Çok bloklu i̇malat için senaryo tabanli dayanikli tesis yerleşim tasarimi: endüstriyel uygulamali veri odakli bir karma tamsayili doğrusal programlama (MILP) çerçevesi

This dissertation addresses the Unequal-Area Facility Layout Problem (UA-FLP) under uncertainty, with a particular focus on multi-block manufacturing environments where material flows are complex and subject to disruption. The motivation for this research stems from the limitations of traditional deterministic layout models, which often fail to accommodate the operational variability and spatial constraints characteristic of modern industrial systems. In response, the study proposes a structured and data-driven modeling framework based on mixed-integer linear programming (MILP), progressively incorporating realistic constraints and robustness features to enhance layout feasibility and long-term performance. The thesis begins with the development of a deterministic MILP model that assigns departments of unequal area to two fixed-size layout blocks. The model minimizes total transportation cost while integrating key industrial features such as AEIOUX-based closeness preferences, ramp-based inter-block material transfer, and capacity-constrained flow routing across multiple ramps. Building on this foundation, the second stage introduces a Gamma-robust optimization model that protects against material flow uncertainty by incorporating worst-case deviation terms. The uncertainty parameters are derived from a three-year historical flow dataset using a weighted two-sigma rule, and a protection level is applied to ensure robust yet cost-efficient layouts. In the final stage, a scenario-based robust MILP model is developed. Ten disruption scenarios are defined to represent potential operational disturbances, including ramp closures, peak demand surges, and supply shortages. Scenario-dependent flow matrices, ramp capacities, and external transportation costs are incorporated while maintaining a single, unified layout across all scenarios. The model allows for flexible flow redistribution and evaluates spatial configuration through penalty terms, offering a balanced trade-off between cost minimization and layout resilience. Scenario probabilities are used to assess expected performance and solution stability. The proposed framework is applied to a real-world case study involving the washing machine production plant of a leading home appliance manufacturer. The layout problem involves assigning 21 departments to two connected blocks under realistic spatial and operational constraints. Historical data from 2022 to 2024 is used to quantify uncertainty and validate the robustness of the proposed solutions. The scenario-based results are analyzed through a detailed cost breakdown, ramp utilization metrics, AEIOUX separation penalties, and layout visualizations. The findings demonstrate that the proposed robust layout configurations maintain feasible and efficient performance across a wide range of disruption scenarios. This dissertation contributes a comprehensive and implementable framework for solving robust multi-block facility layout problems under uncertainty. By integrating practical layout requirements, historical data-driven uncertainty modeling, and scenario-based robustness, the study advances both the theoretical understanding and industrial applicability of robust facility layout design. The resulting methodology provides a scalable decision-support tool for long-term facility planning in dynamic manufacturing environments.