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Journal of Scheduling

, Volume 19, Issue 5, pp 583–600 | Cite as

Scheduling parallel-machine batch operations to maximize on-time delivery performance

  • Shubin Xu
  • James C. BeanEmail author
Article

Abstract

In this paper we study the problem of minimizing total weighted tardiness, a proxy for maximizing on-time delivery performance, on parallel nonidentical batch processing machines. We first formulate the (primal) problem as a nonlinear integer programming model. We then show that the primal problem can be solved exactly by solving a corresponding dual problem with a nonlinear relaxation. Since both the primal and the dual problems are NP-hard, we use genetic algorithms, based on random keys and multiple choice encodings, to heuristically solve them. We find that the genetic algorithms consistently outperform a standard mathematical programming package in terms of solution quality and computation time. We also compare the smaller problem instances to a breadth-first tree search algorithm that gives evidence of the quality of the solutions.

Keywords

Parallel-machine scheduling Batching Total weighted tardiness Optimal and approximate algorithms Nonlinear relaxation Genetic algorithms 

Notes

Acknowledgments

The authors would like to thank two anonymous referees and the Associate Editor for their insightful comments and constructive suggestions which significantly improved the presentation of this paper.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Business and ManagementNortheastern Illinois UniversityChicagoUSA
  2. 2.D’Amore-McKim School of BusinessNortheastern UniversityBostonUSA

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