Accelerated dynamic programming algorithms for a car resequencing problem in automotive paint shops

Abstract

• Novel exact and heuristic algorithms for resequencing cars to minimize setup costs in automotive paint shops are proposed. • Incorporating strong lower and upper bounds in the dynamic programming framework reduces computation time significantly. • Proposed algorithms are more efficient than existing approaches and also more practically applicable. • Shows well-tailored exact algorithms can be more efficient than meta-heuristic algorithms. In this paper, a car resequencing problem (CRP) for automotive paint shops is considered, whereby a set of cars conveyed from an upstream shop to one of the multiple conveyors is retrieved sequentially before the painting operation. The aim of the CRP is to find a car retrieval sequence that minimizes the sequence-dependent changeover cost, which is the cost that is incurred when two consecutive cars do not share the same color. For this problem, we propose accelerated dynamic programming (ADP) algorithms that utilize strong combinatorial lower bounds and effective upper bounds in a standard dynamic programming framework, thus outperforming existing exact algorithms. Testing of our algorithms over a wide range of instances confirmed that they are more efficient than the existing approaches and are also more applicable in practice.