Abstract
In this paper, a hybrid adaptive predictive control approach (HAPC) to solve a dynamic pickup and delivery problem (DPDP) is presented based on a dynamic objective function that includes two dimensions: user and operator costs. Because these two costs are opposite components, the problem was formulated and solved by using an Evolutionary Multiobjective Optimization (EMO) technique. The idea is to minimize both, user and operator costs. At every instant, the use of genetic algorithms is proposed to find the optimal Pareto front associated with the DPDP, whose Pareto Optimal set is a set of solutions of the problem. Since only one solution has to be applied to the system every time a new request appears, several criteria will be utilized in order to properly use the information provided by the dynamic optimal Pareto front. Illustrative experiments through simulation of the process are presented to show the potential benefits of the new approach. Thus, by using EMO, the trade off between the two conflicting objectives will become clear for the dispatcher when making dynamic routing decisions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
