Effective algorithms for single-machine learning-effect scheduling to minimize completion-time-based criteria with release dates

作者：

Highlights：

• Asymptotic optimality of SPTA and EDDA heuristics in mathematical limit sense.

• B&B algorithm with release-date-based branching rule and effective lower bounds.

• DDE algorithm with multi-point insertion scheme and innovative initial population.

• Marginal condition for learning-effect scheduling with lateness criterion.

摘要

•Asymptotic optimality of SPTA and EDDA heuristics in mathematical limit sense.•B&B algorithm with release-date-based branching rule and effective lower bounds.•DDE algorithm with multi-point insertion scheme and innovative initial population.•Marginal condition for learning-effect scheduling with lateness criterion.

论文关键词：Learning effect,Single-machine scheduling,Discrete differential evolution,Asymptotic analysis,Branch and bound

论文评审过程：Received 13 September 2018, Revised 15 February 2020, Accepted 7 April 2020, Available online 1 May 2020, Version of Record 12 May 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113445