An improved model for vehicle routing problem with time constraint based on genetic algorithm
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Reactive power and voltage control based on general quantum genetic algorithms
Expert Systems with Applications: An International Journal
A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding
Computers & Mathematics with Applications
Computing nine new best-so-far solutions for Capacitated VRP with a cellular Genetic Algorithm
Information Processing Letters
An artificial neural network based heuristic for flow shop scheduling problems
Journal of Intelligent Manufacturing
A hybrid quantum-inspired genetic algorithm for flow shop scheduling
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cooperative particle swarm optimization for multiobjective transportation planning
Applied Intelligence
Hi-index | 0.00 |
Logistics faces great challenges in vehicle schedule problem. Intelligence Technologies need to be developed for solving the transportation problem. This paper proposes an improved Quantum-Inspired Evolutionary Algorithm (IQEA), which is a hybrid algorithm of Quantum-Inspired Evolutionary Algorithm (QEA) and greed heuristics. It extends the standard QEA by combining its principles with some heuristics methods. The proposed algorithm has also been applied to optimize a problem which may happen in real life. The problem can be categorized as a vehicle routing problem with time windows (VRPTW), which means the problem has many common characteristics that VRPTW has, but more constraints need to be considered. The basic idea of the proposed IQEA is to embed a greed heuristic method into the standard QEA for the optimal recombination of consignment subsequences. The consignment sequence is the order to arrange the vehicles for the transportation of the consignments. The consignment subsequences are generated by cutting the whole consignment sequence according to the values of quantum bits. The computational result of the simulation problem shows that IQEA is feasible in achieving a relatively optimal solution. The implementation of an optimized schedule can save much more cost than the initial schedule. It provides a promising, innovative approach for solving VRPTW and improves QEA for solving complexity problems with a number of constraints.