Single-machine rescheduling problems with learning effect under disruptions

Mingbao Cheng; Shuxian Xiao; Guosheng Liu

doi:10.3934/jimo.2017085

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2018, Volume 14, Issue 3: 967-980. Doi: 10.3934/jimo.2017085

This issue Previous Article Solutions for bargaining games with incomplete information: General type space and action space Next Article On the convergence properties of a smoothing approach for mathematical programs with symmetric cone complementarity constraints

Single-machine rescheduling problems with learning effect under disruptions

School of Management, Guangdong University of Technology, Gaungzhou, Guangdong 510520, China

* Corresponding author
* Corresponding author

Received: February 29, 2016

Revised: July 31, 2017

Early access: August 2017

Published: June 2018

The research is supported partially by the National Natural Science Foundation Committee of China grant 71571050,71502100,71401044.

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Abstract

Rescheduling in production planning means to schedule the sequenced jobs again together with a set of new arrived jobs so as to generate a new feasible schedule, which creates disruptions to any job between the original and adjusted position. In this paper, we study rescheduling problems with learning effect under disruption constraints to minimize several classical objectives, where learning effect means that the workers gain experience during the process of operation and make the actual processing time of jobs shorter than their normal processing time. The objectives are to find optimal sequences to minimize the makespan and the total completion time under a limit of the disruptions from the original schedule. For the considered objectives under a single disruption constraint or a disruption cost constraint, we propose polynomial-time algorithms and pseudo-polynomial time algorithms, respectively.

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Mathematics Subject Classification: Primary: 90B35; Secondary: 90C26.

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