Mixed-model Sequencing with Stochastic Failures: A Case Study for
Automobile Industry
- URL: http://arxiv.org/abs/2306.12618v1
- Date: Thu, 22 Jun 2023 01:09:18 GMT
- Title: Mixed-model Sequencing with Stochastic Failures: A Case Study for
Automobile Industry
- Authors: I. Ozan Yilmazlar, Mary E. Kurz, Hamed Rahimian
- Abstract summary: In the automotive industry, the sequence of vehicles to be produced is determined ahead of the production day.
This paper proposes a two-stage program for the mixed-model sequencing (MMS) problem with product failures.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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