A versatile algorithm for assembly line balancing

Boysen, Nils; Fliedner, Malte

doi:10.1016/j.ejor.2006.11.006

Cited by 71 publications

(35 citation statements)

References 82 publications

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“…The procedure Avalanche developed by Boysen and Fliedner (2008) is a flexible heuristic procedure for a large variety of ALBPs and can be modified to solve SUALBSP easily. It is an iterative two-phase algorithm based on task sequences.…”

Section: Avalanchementioning

confidence: 99%

“…This approach is best explained by means of our example of Figure To re-optimize each sequence or to combine favorable parts of different (re-optimized) sequences, a so-called grouping graph is constructed (e.g., Klein, 1963;Easton et al, 1989;Boysen and Fliedner, 2008;Boysen and Scholl, 2009). Figure 3 shows the graph for our example which contains a node for each position p of each sequence.…”

Section: Improvement By Shortest-path Computationsmentioning

confidence: 99%

“…When constructing the next sequence, each ant utilizes the pheromon values which are combined with the original priority values to form a composite measure for task selection. For details we refer to Boysen and Fliedner (2008).…”

Section: Avalanchementioning

confidence: 99%

“…This separated form of constructing and optimizing a grouping graph for each single sequence is performed in Avalanche (Boysen and Fliedner, 2008). …”

Section: Improvement By Shortest-path Computationsmentioning

confidence: 99%

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The assembly line balancing and scheduling problem with sequence-dependent setup times: problem extension, model formulation and efficient heuristics

2011

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Assembly line balancing problems (ALBP) consist of distributing the total workload for manufacturing any unit of the products to be assembled among the work stations along a manufacturing line as used in the automotive or the electronics industries.Usually, it is assumed in research papers that, within each station, tasks can be executed in an arbitrary precedence-feasible sequence without changing station times. In practice, however, the task sequence may influence the station time considerably as sequence-dependent setups (e.g., walking distances, tool changes, withdrawal of parts from material boxes) have to be considered. Including this aspect leads to a joint balancing and scheduling problem, which we call SUALBSP (setup assembly line balancing and scheduling problem). In this paper, we modify the problem by modeling setups more realistically, give a new, more compact mathematical model formulation and develop effective heuristic solution procedures. Computational experiments based on existing and new data sets indicate that the new procedures outperform formerly proposed heuristics and are able to solve problem instances of real-world size with small deviations from optimality in short time.

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Section: Avalanchementioning

confidence: 99%

Section: Improvement By Shortest-path Computationsmentioning

confidence: 99%

Section: Avalanchementioning

confidence: 99%

“…This separated form of constructing and optimizing a grouping graph for each single sequence is performed in Avalanche (Boysen and Fliedner, 2008). …”

Section: Improvement By Shortest-path Computationsmentioning

confidence: 99%

See 2 more Smart Citations

The assembly line balancing and scheduling problem with sequence-dependent setup times: problem extension, model formulation and efficient heuristics

2011

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show abstract

“…Another heuristic method that starts an initial solution and realizes trade, and transfer procedures to improve this solution was proposed in [16]. Genetic algorithms [17]- [19], tabu search procedures [20], [21], simulated annealing algorithms [22], [23], ant colony optimization algorithms [24], [25], particle swarm optimization algorithm [26], differential evolution algorithm [27], [28], beam search algorithm [28]- [30] and variable neighbourhood search [31], [32] were also used to solve SALBP-II. In this paper, we propose a large neighbourhood search (LNS) algorithm for solving the SALBP-II.…”

Section: Introductionmentioning

confidence: 99%