The mixed-model U-line balancing problem

Sparling, David; Miltenburg, John

doi:10.1080/002075498193859

Cited by 105 publications

(57 citation statements)

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“…We present our algorithm notation and then our three mixed-model objective functions to be minimized. We base our notation on the work of Scholl (1999) and Sparling and Miltenburg (1998), and we make modifications specific to our representation of the problem. We define the following notation.…”

Section: Methodsmentioning

confidence: 99%

“…The first mixed-model U-line balancing problem (M-UALBP) was addressed by Sparling and Miltenburg (1998). They adapted the four-step mixed-model, serial-line procedure of Thomopolous (1967Thomopolous ( , 1970 and set the initial balance using a branch and bound algorithm developed for serial lines.…”

Section: U-shaped Assembly Line Balancing Literature Reviewmentioning

confidence: 99%

“…To meet the demand for product variety many manufacturers are converting their production lines from a single product or batch production to mixed-model production. Benefits of mixed-model production are the ability to provide customers with a variety of products in a timely and cost effective manner (Sparling and Miltenburg, 1998). This research utilizes a U-shaped assembly line layout for mixed-model production.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

Visich¹,

Khumawala²,

Diaz-Saiz³

2010

IJMTM

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Mixed-model assembly lines often create model imbalance due to differences in task times for the different product models. Smoothing algorithms guided by meta-heuristics that can escape local optimums can be used to reduce model imbalance. In this research we utilize the metaheuristics tabu search (TS), the great deluge algorithm (GDA) and record-to-record travel (RTR) to reduce three objective functions: the absolute deviation from cycle time, the maximum deviation from cycle time, and the sum of the cycle time violations. We found that the GDA was significantly superior to the RTR and TS algorithms across all problem sizes and objective functions. For the 19 task problems, RTR performed significantly better than TS for all three objective functions. On the other hand, for the 61 and 111 task problems TS performed significantly better than RTR for all three objective functions.

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Section: Methodsmentioning

confidence: 99%

Section: U-shaped Assembly Line Balancing Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

Visich¹,

Khumawala²,

Diaz-Saiz³

2010

IJMTM

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“…A station which handles the same workpiece in two different cycles is called a crossover station. With a U-line, the number of required stations can be smaller than with a regular line (for more details on the UALBP see Miltenburg and Wijngaard, 1994;Nakade et al, 1997;Sparling and Miltenburg, 1998;Urban, 1998;Scholl and Klein, 1999;Miltenburg, 2000;Erel et al, 2001;Chiang and Urban, 2006;.…”

Section: Main Contributionsmentioning

confidence: 99%

A survey of the literature on the multiple or parallel assembly line balancing problem

Lusa

2008

EJIE

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“…Both UALBP and SALBP are NP-hard problem (Baybars, 1986; Miltenburg and 44 Sparling, 1995; Miltenburg, 1998;Sparling and Miltenburg, 1998). Obviously large size realistic problems require heuristic approaches.…”

Section: Motivationmentioning

confidence: 99%

Ant colony optimization for the single model U-type assembly line balancing problem

Sabuncuoğlu

Erel

Alp

2009

International Journal of Production Economics

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The assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is the allocation of tasks to an ordered sequence of stations subject to the precedence constraints with the objective of minimizing the number of stations. In a U-line the line is configured into a U-shape topology.In this research, a new heuristic, Ant Colony Optimization (ACO) meta-heuristic, and its variants are proposed for the single model U-type assembly line balancing problem (UALBP). We develop a number of algorithms that can be grouped as:(i) direct methods, (ii) modified methods and (iii) methods in which ACO approach is augmented with some metaheuristic.We also construct an extensive experimental study and compare the performance of the proposed algorithms against the procedures reported in the literature. Ayrıca kapsamlı bir deneysel çalışma yapılmış ve önerilen algoritmaların performansı literatürdeki diğer metodlarla karşılaştırılmıştır.Anahtar sözcükler: U-tipi montaj hattı dengelenmesi problemi, Karınca KolonisiOptimizasyonu meta-sezgiseli.iv

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The mixed-model U-line balancing problem

Cited by 105 publications

References 0 publications

An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

A survey of the literature on the multiple or parallel assembly line balancing problem

Ant colony optimization for the single model U-type assembly line balancing problem

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