Parallel-machine scheduling to minimize tardiness penalty and power cost

Kuei Tang Fang, Miao-Tsong Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

Abstract

Traditional research on machine scheduling focuses on job allocation and sequencing to optimize certain objective functions that are defined in terms of job completion times. With regard to environmental concerns, energy consumption becomes another critical issue in high-performance systems. This paper addresses a scheduling problem in a multiple-machine system where the computing speeds of the machines are allowed to be adjusted during the course of execution. The CPU adjustment capability enables the flexibility for minimizing electricity cost from the energy saving aspect by sacrificing job completion times. The decision of the studied problem is to dispatch the jobs to the machines as well as to determine the job sequence and processing speed of each machine with the objective function comprising of the total weighted job tardiness and the power cost. We give a formal formulation, propose two heuristic algorithms, and develop a particle swarm optimization (PSO) algorithm to effectively tackle the problem. Since the existing solution representations do not befittingly encode the decisions involved in the studied problem into the PSO algorithm, we design a tailored encoding scheme which can embed all decisional information in a particle. A computational study is conducted to investigate the performances of the proposed heuristics and the PSO algorithm.

Original languageEnglish
Pages (from-to)224-234
Number of pages11
JournalComputers and Industrial Engineering
Volume64
Issue number1
DOIs
StatePublished - Jan 2013

Keywords

  • Dynamic voltage scaling
  • Parallel-machine scheduling
  • Particle swarm optimization
  • Total weighted tardiness

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