Swarm intelligence
Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching
Transportation Science
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
The Granular Tabu Search and Its Application to the Vehicle-Routing Problem
INFORMS Journal on Computing
Solving the vehicle routing problem with adaptive memory programming methodology
Computers and Operations Research
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
The Dynamic Assignment Problem
Transportation Science
Transportation Science
Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching
Transportation Science
Incorporating Pricing Decisions into the Stochastic Dynamic Fleet Management Problem
Transportation Science
Pricing in Dynamic Vehicle Routing Problems
Transportation Science
Greenhouse air temperature predictive control using the particle swarm optimisation algorithm
Computers and Electronics in Agriculture
Control of systems integrating logic, dynamics, and constraints
Automatica (Journal of IFAC)
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This paper presents a hybrid adaptive predictive control approach that includes future information in real-time routing decisions in the context of a dynamic pickup and delivery problem (DPDP). We recognize in this research that when the problem is dynamic, an additional stochastic effect has to be considered within the analytical expression of the objective function for vehicle scheduling and routing, which is the extra cost associated with potential rerouting arising from unknown requests in the future. The major contributions of this paper are: first, the development of a formal adaptive predictive control framework to model the DPDP, and second, the development and coding of an ad hoc particle swarm optimization (PSO) algorithm to efficiently solve it. Predictive state-space formulations are written on the relevant variables (vehicle load and departure time at stops) for the DPDP. Next, an objective function is stated to solve the real-time system when predicting one and two steps ahead in time. A problem-specific PSO algorithm is proposed and coded according to the dynamic formulation. Then, the PSO method is used to validate this approach through a simulated numerical example.