Optimization Days 2019

HEC Montréal, May 13-15, 2019


HEC Montréal, 13 — 15 May 2019

Schedule Authors My Schedule

MB3 Stochastic Optimization I

May 13, 2019 10:30 AM – 12:10 PM

Location: Demers Beaulne

Chaired by Raf Jans

4 Presentations

  • 10:30 AM - 10:55 AM

    A multi-stage stochastic programming approach for an integrated maintenance and production planning with demand uncertainty

    • Hamid Reza Zarei, presenter,
    • Masoumeh Kazemi, Concordia University
    • Mustapha Nourelfath, Université Laval

    In this paper, we developed an integrated multi-stage stochastic programming model for production and maintenance planning problems for a multi-state system with random time to failure under uncertain demand for products in each stage. The proposed model is solved for small-sized problems and the results are compared with mean-value deterministic model.
    Keywords: Random demand, Maintenance optimization, Production planning

  • 10:55 AM - 11:20 AM

    Lagrangian dual decision rules for multi-stage stochastic integer programming

    • Maryam Daryalal, presenter, University of Toronto
    • Merve Bodur, University of Toronto
    • Jim Luedtke, University of Wisconsin at Madison

    Multi-stage stochastic programs can be approximated by restricting policies to follow decision rules. This talk introduces Lagrangian dual decision rules (LDDRs) for multi-stage stochastic integer programs. We investigate techniques for using LDDRs to obtain bounds on the optimal value, and compare the strength of the relaxation from these different techniques.

    Keywords: Multi-stage Stochastic Integer Programming, Decision Rules, Lagrangian Relaxation

  • 11:20 AM - 11:45 AM

    Solving stochastic large-scale mixed integer linear problems for industrial production scheduling

    • Zayneb Brika, presenter, École Polytechnique de Montréal
    • Michel Gamache, Polytechnique Montréal
    • Roussos Dimitrakopoulos, COSMO Stochastic Mine Planning Laboratory, Université McGill

    A new linear model is presented to address the topic of an open-pit mine production scheduling accounting for stockpiles and investment decisions in a stochastic context. The solution approach consists in first solving the linear relaxation using an extension of the Bienstock-Zuckerberg algorithm to the stochastic optimization. Then, a rounding heuristic based on the topological sorting is applied followed by a Tabu search with multiple neighbourhoods. A parallelization strategy is used to reduce the time spent creating the neighbourhoods. Real-sized instances are used to test the proposed method.
    Keywords: open-pit scheduling; stochastic mathematical optimization; Bienstock-Zuckerberg algorithm; parallelization.

  • 11:45 AM - 12:10 PM

    Investigating aggregate γ service level constraint in the stochastic lot sizing problem

    • Narges Sereshti, presenter, HEC Montréal
    • Yossiri Adulyasak, HEC Montréal
    • Raf Jans, HEC Montréal

    In this research, we extend the stochastic programming model of the multi-item lot-sizing with γ service level for each individual product to a more practical setting where multiple service levels can be used in conjunction across multiple products to ensure that the business requirements are satisfied on an aggregate level.
    Stochastic Lot-Sizing, Aggregate Service Level