• 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

Raa, B. and Dullaert, W. (2017). Route and fleet design for cyclic inventory routing European Journal of Operational Research, 256(2):404--411.


  • Journal
    European Journal of Operational Research

This paper presents a novel solution approach for planning cyclic distribution from a single depot to multiple customers with constant, deterministic demand rates. The objective is to minimize the total cost rate consisting of fleet costs, distribution costs from the depot to the customers and inventory holding costs at the customers. A solution is built in two phases: designing routes and composing the fleet. When designing vehicle routes in the first phase, the route cycle times are chosen such that the distribution and inventory holding costs are minimized. When assigning routes to vehicles in the second phase, the routes remain unchanged, but their cycle times can be adjusted to minimize the required number of vehicles. The building blocks of this two-phase solution approach are embedded in a metaheuristic framework. Computational experiments show that the resulting solution framework outperforms existing solution approaches.