IDEAS home Printed from https://ideas.repec.org/a/wly/navres/v57y2010i2p159-171.html
   My bibliography  Save this article

Scheduling with variable time slot costs

Author

Listed:
  • Guohua Wan
  • Xiangtong Qi

Abstract

In this article, we study a class of new scheduling models where time slot costs have to be taken into consideration. In such models, processing a job will incur certain cost which is determined by the time slots occupied by the job in a schedule. The models apply when operational costs vary over time. The objective of the scheduling models is to minimize the total time slot costs plus a traditional scheduling performance measure. We consider the following performance measures: total completion time, maximum lateness/tardiness, total weighted number of tardy jobs, and total tardiness. We prove the intractability of the models under general parameters and provide polynomial‐time algorithms for special cases with non‐increasing time slot costs.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010

Suggested Citation

  • Guohua Wan & Xiangtong Qi, 2010. "Scheduling with variable time slot costs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(2), pages 159-171, March.
    • Handle: RePEc:wly:navres:v:57:y:2010:i:2:p:159-171
    DOI: 10.1002/nav.20393
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/nav.20393
    Download Restriction: no
    File URL: https://libkey.io/10.1002/nav.20393?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Schmidt, Gunter, 2000. "Scheduling with limited machine availability," European Journal of Operational Research, Elsevier, vol. 121(1), pages 1-15, February.
    2. Srinivas Bollapragada & Marc Garbiras, 2004. "Scheduling Commercials on Broadcast Television," Operations Research, INFORMS, vol. 52(3), pages 337-345, June.
    3. Cheng, T. C. E. & Ding, Q. & Lin, B. M. T., 2004. "A concise survey of scheduling with time-dependent processing times," European Journal of Operational Research, Elsevier, vol. 152(1), pages 1-13, January.
    4. Zhi-Long Chen & Nicholas G. Hall, 2008. "Maximum Profit Scheduling," Manufacturing & Service Operations Management, INFORMS, vol. 10(1), pages 84-107, February.
    5. Srinivas Bollapragada & Michael R. Bussieck & Suman Mallik, 2004. "Scheduling Commercial Videotapes in Broadcast Television," Operations Research, INFORMS, vol. 52(5), pages 679-689, October.
    6. Al-Anzi, Fawaz S. & Allahverdi, Ali & Kovalyov, Mikhail Y., 2007. "Batching deteriorating items with applications in computer communication and reverse logistics," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1002-1011, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Farnaz Ghazi Nezami & Mojtaba Heydar, 2019. "Energy-aware Economic Production Quantity model with variable energy pricing," Operational Research, Springer, vol. 19(1), pages 201-218, March.
    2. Shen, Liji & Dauzère-Pérès, Stéphane & Maecker, Söhnke, 2023. "Energy cost efficient scheduling in flexible job-shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 310(3), pages 992-1016.
    3. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2016. "Scheduling on a single machine under time-of-use electricity tariffs," Annals of Operations Research, Springer, vol. 238(1), pages 199-227, March.
    4. Lin Chen & Nicole Megow & Roman Rischke & Leen Stougie & José Verschae, 2021. "Optimal algorithms for scheduling under time-of-use tariffs," Annals of Operations Research, Springer, vol. 304(1), pages 85-107, September.
    5. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2021. "Job Scheduling under Time-of-Use Energy Tariffs for Sustainable Manufacturing: A Survey," LIDAM Discussion Papers CORE 2021019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    7. Markó Horváth & Tamás Kis, 2020. "Polyhedral results for position-based scheduling of chains on a single machine," Annals of Operations Research, Springer, vol. 284(1), pages 283-322, January.
    8. Michal Penn & Tal Raviv, 2021. "Complexity and algorithms for min cost and max profit scheduling under time-of-use electricity tariffs," Journal of Scheduling, Springer, vol. 24(1), pages 83-102, February.
    9. Chen, Bo & Zhang, Xiandong, 2019. "Scheduling with time-of-use costs," European Journal of Operational Research, Elsevier, vol. 274(3), pages 900-908.
    10. Trevino-Martinez, Samuel & Sawhney, Rapinder & Shylo, Oleg, 2022. "Energy-carbon footprint optimization in sequence-dependent production scheduling," Applied Energy, Elsevier, vol. 315(C).
    11. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
    12. Briskorn, Dirk & Davari, Morteza & Matuschke, Jannik, 2021. "Single-machine scheduling with an external resource," European Journal of Operational Research, Elsevier, vol. 293(2), pages 457-468.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sylvia Hristakeva & Julie Holland Mortimer, 2023. "Price Dispersion and Legacy Discounts in the National Television Advertising Market," Marketing Science, INFORMS, vol. 42(6), pages 1162-1183, November.
    2. Giovanni Giallombardo & Houyuan Jiang & Giovanna Miglionico, 2016. "New Formulations for the Conflict Resolution Problem in the Scheduling of Television Commercials," Operations Research, INFORMS, vol. 64(4), pages 838-848, August.
    3. Gawiejnowicz, Stanislaw, 2007. "Scheduling deteriorating jobs subject to job or machine availability constraints," European Journal of Operational Research, Elsevier, vol. 180(1), pages 472-478, July.
    4. Bahram Alidaee & Haibo Wang & R. Bryan Kethley & Frank Landram, 2019. "A unified view of parallel machine scheduling with interdependent processing rates," Journal of Scheduling, Springer, vol. 22(5), pages 499-515, October.
    5. Yang, Suh-Jenq & Yang, Dar-Li, 2010. "Minimizing the makespan on single-machine scheduling with aging effect and variable maintenance activities," Omega, Elsevier, vol. 38(6), pages 528-533, December.
    6. Daya Ram Gaur & Ramesh Krishnamurti & Rajeev Kohli, 2009. "Conflict Resolution in the Scheduling of Television Commercials," Operations Research, INFORMS, vol. 57(5), pages 1098-1105, October.
    7. Shinjini Pandey & Goutam Dutta & Harit Joshi, 2017. "Survey on Revenue Management in Media and Broadcasting," Interfaces, INFORMS, vol. 47(3), pages 195-213, June.
    8. Dana G. Popescu & Pascale Crama, 2016. "Ad Revenue Optimization in Live Broadcasting," Management Science, INFORMS, vol. 62(4), pages 1145-1164, April.
    9. Koulamas, Christos & Kyparisis, George J., 2023. "A classification of dynamic programming formulations for offline deterministic single-machine scheduling problems," European Journal of Operational Research, Elsevier, vol. 305(3), pages 999-1017.
    10. Stanisław Gawiejnowicz, 2020. "A review of four decades of time-dependent scheduling: main results, new topics, and open problems," Journal of Scheduling, Springer, vol. 23(1), pages 3-47, February.
    11. José Antonio Carbajal & Wes Chaar, 2017. "Turner Optimizes the Allocation of Audience Deficiency Units," Interfaces, INFORMS, vol. 47(6), pages 518-536, December.
    12. José Antonio Carbajal & Peter Williams & Andreea Popescu & Wes Chaar, 2019. "Turner Blazes a Trail for Audience Targeting on Television with Operations Research and Advanced Analytics," Interfaces, INFORMS, vol. 49(1), pages 64-89, January.
    13. John Turner & Alan Scheller-Wolf & Sridhar Tayur, 2011. "OR PRACTICE---Scheduling of Dynamic In-Game Advertising," Operations Research, INFORMS, vol. 59(1), pages 1-16, February.
    14. M. A. Kubzin & V. A. Strusevich, 2006. "Planning Machine Maintenance in Two-Machine Shop Scheduling," Operations Research, INFORMS, vol. 54(4), pages 789-800, August.
    15. Shi, Yang & Zhao, Ying, 2019. "Modeling Advertisers' Willingness to Pay in TV Commercial Slot Auctions," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 120-133.
    16. A. Beynaghi & F. Moztarzadeh & A. Shahmardan & R. Alizadeh & J. Salimi & M. Mozafari, 2019. "Makespan minimization for batching work and rework process on a single facility with an aging effect: a hybrid meta-heuristic algorithm for sustainable production management," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 33-45, January.
    17. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    18. Wang, Xiuli & Cheng, T.C.E., 2015. "A heuristic for scheduling jobs on two identical parallel machines with a machine availability constraint," International Journal of Production Economics, Elsevier, vol. 161(C), pages 74-82.
    19. C-C He & C-C Wu & W-C Lee, 2009. "Branch-and-bound and weight-combination search algorithms for the total completion time problem with step-deteriorating jobs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1759-1766, December.
    20. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:navres:v:57:y:2010:i:2:p:159-171. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6750 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.