The growing emphasis on sustainable transportation has intensified the need to reduce carbon emissions in railway maintenance and renewal activities. This paper proposes a lexicographic optimisation framework for joint maintenance and renewal planning in urban railway networks, in which CO 2 emissions are prioritised over economic cost. The problem is formulated as a mixed-integer linear program integrating key industrial constraints, including track possession windows, a degradation model, and activity grouping. A two-stage solution strategy is developed: the first stage identifies CO 2 -optimal solutions, while the second stage refines these solutions with respect to cost using either an exact MILP formulation or an iterated local search metaheuristic. Computational experiments on real-world network-level instance show that the proposed MILP–ILS approach preserves CO 2 optimality while achieving notable cost reductions and significantly lower computation times compared to a fully exact lexicographic MILP. In several instances, the approach reduces network-level CO 2 emissions by up to 50% while achieving cost savings of approximately 10% comparing to the company’s current strategy. Finally, an ɛ -constraint analysis is conducted to characterise the Pareto frontier between cost and CO 2 emissions. The results confirm that substantial environmental gains can be achieved with moderate economic trade-offs, reinforcing the practical relevance of the proposed framework.
Saiem et al. (Mon,) studied this question.