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Planning and Scheduling of Presented Bills in Legislative

Afrashteh, Poorya | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57886 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Varmazyar, Mohsen
  7. Abstract:
  8. In recent years, data-driven planning and analysis in political institutions, particularly legislative bodies, have gained significant importance, leading to enhanced decision-making processes and policy effectiveness. Due to their high transparency and direct impact on macro-level political decisions, these institutions are recognized as one of the most critical research domains in this field. In this study, considering the complex and unique structure of legislative processes in parliaments and senates, a mixed-integer programming model was designed to prioritize the scheduling of high-impact legislative proposals. Given the inefficiency of exact solution methods in large-scale problems, metaheuristic algorithms—namely, the Non-dominated Ranked Genetic Algorithm (NRGA) and Multi-Objective Particle Swarm Optimization (MOPSO)—were employed. These algorithms were simulated in MATLAB and demonstrated robust performance in optimizing proposal schedules. Subsequently, by simulating the model in Arena software and incorporating practical constraints such as maximum overtime limits and permissible commission numbers, the model’s alignment with real-world conditions was improved. After evaluating three parallel objective functions, the weighted-sum maximization function of approved proposals was selected due to its 100% reduction in computation time. Optimal input parameters for the metaheuristic algorithms were determined using the Taguchi method. Comparative analysis revealed that the NRGA outperformed others, achieving 70% superior optimization performance and 90% faster computation time. Sensitivity analysis highlighted the substantial influence of temporal factors—including proposal submission, preparation, and review durations in legislative commissions and assemblies—on final outcomes (53.2%, 99.4%, and 89.6%, respectively), emphasizing the necessity for procedural refinements in these areas
  9. Keywords:
  10. Mixed Integer Programming ; Many Objective Metaheuristic Algorithms ; Heuristic Algorithm ; Multiobjective Partial Swarm Optimization (MOPSO) ; Parliament ; Legislative Institutions ; Bill Scheduling ; Ranked Nondominated Genetic Algorithm

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