Work

A constrained multi-objective dynamic corridor allocation problem model for low-carbon manufacturing

Year: 2025

Type: article

Abstract: • A dynamic layout model optimizes costs, carbon emissions, and layout space. • A quadratic-constrained programming integrates energy-based carbon emissions. • An improved NSGA-II enhances solutions and refines path-relinking. • The algorithm outperforms benchmarks in dynamic layout optimization. • This study advances green manufacturing and low-carbon facility design. A well-designed facility layout can effectively reduce carbon emissions by optimising logistics and energy use. Dynamic environments enable more accurate emission estimation, they have rarely been considered in low-carbon manufacturing layout optimisation research. To address this gap, this study proposes an extended constrained multi-objective dynamic corridor allocation problem that integrates three optimisation objectives: logistics costs, carbon emissions, and layout space. A mixed-integer programming model incorporating carbon emissions was developed, considering the energy consumption of material handling equipment, and an estimation method for carbon emissions in a dynamic production environment was proposed. To more accurately represent energy use and spatial interactions, mathematical constraints were imposed to the facility positions, extending the model to a mixed-integer quadratic-constrained programming framework. Given the high computational complexity of the proposed problem, this study designed an improved non-dominated sorting genetic algorithm II (NSGA-II) that enhances the initial solution generation and refines the path-relinking strategy based on problem encoding and multi-objective characteristics, substantially enhance the solution convergence and diversity. Experimental results demonstrate that the proposed algorithm accurately solves four asymmetric material flow multi-objective dynamic instances and outperforms both the traditional NSGA-II and existing literature in benchmark dynamic instances by identifying superior Pareto-optimal solutions. In real-world cases it has been possible to optimise material handling costs by 23.43%, carbon costs by 7.9% and effectively improves shop floor space utilisation. This study provides a theoretical foundation and practical insights for advancing green manufacturing and low-carbon economies.

Source: Swarm and Evolutionary Computation

DOI: 10.1016/j.swevo.2025.102221