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Shortest path problem has always been a hot topic in the study of graph theory, because of its wide application field, extending from operational research to the disciplines of geography, automatic control, computer science and traffic. According to its concrete application, scholars in the relevant field have presented many algorithms, but most of them are solely improvements based on Dijkstra algorithm. The purpose of this paper is to enrich the kinds of (and improve the efficiency of) the shortest path algorithms.

This paper puts forward an improved calculation method of shortest path using cellular automata model, which is designed to search the shortest path from one node to another node. Cellular state set is adjusted with combination of breeding and mature states. Evolution rule is improved to enhance its parallelism. At the same time, recording manner of cellular state turnover is modified to record all information sources.

The result indicates that the improved algorithm is correct and more efficient, in that it could reduce the times of cellular state turnover; meanwhile, it can solve multi‐paths problem.

In this paper, cellular state set in exiting shortest path algorithm based on cellular automata theory is adjusted; evolution rule is improved; and recording manner of cellular state turnover is modified to record all information sources. All of which make the parallelism of this algorithm enhanced and the multi‐paths problem solved.

The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.

A Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.

The numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.

The research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.

Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.

The paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

The purpose of this paper is to obtain the reasonable dimensioning for each part and a full-dimension model of assembly dimensions.

The relational path graph of assembly dimension, the shortest-path spanning tree of functional dimension and a revised spanning tree are established in this paper.

The proposed method can obtain reasonable dimensioning of parts and establishment of dimension model in an assembly.

The proposed method can easily realise by computer and be more suitable to automatic dimensioning and establishment of dimension model of parts.

Two and one half‐dimensional (2.5D) grid maps are useful for navigation in outdoor environment or on non‐flat surface. However, little attention has been given to how to find an optimal path in a 2.5D grid map. The purpose of this paper is to develop a path‐planning method in a 2.5D grid map, which aims to provide an efficient solution to robot path planning no matter whether the robot is equipped with the prior knowledge of the environment.

A 2.5D grid representation is proposed to model non‐flat surface for mobile robots. According to the graph extracted from the 2.5D grid map, an improved searching approach derived from A* algorithm is presented for the shortest path planning. With reasonable assumption, the approach is improved for the path planning in unknown environment.

It is confirmed by experiments that the proposed planning approach provide a solution to the problem of optimal path planning in 2.5 grid maps. Furthermore, the experiment results demonstrate that our 2.5D D* method leads to more efficient dynamic path planning for navigation in unknown environment.

This paper proposes a path‐planning approach in a 2.5D grid map which is used to represent a non‐flat surface. The approach is capable of efficient navigation no matter whether the global environmental information is available at the beginning of exploration.

To determine how the correlational structure of emotion differs for individuals age 60 and above, compared to those under age 60, and to discuss the profound implications these differences may have for the experience and management of emotion.

Structural equation modeling and shortest path analysis of emotion items from the General Social Survey (GSS)’s (1996) emotions module.

Some positive and negative emotion pairs are more distant for individuals over age 60, while others are in fact closer. This variability leads to differences in available shortest paths between emotions, especially when emotional transitions require segueing through intermediary feelings. The segueing emotions most readily available to those over 60 are limited to the poles of affective meaning, whereas those used by ones under age 60 are more variable. The majority of negative emotions are more tightly correlated, whereas the majority of positive emotions are less so, among those over age 60.

Although the measures are limited to 18 of the 19 emotions recorded by the GSS, and are based on self-report data regarding feelings felt over a period of seven days, these results suggest that attempts at intrapersonal and interpersonal emotion management may differ depending up the age of the actor/object.

Addresses the need for more nuanced analyses of emotional experience that goes moves beyond simple frequencies. Also suggests potential bridges between sociological and psychological approaches to the study of emotion.

This research has developed a one-stop service supply chain mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers and consumers in accordance with the Thailand 4.0 economic model. This is an investigation into the agricultural product distribution supply chain which focuses on marketing, distribution and logistics using the Dijkstra’s and Ant Colony Algorithms to respectively explore the major and minor product transport routes. The accuracy rate was determined to be 97%. The application is congruent with the product distribution, supply chain, in a value-based economy. The effectiveness of the mobile application was indicated to be at the highest level of results of learning outcomes, user comprehension and user experience of users. That is, the developed mobile application could be effectively used as a tool to support elderly farmers to distribute their agricultural products in the one-stop service supply chain which emphasizes marketing, distribution and location-based logistics for elderly farmers and consumers with respect to Thailand 4.0.

The purpose of this paper is to propose an efficient path and trajectory planning method to solve online robotic multipoint assembly.

A path planning algorithm called policy memorized adaptive dynamic programming (PM-ADP) combines with a trajectory planning algorithm called adaptive elite genetic algorithm (AEGA) for online time-optimal path and trajectory planning.

Experimental results and comparative study show that the PM-ADP is more efficient and accurate than traditional algorithms in a smaller assembly task. Under the shortest assembly path, AEGA is used to plan the time-optimal trajectories of the robot and be more efficient than GA.

The proposed method builds a new online and efficient path planning arithmetic to cope with the uncertain and dynamic nature of the multipoint assembly path in the Cartesian space. Moreover, the optimized trajectories of the joints can make the movement of the robot continuously and efficiently.

The proposed method is a combination of time-optimal path planning with trajectory planning. The traveling salesman problem model of assembly path is established to transfer the assembly process into a Markov decision process (MDP). A new dynamic programming (DP) algorithm, termed PM-ADP, which combines the memorized policy and adaptivity, is developed to optimize the shortest assembly path. GA is improved, termed AEGA, which is used for online time-optimal trajectory planning in joints space.

Automated guide vehicles (AGVs) are driverless vehicles that perform material handling operations in both flexible and conventional facilities. We provide here a review of recent work on the design of AGV guide paths and dispatching rules, including related issues such as idle vehicle location, and location of pickup and delivery stations. Different types of guide paths and related layouts, including optimal and heuristic approaches to the path design, are reviewed here. Dispatching rules and algorithms, including zone control, are also proposed and compared with commonly‐used rules.