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A fast ant-colony algorithm for single-machine scheduling to minimize the sum of weighted tardiness of jobs

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  • O Holthaus
  • C Rajendran

Abstract

The problem of scheduling on a single machine is considered in this paper with the objective of minimizing the sum of weighted tardiness of jobs. A new ant-colony optimization (ACO) algorithm, called fast ACO (FACO), is proposed and analysed for solving the single-machine scheduling problem. By considering the benchmark problems available in the literature for analysing the performance of algorithms for scheduling on a single machine with the consideration of weighted tardiness of jobs, we validate the appropriateness of the proposed local-search schemes and parameter settings used in the FACO. We also present a comparison of the requirements of CPU time for solving the single-machine total-weighted tardiness problem by the FACO and the existing algorithms.

Suggested Citation

  • O Holthaus & C Rajendran, 2005. "A fast ant-colony algorithm for single-machine scheduling to minimize the sum of weighted tardiness of jobs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 947-953, August.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:8:d:10.1057_palgrave.jors.2601906
    DOI: 10.1057/palgrave.jors.2601906
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    References listed on IDEAS

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    1. Ho, Johnny C., 1995. "Flowshop sequencing with mean flowtime objective," European Journal of Operational Research, Elsevier, vol. 81(3), pages 571-578, March.
    2. Gangadharan, Rajesh & Rajendran, Chandrasekharan, 1993. "Heuristic algorithms for scheduling in the no-wait flowshop," International Journal of Production Economics, Elsevier, vol. 32(3), pages 285-290, November.
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    4. Richard K. Congram & Chris N. Potts & Steef L. van de Velde, 2002. "An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 52-67, February.
    5. Rajendran, Chandrasekharan, 1993. "Heuristic algorithm for scheduling in a flowshop to minimize total flowtime," International Journal of Production Economics, Elsevier, vol. 29(1), pages 65-73, February.
    6. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
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    Cited by:

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    2. A Volgenant & I Y Zwiers, 2007. "Partial enumeration in heuristics for some combinatorial optimization problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 73-79, January.
    3. Juliette García-Alviz & Gina Galindo & Julián Arellana & Ruben Yie-Pinedo, 2021. "Planning road network restoration and relief distribution under heterogeneous road disruptions," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 941-981, December.
    4. Pablo Valledor & Alberto Gomez & Javier Puente & Isabel Fernandez, 2022. "Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    5. Zhichao Li & Jilin Huang, 2019. "How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    6. C Almeder & L Mönch, 2011. "Metaheuristics for scheduling jobs with incompatible families on parallel batching machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2083-2096, December.

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