Assembly Line Balancing Problems Solved By Estimation Of Distribution

In this paper, we propose an new algorithm based estimation of distribution (ED) to solve the type II assembly line balancing problem (ALBP-II). This problem aims to assign a set of assembly operations subject to precedence constraints to a given number of workstations of an assembly line in order to minimize the cycle time. We prove that the optimal solution for determinist ALBP-II problem can maximize the reliability of the least reliable station in the line if the operation times are random and normally distributed with constant variance-to-mean ratio. The ED algorithm is a population-based algorithm. The simulation results show that ED algorithm outperforms the simulated annealing algorithm especially for large-scaled problems.