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Note: Demonstrating Analytics in a Low-Tech Context–Truck-Routing for Solid-Waste Collection in an Indian Metropolis

Kala, K. & Sodhi, M. ORCID: 0000-0002-2031-4387 (2023). Note: Demonstrating Analytics in a Low-Tech Context–Truck-Routing for Solid-Waste Collection in an Indian Metropolis. Transportation Research Part E: Logistics and Transportation Review, 176, article number 103219. doi: 10.1016/j.tre.2023.103219


This paper describes an approach to introducing analytics through various algorithms and applications to users in a low-tech environment as a first step toward understanding such a context. The South Delhi Municipal Corporation (SDMC) of New Delhi, India, have partitioned their collection points into “wards” or clusters, each served by a dedicated truck depot and manually routing trucks for solid waste collection within each ward, with the waste from all wards going to a single landfill. To demonstrate analytics in tactical planning, we implemented the nearest neighbor algorithm mimicking the manual process to provide the baseline cost. Thus, we presented two very different vehicle routing algorithms: (1) a simple but fast revised nearest neighbor algorithm that decreased the baseline total routing cost by 1.57% and (2) an optimal but time-intensive algorithm using a mixed-integer-linear programming model, which decreased the total cost by 4.05%. To demonstrate strategic planning, we tested the efficacy of the cluster structure of collection points by comparing its total routing cost (using the revised nearest neighbor algorithm) to that of other partitions obtained with Minimum Spanning Tree (MST) and K-medoids clustering. The existing wards provided a lower waste pickup cost than the alternative clusters we created, showing SDMC that their existing ward structure was sound.

Publication Type: Article
Additional Information: © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Hierarchical method, vehicle routing problem, municipal solid waste collection, Mixed-Integer Linear Programming (MILP), India
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics
Departments: Bayes Business School > Management
SWORD Depositor:
[thumbnail of Accepted Version 10_July_2023_Accepted.pdf] Text - Accepted Version
This document is not freely accessible until 13 July 2024 due to copyright restrictions.
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