Some extensions to the sweep algorithm

Na, Byungsoo; Jun, Yeowoon; Kim, Byung‐In

doi:10.1007/s00170-011-3240-7

Cited by 23 publications

(13 citation statements)

References 11 publications

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“…SN [13] combines the Nearest Neighbor algorithm with the classical SW. SW groups the customers exclusively by polar angle. On the off chance that the customers are generally isolated yet have less precise distinction, they might be assembled in a similar group.…”

Section: B Sweep Nearest (Sn) Algorithmmentioning

confidence: 99%

“…Fig. 1 shows graphical representation of the clustering with SW and SN for a sample scenario of CVRP customer points [13]. In SW ( Fig.…”

Section: B Sweep Nearest (Sn) Algorithmmentioning

confidence: 99%

“…An extension of Sweep (SW) algorithm, called Sweep Nearest algorithm, has been investigated and found to perform better than standard one. Sweep Nearest (SN) [13] combines the idea of Sweep and Nearest Neighbor concept. Like SW, SN also considers sorted polar angle of the customers and starts a cluster with the customer having smallest polar angle.…”

Section: Introductionmentioning

confidence: 99%

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Distance based Sweep Nearest Algorithm to Solve Capacitated Vehicle Routing Problem

Akhand¹,

Sultana²,

Peya³

et al. 2019

IJACSA

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The Capacitated Vehicle Routing Problem (CVRP) is an optimization problem owing to find minimal travel distances to serve customers with homogeneous fleet of vehicles. Clustering customers and then assign individual vehicles is a widely-studied way, called cluster first and route second (CFRS) method, for solving CVRP. Cluster formation is important between two phases of CFRS for better CVRP solution. Sweep (SW) clustering is the pioneer one in CFRS method which solely depends on customers' polar angle: sort the customers according to polar angle; and a cluster starts with customer having smallest polar angle and completes it considering others according to polar angle. On the other hand, Sweep Nearest (SN) algorithm, an extension of Sweep, also considers smallest polar angle customer to initialize a cluster but inserts other customer(s) based on the nearest neighbor approach. This study investigates a different way of clustering based on nearest neighbor approach. The proposed Distance based Sweep Nearest (DSN) method starts clustering from the farthest customer point and continues for a cluster based on nearest neighbor concept. The proposed method does not rely on polar angle of the customers like SW and SN. To identify the effectiveness of the proposed approach, SW, SN and DSN have been implemented in this study for solving benchmark CVRPs. For route optimization of individual vehicles, Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are considered for clusters formation with SW, SN and DSN. The experimental results identified that proposed DSN outperformed SN and SW in most of the cases and DSN with PSO was the best suited method for CVRP.

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Section: B Sweep Nearest (Sn) Algorithmmentioning

confidence: 99%

“…Fig. 1 shows graphical representation of the clustering with SW and SN for a sample scenario of CVRP customer points [13]. In SW ( Fig.…”

Section: B Sweep Nearest (Sn) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distance based Sweep Nearest Algorithm to Solve Capacitated Vehicle Routing Problem

Akhand¹,

Sultana²,

Peya³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…was proposed named Sweep Nearest algorithm (SN) 15 , which combines the classical Sweep and the Nearest Neighbor algorithm. SN first assigns a vehicle to the customer with the smallest polar angle among the remaining customers and then finds the nearest stop to those already assigned and then inserts that customer.…”

Section: Sweep Algorithmmentioning

confidence: 99%

“…Based on these observations, the authors of 15 proposed two different kinds of reference points rather than the depot: every node and distant point. , y + , y -.…”

Section: Sweep Reference Point Algorithmmentioning

confidence: 99%

Constructive and Clustering Methods to Solve Capacitated Vehicle Routing Problem

et al. 2017

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Vehicle Routing Problem (VRP) is a real life constraint satisfaction problem to find minimal travel distances of vehicles to serve customers. Capacitated VRP (CVRP) is the simplest form of VRP considering vehicle capacity constraint. Constructive and clustering are the two popular approaches to solve CVRP. A constructive approach creates routes and attempts to minimize the cost at the same time. Clarke and Wright's Savings algorithm is a popular constructive method based on savings heuristic. On the other hand, a clustering based method first assigns nodes into vehicle wise cluster and then generates route for each vehicle. Sweep algorithm and its variants and Fisher and Jaikumar algorithm are popular among clustering methods. Route generation is a traveling salesman problem (TSP) and any TSP optimization method is useful for this purpose.

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Constraint Programming Based Algorithm for Solving Large-Scale Vehicle Routing Problems

Rabbouch

Saâdaoui

Mraïhi

2019

Lecture Notes in Computer Science

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Some extensions to the sweep algorithm

Cited by 23 publications

References 11 publications

Distance based Sweep Nearest Algorithm to Solve Capacitated Vehicle Routing Problem

Distance based Sweep Nearest Algorithm to Solve Capacitated Vehicle Routing Problem

Constructive and Clustering Methods to Solve Capacitated Vehicle Routing Problem

Constraint Programming Based Algorithm for Solving Large-Scale Vehicle Routing Problems

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