Optimizing Planning Service Territories by Dividing Into Compact Several Sub-areas Using Binary K-means Clustering According Vehicle Constraints
Abstract
VRP (Vehicle Routing Problem) is an NP hard problem, and it has attracted a lot of research interest. In contexts where vehicles have limited carrying capacity, such as volume and weight but needed to deliver items at various locations. Initially before creating a route, each vehicle needs a group of delivery points that are not exceeding their maximum capacity. Drivers tend to deliver only to certain areas. Cluster-based is one of the approaches to give a basis for generating tighter routes. In this paper we propose new algorithms for producing such clusters/groups that do not exceed vehicles maximum capacity. Our basic assumptions are each vehicle originates from a depot, delivers the items to the customers and returns to the depot, also the vehicles are homogeneous. This methods are able to compact sub-areas in each cluster. Computational results demonstrate the effectiveness of our new procedures, which are able to assist users to plan service territories and vehicle routes more efficiently.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2020
- DOI:
- 10.48550/arXiv.2010.10934
- arXiv:
- arXiv:2010.10934
- Bibcode:
- 2020arXiv201010934H
- Keywords:
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- Mathematics - Optimization and Control;
- Computer Science - Machine Learning
- E-Print:
- 7 pages, 3 figures, preliminary research for implementing VRP