A BATCHING PROBLEM WITH LEARNING EFFECT CONSIDERATIONS

Wen-Hua Yang ()
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Wen-Hua Yang: Department of Business Administration, Chaoyang University of Technology, 168, Gifong E. Rd., Wufong, Taichung County, 413, Taiwan

Asia-Pacific Journal of Operational Research (APJOR), 2009, vol. 26, issue 02, 307-317

Abstract: Consider a batch-sizing problem, where all jobs are identical or similar, and aunitprocessing time(p = 1)is specified for each job. To minimize the total completion time of jobs, partitioning jobs into batches may be necessary. Learning effect from setup repetition makes small-sized batches; on the contrary, job's learning effect results in large-sized batches. With their collaborative influence, we develop a forward dynamic programming (DP) algorithm to determine the optimal number of batches and their optimal integer sizes. The computation effort required by this DP algorithm is a polynomial function of job size.

Keywords: Scheduling; batch; setup; learning effect (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1142/S0217595909002213

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