• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni

Van Hoesel, C.P.M. and Wagelmans, A.P.M. (1996). An O(T3) algorithm for the economic lot-sizing problem with constant capacities Management Science, 42(1):142--150.


  • Affiliated authors
    Roger van Hoesel, Albert Wagelmans
  • Publication year
    1996
  • Journal
    Management Science

We develop an algorithm that solves the constant capacities economic lot-sizing problem with concave production costs and linear holding costs in O(T3) time. The algorithm is based on the standard dynamic programming approach which requires the computation of the minimal costs for all possible subplans of the production plan. Instead of computing these costs in a straightforward manner, we use structural properties of optimal subplans to arrive at a more efficient implementation. Our algorithm improves upon the O(T4) running time of an earlier algorithm.