Constructive metaheuristics for solving the Car Sequencing Problem under uncertain partial demand

The car sequencing problem is a well established problem that models the conflicts arising from scheduling cars into an assembly line. However, the existing approaches to this problem do not consider non-regular or out-of-catalog vehicles, which are commonly manufactured in assembly lines. In this paper, we propose a new problem definition that deals with non-regular vehicles. This novel model is called robust Car Sequencing Problem. We model this realistic optimization problem using scenarios defined by different production plans. The problem can be solved by measuring the impact of the plans’ variability and by observing the violations of the problem constraints that appear when switching from one plan to another. In addition to our model formulation, we design and implement a set of constructive metaheuristics to tackle the traditional and the novel robust car sequencing problem. The selected metaheuristics are based on the greedy randomized adaptive search procedure, ant colony optimization, and variable neighborhood search. We have generated compatible instances from the main benchmark in the literature (CSPLib) and we have applied these metaheuristics for solving the new robust problem extension. We complement the experimental study by applying a post hoc statistical analysis for detecting statistically relevant differences between the metaheuristics performance. Our results show that a memetic ant colony optimization with local search is the best method since it performs well for every problem instance regardless of the difficulty of the problem (i.e., constraints and instance size).