By W. Grzechca
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G. , 1986), most of which are based on the precedence relations and the cycle time. Solutions of good quality were obtained for problems involving up to 305 tasks and 11 assembly subgraphs. In this work the use of weighted, rather than nominal, values for the priority rules is explored. , 2004). A GRASP overview and literature reviews can be found in (Festa & Resende, 2008a, 2008b). The former research work discusses the algorithmic aspects of this type of procedures; the second one presents GRASP applications covering a wide range of optimization problems, including scheduling, routing, graph theory, partitioning, location, assignment, and manufacturing.
In the proposed procedures (see (Capacho, 2007)), the construction phase generates a feasible solution by applying a construction method in which both the subgraphs and the assembly tasks are randomly selected following probability distributions based on weighted priority rule values. Weighted values are proportional or inversely proportional (when using a maximizing or minimizing criterion, respectively) to the values obtained considering a given priority rule. , the one that requires the fewest number of workstations).
Stochastic assembly line balancing using simulated annealing, International journal of production research, Vol. 32, No. 8, pp. O. & Tunali, S. (2006). A review of current application of genetic algorithms in assembly line balancing, Journal of intelligent manufacturing, Vol. 19, No. 1 (February, 2008), pp. M. S. (2006). ANTBAL: An ant colony optimization algorithm for balancing mixed-model assembly lines with parallel workstations, International journal of production research, Vol 44, Issue 2, pp.
Assembly Line - Theory and Practice by W. Grzechca