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Journal Cover Asian Journal of Management Science and Applications
  [4 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2049-8683 - ISSN (Online) 2049-8691
   Published by Inderscience Publishers Homepage  [432 journals]
  • Relative incentive rate in a multi-period and multi-task agency
    • Authors: Christina F. Rusnock, Christopher D. Geiger
      Pages: 1 - 23
      Abstract: This study explicitly calculates the relative incentive rate in an N-period contract with multiple tasks. The inter-temporal covariance risk, as well as the within-period risk premium, prevents the first best allocation of effort from being endogenously achieved even if the first best allocation is feasible. The inter-temporal covariance risk reduces the effective sensitivity of a performance measure, and thus the performance measure with a bigger inter-temporal covariance risk is assigned a weaker relative incentive rate. From these results, an empirical prediction is derived that a performance measure with larger positive (negative) inter-temporal covariances is assigned a weaker (stronger) relative incentive rate in multi-period contracts.
      Keywords: relative incentive rate; performance measures; multi-period; multi-task; inter-temporal covariance
      Citation: Asian J. of Management Science and Applications, Vol. 3, No. 1 (2017) pp. 1 - 23
      PubDate: 2017-04-08T23:20:50-05:00
      DOI: 10.1504/AJMSA.2017.083496
      Issue No: Vol. 3, No. 1 (2017)
  • The analysis based on principal matrix decomposition for 3-mode binary
    • Authors: Haruka Yamashita, Masayuki Goto
      Pages: 24 - 37
      Abstract: Recently, principal points for a multivariate binary distribution (Yamashita and Suzuki, 2014, 2015) have been proposed as the binary vectors that optimally represent a distribution, in terms of the average Euclidian squared distance between a multivariate binary distribution and the vectors. In this paper, we proposes a new analysis procedure for 3-mode binary data, based on principal points for a multivariate binary distribution (Yamashita and Suzuki, 2014, 2015). Moreover, we propose a method that decomposes principal matrixes for 3-mode binary data into a small number of vectors based on vector products. In order to investigate our method's applicability to real-world data, we use the method to analyse 3-mode structured data from annual all-star games for Japanese professional baseball.
      Keywords: principal points; 3-mode data; binary data; clustering; data analysis
      Citation: Asian J. of Management Science and Applications, Vol. 3, No. 1 (2017) pp. 24 - 37
      PubDate: 2017-04-08T23:20:50-05:00
      DOI: 10.1504/AJMSA.2017.083504
      Issue No: Vol. 3, No. 1 (2017)
  • Hedging financial and environmental risk in portfolios: constructing and
           evaluating eco-funds
    • Authors: Kan Nakayashiki, Wei Zang, Satoshi Kumagai
      Pages: 38 - 49
      Abstract: Reducing their environmental load has recently become a key concern to firms. Many financial products that invest in companies with a strong environmental consciousness, such as the Nikko Eco-fund, have been released. However, choosing an ecological fund based on an interview or questionnaire is a qualitative process, as an ecological fund does not reflect a firm's environmental performance, nor reduces environmental risk. This study calculates an environmental beta value (environmental risk) using emissions data on environmental load material, applying eco performance and economic screening. We, then, decide on investment brands and ratios using the environmental beta value and efficient frontier. Finally, we propose an environmental portfolio that reduces environmental and financial risks. This methodology enables to assess environmental risk in a quantitative manner. Two brands of company that simultaneously reduce environmental and financial risks are identified.
      Keywords: environmental risks; beta-value; portfolio management; eco-funds; economic screening; eco performance screening
      Citation: Asian J. of Management Science and Applications, Vol. 3, No. 1 (2017) pp. 38 - 49
      PubDate: 2017-04-08T23:20:50-05:00
      DOI: 10.1504/AJMSA.2017.083509
      Issue No: Vol. 3, No. 1 (2017)
  • Random assignment under ordinal preferences: a separation
    • Authors: Kan Nakayashiki, Wei Zang, Satoshi Kumagai
      Pages: 50 - 60
      Abstract: Assignment problems include allocating a set of objects among agents; here, only ordinal preferences are revealed. In this paper, we establish a condition of feasible solutions for deterministic assignments. Related to it, we show then a separation characterisation for probabilistic serial (PS) mechanism, based on sd-efficiency, sd-envy-freeness and the definition of PS (where 'sd' stands for first-order stochastic dominance). An application to recent result about PS is also described. Models here are suitable for assignment problems in various fields, such as fair sharing of resources in industry. The separation structure proposed here provides a possibility to divide a large-scale problem into several sub-problems.
      Keywords: bipartite-graph; deterministic and random assignment; serial rule; ordinal efficiency; leximin; sharing problem
      Citation: Asian J. of Management Science and Applications, Vol. 3, No. 1 (2017) pp. 50 - 60
      PubDate: 2017-04-08T23:20:50-05:00
      DOI: 10.1504/AJMSA.2017.083508
      Issue No: Vol. 3, No. 1 (2017)
  • Data pair selection for accurate classification based on
           information-theoretic metric learning
    • Authors: Takashi Maga, Kenta Mikawa, Masayuki Goto
      Pages: 61 - 74
      Abstract: Data classification is one of the main technique in data analysis which has become more and more important in various fields of business. Automatic classification is the problem that classification category label is learned from training data. One of the effective approaches for automatic classification is the k-nearest neighbour (kNN) method based on distances between data pairs, combining with the well-known distance metric learning. In this study, we focus on information-theoretic metric learning (ITML) method. In ITML, the optimisation problem is formulated as learning metric matrix so that the distance between each pair of data belonging to the same class becomes smaller than a constant, while the distance between each pair of data belonging to different classes becomes larger than the other constant. In this study, we propose an improved procedure by choosing the data-pairs which affect clarifying the boundaries effectively. We verify the effectiveness of our proposed method by conducting the simulation experiment with benchmark dataset.
      Keywords: automatic classification; distance metric learning; Mahalanobis distance; information-theoretic metric learning; ITML
      Citation: Asian J. of Management Science and Applications, Vol. 3, No. 1 (2017) pp. 61 - 74
      PubDate: 2017-04-08T23:20:50-05:00
      DOI: 10.1504/AJMSA.2017.083512
      Issue No: Vol. 3, No. 1 (2017)
  • Designing insert buffers for mixed-model assembly lines
    • Authors: Sho Matsuura, Haruki Matsuura, Akiko Asada
      Pages: 75 - 95
      Abstract: To meet a diverse range of manufacturing specifications and keep pace with rapid changes in consumer requirements, mixed-model assembly lines must become more efficient and flexible than they are at present. This study examines the characteristics of an insert buffer that uses the curved portion of a mixed-model assembly line to provide guidelines for designing the buffer, and then analyses the number of sequences generated with the buffer. Also analysed is the effect of the insert buffer on the working time and line length. Based on these results, a procedure for designing an insert buffer is proposed.
      Keywords: mixed-model line; insert buffer; line length; flexibility
      Citation: Asian J. of Management Science and Applications, Vol. 3, No. 1 (2017) pp. 75 - 95
      PubDate: 2017-04-08T23:20:50-05:00
      DOI: 10.1504/AJMSA.2017.083514
      Issue No: Vol. 3, No. 1 (2017)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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