A new branch and bound algorithm for cell formation problem

Arkat, Jamal; Abdollahzadeh, Hadi; Ghahve, Homa

doi:10.1016/j.apm.2011.12.047

Cited by 24 publications

(20 citation statements)

References 21 publications

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“…Tavakkoli-Moghaddam et al 4 discussed the CF problem in dynamic condition by using genetic algorithm, simulated annealing and tabu search. Arkat et al 5 formulated the CF problem as an integer linear programming model. They proposed three types of branch and bound algorithms to minimize the total number of intercellular movements.…”

Section: Introductionmentioning

confidence: 99%

“…In the first set, P is allowed to vary, given M = 20, W = 5, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4]. In the second set, M is allowed to vary, given P = 20, W = 5, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4].…”

Section: Computational Experimentsmentioning

confidence: 99%

“…In the fifth set, the distribution of T pkmw is allowed to vary, given M = 20, P = 30, W = 5, B u = 8 and β ∼ DU [1,4]. In the sixth set, the distribution of β is allowed to vary, given M = 20, P = 30, W = 5, B u = 8 and T pkmw ∼ DU [5,40]. Given K p ∼ DU [20,40], The experiments have been performed on a Pentium-based Lenovo-compatible personal computer with 2.3 GHz clock-pulse and 4 GB RAM.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…In the second set, M is allowed to vary, given P = 20, W = 5, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4]. In the third set, W is allowed to vary, given M = 20, P = 20, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4]. In the fourth set, B u is allowed to vary, given M = 30, P = 30, W = 5, T pkmw ∼ DU [5,20] and β ∼ DU [1,4].…”

Section: Computational Experimentsmentioning

confidence: 99%

“…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%

See 4 more Smart Citations

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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Section: Introductionmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

See 3 more Smart Citations

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

Liu¹,

Wang²

2016

IJCIS

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A Branch and Bound Algorithm for a Fractional 0-1 Programming Problem

Utkina

Batsyn

Batsyna

2016

Discrete Optimization and Operations Research

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We consider a fractional 0-1 programming problem arising in manufacturing. The problem consists in clustering of machines together with parts processed on these machines into manufacturing cells so that intra-cell processing of parts is maximized and inter-cell movement is minimized. This problem is called Cell Formation Problem (CFP) and it is an NP-hard optimization problem with Boolean variables and constraints and with a fractional objective function. Because of its high computational complexity there are a lot of heuristics developed for it. In this paper we suggest a branch and bound algorithm which provides exact solutions for the CFP with a variable number of cells and grouping efficacy objective function. This algorithm finds optimal solutions for 21 of the 35 popular benchmark instances from literature and for the remaining 14 instances it finds good solutions close to the best known.

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