Proposal of an Effective Computation Environment for the Traveling Salesman Problem Using Cloud Computing

Mizuno, Shinya; Iwamoto, Shogo; Yamaki, Naokazu

doi:10.1299/jamdsm.6.703

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…We specifically use GDirections of the Google Maps API version 2, which requires approximately 1 s to acquire the parameters for each combination of nodes (Mizuno et al. 2012). We use either the transit time or distance as the distance parameter for the gravity model.…”

Section: Configuration Of the Proposed Systemmentioning

confidence: 99%

Proposal for optimal placement platform of bikes using queueing networks

Mizuno

Iwamoto²,

Seki

et al. 2016

SpringerPlus

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In recent social experiments, rental motorbikes and rental bicycles have been arranged at nodes, and environments where users can ride these bikes have been improved. When people borrow bikes, they return them to nearby nodes. Some experiments have been conducted using the models of Hamachari of Yokohama, the Niigata Rental Cycle, and Bicing. However, from these experiments, the effectiveness of distributing bikes was unclear, and many models were discontinued midway. Thus, we need to consider whether these models are effectively designed to represent the distribution system. Therefore, we construct a model to arrange the nodes for distributing bikes using a queueing network. To adopt realistic values for our model, we use the Google Maps application program interface. Thus, we can easily obtain values of distance and transit time between nodes in various places in the world. Moreover, we apply the distribution of a population to a gravity model and we compute the effective transition probability for this queueing network. If the arrangement of the nodes and number of bikes at each node is known, we can precisely design the system. We illustrate our system using convenience stores as nodes and optimize the node configuration. As a result, we can optimize simultaneously the number of nodes, node places, and number of bikes for each node, and we can construct a base for a rental cycle business to use our system.

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Section: Configuration Of the Proposed Systemmentioning

confidence: 99%

Proposal for optimal placement platform of bikes using queueing networks

Mizuno

Iwamoto²,

Seki

et al. 2016

SpringerPlus

Self Cite

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“…With this model, the closed queuing network [1] [2] focused solely on the movement within the system to determine the overall hospital system equilibriu m within a local area. The study considered:…”

Section: Modeling By a Closed Queuing Networkmentioning

confidence: 99%

Expression of Relation of Medical Institutions and the Simulation of the Relation Recovery for Disaster

Mizuno¹,

Seki²,

Fujisawa³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Abstract-The present medical institution cooperate each other, such as specialty skills or regional peculiarity. However, when disasters, such as an earthquake, an institution may not have function as a medical institution and the balance of the medical service at the time will usually unstable. Restoring medical service, each medical institution also becomes to be stable. In this research, we express relation of the medical institutions as a medical network. Moreover, we get the simulation of the degree of relation of a medical institution. By expressing a medical network, it becomes clear where it needs medical services. And it becomes possible where we should focus to advance restoration by medical network simulation.

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“…Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. The key spirit of the cloud concept can be summarized as the capability of providing distributed, fast-responding, on-demand and quantifiable services (Xu, et al, 2012, Mizuno, et al, 2012, Herrmann, et al, 2014. Therefore, these advantages of the cloud computing can be applied for the IMS to increase the efficiency of this system in the wide network application.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a cloud based smart manufacturing system

Park¹,

Tran

2015

JAMDSM

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Smart manufacturing considered as a new trend of modern manufacturing helps to satisfy objectives associated with the productivity, quality, cost and competiveness. The smart manufacturing system is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of smart manufacturing is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee's foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-healing. In this paper, a cloud based smart manufacturing system for machining transmission cases is considered as research object in which the advanced information and communication technology such as cognitive agent, swarm intelligence, and cloud computing are used to integrate, organize and allocate the machining resources.

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Proposal of an Effective Computation Environment for the Traveling Salesman Problem Using Cloud Computing

Cited by 3 publications

References 13 publications

Proposal for optimal placement platform of bikes using queueing networks

Proposal for optimal placement platform of bikes using queueing networks

Expression of Relation of Medical Institutions and the Simulation of the Relation Recovery for Disaster

Development of a cloud based smart manufacturing system

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