Heffley (1977), Los (1978), Krackhardt (1988), Khare et al (1988aKhare et al ( , 1988b, Bland & Dawson (1991), Lacksonen & Enscore (1993), Medova (1994), Gouveia & Voß (1995), Bozer & Suk-Chul (1996), Mason & Rönnqvist (1997), Ostrowski & Ruoppila (1997), Ball et al (1998), Haghani & Chen (1998), Kochhar et al (1998), Martin (1998) Desde a sua primeira formulação, o PQA tem atraído a atenção de pesquisadores em todo o mundo, não apenas pela sua importância prática e teórica, mas principalmente pela sua complexidade. Trata-se de um dos mais difíceis problemas de otimização combinatória: instâncias de ordem n > 30 não podem, em geral, ser resolvidas exatamente em tempo computacional aceitável.…”

Section: Introductionunclassified

Uma revisão comentada das abordagens do problema quadrático de alocação

2004

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ResumoO Problema Quadrático de Alocação, PQA, um dos mais difíceis da classe NP-hard, modela diversas aplicações em diferentes áreas como pesquisa operacional, computação paralela e análise estatística de dados discretos. Além disso, problemas conhecidos como o do caixeiro viajante, o da clique maximal, o de particionamento e o de isomorfismo de grafos podem ser formulados como um PQA. Na tentativa de identificar novas propriedades estruturais para este problema, diversas formulações aparecem na literatura. Reunimos tais formulações, destacando suas principais características para classificá-las segundo as técnicas matemáticas nelas adotadas. Finalizamos o artigo avaliando a extensão das contribuições dadas ao problema, quer na elaboração de algoritmos, quer no cálculo de limites inferiores ou na caracterização de classes de exemplares polinomiais ou não, oriundas das diferentes abordagens aqui estudadas.Palavras-chave: problema quadrático de alocação; formulações; otimização combinatória. AbstractThe Quadratic Assignment Problem, QAP, one of the most difficult problems in NP-hard class, models many applications in several areas such as operational research, parallel and distributed computing, and combinatorial data analysis. Besides, other optimization combinatorial problems such as the traveling salesman problem, maximal clique, isomorphism and graph partitioning can be formulated as a QAP. In this paper, we survey some of the most important formulations available, in order to classify them according to their mathematical resources. Finally, we analyze the extension of the contributions brought by the studies of different approaches.

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Combined computer-aided approach for the facilities design problem and estimation of the distribution parameter in the case of multigoal optimization

Cited by 33 publications

References 11 publications

Dynamic Facility Layout With Multi-Objectives

Dynamic Facility Layout With Multi-Objectives

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

Uma revisão comentada das abordagens do problema quadrático de alocação

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