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Scheduling pre-printed newspaper advertising inserts using genetic algorithms

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  • Carter, Arthur E.
  • Ragsdale, Cliff T.

Abstract

In recent years, the use of pre-printed advertising inserts in newspapers has increased dramatically. Pre-printed inserts allow advertisers to deliver colorful, high-quality marketing material to targeted groups of consumers within the newspaper's delivery zone structure. To accommodate the increased workload associated with pre-printed inserts without negatively impacting the news deadline or delivery schedules, many newspaper companies face increasingly complex post-press scheduling decisions. This paper presents a spreadsheet model developed to represent the pre-printed insert scheduling problem in a case study of an actual medium-size newspaper company. The performance of two commercial genetic algorithm (GA) optimizers is compared on this problem. Computational testing shows the GAs develop schedules that substantially reduce the post-press production department's insert processing time.

Suggested Citation

  • Carter, Arthur E. & Ragsdale, Cliff T., 2002. "Scheduling pre-printed newspaper advertising inserts using genetic algorithms," Omega, Elsevier, vol. 30(6), pages 415-421, December.
    • Handle: RePEc:eee:jomega:v:30:y:2002:i:6:p:415-421
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    References listed on IDEAS

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    1. Shiromaru, Isao & Inuiguchi, Masahiro & Sakawa, Masatoshi, 2000. "A fuzzy satisficing method for electric power plant coal purchase using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 126(1), pages 218-230, October.
    2. Van Buer, Michael G. & Woodruff, David L. & Olson, Rick T., 1999. "Solving the medium newspaper production/distribution problem," European Journal of Operational Research, Elsevier, vol. 115(2), pages 237-253, June.
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    Cited by:

    1. Chiang, Wen-Chyuan & Russell, Robert & Xu, Xiaojing & Zepeda, David, 2009. "A simulation/metaheuristic approach to newspaper production and distribution supply chain problems," International Journal of Production Economics, Elsevier, vol. 121(2), pages 752-767, October.
    2. Bektas, Tolga, 2006. "The multiple traveling salesman problem: an overview of formulations and solution procedures," Omega, Elsevier, vol. 34(3), pages 209-219, June.
    3. Carter, Arthur E. & Ragsdale, Cliff T., 2006. "A new approach to solving the multiple traveling salesperson problem using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 175(1), pages 246-257, November.
    4. Liu, Ran & Xie, Xiaolan & Garaix, Thierry, 2014. "Hybridization of tabu search with feasible and infeasible local searches for periodic home health care logistics," Omega, Elsevier, vol. 47(C), pages 17-32.
    5. Liang, Wen-Yau & Huang, Chun-Che, 2008. "A hybrid approach to constrained evolutionary computing: Case of product synthesis," Omega, Elsevier, vol. 36(6), pages 1072-1085, December.
    6. Lawson, Barry R. & Baker, Kenneth R. & Powell, Stephen G. & Foster-Johnson, Lynn, 2009. "A comparison of spreadsheet users with different levels of experience," Omega, Elsevier, vol. 37(3), pages 579-590, June.
    7. Russell, Robert, 2013. "A constraint programming approach to designing a newspaper distribution system," International Journal of Production Economics, Elsevier, vol. 145(1), pages 132-138.
    8. John F. Wellington & Stephen A. Lewis, 2021. "Getting Beyond the First Result of Solving a Vehicle Routing Problem," INFORMS Transactions on Education, INFORMS, vol. 22(1), pages 9-27, September.
    9. Tamás Kalmár-Nagy & Giovanni Giardini & Bendegúz Dezső Bak, 2017. "The Multiagent Planning Problem," Complexity, Hindawi, vol. 2017, pages 1-12, February.

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