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Annals of Operations Research

, Volume 273, Issue 1–2, pp 111–134 | Cite as

Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network

  • Chen Chao
  • Tian Zhihui
  • Yao BaozhenEmail author
OR in Transportation

Abstract

High transportation cost and low service quality are common weaknesses in different logistics networks, especially in food delivery. Due to its perishable features, the quality of the food will deteriorate during the while delivery process. In this paper, a two-stage location–routing–inventory problem with time windows (2S-LRITW) for food products is proposed. The first stage corresponds to a location–routing–inventory problem with time windows and the second stage is the transportation problem with vehicle capacity constraints. The problem is formulated as a mixed integer-programming model. Then a hybrid heuristic is proposed, in which distance-based clustering approach, mutation operation and the relocate exchange method are introduced to improve performance of algorithm. At last, the hybrid heuristic was tested using several cases, including some small instances and a real-life case. The results show that the distance-based clustering approach can efficiently improve the convergence speed. And the IACO and the relocate exchange method can enlarge the search space. As a result, the hybrid heuristic is suitable for solving practical larger scale problem. Furthermore, the results also indicate that customer sequence has great effect on the object due to the energy cost considered.

Keywords

Location–routing–inventory problem with time-windows (LRITW) Food distribution IACO Clustering 

Notes

Acknowledgements

This research was supported in National Natural Science Foundation of China 51578112 and the fundamental research funds for the central universities (DUT16YQ104).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Automotive EngineeringDalian University of TechnologyDalianChina

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