Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

Vincent F. Yu, Grace Aloina, Hadi Susanto, Mohammad Khoirul Effendi, Shih Wei Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Municipal waste management has become a challenging issue with the rise in urban pop-ulations and changes in people’s habits, particularly in developing countries. Moreover, government policy plays an important role associated with municipal waste management. Thus, this research proposes the regional location routing problem (RLRP) model and multi-depot regional location routing problem (MRLRP) model, which are extensions of the location routing problem (LRP), to provide a better municipal waste collection process. The model is constructed to cover the minimum number of depot facilities’ policy requirements for each region due to government policy, i.e., the large-scale social restrictions in each region. The goal is to determine the depot locations in each region and the vehicles’ routes for collecting waste to fulfill inter-regional independent needs at a minimum total cost. This research conducts numerical examples with actual data to illustrate the model and implements a hybrid genetic algorithm and simulated annealing optimization to solve the problem. The results show that the proposed method efficiently solves the RLRP and MRLRP.

Original languageEnglish
Article number2131
Issue number12
Publication statusPublished - 1 Jun 2022


  • genetic algorithm
  • multi-depot
  • regional location routing problem
  • simulated annealing
  • waste collection


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