No abstract

The human mobility is nowadays always organized in a multimodal context. However, the transport system has become more complex. Consequently, for the sake of helping passengers, building Advanced Travelers Information Systems (ATIS) has become a certain need. Since passengers tend to consider several other criteria than the travel time, an efficient routing system should incorporate a multi-objective analysis. Besides, the transport system may behave in an uncertain manner. Integrating uncertainty into routing algorithms may thus provide more robust itineraries. The main objective of this paper is to propose a Memetic Algorithm (MA) in which a Genetic Algorithm (GA) is combined with a Hill Climbing (HC) local search procedure in order to solve the multicriteria shortest path problem in stochastic multimodal networks. As transport modes, railway, bus, tram and metro are considered. As optimization criteria, stochastic travel time, travel cost, number of transfers and walking time are taken into account. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that unlike classical deterministic algorithms and pure GA and HC, the proposed MA is efficient enough to be integrated within real world journey-planning systems.

Abstract-One-way carsharing system is a mobility service that offers short-time car rental service for its users in an urban area. This kind of service is attractive since users can pick up a car from a station and return it to any other station unlike round-trip carsharing systems where users have to return the car to the same station of departure. Nevertheless, uneven users' demands for cars and for parking places throughout the day poses a challenge on the carsharing operator to rebalance the cars in stations to satisfy the maximum number of users' requests. We refer to a rebalancing operation by car relocation. These operations increase the cost of operating the carsharing system. As a result, optimizing these operations is crucial in order to reduce the cost of the operator. In this paper, the problem is modeled as an Integer Linear Programming model (ILP). Then we present three different car relocation policies that we implement in a greedy search algorithm. The comparison between the three policies shows that car relocation operations that do not consider future demands do not effectively decrease rejected demands. On the contrary, they can generate more rejected demands. Results prove that solutions provided by our greedy algorithm when using a good policy, are competitive with CPLEX solutions. Furthermore, adding stochastic modification on the input data proves that the results of the two presented approaches are highly affected by the input demand even after adding threshold values constraints.

In this paper, we present a multiobjective approach for solving the one-way car relocation problem. We fix three objectives that include the number of remaining rejected demands, the number of jockeys used for the relocation operations, and the total time used by these jockeys. For this sake, we propose to apply two algorithms namely NSGA-II and an adapted memetic algorithm (MA) that we call MARPOCS which stands for memetic algorithm for the one-way carsharing system. The NSGA-II is used as a reference to compare the performance of MARPOCS. The comparison of the approximation sets obtained by both algorithms shows that the hybrid algorithm outperforms the classical NSGA-II and so solutions generated by the MARPOCS are much better than the solutions generated by NSGA-II. This observation is proved by the comparison of different quality indicators' values that are used to compare the performance of each algorithm. Results show that the MARPOCS is promising to generate very good solutions for the multiobjective car relocation problem in one-way carsharing system. It shows a good performance in exploring the search space and in finding solution with very good fitness values.

In an electric distribution system, the management of peak demands is becoming increasingly difficult. Every method we have to flatten the consumption curve greatly reduces the energy generation costs, CO2 emissions, and congestion in the 15 generation, transmission, and distribution systems. Therefore, we can act on consumers’ consumption, especially when we know that some consumers can be interested in reducing their consumption levels for monetary compensations. This can be done by reporting a part of consumption or shifting it. For this, a new profession called aggregation is born to manage the flexible consumers and meet the network requirements. To maximize their revenues, aggregators need to own an intelligent system to manage their portfolio of flexible consumers. They should optimize the way they modify the load curve of their flexible assets by respecting the system requirements and a set of consumer constraints. In this article, we address this task by proposing a Mixed Integer Linear Programming (MILP) formulation for two different modes: The economic mode (Evaluation of the potential of the portfolio to generate benefits. The aggregator uses this mode to make bids on the energy market) and the dispatch mode (to be used in an operational situation to respect the bids already submitted). Experimentation studies on real and random in-stances (>1000 instances) demonstrate the effectiveness of the proposed MILP.