Integrated cost optimization in a two-stage, automotive supply chain

• We study integrated supply chain functions of a Tier-1 automotive supplier.
• We interact with industry to formulate our model based on realistic assumptions.
• We develop a hybrid simulated annealing algorithm to solve the proposed problem.
• Our solution approach employs an effective encoding–decoding strategy.
• Our method solves the proposed problem in a timely manner for industry use.

The efficiency of the automotive supply chain is crucial for ensuring the competitiveness of the automotive industry, which represents one of the most significant manufacturing sectors. We model the integrated production and transportation planning problem of a Tier-1 automotive supplier while taking into account realistic conditions such as sequence-dependent setups on multiple injection molding machines operating in parallel, auxiliary resource assignments of overhead cranes, and multiple types of costs. Finished parts go to the integrated supply chain׳s second stage, transportation, for subsequent delivery by capacitated vehicles to multiple distribution centers for meeting predefined due date requirements. We develop a mixed-integer, linear programming model of the problem, and then present a hybrid simulated annealing algorithm (HSAA), including a constructive heuristic. Our proposed HSAA employs an effective encoding-decoding strategy to solve the NP-hard problem to near optimality in a timely manner. Computational results demonstrate the promising performance of the proposed solution approach.