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

Citation data:

Computers & Industrial Engineering, ISSN: 0360-8352, Vol: 105, Page: 201-209

Publication Year:
2017
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DOI:
10.1016/j.cie.2017.01.007
Author(s):
Sayak Roychowdhury; Theodore T. Allen; Nicholas B. Allen
Publisher(s):
Elsevier BV
Tags:
Computer Science; Engineering
article description
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.