Territorial design for customers with demand frequency

Lu Zhen, Jingjing Gao, Zheyi Tan, Gilbert Laporte, Roberto Baldacci

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)


Territorial design is an important long-term decision for urban delivery service companies, in contexts where customers are partitioned into districts. This study focuses on a territorial design problem given the demand frequency of each customer, i.e., the estimated percentage of days with demand, over the planning horizon. This study formulates a set partitioning model and designs a column generation based algorithm to solve the problem. The algorithm decomposes the original problem into a restricted master problem (RMP) and a series of pricing problems (PPs), each limited to one district. A dynamic programming based method is designed to solve the PPs efficiently. To further accelerate the solution processes of the PPs and of the RMP, some tailored strategies are also embedded within the algorithm. Numerical experiments are conducted to validate the contributions of the dynamic programming and of the acceleration strategies. Some tests based on real-world cases are also performed in order to derive some managerial insights to support the practitioners’ decisions on service territory design.

Original languageEnglish
Pages (from-to)82-101
Number of pages20
JournalEuropean Journal of Operational Research
Issue number1
Early online date17 Jan 2023
Publication statusPublished - 16 Aug 2023


The authors thank the editor (Professor J.F. Oliveira) and three anonymous reviewers for their constructive suggestions on improving this paper. This research was supported by the National Natural Science Foundation of China (Grant numbers 72025103 and 71831008 ). Thanks are due to the referees for their valuable comments.


  • Column generation
  • OR in service industries
  • Set partitioning
  • Territorial design problem

ASJC Scopus subject areas

  • Information Systems and Management
  • General Computer Science
  • Industrial and Manufacturing Engineering
  • Modelling and Simulation
  • Management Science and Operations Research


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