Dynamic Street Parking Space Using Memetic Algorithm for Optimization

In recent years, there have been an increasing number of automobiles in cities around the world. This is due to more people living and working in cities as a result of urbanization. Street parking remains a common option for motorists, due to it being cheap and convenient. However, this option leads to a high concentration of vehicles causing congestion and obstruction of traffic. This problem is compounded as motorists wait for others to pull out of parking bays or look for empty parking spaces. In order to provide relief to this problem, an intelligent approach is proposed that generates an optimal parking space based on the vehicle location and desired destination. The proposed approach applies its operators adaptively and it derives optimality from the synergy between genetic algorithm and a local search technique in the search optimization process. The proposed method exhibits superior performance when compared with the existing methods over multiple iterations

Cooperative self Organization of agents for optimization : the electrical wiring example

International audienceIn aircrafts, densifying electrical systems and oversizing cables in order to respect constraints induce a useless increase in cable weight. This increase leads to additional costs of operation and to an unnecessary pollution during the plane operating life. In this paper we address optimization of harness weight which is a mono-objective problem with manifold and interdependent constraints. To solve this problem, we use a multi-agent approach based on the cooperative self-organization of agents. Performances obtained by the 'Smart Harness Optimizer' software that we have developed are promising for problems considered by the experts as being very difficult. In this article, we expose the method used to solve this Constraint Optimization Problem. Then we apply it to the addressed problem and finally we give results on typical cases and analyze them

Multiple depots vehicle routing based on the ant colony with the genetic algorithm

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing, the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the efficiency multiple depots vehicle routing.Peer Reviewe

Two-step Meta-heuristic Approach for a Vehicle Assignment Problem – Case from İstanbul/Turkey

In this paper, a two-step meta-heuristic approach is proposed for vehicle assignment problem with geometric shape-based clustering and genetic algorithm. First, the geometric shape-based clustering method is used and then the solution of this method is given to the genetic algorithm as initial solution. The solution process is continued by genetic algorithm. There are 282 bus lines in İstanbul European side. Those buses should be assigned to six bus garages. The proposed method is used to determine the minimum distance between the bus lines and garages by assigning buses to garages. According to the computational results, the proposed algorithm has better clustering performance in terms of the distance from each bus-line start point to each bus garage in the cluster. The crossover rate changing method is also applied as a trial in order to improve the algorithm performance. Finally, the outputs that are generated by different crossover rates are compared with the results of the k-Nearest Neighbour algorithm to prove the effectiveness of the study.</p

Evolutionary Computation methods applied to Operational Control Centers

Durante a execução de um plano operacional, existe a possibilidade do mesmo sofrer rupturas causadas por eventos não esperados. As rupturas afetam pelo menos três dimensðes sobre as quais as companhias aéreas e os centros de controlo operacional devem ter em conta, nomeadamente os passageiros, a tripulação e os aviðes. Normalmente, um ruptura é um estado durante o qual uma operação que esteja a ser executada é afetada por um desvio (que é grande o suficiente para causar uma mudança) do plano original e, por vezes, levando a que o plano não seja execuível. Exem- plos de eventos que podem causar rupturas são condiçðes meteorológicas, ameaças ou ataques terroristas e avarias nos aviðes.Disruption Management, pode então ser definido como o processo que começa após detectar o desvio do plano original. Depois da ruptura, o plano é mudado e nunca mais vai estar tão perto da solução ótima quanto estava antes da ruptura, sendo que pode mesmo vir a ser impossível a continuação do plano. De qualquer maneira existe a necessidade de rever o plano e de minimizar o impacto causado pela ruptura.O MASDIMA é uma grande ajuda para os Centros de Controlo Operacionais das companhias aéreas encontrarem soluçðes para rupturas durante a execução de um plano operacional, e para melhorar quer o tempo de computação quer a qualidade das soluçðes, existe a necessidade de melhorar o sistema. Isto traduz-se em minimizar o impacto quer a ao nvel dos custos quer ao nvel dos atrasos.Para lidar com este problema serão implementados três agentes, sendo que cada um representa um algoritmo evolutivo diferente (Particle Swarm Optimisation, Ant Colony Optimisation e Ge- netic Algorithms) e estarão relacionados com a dimensão avião relativa ao problema. Estes três agentes serão implementados num Sistema Multi-Agente chamado de MASDIMA que represen- tará o Centro de Controlo Operacional.During the execution of an operational plan, there is the likelihood of this plan being affected by some disruptions caused by unexpected events. The disruptions affect at least three dimensions that airline companies and the operational control centers must take into account which are pas- senger, crew and aircraft. Usually, a disruption is a state during which the current operation being executed is affected by a deviation (which is large enough to cause a change) from the original plan, and sometimes unfortunately it leads to an unfeasible plan. Examples of events that might cause disruptions are bad weather, threats or terrorist attacks and aircraft malfunctions.Disruption Management, can be defined as the process that starts after the deviation from the original plan is detected. After the disruption, the plan is changed and it will no longer be as close as it was from an optimal plan or it can even turn into an unfeasible plan. Either way there is a need to review the plan and try to minimize the impact caused by the disruption.MASDIMA is useful to help Airline Operation Control Centers finding a solution to disrup- tions during an operational plan, and in order to improve both computing time and the quality of solutions, there is a need to improve the system. This will translate in minimising the impact both in terms of costs or delays.To deal with that problem three agents will be implemented that will reflect, each one, different evolutionary computation algorithms (Particle Swarm Optimisation, Ant Colony Optimisation and Genetic Algorithms) and are related to the aircraft dimension of the problem. These agents will be implemented on a Multi-Agent System named MASDIMA that represents an Operation Control Center

Smart charging strategies for electric vehicle charging stations

Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, consumer concerns over the limited availability of charging stations and long charging/waiting times are major contributors to the slow uptake of plug-in electric vehicles (PEVs) in many countries. To address the consumer concerns, many countries have undertaken projects to deploy a network of both fast and slow charging stations, commonly known as electric vehicle charging networks. While a large electric vehicle charging network will certainly be helpful in addressing PEV owners\u27 concerns, the full potential of this network cannot be realised without the implementation of smart charging strategies. For example, the charging load distribution in an EV charging network would be expected to be skewed towards stations located in hotspot areas, instigating longer queues and waiting times in these areas, particularly during afternoon peak traffic hours. This can also lead to a major challenge for the utilities in the form of an extended PEV charging load period, which could overlap with residential evening peak load hours, increasing peak demand and causing serious issues including network instability and power outages. This thesis presents a smart charging strategy for EV charging networks. The proposed smart charging strategy finds the optimum charging station for a PEV owner to ensure minimum charging time, travel time and charging cost. The problem is modelled as a multi-objective optimisation problem. A metaheuristic solution in the form of ant colony optimisation (ACO) is applied to solve the problem. Considering the influence of pricing on PEV owners\u27 behaviour, the smart charging strategy is then extended to address the charging load imbalance problem in the EV network. A coordinated dynamic pricing model is presented to reduce the load imbalance, which contributes to a reduction in overlaps between residential and charging loads. A constraint optimization problem is formulated and a heuristic solution is introduced to minimize the overlap between the PEV and residential peak load periods. In the last part of this thesis, a smart management strategy for portable charging stations (PCSs) is introduced. It is shown that when smartly managed, PCSs can play an important role in the reduction of waiting times in an EV charging network. A new strategy is proposed for dispatching/allocating PCSs during various hours of the day to reduce waiting times at public charging stations. This also helps to decrease the overlap between the total PEV demand and peak residential load

Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.</p