The traveling salesman problem with backhauls
Computers and Operations Research
Computers and Operations Research
A heuristic for the pickup and delivery traveling salesman problem
Computers and Operations Research
A Branch & Cut Algorithm for the Asymmetric Traveling Salesman Problem with Precedence Constraints
Computational Optimization and Applications
An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem
INFORMS Journal on Computing
Heuristics for the One-Commodity Pickup-and-Delivery Traveling Salesman Problem
Transportation Science
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
A branch-and-cut algorithm for a traveling salesman problem with pickup and delivery
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
A hybrid GRASP/VND heuristic for the one-commodity pickup-and-delivery traveling salesman problem
Computers and Operations Research
Mathematical Programming: Series A and B
Matheuristics: Hybridizing Metaheuristics and Mathematical Programming
Matheuristics: Hybridizing Metaheuristics and Mathematical Programming
A branch-and-cut algorithm for solving the Non-Preemptive Capacitated Swapping Problem
Discrete Applied Mathematics
Models for the single-vehicle preemptive pickup and delivery problem
Journal of Combinatorial Optimization
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This paper addresses an extension of the Traveling Salesman Problem where a vehicle with a limited capacity must transport commodities. Each commodity has a weight, and exactly one origin and one destination. The objective is to find a minimum length Hamiltonian tour satisfying all the transportation requests without ever violating the capacity constraint. We propose for this problem a hybrid heuristic approach that combines the GRASP and VND metaheuristic techniques. Two variants of the method are presented, one of them using a mathematical programming based local search. We conduct computational experiments to compare the developed algorithms. The experiments show that they improve the best known solutions for a set of instances from the literature, and are able to cope with instances with up to 300 customers and 600 commodities in a reasonable amount of computation time.