By processing a large dataset composed of daily trajectories of thousands of students in Singapore, we find that, instead of simply picking up students from their homes, an optimal school shuttle planning system needs...
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By processing a large dataset composed of daily trajectories of thousands of students in Singapore, we find that, instead of simply picking up students from their homes, an optimal school shuttle planning system needs to learn the real transportation usage and plan across all potential pickup locations for every student to generate need-satisfying routes. It is challenging, however, to perform route planning over a large number of students each having multiple potential pickup locations. We develop a graph-based data structure that embeds potential pickup locations of all students with the awareness of real-world constraints and existing public transits. based on the graphstructure, we prove that the optimal last-mile school shuttle planning problem is NP-hard and thereafter design a Tabu-based expansion algorithm to solve the problem, which strikes at a proper balance between the savings of students' commute time and the total cost of operating the shuttle buses. Extensive experiments with large-scale real-world crowdsensed trajectory data demonstrate that our last-mile school shuttles can save the traveling time for most students by over 20% and the savings can be up to 65% for 10% of the students.
The authors consider the general problem of graph clustering. graph clustering manipulates the graph-based data structure and the entries of the solution vectors are only allowed to take non-negative discrete values. ...
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The authors consider the general problem of graph clustering. graph clustering manipulates the graph-based data structure and the entries of the solution vectors are only allowed to take non-negative discrete values. Finding the optimal solution is NP-hard, so relaxations are usually considered. Spectral clustering retains the orthogonality rigorously but ignores the non-negativity and discreteness of the solution. Sym non-negative matrix factorisation can retain the non-negativity rigorously but it is hard to reach the orthogonality. In this study, they proposed a novel method named congruent approximate graph clustering (CAC), which can retain the non-negativity rigorously and can reach the orthogonality properly by congruency approximation. Furthermore, the solution obtained by CAC is sparse, which is approximate with the ideal discrete solution. Experimental results on several real image benchmark datasets indicate that CAC achieves encouraging results compared with state-of-the-art methods.
Dimensional quality control remains a challenge in assembly of complex products. Currently dimensional variation control research efforts include tolerance analysis and allocation, fixture layout design, assembly sequ...
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Dimensional quality control remains a challenge in assembly of complex products. Currently dimensional variation control research efforts include tolerance analysis and allocation, fixture layout design, assembly sequence planning, and others, without systematically viewing assembly as a proxy for a wide range of design decisions to produce final products with least cost, most productivity and best quality. A literature review in current assembly modelling methods shows that a unified datastructure has yet to be developed at a preliminary design phase in order to design product and plan assembly process automatically. This paper serves to develop such a datastructure, which captures heterogeneous product and assembly process information available at a preliminary design phase and unifies them in a datastructure represented as a hierarchical graph. The graph-based data structure, on one side, facilitates automatic product design and assembly process planning at the preliminary design phase by utilising research results developed in graph theory, and on the other side, allows easier embedding the assembly model into computer aided design (CAD) software since they share the similar datastructure. The presented graph-based data structure is illustrated by a sport utility vehicle (SUV) side frame assembly.
This paper aims at contributing to a better understanding of essential concepts of supporting system for initial visual design. Towards this end, we first outline the system architecture corresponding to our model of ...
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ISBN:
(纸本)9780857297983
This paper aims at contributing to a better understanding of essential concepts of supporting system for initial visual design. Towards this end, we first outline the system architecture corresponding to our model of conceptual design aided by computer, in which designer's drawings created on the monitor screen are automatically transformed into elements of a graph-based data structure and next into the first-order logic formulas. Then, we describe particular modules of the proposed model paying attention to the role of a graph-based data structure gathering information on which design knowledge is based. Finally, the approach is illustrated on examples of designing floor layouts where fire code regulations and ranges of sensors are checked on the base of the proposed design knowledge reasoning module.
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