Serial-batching scheduling with time-dependent setup time and effects of deterioration and learning on a single-machine

Jun Pei (), Xinbao Liu, Panos M. Pardalos, Athanasios Migdalas and Shanlin Yang
Additional contact information
Jun Pei: Hefei University of Technology
Xinbao Liu: Hefei University of Technology
Panos M. Pardalos: University of Florida
Athanasios Migdalas: Lulea University of Technology
Shanlin Yang: Hefei University of Technology

Journal of Global Optimization, 2017, vol. 67, issue 1, No 12, 262 pages

Abstract: Abstract This paper deals with serial-batching scheduling problems with the effects of deterioration and learning, where time-dependent setup time is also considered. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on a single serial-batching machine. The actual job processing time is a function of its starting time and position. In addition, a setup time is required when a new batch is processed, and the setup time of the batches is time-dependent, i.e., it is a linear function of its starting time. Structural properties are derived for the problems of minimizing the makespan, the number of tardy jobs, and the maximum earliness. Then, three optimization algorithms are developed to solve them, respectively.

Keywords: Scheduling; Serial-batching; Deteriorating jobs; Learning effect; Single-machine (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10898-015-0320-5

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