A Novel Matheuristic based on bi-level optimization for the multi-Objective design of hydrogen supply chains

• Reformulation of the original bi-objective Mixed-Integer Linear Programming model as a bi-level optimization problem. Development an adapted solution tool, which is matheuristic involving Multi-Objective Evolutionary Algorithm particularly efficient for problems (SMS-EMOA) and Programming. Numerical evaluation proposed with application to design HSC in Midi-Pyrénées region (southern France), demonstrates benefits our approach, regarding both quality computational efficiency. This work introduces tool sustainable hydrogen supply chains (HSCs), considering economic environmental concerns, through appropriate multi-objective strategy. The problem, being formulated mixed-integer linear programming (MILP) takes into consideration availability different energy sources, installation operation facilities sizes technologies, transportation from production units storage facilities. area study divided grids have specific demand that evolves over time, thus multi-period considered. In order overcome burden associated large size instances resulting we strategy consisting hybrid algorithm. problem reformulated problem: upper level (discrete problem) consists finding optimal location plants facilities, whereas lower (continuous minimizes their corresponding costs facility operation. A evolutionary algorithm employed level, decomposed using scalarizing function, then solved solver. methodology validated comparison true Pareto fronts given by CPLEX ε -constraint method, six increasing instances. results prove approach produces accurate approximation Pareto-optimal fronts, more efficiently than exact approach.