A sustainability driven multi-objective model for unified dynamic facility layouts and cellular manufacturing system optimization

The design of Dynamic Cellular Manufacturing Systems (DCMS) presents a challenge in simultaneously balancing economic performance, environmental impact, and spatial efficiency. Conventional DCMS formulations frequently neglect explicit facility layout considerations, particularly cell positioning and overlap constraints, leading to solutions that are optimal in theory but impractical for real-world implementation. This study introduces a multi-objective framework that jointly minimizes material handling cost, floor area utilization, and carbon emissions within a unified model. A hybrid Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed and coupled with a greedy packing algorithm for spatial placement. The proposed solver employs a discrete grid-based layout representation, and segment-specific crossover and mutation tailored to mixed-integer decision variables. Polyominoes are used to model machine groupings within cells, enabling unequal-sized, regular, and irregularly shaped cells while explicitly preventing cell and machine overlaps. Solution quality is assessed using Pareto dominance and crowding distance, with constraint violations handled through a penalty-based mechanism. Computational experiments demonstrate that integrating layout planning with dynamic reconfiguration decisions produces layout-aware solutions that are both spatially feasible and industrially realistic. Results further reveal a nonlinear convex trade-off between economic cost, carbon emissions, and utilized floor space, offering valuable insights for sustainable and efficient manufacturing system designs.