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Journal : Journal of Applied Data Sciences

SAGOMECON: An Adaptive ε-Constraint-Based Optimization Method for Multi-Criteria Decision-Making in Collaborative Industrial Networks Mesran, M; Sihombing, Poltak; Efendi, Syahril; Zarlis, Muhammad
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.941

Abstract

In collaborative industrial networks, decision-making processes often involve conflicting objectives such as minimizing operational costs and risks, while simultaneously maximizing efficiency and inter-organizational collaboration. Existing multi-objective optimization methods, including the ε-constraint and its variants, face significant challenges in handling dynamic constraints and achieving computational efficiency in real-world scenarios. To address these limitations, this study introduces SAGOMECON (Simplified Adaptive Optimization with Modified ε-Constraint), an enhanced optimization approach designed to support adaptive and efficient multi-criteria decision-making in dynamic environments. SAGOMECON extends the conventional SAUGMECON framework by incorporating real-time constraint updates, adaptive slack handling, and iterative refinement mechanisms, enabling it to maintain solution feasibility under shifting priorities and evolving operational conditions. The proposed method was evaluated using simulated datasets representing partner selection scenarios in collaborative networked organizations (CNOs). Comparative analysis against the traditional ε-constraint and SAUGMECON methods demonstrates that SAGOMECON consistently delivers Pareto-optimal solutions with reduced computational time and superior adaptability to dynamic changes. The findings suggest that SAGOMECON offers a practical and scalable solution for decision-makers in collaborative industrial settings, particularly where trade-offs between competing objectives must be navigated under uncertainty. This contribution is significant for industries seeking intelligent optimization strategies that align with agile and data-driven decision-making frameworks.
Co-Authors , Rahmad Sembiring Achmad Noerkhaerin Putra Adisasmito, Wiku Bakti Bawono Ady Putra, Wahyu Aidil Halim Lubis AIRLANGGA, EKA Aminuyati Andrian, Kevin Ayodhia P. Pasaribu, Ayodhia P. Benfano Soewito Buaton, Relita Budhiarti Nababan, Erna Bugis M. Lubis, Bugis M. Christefa, Dea Cut Ita Erliana Dahlan Abdullah Defi Irwansyah Deny Jollyta Dewi, Rafiqa Efendi, Syahril Efendi, Syahril Eka Irawan Elviwani, Elviwani Erlina Erlina Erma Julita, Erma Erna Budhiarti Nababan Erna Budiarti Ghazali, Alfin Ginting, Emnita Boru Gunawan Gunawan Hadistio, Ryan Rinaldi Haq, Fesa Asy Syifa Nurul Harahap, Eka Purnama Hartama, Dedy Hasibuan, Nisma Novita Herman Mawengkang Hidayati, Indri Husna, Lina Naelal Indra Gunawan Lewis, Andreas Liana Liana Lidya Rosnita Mahyuddin K. M Nasution Malisie, Ririe F. Marischa Elveny, Marischa Marpaung, Tulus Joseph Herianto Mesran, Mesran Miralda, Viya Mohammad Andri Budiman Muhammad Reza Aulia Muliati, Vika Febri Nasution, Zulaini Masruro Novi Dian Nathasia Nurhayati Siregar, Nurhayati Nurwita, Siti Rakhmawati Ovirianti, Nurul Huda Pasaribu, Roni Fredy Halomoan Poltak Sihombing Prayoga, Nanda Dimas Pulungan, Annisa Fadhillah purba, lia cintia Purba, Roimal Hafizi Purnomo Sidi Priambodo Rahmad, Sofyan Rahman Aulia, Rahman Rahman, Abdu Riansyah, Muhammad Romanus Damanik Saib Suwilo Saifullah Saifullah Santoso, Ahmad Imam Sawaluddin Sembiring, Rahmat Widia Siregar, Jelita Siti Sarah Harahap Sri Melvani Hardi Suherman, Suherman Sukiman, T. Sukma Achriadi Sumarno . Sutarman Sutarman Suyanto Suyanto Syah, Rahmad B. Y. Syahputra, Muhammad Romi Syahril Effendi Syauqi, Muhammad Irfan Tanjung, Yulia Windi Tobing, Ricardo Joynest Tulus Tulus Tulus Ucuk Darusalam Wahyuni, Arlinda S. Wardhani, Widiastuti Kusumo Zakaria Zakaria Zakarias Situmorang Zulham Zulkarnain Lubis