Simulated annealing: theory and applications
Simulated annealing: theory and applications
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A Metaheuristic for the Pickup and Delivery Problem with Time Windows
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Ant Colony Optimization
Using metaheuristic algorithms remotely via ROS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computers and Industrial Engineering
A comparison of five heuristics for the multiple depot vehicle scheduling problem
Journal of Scheduling
A new hybrid ant colony optimization algorithm for the vehicle routing problem
Pattern Recognition Letters
An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup
Computers and Operations Research
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
A hybrid genetic - Particle Swarm Optimization Algorithm for the vehicle routing problem
Expert Systems with Applications: An International Journal
Benefits of plugin-based heuristic optimization software systems
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Hybrid particle swarm algorithm for grain logistics vehicle routing problem
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Particle swarm optimization with triggered mutation and its implementation based on GPU
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Bio-inspired metaheuristics for the vehicle routing problem
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
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The objective of vehicle routing problem (VRP) is to design a set of vehicle routes in which a fixed fleet of delivery vehicles from one or several depots to a number of customers have to be set with some constraints. To this date in the literature, many instances of VRP model have been introduced and applied for various types of scheduling problems. However, when implemented in a real life application, the VRP models proved to be very complex and time consuming, especially in the development phase. It is due to the fact that there are technical hurdles to overcome such as the steep learning curve, the diversity and complexity of the algorithms. This paper presents a generalize software framework for an effective development of VRP models. The software framework presented here is hybridized algorithm of two metaheuristics namely as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The hybrid algorithm is used to optimize the best route for the vehicles that also incorporates a mechanism to trigger swarm condition for PSO algorithm. In order to test the functionality of the software framework, the applications of Pickup and Delivery Problem with Time Windows (PDPTW) and Vehicle Routing Problem with Time Windows (VRPTW) are developed based on the software framework. Experiments have been carried out by running the hybrid PSO with the VRPTW and PDPTW benchmark data set. The results indicate that the algorithm is able to produce significant improvement mostly to the PDPTW.