A genetic algorithm with an earliest due date encoding for scheduling automotive stamping operations

• Methods are proposed for a real-world stampings scheduling at an automotive company.
• A comparison is provided with 5 alternatives on 6 test problems for 4 metrics.
• The proposed genetic algorithm generalized earliest due date method is viable.
• Conditions for the global optimality of earliest due date scheduling are clarified.

This article considers a manufacturing scheduling problem related to automotive stamping operations. A mathematical program of the associated single machine problem is formulated with known demand, production constraints involving stamping dies, and limited storage space availability. It is demonstrated that a generalized version of the standard earliest due-date heuristic efficiently generates optimal solutions for specific problem instances (relatively high initial inventory cases and no tardiness) but poor solutions for cases involving relatively low initial inventories and/or longer time horizons. Branch and bound is shown to be inefficient in terms of computational time for relevant problem sizes. To build a viable decision support tool, we propose a meta-heuristic, “genetic algorithms with generalized earliest due dates” (GAGEDD), which builds on earliest due date scheduling. Alternative methods are illustrated and compared using a real-world case study of stamping press scheduling by an automotive manufacturer.