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This research aims to optimize queuing systems in oil depots by balancing costs, carbon emissions, and customer satisfaction.

August 13, 2026SustainabilityOpen Access

Multi-Objective Queuing Optimization for Oil Depots Balancing Cost, Carbon Emissions and Customer Satisfaction

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This research aims to optimize queuing systems in oil depots by balancing costs, carbon emissions, and customer satisfaction.
Proposed a modified finite capacity Markovian queuing system with random service interruptions.
Implemented a multi-objective model focusing on expected cost, carbon emissions, and customer satisfaction.
Utilized a Bayesian optimization-based NSGA-II hybrid algorithm and compared its performance against four other algorithms.
The Bayesian optimization-based NSGA-II achieved a hypervolume of 0.657, with low values for IGD (0.0066), GD (0.0009), and spacing (0.0069).
Expected cost increased by only 1.15% compared to the minimum cost solution from MOEA/D.
Carbon emissions reduced to 11.25 kgCO2/h while customer satisfaction improved to 0.9769.

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Randomized trial analyzes cost, carbon emissions, and customer satisfaction in oil depots, indicating optimal resource allocation strategies.

Kong et al. (2026) studied this question.

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Randomized trial analyzes cost, carbon emissions, and customer satisfaction in oil depots, indicating optimal resource allocation strategies.