Showing 21 - 40 results of 42 for search '"TARDIS"', query time: 0.04s Refine Results
  1. 21

    Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem by S. Molla-Alizadeh-Zavardehi, R. Tavakkoli-Moghaddam, F. Hosseinzadeh Lotfi

    Published 2014-01-01
    “…This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. …”
    Get full text
    Article
  2. 22

    Mathematical Model and Algorithm for the Reefer Mechanic Scheduling Problem at Seaports by Jiantong Zhang, Yujian Song

    Published 2017-01-01
    “…To minimize the sum of the total tardiness of all tasks and the total working distance of all mechanics, we formulate a mathematical model. …”
    Get full text
    Article
  3. 23

    Production Scheduling considering Outsourcing Options and Carrier Costs by Byung-Cheon Choi, Yunhong Min, Myoung-Ju Park, Kyung Min Kim

    Published 2020-01-01
    “…An in-house job incurs a stepwise penalty cost for tardiness, and an outsourced job incurs an outsourcing cost. …”
    Get full text
    Article
  4. 24

    A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory by Toly Chen, Yi-Chi Wang

    Published 2013-01-01
    “…The proposed rule considers the uncertainty in the remaining cycle time and is aimed at simultaneous improvement of the average cycle time, cycle time standard deviation, the maximum lateness, and number of tardy jobs. Existing publications rarely discusse ways to optimize all of these at the same time. …”
    Get full text
    Article
  5. 25

    A PMBGA to Optimize the Selection of Rules for Job Shop Scheduling Based on the Giffler-Thompson Algorithm by Rui Zhang, Cheng Wu

    Published 2012-01-01
    “…So in this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. Several dispatching rules are adopted in the Giffler-Thompson algorithm for constructing active schedules. …”
    Get full text
    Article
  6. 26

    Modified artificial fish swarm algorithm to solve unrelated parallel machine scheduling problem under fuzzy environment by Azhar Mahdi Ibadi, Rosshairy Abd Rahman

    Published 2024-12-01
    “…Hence, a metaheuristic algorithm based on a modified artificial fish swarm algorithm (AFSA) is presented in this study to minimize the multi-objective makespan and total tardiness. Three modifications were made to the proposed algorithm. …”
    Get full text
    Article
  7. 27

    A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems by Xinnian Wang, Keyi Xing, Chao-Bo Yan, Mengchu Zhou

    Published 2019-01-01
    “…., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. …”
    Get full text
    Article
  8. 28

    A hybrid particle swarm optimization algorithm for single machine scheduling with sequence-dependent setup times and learning effects by Payam Chiniforooshan, Dragan Marinkovic

    Published 2023-06-01
    “…This paper deals with the single machine scheduling problem with sequence-dependent setup time and learning effect on processing time, where the objective is to minimize total earliness and tardiness of the jobs. A Mixed Integer Linear Programming (MILP) model capable of solving small-sized problems is proposed to formulate this problem. …”
    Get full text
    Article
  9. 29

    Multiproject Resources Allocation Model under Fuzzy Random Environment and Its Application to Industrial Equipment Installation Engineering by Jun Gang, Jiuping Xu, Yinfeng Xu

    Published 2013-01-01
    “…On the upper level, the company manager aims to allocate the company's resources to multiple projects to achieve the lowest cost, which include resource costs and a tardiness penalty. On the lower level, each project manager attempts to schedule their resource-constrained project, with minimization of project duration as the main objective. …”
    Get full text
    Article
  10. 30

    Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics by Alonso Vela, Gerardo Humberto Valencia-Rivera, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Ivan Amaya

    Published 2025-01-01
    “…Despite the robust development in hyper-heuristic methodologies, our analysis indicates an under-representation of multi-objective optimization and a limited use of performance metrics beyond makespan and tardiness. We also identify potential areas for future research, such as expanding hyper-heuristic applications to underexplored industries and exploring less conventional performance metrics. …”
    Get full text
    Article
  11. 31

    Performance Evaluation of New Joint EDF-RM Scheduling Algorithm for Real Time Distributed System by Rashmi Sharma, Nitin

    Published 2014-01-01
    “…Results are achieved and evaluated in terms of Success Ratio (SR), Average CPU Utilization (ECU), Failure Ratio (FR), and Maximum Tardiness parameters. In the end, the results are compared with the existing (EDF, RM, and D_R_EDF) algorithms. …”
    Get full text
    Article
  12. 32

    L’illusion de la désillusion : Essai d’interprétation génétique de L’Éducation sentimentale by Kazuhiro Matsuzawa

    Published 2010-09-01
    “…“Excuse my tardiness”. With these words spoken sixteen years later Madame Arnoux justifies her unexpected visit in the penultimate chapter of the Éducation sentimentale. …”
    Get full text
    Article
  13. 33

    Differential Evolution Algorithm Combined with Uncertainty Handling Techniques for Stochastic Reentrant Job Shop Scheduling Problem by Rong Hu, Xing Wu, Bin Qian, Jianlin Mao, Huaiping Jin

    Published 2022-01-01
    “…., the SRJSSP with the maximum tardiness criterion and the SRJSSP with the makespan criterion. …”
    Get full text
    Article
  14. 34

    Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time by Hongwei Li, Yuvraj Gajpal, Chirag Surti, Dongliang Cai, Amit Kumar Bhardwaj

    Published 2020-01-01
    “…The first agent tries to minimize the total completion time, while the second agent tries to minimize the number of tardy jobs. The two agents’ scheduling problem, an NP-hard problem, has a wide variety of applications ranging from the manufacturing industry to the cloud computing service provider. …”
    Get full text
    Article
  15. 35

    Federated Digital Twins: A Scheduling Approach Based on Temporal Graph Neural Network and Deep Reinforcement Learning by Young-Jin Kim, Hanjin Kim, Beomsu Ha, Won-Tae Kim

    Published 2025-01-01
    “…Experimental results demonstrate that, in scenarios where multiple DTs exhibit diverse speed profiles, the proposed approach improves makespan by 16.9% and reduces tardiness by 76.2%, demonstrating both flexibility and superior performance. …”
    Get full text
    Article
  16. 36
  17. 37
  18. 38
  19. 39

    Presenting a model for solving lot-streaming hybrid flow shop scheduling problem by considering independent setup time and transportation time by Roja Ruhbakhsh, Esmaeil Mehdizadeh, Mohammad Amin Adibi

    Published 2023-09-01
    “…After performing the experiments and comparing the algorithms with each other, the results show NRGA algorithm performs bether than NSGA-II.Originality/Value: In this paper, for solving a multi objective hybrid flow shop scheduling problem with lot-streamingm mathematical model with the aim of minimizing the makespan and total tardiness, the sequence-dependent setup time and the transportation time constraints between consecutive stages are considered. …”
    Get full text
    Article
  20. 40

    Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal by Supath Dhital, Kapil Lamsal, Sulav Shrestha, Umesh Bhurtyal

    Published 2024-06-01
    “…In Nepal, the Department of Hydrology and Meteorological uses a numerical modeling approach to forecast the weather, which is tardy and requires high-end equipment to process the information, so a deep learning approach will be the best alternative. …”
    Get full text
    Article