Identifying multiple solutions for assembly line balancing having stochastic task times

Betts, Judith; Mahmoud, Karimi

doi:10.1016/0360-8352(89)90161-7

Cited by 17 publications

(2 citation statements)

References 8 publications

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“…There is an overwhelming number of research works that cover the balancing of manual assembly lines [20]. Nearly all their methodologies consider a single-model line with various objective functions such as (i) maximization of profit [21], or minimization of (ii) the number of stations for a given cycle time [22], [23]; (iii) the cycle time for a given number of stations [24]; or (iv) expected total costs [25], [26].…”

Section: B Optimization In a Mixed Model Assembly Linementioning

confidence: 99%

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

et al. 2019

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Increasing production variability while maintaining operation efficiency remains a critical issue in many manufacturing industries. While the adoption of mixed-model assembly lines enables the production of high product variety, it also makes the system more complex as variety increases. This paper proposes an information entropy-based methodology that quantifies and then minimizes the complexity through product sequencing. The theory feasibility is demonstrated in a series of simulations to showcase the impact of sequencing in controlling the system predictability and complexity. Hence, the framework not only serves as a tool to quantitatively assess the impact of complexity on total system performance but also provides means and insights into how complexity can be mitigated without affecting the overall manufacturing workload.

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Section: B Optimization In a Mixed Model Assembly Linementioning

confidence: 99%

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

et al. 2019

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“…To deal with this uncertainty, the following models were proposed in the literature. Task times were assumed to be random variables with either known continuous probability distributions (Zhao, Liu, Ohno, & Kotani, 2007), or known or unknown symmetric probability distributions (Betts & Mahmoud, 1989;Raouf & Tsui, 1982), or known independent normal probability distributions. This third case has received quite some attention: earlier papers have focused on optimizing straight assembly lines where heuristic (Carter & Silverman, 1984;Chakravarty & Shtub, 1986;Fazlollahtabar, Hajmohammadi, & Es'haghzadeh, 2011;Kao, 1979;Lyu, 1997;Shin, 1990;Silverman & Carter, 1986), metaheuristic (Cakir, Altiparmak, & Dengiz, 2011;Erel, Sabuncuoglu, & Sekerci, 2005) and exact solution methods (Henig, 1986;Kao, 1976;Sarin, Erel, & Dar-el, 1999) were proposed.…”

Section: Assembly Line Design and Balancingmentioning

confidence: 99%

Second order conic approximation for disassembly line design with joint probabilistic constraints

Bentaha

Battaïa

Dolgui

et al. 2015

European Journal of Operational Research

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a b s t r a c tA problem of profit oriented disassembly line design and balancing with possible partial disassembly and presence of hazardous parts is studied. The objective is to design a production line providing a maximal revenue with balanced workload. Task times are assumed to be random variables with known normal probability distributions. The cycle time constraints are to be jointly satisfied with at least a predetermined probability level. An AND/OR graph is used to model the precedence relationships among tasks. Several lower and upper-bounding schemes are developed using second order cone programming and convex piecewise linear approximation. To show the relevance and applicability of the proposed approach, a set of instances from the literature are solved to optimality.

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Littger

1992

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Identifying multiple solutions for assembly line balancing having stochastic task times

Cited by 17 publications

References 8 publications

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

Sequence Based Optimization of Manufacturing Complexity in a Mixed Model Assembly Line

Second order conic approximation for disassembly line design with joint probabilistic constraints

Überblick über anwendungsbezogene Veröffentlichungen

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