Batch Scheduling for a Single Machine with Learning Effect to Minimize Total Actual Flow Time  

Oleh:

Yusriski, Rinto ; Sukoyo ; Samadhi, TMA Ari ; Hakim, Abdul Halim

Jenis: Article from Proceeding
Dalam koleksi: The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), 3-6 December 2013 Cebu, Philippines, page 1-9.
Topik: learning effect; batch scheduling; total actual flow times
Fulltext: 1223.pdf (654.33KB)

Isi artikel

This research addresses a batch scheduling model for a single machine with learning effect leading to a situation where as the operator becomes experienced in processing similar tasks sequentially, the processing time for a job will be shorter if the job is processed in latter position in the production schedule. The objective for the models is to minimize the so-called total actual flow time of all parts in the shop, defined as the total interval times between the respective arrival times of all parts in all batches which is processed by the machine and their common due date. The problem is formulated as a non-linear programming model for which a relaxation is applied by considering variable N (the number of batches) to be a parameter, i.e., setting several values of N for the model, started from N = 1 and increased by 1 iteratively until a stopping rule is satisfied. Numerical experiments to show the effectiveness of the methods are also provided.

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