The Algorithm and Simulation of Multi-Objective Sequence and Balancing Problem for Mixed Mode Assembly Line

Abstract

In order to solve the balancing problem of product processing in a mixed flow assembly line, a modified genetic algorithm is proposed to optimize the instantaneous load and average load in the assembly line. An improved discrete particle swarm optimization algorithm is used to address the disordered and inefficient sequencing problem in processing products in an assembly line. Through a comprehensive consideration of the operating sequence, minimum production cycle, and the average load and instantaneous load of all workstations, the optimal solution was obtained and its load balancing conditions were studied. Based on the final solution and simulation results, the optimal solution was selected as the assembly line balancing alternative. The sequencing analysis result shows that by introducing the modified discrete PSO algorithm in the sequencing solution seeking in a mixed mode assembly line, the disordered and inefficient multi-objective sequencing problem can be effectively solved. According to the simulation result and calculated result, we set the ratio of the number of workstations to transmission rate as 10 and the product launch intervals as 45 s. Compared to the traditional algorithm, the improved algorithm has a smaller targeted function value, much shorter distance between the optimal solution and the ideal solution, and greater convergence capability.

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