Perturbation heuristics for the pickup and delivery traveling salesman problem

Authors:
Jacques Renaud;Fayez F. Boctor;Gilbert Laporte
Affiliations:
Télé-Université, Tour de la Cité, Sainte-Foy, Canada and Centre de recherche sur les technologies de l'organisation réseau, Université Laval, Que., Canada;Centre de recherche sur les technologies de l'organisation réseau, Université Laval, Que., Canada and Faculté des sciences de l'administration, Université Laval, Que., Canada;École des Hautes Études Commerciales, Montréal, Que., Canada and Centre de recherche sur les transports, Université de Montréal, Montréal, Que., Canada
Venue:
Computers and Operations Research
Year:
2002

Citing 7
Cited 4

A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems

SIAM Review
New search spaces for sequencing problems with application to job shop scheduling

Management Science
The traveling salesman problem with backhauls

Computers and Operations Research
Variable neighborhood search

Computers and Operations Research
A heuristic for the pickup and delivery traveling salesman problem

Computers and Operations Research
Tabu Search

Tabu Search
An Exact Method for the Vehicle Routing Problem with Backhauls

Transportation Science

Heuristics for the traveling purchaser problem

Computers and Operations Research
The pickup and delivery traveling salesman problem with first-in-first-out loading

Computers and Operations Research
Branch-and-cut for the pickup and delivery traveling salesman problem with FIFO loading

Computers and Operations Research
Models for the single-vehicle preemptive pickup and delivery problem

Journal of Combinatorial Optimization

Quantified Score

Hi-index	0.01

Visualization

Abstract

This article describes and compares seven perturbation heuristics for the Pickup and Delivery Traveling salesman Problem (PDTSP). In this problem, a shortest Hamiltonian cycle is sought through a depot and several pickup and delivery pairs. Perturbation heuristics are diversification schemes which help a local search process move away from a local optimum. Three such schemes have been implemented and compared: Instance Perturbation, Algorithmic Perturbation, and Solution Perturbation. Computational results on PDTSP instances indicate that the latter scheme yields the best results. On instances for which the optimum is known, it consistently produces optimal or near-optimal solutions.