2016 Optimization Days

HEC Montréal, Québec, Canada, May 2 — 4, 2016

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WA2 Optimal Allocation

May 4, 2016 10:30 AM – 12:10 PM

Location: Saine Marketing

Chaired by Koukla Azeuli

3 Presentations

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    10:30 AM - 10:55 AM

    Constrained TCP-nets

    • Malek Mouhoub, presenter, University of Regina
    • Samira Sadaoui, University of Regina
    • Shu Zhang, University of Regina

    A Conditional Preference Network (CP-net) is a widely used graphical model for expressing qualitative and conditional preferences over attributes values. CP-nets have been recently extended to Tradeoffs-enhanced Conditional Preference Networks (TCP-nets) in order to capture the relative importance among attributes. In this paper, we extend the TCP-net to hard constraints and call the new proposed model constrained TCP-net. More precisely, the constrained TCP-net has the ability to represent and manage a given application under constraints as well as qualitative and conditional preferences over the attributes and their values. Solving the constrained TCP-net consists of finding the set of Pareto optimal solutions satisfying all the constraints and maximizing all the preferences. This task is addressed in this paper using a variant of the branch and bound algorithm enhanced with constraint propagation and variable ordering heuristics.

  • Cal add eabad1550a3cf3ed9646c36511a21a854fcb401e3247c61aefa77286b00fe402
    10:55 AM - 11:20 AM

    Multi-objective optimization in multi-attribute combinatorial reverse auctions

    • Shubhashis Kumar Shil, presenter, University of Regina
    • Samira Sadaoui, University of Regina

    Combinatorial Reverse Auctions (CRAs) often involve multiple conflicting objectives. Winner determination in CRAs is a NP-complete problem. Considering multi-unit and multi-attribute along with buyer's and sellers' constraints adds more complexity. Our ambition is to develop a multi-objective genetic algorithm to find the Pareto-optimal solutions for our new winner determination problem.

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    11:20 AM - 11:45 AM

    Allocation optimale des budgets de campagne en marketing interactif

    • Koukla Azeuli, presenter, Polytechnique Montréal
    • Michel Gamache, Polytechnique Montréal
    • Alain Hertz, GERAD, Polytechnique Montréal

    Nous proposons un modèle non-linéaire d'optimisation de campagnes publicitaires en ligne à partir de données de navigation partielles. Ce modèle permet de déterminer les valeurs d'enchères à miser sur les mots-clés dans les moteurs de recherche permettant d'améliorer la rentabilité de la campagne. Nous avons également développé un algorithme de résolution basé sur la recherche taboue.

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