Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach

Cao, Zhiguang; Guo, Hongliang; Zhang, Jie; Niyato, Dusit; Fastenrath, Ulrich

doi:10.1109/tits.2015.2498160

Cited by 85 publications

(38 citation statements)

References 26 publications

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“…In future, we will study the obstacle avoidance in navigation, where the autonomous ships can be considered as intelligent agents or vehicles. Therefore, we will investigate the routing algorithms for both independent and cooperative agents (or vehicles) in land and marine transportation [31][32][33][34][35][36][37][38], to achieve the obstacle avoidance for autonomous ships.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Processing of Situation Awareness for Autonomous Ships Navigation

Zhou¹,

Liu²,

Zhao³

et al. 2019

TransNav

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The first ever attempt at fully autonomous dock-to-dock operation has been tested and demonstrated successfully at the end of 2018. The revolutionary shift is feared to have a negative impact on the safety of navigation and the getting of real-time situation awareness. Especially, the centralized context onboard could be changed to a distributed context. In navigation safety domain, monitoring, control, assessment of dangerous situations, support of operators of decision-making support system should be implemented in real time. In the context of autonomous ships, decision-making processes will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. At the same time, situation awareness is the key element of the decision-making processes. Although there is substantial research on situation awareness measurement techniques, they are not suitable to directly execute quantitative processing for the situation awareness of autonomous ships navigation. Hence, a novel quantitative model of situation awareness is firstly proposed based on the system safety control structure of remotely controlled vessel. The data source is greatly limited, but the main result still indicates that the probability of operator lose adequate situation awareness of the autonomous ship is significantly higher than the conventional ship. Finally, the paper provides a probabilistic theory and model for high-level abstractions of situation awareness to guide future evaluation of the navigation safety of autonomous ships.

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Section: Discussionmentioning

confidence: 99%

Quantitative Processing of Situation Awareness for Autonomous Ships Navigation

Zhou¹,

Liu²,

Zhao³

et al. 2019

TransNav

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“…For convenience, they are denoted as x 1 , x 2 , x 3 , x 4 . Residents had certain routes to leave and back under certain residential area layout type, and the routes decided travel time, speed, distance and possible conflicts of their trips [32,33]. The routes in the open residential area were different from those in the gated residential area.…”

Section: Examplementioning

confidence: 99%

Novel Parameterized Utility Function on Dual Hesitant Fuzzy Rough Sets and Its Application in Pattern Recognition

Zhang

et al. 2019

Information

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Based on comparative studies on correlation coefficient theory and utility theory, a series of rules that utility functions on dual hesitant fuzzy rough sets (DHFRSs) should satisfy, and a kind of novel utility function on DHFRSs are proposed. The characteristic of the introduced utility function is a parameter, which is determined by decision-makers according to their experiences. By using the proposed utility function on DHFRSs, a novel dual hesitant fuzzy rough pattern recognition method is also proposed. Furthermore, this study also points out that the classical dual tool is suitable to cope with dynamic data in exploratory data analysis situations, while the newly proposed one is suitable to cope with static data in confirmatory data analysis situations. Finally, a medical diagnosis and a traffic engineering example are introduced to reveal the effectiveness of the newly proposed utility functions on DHFRSs.

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“…Thus, we reformulate the system model as the cardinality minimization problem to handle this randomness. Since the solution of L1-norm formulation in [5] does not guarantee to be optimal, we directly minimize the cardinality of all samples instead of minimize the total exceeding time of all samples. Furthermore, to speed up the computation, the L-shaped decomposition [9] is applied into the ODPD.…”

Section: Related Workmentioning

confidence: 99%

Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

Sawadsitang

Jiang²,

Niyato

et al. 2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

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Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers. Most of the existing VRPPC works consider deterministic parameters which may not be practical and uncertainty has to be taken into account. In this paper, we propose the Optimal Stochastic Delivery Planning with Deadline (ODPD) to help a supplier plan and optimize the package delivery. The aim of ODPD is to service all customers within a given deadline while considering the randomness in customer demands and traveling time. We formulate the ODPD as a stochastic integer programming, and use the cardinality minimization approach for calculating the deadline violation probability. To accelerate computation, the L-shaped decomposition method is adopted. We conduct extensive performance evaluation based on real customer locations and traveling time from Google Map.

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Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach

Cited by 85 publications

References 26 publications

Quantitative Processing of Situation Awareness for Autonomous Ships Navigation

Quantitative Processing of Situation Awareness for Autonomous Ships Navigation

Novel Parameterized Utility Function on Dual Hesitant Fuzzy Rough Sets and Its Application in Pattern Recognition

Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

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