A bacteria foraging algorithm based cell formation considering operation time

Nouri, Hossein; Hong, Tang Sai

doi:10.1016/j.jmsy.2012.03.001

Cited by 23 publications

(6 citation statements)

References 48 publications

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“…Based on operational time required for part processing, Nouri et al. [26] introduced the bacteria foraging algorithm (BFA) as an optimization method for operation time required for the cell formation issue. The study considered the number of voids and exceptional elements in cells as decision variables that can determine the cell formation.…”

Section: Literature Reviewmentioning

confidence: 99%

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

Al‐Zuheri

Ketan

Vlachos

2022

IET Collab Intel Manufact

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Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re-arrange machines enabling the minimisation of inter/intra-cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Literature Reviewmentioning

confidence: 99%

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

Al‐Zuheri

Ketan

Vlachos

2022

IET Collab Intel Manufact

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“…Moreover, to determine whether the range of T pkmw and β may have any impact on the performance of the HSA, four different distributions of T pkmw are used, including T pkmw ∼ DU [5,10], T pkmw ∼ DU [5,20], T pkmw ∼ DU [5,30] and T pkmw ∼ DU [5,40], and four different distributions of β are used, including β ∼ DU [1,5], β ∼ DU [1,6], β ∼ DU [1,7] and β ∼ DU [1,8], where DU[a, b] represents a discrete uniform distribution with an integer range from a to b.…”

Section: Computational Experimentsmentioning

confidence: 99%

Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing

Liu¹,

Wang²

2016

IJCIS

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This paper designs a non-linear integer mathematical model for the cellular manufacturing system (CMS) with dual-resource constrained setting. The multi-functional machines and the multi-skilled workers need to be grouped and assigned to the cells. Moreover, each operation of the parts has different processing times if processed by different machines or workers. Each part with operation sequence is allowed to move from one machine to another for processing subsequent operation, which might reduce processing time although it will incur additional movement time. In order to solve the simultaneous and intertwined optimization problem, a hybrid simulated annealing (HSA) which embedding priority rule based heuristic algorithm is proposed to minimize the makespan. Computational experiments are conducted to show that the proposed HSA performs well with respect to accuracy and efficiency of solution than the traditional simulated annealing algorithm.

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“…For instance, a detailed GA algorithm is proposed in the paper of Jahanzaib et al (2013). Recently another evolutionary algorithm, a bacteria foraging algorithm (BFA), has been proposed to solve cell formation considering operation time (Nouri and Hong, 2012). The new algorithm has been successfully tested with benchmark problems from the literature.…”

Section: Solving Algorithms Formulated For Models (Metaheuristics)mentioning

confidence: 99%

A new mathematical approach to supporting group technology

Tóth

Radeleczki

Veres

et al. 2014

EJIE

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This paper presents a mathematical approach which can be successfully applied in different group technology problems. The method is originated from formal concept analysis (FCA). We produce some particular extent partitions of a formal context which encodes the relationship between given technical objects and their attributes. We show how to define the part type notions and also how to solve the cell formation problem by means of these extent partitions. The main focus of the paper is a solution method for the determination of master parts. The method is based on notions defined by modal operators analogously to formal concepts and uses the extent partitions of the complementary context. It follows from the nature of the problem that several solutions can exist; a consistency measure is introduced in order to rank them. [

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A bacteria foraging algorithm based cell formation considering operation time

Cited by 23 publications

References 48 publications

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing

A new mathematical approach to supporting group technology

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