Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3485190.3485231acmotherconferencesArticle/Chapter ViewAbstractPublication PagesimmsConference Proceedingsconference-collections
research-article

Introduction to A Compromise Programming Based Method for Complex Scheduling and Planning Problems

Published: 02 December 2021 Publication History

Abstract

Planning and Planning (SP) plays a vital role in many fields. However, SP problems become more complex when they require to archive multi goals in decision-making processes that are more difficult to solve and push the decision-maker into a dilemma. This paper introduces an adaptive method based on the compromise programming approach to multi-objective optimization (MOP) in scheduling and planning (SP) problems. The proposed method gives an effective integration of mathematical programming with evolutionary algorithms (EA). Through the technique, decision-makers can validate the models as well as evaluate different decision alternatives. The method is in the development progress. However, we have obtained preliminary results by applying the method for solving some SP problems. These results show the feasibility of the proposed method.

References

[1]
J. Blazewicz, K. H. Ecker, E. Pesch, G. Schmidt, M. Sterna, and J. Weglarz, Handbook on Scheduling. Cham: Springer International Publishing, 2019.
[2]
M. Padberg, “Combinatorial Optimization: An Introduction,” 1999.
[3]
N. Gunantara and N. P. Sastra, “Cooperative Diversity Selection Protocol Using Pareto Method with Multi Objective Criterion in Wireless Ad Hoc Networks,” Int. J. Multimed. Ubiquitous Eng., vol. 11, no. 5, pp. 43–54, May 2016.
[4]
T. Chugh, K. Sindhya, J. Hakanen, and K. Miettinen, “A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms,” Soft Comput., vol. 23, no. 9, pp. 3137–3166, May 2019.
[5]
J. R. Meredith, “Reconsidering the Philosophical Basis of OR/MS,” Oper. Res., vol. 49, no. 3, pp. 325–333, Jun. 2001, [Online]. Available: http://www.jstor.org/stable/3088630.
[6]
P. Taillandier and J. Gaffuri, “Objective Function Designing Led by User Preferences Acquisition,” CoRR, vol. abs/1204.4, 2012, [Online]. Available: http://arxiv.org/abs/1204.4990.
[7]
R. N. Monemi, S. Gelareh, A. Nagih, and D. Jones, “Bi-objective load balancing multiple allocation hub location: a compromise programming approach,” Ann. Oper. Res., vol. 296, no. 1–2, Jan. 2021.
[8]
T. S. Ngo, “Some Algorithms to Solve a Bi-Objectives Problem for Team Selection,” Appl. Sci., vol. 10, no. 8, Apr. 2020.
[9]
Ngo Tung Son, Le Van Thanh, Tran Binh Duong, and Bui Ngoc Anh. 2018. A decision support tool for cross-functional team selection: case study in ACM-ICPC team selection. In Proceedings of the 2018 International Conference on Information Management & Management Science (IMMS '18). Association for Computing Machinery, New York, NY, USA, 133–138.
[10]
M. Koppen and K. Yoshida, “Visualization of Pareto-Sets in Evolutionary Multi-Objective Optimization,” Sep. 2007.
[11]
M. López-Ibáñez, L. Paquete, and T. Stützle, “Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization,” in Experimental Methods for the Analysis of Optimization Algorithms, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
[12]
F. Kudo and T. Yoshikawa, “Knowledge extraction in multi-objective optimization problem based on visualization of Pareto solutions,” Jun. 2012.
[13]
S. T. Ngo, J. Jaafar, I. A. Aziz, and B. N. Anh, “A Compromise Programming for Multi-Objective Task Assignment Problem,” Computers, vol. 10, no. 2, Jan. 2021.
[14]
N. T. Son, J. Jaafar, I. A. Aziz, and B. N. Anh, “Meta-Heuristic Algorithms for Learning Path Recommender at MOOC,” IEEE Access, vol. 9, pp. 59093–59107, 2021.
[15]
S. T. Ngo, J. B. Jaafar, I. A. Aziz, G. H. Nguyen, and A. N. Bui, “Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem,” Int. J. Emerg. Technol. Learn., vol. 16, no. 11, p. 4, Jun. 2021.
[16]
Son Tung Ngo, Jaafar Jafreezal, Giang Hoang Nguyen, and Anh Ngoc Bui. 2021. A Genetic Algorithm for Multi-Objective Optimization in Complex Course Timetabling. In 2021 10th International Conference on Software and Computer Applications (ICSCA 2021). Association for Computing Machinery, New York, NY, USA, 229–237.
[17]
Son, N. T., Jaafar, J., Aziz, I. A., Anh, B. N., Binh, H. D. (2021). A Compromise Programming to Task Assignment Problem in Software Development Project. CMC-Computers, Materials & Continua, 69(3), 3429–3444.

Cited By

View all
  • (2023)Some Metaheuristics for Tourist Trip Design Problem2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA58478.2023.10212154(1-10)Online publication date: 15-Jul-2023
  • (2022)Some metaheuristic algorithms for solving multiple cross-functional team selection problemsPeerJ Computer Science10.7717/peerj-cs.10638(e1063)Online publication date: 9-Aug-2022
  • (2022)Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment ProblemEntropy10.3390/e2403038824:3(388)Online publication date: 9-Mar-2022

Index Terms

  1. Introduction to A Compromise Programming Based Method for Complex Scheduling and Planning Problems
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    IMMS '21: Proceedings of the 4th International Conference on Information Management and Management Science
    August 2021
    332 pages
    ISBN:9781450384278
    DOI:10.1145/3485190
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 02 December 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Combinatorial Optimization
    2. Compromise Programming
    3. Evolutionary Algorithm
    4. Multi-Objective Optimization
    5. Planning
    6. Scheduling

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    IMMS 2021

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)54
    • Downloads (Last 6 weeks)6
    Reflects downloads up to 30 Nov 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Some Metaheuristics for Tourist Trip Design Problem2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA58478.2023.10212154(1-10)Online publication date: 15-Jul-2023
    • (2022)Some metaheuristic algorithms for solving multiple cross-functional team selection problemsPeerJ Computer Science10.7717/peerj-cs.10638(e1063)Online publication date: 9-Aug-2022
    • (2022)Metaheuristic Algorithms Based on Compromise Programming for the Multi-Objective Urban Shipment ProblemEntropy10.3390/e2403038824:3(388)Online publication date: 9-Mar-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media