Abstract
Assigning tasks to work stations is an essential problem which needs to be addressed in an assembly line design. The most basic model is called simple assembly line balancing problem type 1 (SALBP-1). We provide a survey on 12 heuristics and 9 lower bounds for this model and test them on a traditional and a lately-published benchmark dataset. The present paper focuses on algorithms published before 2011.We improve an already existing dynamic programming and a tabu search approach significantly. These two are also identified as the most effective heuristics; each with advantages for certain problem characteristics. Additionally we show that lower bounds for SALBP-1 can be distinctly sharpened when merging them and applying problem reduction techniques.
Highlights
Lines are a common way to organise mass production of standardised products
For simple assembly line balancing problem type 1 (SALBP-1) a solution is feasible if (i) the tasks of each station do not have a task time sum larger than c and (ii) no direct or indirect predecessor of any task j is assigned to a later station than j is assigned to
The second goal is the improvement of some already-known procedures, namely tabu search, dynamic programming, lower bound 7 and 8, as well as SALBP-1 reduction techniques
Summary
Lines are a common way to organise mass production of standardised products. Algorithms should be analysed properly on their effectiveness on SALBP-1 before adapting them to more sophisticated GALBPs. Comparing the effectiveness of procedures only with the results reported in their original papers may be distorting due to different computational environments, incomparable CPU times, or different datasets. The second goal is the improvement of some already-known procedures, namely tabu search, dynamic programming, lower bound 7 and 8, as well as SALBP-1 reduction techniques. Heuristics and lower bounds for the simple assembly line balancing problem type 1: Overview, computational tests and improvements. General idea: Sabuncuoglu et al introduce a genetic algorithm in which the tasks as genes are always ordered on the chromosome in a sequence that obeys the precedence graph (order encoding) They apply a crossover technique which completely avoids infeasibility.
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