Journées de l'optimisation 2018

HEC Montréal, Québec, Canada, 7 — 9 mai 2018

Horaire Auteurs Mon horaire
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MB5 Scheduling II

7 mai 2018 15h30 – 17h10

Salle: Manuvie (54)

Présidée par Rupinder Kaur

3 présentations

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    15h30 - 15h55

    Cross-dock scheduling with fixed outbound departures, multiple transshipment trips and known order of shipments

    • Thuy Vu, Concordia University
    • Wael Nassief, prés., Université Concordia
    • Brigitte Jaumard, CIISE, Concordia University

    We study a cross-dock inbound scheduling problem with fixed outbound departure times to minimize the number of tardy products per shipment. We consider two extensions: multiple trips and known unloading order of shipments. A time-indexed formulation is introduced and compared with the state-of-the-art. Computational experiments on benchmark instances are reported.

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    15h55 - 16h20

    Tactical aerial search and rescue fleet optimization

    • Jair Feldens Ferrari, prés., Concordia University
    • Mingyuan Chen,

    When an accident occurs far in the ocean, an ad hoc fleet is usually formed for searching survivors and debris. However, it presents challenges to obtain a high search efficiency in combining a set of aircraft from different sources and varied performances, especially when it is not possible to forecast for how long the aerial operation will last. This research considers the problem for planning such fleet as a resource allocation problem and proposes a binary integer programming model to calculate the best combination of available aircraft for maximizing the area searched while minimizing costs and considering operational, time and crew constraints. Instances are used to demonstrate the potential of the model for planning the aerial fleet at the tactical level for solving real-world problems.

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    16h20 - 16h45

    Cross-disciplinary workforce planning for Industry 4.0

    • Rupinder Kaur, prés., Concordia University
    • Anjali Awasthi, Concordia University

    Since smart factories are capable of adopting changes, the staff should be trained and qualified on multiple skills. We propose a genetic algorithm based approach to address the multi skill workforce planning and scheduling problem for Industry 4.0. A case study is provided.​

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