Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem

This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem

Where to park an autonomous vehicle?:Results of a stated choice experiment

The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people's parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.</p

Back-stepping variable structure controller design for off-road intelligent vehicle

In this paper, off-road path recognition and navigation control method are studied to realize intelligent vehicle autonomous driving in unstructured environment. Firstly, the traversable path is achieved by vision and laser sensors. The vehicle steering and driving coupled dynamic model is established. Secondly, a coordinated controller for steering and driving is proposed via the back-stepping variable structure control method, which can be used to deal with the unmatched uncertainties of the control system model. To reduce the chattering phenomenon caused by variable structure, the boundary layer approach is introduced. The results of simulation and off-road experiment show the effectiveness and robustness of the proposed controller

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Effective bus travel time prediction is essential in transit operation system. An improved support vector machine (SVM) is applied in this paper to predict bus travel time and then the efficiency of the improved SVM is checked. The improved SVM is the combination of traditional SVM, Grubbs’ test method and an adaptive algorithm for bus travel-time prediction. Since error data exists in the collected data, Grubbs’ test method is used for removing outliers from input data before applying the traditional SVM model. Besides, to decrease the influence of the historical data in different stages on the forecast result of the traditional SVM, an adaptive algorithm is adopted to dynamically decrease the forecast error. Finally, the proposed approach is tested with the data of No. 232 bus route in Shenyang. The results show that the improved SVM has good prediction accuracy and practicality

Dynamic Extra Buses Scheduling Strategy in Public Transport

This paper presents a dynamic extra buses scheduling strategy to improve the transit service of transit routes. In this strategy, in order to decide when to dispatch an extra bus, the service reliability of transit route is assessed firstly. A model aimed at maximizing the benefit of the extra buses scheduling strategy is constructed to determine how many stops extra buses need to skip from the terminal to accommodate passengers at the following stops. A heuristic algorithm is defined and implemented to estimate the service reliability of transit route and to optimize the initial stop of extra buses scheduling strategy. Finally, the strategy is tested on two examples: a simple and a real-life transit route in the Dalian city in China. The results show that the extra buses scheduling strategy based on terminal stops with a reasonable threshold can save 8.01% waiting time of passengers

Novel compound heterozygous variants in the CSPP1 gene causes Joubert syndrome: case report and literature review of the CSPP1 gene’s pathogenic mechanism

Joubert syndrome (JS) is a rare autosomal recessive neurodevelopmental condition characterized by congenital mid-hindbrain abnormalities and a variety of clinical manifestations. This article describes a case of Joubert syndrome type 21 with microcephaly, seizures, developmental delay and language regression, caused by a CSPP1 gene variant and examines the contributing variables. This paper advances the understanding of JS by summarizing the literature and offering detection patterns for practitioners with clinical suspicions of JS

Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub

The building density of Central Business District (CBD) is usually high. Land for a bus terminal is insufficient. In this situation, passengers in CBD have to walk far to take a bus, or take a long time to wait for a taxi. To solve this problem, this paper proposes an indirect approach: the design of a circle line of shuttle bus as a dynamic bus terminal in CBD. The shuttle bus can deliver people to the bus station through a circle line. This approach not only reduces the traffic pressure in CBD, but also saves travel time of the passenger. A bi-objective model is proposed to design a circle line of a shuttle bus for CBD. The problem is solved by non-dominated sorting genetic algorithm (NSGA-II). Furthermore, the Dalian city in China has been chosen as the case study to test the proposed method. The results indicate that the method is effective for circle line optimization of shuttle bus in central business district without a bus terminal

Machine learning in automotive industry

Not available

Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network

High transportation cost and low service quality are common weaknesses in different logistics networks, especially in food delivery. Due to its perishable features, the quality of the food will deteriorate during the while delivery process. In this paper, a two-stage location–routing–inventory problem with time windows (2S-LRITW) for food products is proposed. The first stage corresponds to a location–routing–inventory problem with time windows and the second stage is the transportation problem with vehicle capacity constraints. The problem is formulated as a mixed integer-programming model. Then a hybrid heuristic is proposed, in which distance-based clustering approach, mutation operation and the relocate exchange method are introduced to improve performance of algorithm. At last, the hybrid heuristic was tested using several cases, including some small instances and a real-life case. The results show that the distance-based clustering approach can efficiently improve the convergence speed. And the IACO and the relocate exchange method can enlarge the search space. As a result, the hybrid heuristic is suitable for solving practical larger scale problem. Furthermore, the results also indicate that customer sequence has great effect on the object due to the energy cost considered