Transactions of the Institute of Systems, Control and Information Engineers
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ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668

Transactions of the Institute of Systems, Control and Information Engineers Vol. 33 (2020), No. 2

  • Environment-Adaptive Genetic Algorithm-based Nesting Scheduling for Sheet-Metal Processing

    pp. 39-48

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    DOI:10.5687/iscie.33.39

    Sheet metal processing is a popular machining technique. In sheet metal processing, as many parts as possible are cut from a metal sheet to effectively use the metal without waste. The parts cut from the sheet metal are processed by a specified due-date. To meet the due-date, scheduling is important. The optimizations of the cutting layout and schedule are called nesting and scheduling, respectively. The relation between them sometimes exhibits a trade-off. To enhance the efficiency of the entire manufacturing process, nesting and scheduling should be considered simultaneously. Therefore, in this study, we proposed an environment-adaptive genetic-algorithm-based nesting scheduling method for the simultaneous consideration of two related problems with different optimization targets. We treated the problems as different environments, and the cutting layout and processing order of the parts evolved in each environment using the genetic algorithm.
  • Minimum Implementation of Linear Traffic Lights for One-way Intersections

    pp. 49-56

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    DOI:10.5687/iscie.33.49

    Recent years, a new type of traffic lights, called the linear traffic lights, has been proposed. The linear traffic lights, which are placed at an intersection, monitor the traffic status of the crossing roads and control the traffic flow around the intersection. This paper considers the minimum configuration of the linear traffic lights to guarantee non-stopping crossing without collision at the intersection of one-way roads.
  • Analysis of Distributed Thompson Sampling based on Consensus Control

    pp. 57-65

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    DOI:10.5687/iscie.33.57

    Recently, distributed control for multi-agent systems has attracted much attention. Each agent makes a decision through interaction over a communication network. In general, there exists a trade-off between exploration of the best choice and exploitation of the obtained knowledge. Such a trade-off can be formulated as the bandit problem. In this paper, we investigate a distributed bandit problem where a group of agents cooperatively searches the best choice in a distributed manner. We propose a cooperative Thompson sampling based on the consensus algorithm of multi-agent systems. The theoretical analysis of a regret bound is carried out for the case when the communication network is represented by a complete graph. The numerical examples show that the regret can be reduced by the proposed cooperative Thompson sampling compared to the case when agents individually search the best choice without cooperation.

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