Multi-objective robust optimisation model for MDVRPLS in refined oil distribution

At depots with refined oil shortage, arranging a reasonable distribution scheme limited supply affects operation costs, demand satisfaction rate of gasoline stations (hereafter, ‘station satisfaction’), and overtime penalty. This study considers the problem shortages using multi-objective optimisation approach from perspective decision makers marketing companies. The modelling solving process involves (i) formulation crisp multi-depot vehicle routing model (MDVRPLS) which station priority soft time windows, (ii) development robust (ROM) to manage uncertainty in demand, (iii) proposal particle swarm (MOPSO)algorithm. Results numerical experiments show that can better balance satisfaction, penalty, produces 3.33% 4.60% increase at an increased unit cost penalty respectively; ROM successfully addresses compared model, requires additional 8.81% 12.85% penalty; MOPSO manages these MDVRPLS models more effectively than other heuristic algorithms. Therefore, applying shortage management significantly improves efficiency resists disturbance caused by external uncertainties, providing scope for efficient scarce resources.