Development of an integrated scenario-based stochastic rolling-planning multistage logistics model considering various risks

Abstract

In this study, a new integrated scenario-based stochastic rolling-planning multistage logistics model is proposed to reduce overall logistics costs. To achieve this goal, two phases were considered in the model. In the first phase, a multi-criteria group decision-making model was developed to select a trustworthy supplier. In the second stage, the selected suppliers were integrated with other stakeholders to develop a rolling-planning-based logistics model using a variety of risky scenarios. Several risk factors including price variability, demand, and quality risks were considered in the model. By considering these risk factors, a new risk-embedded rolling-planning logistics method was established that regulates inventory, stock-out, and overstock problems by constantly controlling the production volume at the manufacturing site based on actual demands. In this model, the supplier's side material quality, price fluctuation risks, and customer-side demand risks were considered simultaneously. To evaluate the performance of the proposed model, a numerical example was set up, and the obtained results were compared with those of another model where fixed volume production and delivery approach was used instead of the rolling-planning approach. To verify the superiority and robustness of the proposed model, its performance was verified through a sensitivity analysis under different experimental conditions. The findings show that in a risk environment, the proposed model estimates lower logistics costs of 2697648.00 units compared to another model whose costs were 2721843.00 units.