Using distributed search methods for balancing mixed-model assembly lines in the automotive industry

Abstract

Currently, modern companywide PC networks usually possess significant unused calculation capacity. Since the connected personal computers are mainly used for office applications, considerable off-peak times occur. Consequently, in order to solve planning problems more efficiently, it is promising to apply distributed search procedures that make use of those available off-peak times. This applies in particular to complex problems where insights into the structure of the solution space are lacking. The paper at hand illustrates the application of distributed search methods to automotive assembly line balancing. Modern mass customization programs in the automotive industry frequently comprise more than a billion theoretical variants. Since this causes an oscillating capacity demand at the line, deliberately designing the layout of a mixed-model assembly line is of significant importance. The paper at hand provides a new mixed-model assembly line balancing approach that integrates specific aspects relevant for the automotive industry. However, by integrating several $${{\mathcal NP}}$$ -hard subproblems like a detailed personnel planning or a flexible process planning of each task, the resulting model has significant complexity. Consequently, in order to find appropriate line layouts in reasonable time, specifically designed distributed solution approaches are provided and evaluated. Among these approaches, particularly the use of a specific clustered Tabu Search algorithm attains promising results. By making use of an adaptive dynamic load balancer, substantial improvements of the solution quality can be obtained even under unfavorable circumstances like oscillating background loads in the PC network.