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Solving a two-agent single-machine scheduling problem considering learning effect
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AI
DOI：10.1016/j.cor.2011.09.018.png)
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Abstract
En 中文
Scheduling with multiple agents and learning effect has drawn much attention. In this paper, we investigate the job scheduling problem of two agents competing for the usage of a common single machine with learning effect. The objective is to minimize the total weighted completion time of both agents with the restriction that the makespan of either agent cannot exceed an upper bound. In order to solve this problem we develop several dominance properties and a lower bound based on a branch-and-bound to find the optimal algorithm, and derive genetic algorithm based procedures for finding near-optimal solutions. The performances of the proposed algorithms are evaluated and compared via computational experiments. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Scheduling
Single-machine
Two-agent
Learning effect
Genetic algorithm
Journal
C
IF:
4.3
Papers:
6.5K
Citations:
1.8W