Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks

Abstract

The closer the supply chain network (SCN) modeling to the real world, the more accurate decisions can be made. In this regard, it is essential to take into account of the factors in the real world such as the formation of the queuing system in the producers of the SCN, and batch transportation and processing (BTP) approaches, as well as the operational and disruption risks. In this research, batch sizes are considered equal to the potential transportation capacity of vehicles. The present paper considers simultaneous optimization of total cost, total time and average total number of commodities dispatched from any echelon of the SCN to the next echelon on a five-echelon multi-period SCN including suppliers, manufacturing plants, assemblers, distribution centers, and customers. To deal with the disruption risk, the reliability of facilities in sending the commodities is computed based on the exponential distribution and is maximized in the third objective function. Additionally, to tackle the operational risk of the problem, we convert the proposed tri-objective mathematical model into the robust mathematical model using Ben-Tal method (ρ-Robust method). Then five multi-objective decision-making (MODM) solution methods are selected to solve the problem. The results of these five methods are compared and the best MODM solution method is selected based on the objective function values and the CPU times. Finally, a novel solution is proposed to find the dominant and dominated objective functions in multi-objective models based on the uncertainty level (⍴) of the model.