This paper presents and analyzes the one-to-many vehicle routing and scheduling problem with electric vehicles. Initially, focus is given on the problem formulation and the restrictions imposed in practice are examined. EVRP is NP-hard in the strong sense since it is natural extension of the well-known Capacitated Vehicle Routing Problem and requires substantial computational effort for determining optimal or near optimal solutions for medium and large scale problem instances. A comprehensive mathematical formulation is developed in order to model the EVRP and the multiple constraints appeared due to capacity limitations, time window restrictions and the predefined charging level of the vehicles. In addition, recent trends for the EVRP are analyzed producing valuable insights for future research regarding extra operational constraints, real-life data sets and solution frameworks that embody approximation algorithms for an efficient and effective search of the solution space

This paper deals with one-to-many-to-one vehicle routing and scheduling problems with pickups and deliveries and studies the effect of various backhauling strategies. Initially, focus is given on problem instances with clustered backhauls where all delivery customers must be visited before pickup customers. Afterward, operational settings with mixed backhauls and varying visiting sequence restrictions with respect to the capacity of the vehicles are examined. The proposed solution method evolves a set of reference solutions on the basis of a novel Adaptive Path Relinking framework. The latter encompasses an adaptive multisolution recombination procedure to generate provisional solutions based on the recurrence of particular solution attributes. On return, these solutions are used as guiding points for performing search trajectories from initial reference solutions via tunneling. Computational results on benchmark data sets of the literature illustrate the competitiveness and robustness of the proposed approach compared to state-of-the-art solution methods for well-known vehicle routing and scheduling problems. Finally, various experiments are also reported to demonstrate the economic effect of different mixing levels and densities of linehaul and backhaul customers.

In this paper, a decision-making tool is proposed that can utilize different strategies to deal with passenger flows in airport terminals. A simulation model has been developed to investigate these strategies, which can be updated and modified based on the current requirements of an airport terminal. The proposed tool could help airport managers and relevant decision makers proactively mitigate potential risks and evaluate crowd management strategies. The aim is to eliminate risk factors due to overcrowding and minimize passenger waiting times within the terminal to provide a seamless, safe and satisfying travel experience. Overcrowding in certain areas of the terminal makes it difficult for passengers to move freely and increases the risk of accidents (especially in the event of an emergency), security problems and service interruptions. In addition, long queues can lead to frustration among passengers and increase potential conflicts or stress-related incidents. Based on the derived results, the optimized routing of passengers using modern technological solutions is the most promising crowd management strategy for a sample airport that can handle 800 passengers per hour.