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

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  • Vincent F. Yu

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
    Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

  • Grace Aloina

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

  • Hadi Susanto

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

  • Mohammad Khoirul Effendi

    (Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia)

  • Shih-Wei Lin

    (Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei 243303, Taiwan
    Department of Emergency Medicine, Keelung Chang Gung Memorial Hospital, Keelung 20401, Taiwan)

Abstract

Municipal waste management has become a challenging issue with the rise in urban populations 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.

Suggested Citation

  • Vincent F. Yu & Grace Aloina & Hadi Susanto & Mohammad Khoirul Effendi & Shih-Wei Lin, 2022. "Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing," Mathematics, MDPI, vol. 10(12), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2131-:d:842475
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    References listed on IDEAS

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    Cited by:

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    5. Cristina Lopes & Ana Maria Rodrigues & Valeria Romanciuc & José Soeiro Ferreira & Elif Göksu Öztürk & Cristina Oliveira, 2023. "Divide and Conquer: A Location-Allocation Approach to Sectorization," Mathematics, MDPI, vol. 11(11), pages 1-19, June.

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