The multiple origins multiple destinations routing (MOMDR) problem becomes extremely complicated when considering the traffic volumes on road sections. When solving this kind of problem, only heuristic algorithms have practical values because it is a typical NP-Hard problem. This paper applies Genetic Algorithm (GA) to enhance Sorting-RandomizingAdjusting-Updating (SRAU) algorithm [1]. The previous paper shows that different processing orders of the origin-destinations (ODs) result in different solutions with different performances. Therefore, an algorithm for finding the best processing order of ODs can optimize SRAU algorithm. In this paper, the processing order of ODs is transformed into a gene/chromosome of the individual of GA; then, the best gene can be found by evolution; finally, the best gene is transformed back to find the optimal solution of the problem. Sufficient simulations show that the proposed algorithm is more efficient than original SRAU algorithm. Comparisons also show that the proposed algorithm has higher performance and faster convergence speed than RAND algorithm which uses the random policy to find the proper processing order of ODs. Moreover, the consideration of the traffic volumes on the road sections enables the proposed algorithm to be applied to real traffic systems.

Taxi service usually benefits people by providing comfortable and flexible ride experiences. However, an inherent problem, the insufficient number of taxis at traffic peak, has baffled taxi service ever since it existed. This paper hereby proposes a Multi-Customer Taxi Dispatch System (MCTDS), where taxis are granted a right to take customers with different Origin-Destination (OD) pairs simultaneously, to shorten the total waiting time and traveling time. In addition, to mitigate the damage of detours, MCTDS is built based on Genetic Network Programming, a graph-based evolutionary algorithm that has shown excellent performances previously in some complicated applications. We also modify the structure of GNP to achieve an improvement in performance. In the simulation part, we demonstrate that MCTDS outperforms the conventional GNP and some heuristic taxi dispatch approaches.

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Genetic Network Programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data mining, etc. This paper proposes a new method to accumulate evolutionary experiences and guide agent's actions by extracting and using generalized rules. Each generalized rule is a stateaction chain which contains the past information and the current information. These generalized rules are accumulated and updated in the evolutionary period and stored in the rule pool which serves as an experience set for guiding new agent's actions. We designed a two-stage architecture for the proposed method and applied it to the Tile-world problem, which is an excellent benchmark for multi-agent systems. The simulation results demonstrated the efficiency and effectiveness of the proposed method in terms of both generalization ability and average fitness values and showed that the generalized rule accumulation method is especially remarkable when dealing with non-markov problems.

Among the running faults of railway freight cars, wheelset faults still occupy a very large proportion in all operation faults. These faults seriously affect and threaten the operation safety of railway freight cars. Focusing on the data of the operating mileage of C80 wheelset, application of the two-parameter Birnbaunm-Saunders (BS) fatigue life distribution and the Weibull distribution are introduced and reviewed for analyzing the data of three high-risk wheelset faults, namely the wheel diameter difference, the circumferential wear of the tread and the location depression of tread. The analysis results show that the application of BS fatigue life distribution in the prediction of wheelset reliability is feasible.