IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v33y2013i11p2056-2067.html

An Approach for Optimal Allocation of Safety Resources: Using the Knapsack Problem to Take Aggregated Cost‐Efficient Preventive Measures

Author

Listed:
  • Genserik L. L. Reniers
  • Kenneth Sörensen

Abstract

On the basis of the combination of the well‐known knapsack problem and a widely used risk management technique in organizations (that is, the risk matrix), an approach was developed to carry out a cost‐benefits analysis to efficiently take prevention investment decisions. Using the knapsack problem as a model and combining it with a well‐known technique to solve this problem, bundles of prevention measures are prioritized based on their costs and benefits within a predefined prevention budget. Those bundles showing the highest efficiencies, and within a given budget, are identified from a wide variety of possible alternatives. Hence, the approach allows for an optimal allocation of safety resources, does not require any highly specialized information, and can therefore easily be applied by any organization using the risk matrix as a risk ranking tool.

Suggested Citation

  • Genserik L. L. Reniers & Kenneth Sörensen, 2013. "An Approach for Optimal Allocation of Safety Resources: Using the Knapsack Problem to Take Aggregated Cost‐Efficient Preventive Measures," Risk Analysis, John Wiley & Sons, vol. 33(11), pages 2056-2067, November.
    • Handle: RePEc:wly:riskan:v:33:y:2013:i:11:p:2056-2067
    DOI: 10.1111/risa.12036
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.12036
    Download Restriction: no
    File URL: https://libkey.io/10.1111/risa.12036?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. Martello, Silvano & Pisinger, David & Toth, Paolo, 2000. "New trends in exact algorithms for the 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 123(2), pages 325-332, June.
    2. RENIERS, Genserik & SOUDAN, Karel, 2003. "Risicoanalyseprocedures in de scheikundige nijverheid: Resultaten van een semi-kwalitatief onderzoek bij 24 chemische plants," Economic and Social Journal (Economisch en Sociaal Tijdschrift), University of Antwerp, Faculty of Business and Economics, vol. 57(3), pages 249-274, December.
    3. Eric D. Smith & William T. Siefert & David Drain, 2009. "Risk matrix input data biases," Systems Engineering, John Wiley & Sons, vol. 12(4), pages 344-360, December.
    Full references (including those not matched with items on IDEAS)

    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. Büther, Marcel & Briskorn, Dirk, 2007. "Reducing the 0-1 knapsack problem with a single continuous variable to the standard 0-1 knapsack problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 629, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    2. R. G. van der Vegt, 2018. "Risk Assessment and Risk Governance of Liquefied Natural Gas Development in Gladstone, Australia," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1830-1846, September.
    3. Ben O’Neill, 2022. "Smallest covering regions and highest density regions for discrete distributions," Computational Statistics, Springer, vol. 37(3), pages 1229-1254, July.
    4. R. Pablo Arribillaga & G. Bergantiños, 2022. "Cooperative and axiomatic approaches to the knapsack allocation problem," Annals of Operations Research, Springer, vol. 318(2), pages 805-830, November.
    5. Sbihi, Abdelkader, 2010. "A cooperative local search-based algorithm for the Multiple-Scenario Max-Min Knapsack Problem," European Journal of Operational Research, Elsevier, vol. 202(2), pages 339-346, April.
    6. Stefan Hajkowicz & Andrew Higgins & Kristen Williams & Daniel P. Faith & Michael Burton, 2007. "Optimisation and the selection of conservation contracts," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(1), pages 39-56, March.
    7. Andrea Chaves & A. Terry Bahill, 2014. "Comparison of Risk Analysis Approaches and a Case Study of the Risk of Incorporating Solar Photovoltaic Systems into a Commercial Electric Power Grid," Systems Engineering, John Wiley & Sons, vol. 17(1), pages 89-111, March.
    8. Joonyup Eun & Chang Sup Sung & Eun-Seok Kim, 2017. "Maximizing total job value on a single machine with job selection," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 998-1005, September.
    9. Alessandro Capocchi & Paola Orlandini & Stefano Amelio, 2021. "Hospital risk management at the time of Covid-19: An analysis of the Lombardy Region," MECOSAN, FrancoAngeli Editore, vol. 0(118), pages 97-116.
    10. Wishon, Christopher & Villalobos, J. Rene, 2016. "Robust efficiency measures for linear knapsack problem variants," European Journal of Operational Research, Elsevier, vol. 254(2), pages 398-409.
    11. Gema Sakti Raspati & Stian Bruaset & Camillo Bosco & Lars Mushom & Birgitte Johannessen & Rita Ugarelli, 2022. "A Risk-Based Approach in Rehabilitation of Water Distribution Networks," IJERPH, MDPI, vol. 19(3), pages 1-24, January.
    12. Furini, Fabio & Ljubić, Ivana & Sinnl, Markus, 2017. "An effective dynamic programming algorithm for the minimum-cost maximal knapsack packing problem," European Journal of Operational Research, Elsevier, vol. 262(2), pages 438-448.
    13. Zhenbo Wang & Wenxun Xing, 2009. "A successive approximation algorithm for the multiple knapsack problem," Journal of Combinatorial Optimization, Springer, vol. 17(4), pages 347-366, May.
    14. Syam Menon & Ali Amiri, 2004. "Scheduling Banner Advertisements on the Web," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 95-105, February.
    15. Shah, Ruchit & Reed, Patrick, 2011. "Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems," European Journal of Operational Research, Elsevier, vol. 211(3), pages 466-479, June.
    16. Jianping Li & Chunbing Bao & Dengsheng Wu, 2018. "How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 99-117, January.
    17. Novak, Antonin & Sucha, Premysl & Hanzalek, Zdenek, 2019. "Scheduling with uncertain processing times in mixed-criticality systems," European Journal of Operational Research, Elsevier, vol. 279(3), pages 687-703.
    18. Ewa M. Bednarczuk & Janusz Miroforidis & Przemysław Pyzel, 2018. "A multi-criteria approach to approximate solution of multiple-choice knapsack problem," Computational Optimization and Applications, Springer, vol. 70(3), pages 889-910, July.
    19. Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.
    20. Reilly, Charles H. & Sapkota, Nabin, 2015. "A family of composite discrete bivariate distributions with uniform marginals for simulating realistic and challenging optimization-problem instances," European Journal of Operational Research, Elsevier, vol. 241(3), pages 642-652.

    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:riskan:v:33:y:2013:i:11:p:2056-2067. 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/10.1111/(ISSN)1539-6924 .

    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.