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Journal of Global Optimization

, Volume 67, Issue 1–2, pp 251–262 | Cite as

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
  • Shanlin Yang
Article

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 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 71231004, 71171071, 71131002, 71201042), and the Fundamental Research Funds for the Central Universities (Nos. JZ2015HGBZ0116, JZ2015HGBZ0117, JZ2015HGQC0187). Panos M. Pardalos is partially supported by the project of “Distinguished International Professor by the Chinese Ministry of Education” (MS2014HFGY026).

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Jun Pei
    • 1
    • 2
  • Xinbao Liu
    • 1
    • 3
  • Panos M. Pardalos
    • 2
  • Athanasios Migdalas
    • 4
    • 5
  • Shanlin Yang
    • 1
    • 3
  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.Department of Industrial and Systems Engineering, Center for Applied OptimizationUniversity of FloridaGainesvilleUSA
  3. 3.Key Laboratory of Process Optimization and Intelligent Decision-MakingMinistry of EducationHefeiChina
  4. 4.Division of Industrial Logistics, Department of Industrial EngineeringLulea University of TechnologyLuleåSweden
  5. 5.Division of Transportation, Construction Management and Regional Planning, Department of Civil EngineeringAristotle University of ThessalonikiThessaloníkiGreece

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