The traveling salesman problem with backhauls
Computers and Operations Research
Vehicle routing with pick-up and delivery: tour-partitioning heuristics
Computers and Industrial Engineering
Heuristics for the traveling salesman problem with pickup and delivery
Computers and Operations Research
An Exact Method for the Vehicle Routing Problem with Backhauls
Transportation Science
The traveling salesman problem with delivery and backhauls
Operations Research Letters
A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
A genetic algorithm for the simultaneous delivery and pickup problems with time window
Computers and Industrial Engineering
New best solutions to VRPSPD benchmark problems by a perturbation based algorithm
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm
Expert Systems with Applications: An International Journal
A Genetic Algorithm-based optimization model for supporting green transportation operations
Expert Systems with Applications: An International Journal
Hi-index | 0.02 |
The vehicle routing problem with simultaneous pick-up and delivery (VRP_SPD) is a variant of the classical vehicle routing problem (VRP) where clients require simultaneous pick-up and delivery service. Deliveries are supplied from a single depot at the beginning of the vehicle's service, while pick-up loads are taken to the same depot at the conclusion of the service. One important characteristic of this problem is that a vehicle's load in any given route is a mix of pick-up and delivery loads. In this paper we develop a tabu search algorithm to solve VRP_SPD. This algorithm uses three types of movements to obtain inter-route adjacent solutions: the relocation, interchange and crossover movements. A 2-opt procedure is used to obtain alternative intra-route solutions. Four types of neighbourhoods were implemented, three of them defined by the use of each of the single inter-route movements and the fourth by using a combination of these movements. Two different search strategies were implemented for selecting the next movement: first admissible movement and best admissible movement. Intensification and diversification of the search were achieved through frequency penalization. Computational results are reported for a set of 87 test problems with between 50 and 400 clients.