摘要
This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.
This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.
