Curriculum Vitaes

Gonsalves Tad

  (ゴンサルベス タッド)

Profile Information

Affiliation
Professor, Faculty of Science and Technology, Department of Information and Communication Sciences, Sophia University
Degree
博士(工学)(上智大学)

Researcher number
90407338
J-GLOBAL ID
201301073146868965
researchmap Member ID
7000004362

External link

Papers

 133
  • Tshewang Phuntsho, Tad Gonsalves
    Algorithms, 17(5) 180-180, Apr 28, 2024  Peer-reviewed
    Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The Estimation of Distribution Algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP–DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased.
  • Tshewang Phuntsho, Tad Gonsalves
    Connection Science, Taylor and Francis, Dec, 2023  Peer-reviewed
  • H. D. Purnomo, T. Gonsalves, T. Wahyono, P. O. N. Saian
    20(2) 125-134, Aug, 2023  Peer-reviewed
  • Zhihan Xue, Tad Gonsalves
    Connection Science, 35(1), Mar 8, 2023  Peer-reviewed
  • Tshewang Phuntsho, Tad Gonsalves
    2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 53 524-530, Jan 5, 2023  Peer-reviewed

Misc.

 2
  • GONSALVES Tad, ITOH Kiyoshi, KAWABATA Ryo
    Technical report of IEICE. KBSE, 103(604) 1-6, Dec, 2004  
    In this paper we propose a composite-server model and make use of the knowledge of the intrinsic composition of its service providing units (personnel or equipment) to derive Qualitative knowledge-based rules for its performance evaluation. The composite server model that takes into account the composite nature of service has wider scope in its applications and can be used to represent a variety of system classes. We use this novel concept in the performance design and improvement of collaborative engineering systems. System modeling is done by Multi-Context Map (MCM) technique. MCM is a descriptive model that expresses the collaborative activity performed through the exchange of token, material and information; bottlenecks primarily arise due to the non-uniformity in the flow of token, material and information. Another source of bottlenecks in collaborative engineering systems is the lack or surplus of service-providing units, known as "Perspectives" in the MCM terminology. Bottlenecks due to inappropriate Perspective allocation are resolved by the Qualitative Reasoning approach. We have found this method successful in the performance design, evaluation and improvement of a practical collaborative engineering system presented at the end of this paper.
  • GONSALVES Tad, ITOH Kiyoshi, KAWABATA Ryo
    IEICE technical report. Artificial intelligence and knowledge-based processing, 103(306) 15-20, Sep 9, 2003  
    This paper discusses the design and implementation of a novel system performance improvement Expert System (ES) with a Qualitative inference engine. The motive for using Qualitative Reasoning is to overcome the computational complexity posed by the triple-input-triple-output contexts interactions in the Multi-Context Map (MCM) queuing network which models the system. The ES analyses the GPSS simulation data of system performance, consults the MCM knowledge base of the system, and with its inference engine driven by qualitative rules draws the parameter-tuning plan to resolve bottlenecks. The ES has been successfully applied in improving a typical benchmarking system in Collaboration Engineering.

Books and Other Publications

 2

Presentations

 63