An Optimization Approach to Model the Waste Collection Process

  • Authors

    • Zainab Adnanjwad
    • Saad Talib Hasson
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.27970
  • Waste collection, Capacitated Clustering problem, uniform distribution, waste value estimation, distinct node approach, nearest neighbor.

  • Abstract

    Optimizing the waste collection process is helping in reducing the costs and the waste environmental effects.Waste collection requires applying modified technologies in designing and managing the waste collection areas.Waste collection problem represents a special case of the general collection problems. It needs to consider additional constraints and qualified resources. In this study a modified capacitated clustering approach is applied and implemented in order to distribute the waste bin nodes into dissimilar groups (clusters). These clusters are having different weights depending on the available trucks. Each truck has certain capacity (size) and it must visit each waste bin ones. It must also assign to one cluster only. The number of cluster can be estimated according to the available number of trucks and their capacities. The developed clustering process will optimize the total distances joining the waste bins in each cluster. 

    Waste quantity generation is applied and generated as uniform probability distribution random variables based on the historical data of the collected averages. The final result shows a large reduction (about 40 %) in the travelled route in comparing this study suggestion and the municipality route. This study develops scheduling process to assign the available trucks into shifts to collect and empty all the waste bin nodes.

     

     

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  • How to Cite

    Adnanjwad, Z., & Talib Hasson, S. (2018). An Optimization Approach to Model the Waste Collection Process. International Journal of Engineering & Technology, 7(4.19), 615-619. https://doi.org/10.14419/ijet.v7i4.19.27970

    Received date: 2019-02-26

    Accepted date: 2019-02-26

    Published date: 2018-11-27