On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Abstract

Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time.

Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods

“…The biased-randomized heuristic in AO typically involves selecting building steps based on skewed probability distributions, where certain steps are favored over others [39]. This controlled randomness enables AO to adapt to changing conditions, explore alternative solutions, and effectively address the complexities of real-world optimization problems [40]. Figure 3 illustrates the AO framework.…”

Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy

“…However, more and more citizens in developed countries show a preference for using carsharing and ridesharing options for commutes and trips [32]. In the case of using EVs in carsharing, in addition to relocation and route scheduling, the area of the charging stations and charging scheduling are some of the current subjects of the new research in the field of carsharing [33].…”

“…On the one hand, cities and countries with a lower urban density, such as USA and Canada, have a higher energy consumption. On the other hand, more densely-populated countries such as Japan and China have less energy consumption [33]. Two fundamental reasons justify this reverse relation.…”

Optimal Route Crowd-Shipping System for Sustainable Rapid Delivery: Algorithm, Simulation, and Feasibility Evaluation

“…[12] focuses on the problem of matching crowd-shipping supply and demand, presenting a procedure that maximizes the number of matched shipments while considering the employees' minimum expected earnings per time unit, this problem was also considered in [13]. In [14], the authors proposed mechanisms to achieve outcome prediction in crowdsourcing, while the authors of [15] suggest agile optimization to handle problems related to routing time and distance or to reducing energy consumption. In [16], they address energy optimization and constraints using linear programming.…”