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.

Due to increasing competition and differentiated demand, assembly line is no longer sufficient to offer only standardized products. To satisfy the requirement of customers with the advantages of an efficient flow line, some product variants are allowed to produce simultaneously on the same assembly line. This paper proposes a multi-agent based algorithm for a mixed-assembly line balancing problem (MALBP). The model is constructed by two-level agent architecture. In the first level, a planning agent is built to determine line cycle time based on customer demand. According to the cycle time, the ideal number of workstation is determined. In the second level, there are a balancing agent and multiple machine agents collaborating to balance the workloads of all workstations. The balancing agent records the task precedence constraints and the workload of each workstation, and calculates the efficiency of line balancing. Tabu search algorithm is applied as a communication mechanism between machine agents to adjust the workloads of all workstations. The primary objective is to minimize the number of workstation for a given cycle time, and secondary objective is to minimize the variance of the workload of workstations. In this paper, a workstation is viewed as an agent. Adjacent swap and insert mechanism is applied to search the neighborhood, and, furthermore, tabu list is used to avoid the solution trapped into local optimal.

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