4.4 Article

Local Cuts and Two-Period Convex Hull Closures for Big-Bucket Lot-Sizing Problems

Journal

INFORMS JOURNAL ON COMPUTING
Volume 28, Issue 4, Pages 766-780

Publisher

INFORMS
DOI: 10.1287/ijoc.2016.0712

Keywords

lot-sizing; integer programming; local cuts; convex hull closure; quadratic programming; column generation

Funding

  1. EPSRC entitled MultiItem Production Planning: Theory, Computation and Practice, [EP/L000911/1]
  2. NSF [CMMI-0323299, CMMI-0521953]
  3. EPSRC [EP/L000911/1] Funding Source: UKRI
  4. Engineering and Physical Sciences Research Council [EP/L000911/1] Funding Source: researchfish

Ask authors/readers for more resources

Despite the significant attention they have drawn, big-bucket lot-sizing problems remain notoriously difficult to solve. Previous literature contained results (computational and theoretical) indicating that what makes these problems difficult are the embedded single-machine, single-level, multiperiod submodels. We therefore consider the simplest such submodel, a multi-item, two-period capacitated relaxation. We propose a methodology that can approximate the convex hulls of all such possible relaxations by generating violated valid inequalities. To generate such inequalities, we separate two-period projections of fractional linear programming solutions from the convex hulls of the two-period closure we study. The convex hull representation of the two-period closure is generated dynamically using column generation. Contrary to regular column generation, our method is an outer approximation and can therefore be used efficiently in a regular branch-and-bound procedure. We present computational results that illustrate how these two-period models could be effective in solving complicated problems.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available