Mixed-model Sequencing with Stochastic Failures: A Case Study for Automobile Industry

TL;DR

This paper develops a stochastic programming approach for mixed-model vehicle sequencing that accounts for potential failures, improving scheduling robustness and reducing expected work overload in automotive production.

Contribution

It introduces a two-stage stochastic model with scenario-based solutions and proposes novel algorithms, including an L-shaped decomposition, for the automotive sequencing problem with failures.

Findings

Robust sequences reduce expected work overload by over 20%.

The L-shaped algorithm outperforms standard solvers.

Scenario-based solutions improve scheduling resilience.

Abstract

In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day. However, there are some vehicles, failed vehicles, that cannot be produced due to some reasons such as material shortage or paint failure. These vehicles are pulled out of the sequence, and the vehicles in the succeeding positions are moved forward, potentially resulting in challenges for logistics or other scheduling concerns. This paper proposes a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provides improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. Moreover, we provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances.