A multiobjective genetic algorithm and robustness information function for time and space assembly line balancing

Abstract

The time and space assembly line balancing problem (TSALBP) is a realistic multiobjective version of assembly line balancing industrial problems involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and the area of these stations. However, the existing problem formulation does not consider the industrial scenario where the demand of a set of mixed products is variable and uncertain. In this contribution we introduce a novel robustness function to measure the robustness of the line configuration when the production plans demand changes. The function is used as additional a posteriori information for the non-dominated solutions found by an advanced multiobjective genetic algorithm. Because of their independence, these robustness functions can be used in conjunction with any other multiobjective metaheuristic. Results show how the use of the robustness function can help the decision maker to select robust non-dominated solutions when future demand conditions vary in the assembly line configurations.