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A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 1 (2025): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Highly complex systems such as smart grids, autonomous transportation networks, and large-scale supply chains present significant challenges for optimization due to high dimensionality, nonlinear interactions, and pervasive uncertainty. Traditional deterministic models often fail under dynamic conditions, while many AI-based approaches lack robustness and stability when confronted with noisy or incomplete data. Addressing these issues, this study proposes a probabilistic decision model designed to enhance AI-driven optimization in uncertain and rapidly changing environments. The model integrates probabilistic graphical structures, Bayesian inference, and AI-based optimization techniques to quantify uncertainty and support adaptive decision-making. Experimental evaluations were conducted using a combination of synthetic datasets, simulation environments, and benchmark scenarios representative of real-world complex systems. Results show that the proposed model achieves significantly higher decision accuracy, improved stability under noisy conditions, and more efficient performance in high-dimensional settings compared with classical optimization, reinforcement learning, and standard probabilistic approaches. The model consistently reduces uncertainty and delivers robust, reliable solutions across a wide range of test conditions.The study presents a scalable, interpretable, and highly effective framework for uncertainty-aware optimization. Its strong performance and generalizability highlight its potential for deployment in critical real-world applications where reliability, safety, and adaptability are essential.

A Probabilistic Decision Model for AI-Driven Optimization in Highly Complex Systems Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 2 (2025): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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This research proposes a novel Probabilistic Decision Model (PDM) designed to address the challenges of optimization in highly complex systems characterized by high-dimensional states, nonlinear interactions, and deep uncertainty. Traditional deterministic, heuristic, and deep learning-based methods often fail to provide reliable decisions under such conditions due to their limited scalability, lack of uncertainty quantification, or inability to guarantee constraint satisfaction. The proposed model integrates probabilistic constraints, expectation-based objective functions, and adaptive AI-driven scenario generation to deliver a robust and flexible optimization framework. A rigorous mathematical formulation is presented, including probability space definitions, risk measures, and feasible neighborhood rules. Validation through numerical simulations demonstrates that the model maintains high feasibility, reduces worst-case risks, and remains stable even under extreme uncertainty. Case studies in smart grid optimization, logistics routing, and manufacturing scheduling further highlight significant performance improvements over classical stochastic optimization, MDP/POMDP models, and deep reinforcement learning without probabilistic modeling. The results confirm the model’s strong scalability, enhanced uncertainty modeling, and practical relevance for real-world industrial environments. This research contributes a hybrid probabilistic-AI framework that advances the reliability, resilience, and intelligence of decision-making in modern complex systems, while opening pathways for future exploration in multi-agent coordination, automated parameter tuning, and real-time adaptive optimization.

Theoretical Advances in Hungarian Maximization Models for Multi-Site Human Resource Allocation Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 3 (2025): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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This study presents a theoretical and methodological advancement of the Hungarian maximization model for optimizing multi-site human resource allocation. Traditional Hungarian algorithms focus on single-site, cost-minimization assignments, limiting their applicability in modern workforce environments characterized by distributed operations and diverse employee attributes. To address these gaps, the study reformulates the classical objective function into a maximization framework and incorporates multi-site constraints, multi-criteria employee attributes, and workload balancing requirements. The enhanced model is evaluated through mathematical analysis and simulation-based case studies to assess its performance relative to baseline assignment and heuristic optimization methods. The results demonstrate that the proposed model achieves higher organizational productivity, reduces operational costs, improves staff distribution equity, and significantly accelerates computation time compared with existing approaches. Moreover, the model ensures more consistent alignment between employee capabilities and site-level demands, offering a more robust foundation for strategic workforce deployment. Comparisons with previous studies show that this research provides the first Hungarian-based maximization framework specifically tailored for multi-site HR allocation, overcoming key limitations related to scalability, fairness, and optimality. Overall, this study contributes a rigorous theoretical extension of the Hungarian method and offers practical implications for workforce scheduling, supply-chain staffing, healthcare deployment, and emergency response operations. The findings underscore the potential of deterministic optimization models to support intelligent and equitable human resource decision-making in increasingly complex organizational settings.

A Unified Mathematical Framework for NWC, MODI, and Stepping Stone as Foundational Models in Optimal Transport Theory Riandari, Fristi; Panjaitan, Firta Sari
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 4 (2025): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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This research introduces a unified mathematical framework connecting three classical transportation problem methods Northwest Corner Rule (NWC), Modified Distribution Method (MODI), and the Stepping Stone Method to the modern theory of Optimal Transport (OT). Despite their long-standing use in operations research, these classical algorithms have traditionally been treated as heuristic procedures without a formal theoretical link to the rigorous Monge Kantorovich formulation. This study demonstrates that each method corresponds directly to fundamental geometric and dual structures of the transportation polytope: NWC generates an initial extreme-point solution, MODI computes dual potentials analogous to Kantorovich potentials, and Stepping Stone identifies improvement cycles consistent with movements along polytope edges. Using formal definitions, algebraic mappings, and geometric interpretation, the research establishes a coherent connection between classical OR algorithms and OT duality theory. The results show that these methods are not isolated heuristics, but structured approximations of optimal transport processes. The unified framework improves theoretical understanding, simplifies instructional explanations, and offers methodological insights that may support future algorithmic enhancements. Limitations include scalability challenges and reduced applicability to complex continuous OT settings. Overall, this research contributes a foundational unification that bridges classical transportation algorithms with contemporary optimal transport theory, advancing both theoretical rigor and practical comprehension.

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