Operations Research
A vehicle routing problem with stochastic demand
Operations Research
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Fuzzy Modeling for Control
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Genetic Algorithms: Concepts and Designs with Disk
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Operations Research
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
Stochastic Vehicle Routing Problem with Restocking
Transportation Science
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
Exact and heuristic dynamic programming algorithms for the vehicle routing problem with stochastic demands
A Diversity-Controlling Adaptive Genetic Algorithm for the Vehicle Routing Problem with Time Windows
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Ant Colony Optimization
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Computers and Operations Research
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Computers and Operations Research
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
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Transportation Science
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
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Automatica (Journal of IFAC)
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Computers and Operations Research
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Computers and Industrial Engineering
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Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
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Applied Soft Computing
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Computers in Industry
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In this paper, we develop a family of solution algorithms based upon computational intelligence for solving the dynamic multi-vehicle pick-up and delivery problem formulated under a hybrid predictive adaptive control scheme. The scheme considers future demand and prediction of expected waiting and travel times experienced by customers. In addition, this work includes an analytical formulation of the proposed prediction models that allow us to search over a reduced feasible space. Predictive models consider relevant state space variables as vehicle load and departure time at stops. A generic expression of the system cost function is used to measure the benefits in dispatching decisions of the proposed scheme when solving for more than two-step ahead under unknown demand. The demand prediction is based on a systematic fuzzy clustering methodology, resulting in appropriate call probabilities for uncertain future. As the dynamic multi-vehicle routing problem considered is NP-hard, we propose the use of genetic algorithms (GA) that provide near-optimal solutions for the three, two and one-step ahead problems. Promising results in terms of computation time and accuracy are presented through a simulated numerical example that includes the analysis of the proposed fuzzy clustering, and the comparison of myopic and new predictive approaches solved with GA.