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Related Concept Videos

Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

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Castigliano's Theorem: Problem Solving01:14

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Related Experiment Videos

The bi-objective stochastic covering tour problem.

Fabien Tricoire1, Alexandra Graf, Walter J Gutjahr

  • 1Department of Business Administration, University of Vienna, Bruenner Strasse 72, 1210 Vienna, Austria ; NICTA/UNSW, Sydney, Australia.

Computers & Operations Research
|March 9, 2013
PubMed
Summary
This summary is machine-generated.

This study presents a bi-objective model for humanitarian logistics, optimizing distribution center costs and minimizing unmet demand. Computational results demonstrate a viable approach for real-world applications in rural communities.

Keywords:
Branch-and-cutCovering tour problemDisaster reliefMulti-objective optimizationStochastic optimization

Related Experiment Videos

Area of Science:

  • Operations Research
  • Logistics Management
  • Stochastic Optimization

Background:

  • Humanitarian logistics faces challenges in balancing operational costs with service levels.
  • Stochastic demand and client behavior (e.g., traveling to distribution centers) complicate planning.
  • Efficient distribution network design is crucial for effective aid delivery.

Purpose of the Study:

  • To develop a bi-objective model for distribution network design considering both cost and expected uncovered demand.
  • To incorporate stochastic demand and client proximity to distribution centers.
  • To provide a computationally viable solution for humanitarian logistics applications.

Main Methods:

  • Formulation of a bi-objective two-stage stochastic program with recourse.
  • Application of a branch-and-cut technique within an epsilon-constraint algorithm.
  • Utilizing a sample-average approximation for computational tractability.

Main Results:

  • The proposed model effectively balances cost and service level objectives.
  • Computational results on real-world data from Senegal confirm the approach's viability.
  • Demonstrated the effectiveness of the branch-and-cut and epsilon-constraint methods.

Conclusions:

  • The developed model offers a practical tool for optimizing humanitarian logistics networks.
  • The approach is suitable for addressing stochastic demand and real-world complexities.
  • Successful application in rural Senegal highlights its potential for similar contexts.