Mixed-model Sequencing with Reinsertion of Failed Vehicles: A Case Study for Automobile Industry

TL;DR

This paper develops a stochastic programming model and optimization algorithms for mixed-model vehicle sequencing that accounts for failures and reinsertion, reducing work overload and vehicle waiting times in automotive production.

Contribution

It introduces a novel bi-objective two-stage stochastic model and hybrid optimization approach for vehicle sequencing with failure reinsertion, enhancing production efficiency.

Findings

Hybrid algorithm better explores Pareto front.

Local search provides more reliable work overload solutions.

Reinsertion reduces work overload by ~20% and vehicle waiting times.

Abstract

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimization algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by ~20\% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem.