Optimizing Planning Service Territories by Dividing Into Compact Several Sub-areas Using Binary K-means Clustering According Vehicle Constraints

• URL: http://arxiv.org/abs/2010.10934v1
• Date: Wed, 21 Oct 2020 12:19:08 GMT
• Title: Optimizing Planning Service Territories by Dividing Into Compact Several Sub-areas Using Binary K-means Clustering According Vehicle Constraints
• Authors: Muhammad Wildan Abdul Hakim, Syarifah Rosita Dewi, Yurio Windiatmoko, Umar Abdul Aziz
• Abstract summary: VRP (Vehicle Routing Problem) is an NP hard problem, and it has attracted a lot of research interest. In this paper we propose new algorithms for producing such clusters/groups that do not exceed vehicles maximum capacity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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