Electric vehicles have recently received attention due to the environmental and financial implication. However, they are still not popular because of their inconvenience, limited battery capacities and long recharge times. In order to grow popular of electric vehicles, several studies in the field of optimization have been engaged. One of the studies is routing problem for energy-optimized driving. In this paper, we deal with the electric and fuel-engined vehicle routing problem (EFVRP), this problem is a type of the vehicle routing problem where we use electric vehicles and fuel-engined vehicles (e.g. Gasoline-powered vehicles). We model the EFVRP and propose a column generation approach for the EFVRP. Experiments compare the results obtained using our model and algorithm with their lower bounds.

A hypergraph ℱ is a set family defined on vertex set V. The dual of ℱ is the set of minimal subsets H of V such that F ∩ H ≠ ø for any F ∊ F. The computation of the dual is equivalent to many problems, such as minimal hitting set enumeration of a subset family, minimal set cover enumeration, and the enumeration of hypergraph transversals. In this paper, we introduce a new set system induced by the minimality condition of the hitting sets, that enables us to use efficient pruning methods. We further propose an efficient algorithm for checking the minimality, that enables us to construct time efficient and polynomial space dualization algorithms. The computational experiments show that our algorithms are quite fast even for large-scale input for which existing algorithms do not terminate in practical time.

Printed circuit boards (PCBs) are designed for packing two or more semiconductor chips. On these PCBs, there are various types of faults in the wiring. We must therefore test the PCBs to detect these faults, and it is essential to establish an efficient testing method. One type of test method uses two probes. Two probes, each touching one edge (end) of an inter-chip wiring, are used to check for the presence of faults. Testing is complete when we have confirmed that no faults exist on the PCB. The objective is to minimize the completion time for the testing, that is, our aim is to design efficient routes for the two probes; this is a two-probe routing problem. Several previous studies have proposed an approach involving a two-phase method. Although the two-phase method quickly finds a feasible solution, there is no guarantee that the solution is good. Besides, in recent years, PCB technology provides dense assembly capability and increased chip packaging; this implies that the scale of the two-probe routing problem becomes larger and the problem is difficult to be solved. We therefore propose a new approach, where we formulate a two-probe routing problem as a combinatorial optimization problem and then, we solve the combinatorial optimization problem by using genetic algorithm.We expect that our approach is useful for solving the large-scale problems. In computational experiments, we compare the results of our approach with those obtained using the two-phase method, and we show that our approach outperforms the two-phase method.

This article shows our research efforts toward the development of an olfactory display system for presenting odors with a vivid sense of reality. A multi-component olfactory display is developed, which enables generation of a variety of odors by blending multiple odor vapors with arbitrary ratios. In this research demo, the two contents using the olfactory display will be demonstrated. The first one is the experiment on odor approximation using odor components extracted from a mass spectrum database. The second one is the reproduction of video with smell. The temporal changes of odor kind and its concentration can be recorded and can be presented together with movie obtained from a Web camera. People can enjoy the recorded video with scents.

A variety of smells can be realized by blending multiple odor components using an olfactory display. Since a set of odor components to cover the entire range of smells has not yet been known, we studied a method of selecting odor components using a large-scale mass spectrum database. Basis vectors corresponding to odor components were extracted by the NMF (nonnegative matrix factorization) method. Then, the recipe of the target odor was obtained using the nonnegative least-squares method. The basis vectors were successfully obtained from 10,000 compounds within a tolerable error. Moreover, the mass spectra of 104 odors composed of 322 compounds could be approximated using 32-50 basis vectors.