This paper develops a bi-objective mixed possibilistic-stochastic model for a comprehensive supply chain master planning problem. The model integrates physical/material and financial tactical plans by accounting for the reciprocal effects of supply chain's functions and flows. Given the existence of several sources of uncertainty and the uncertainty level of input data, a mixture of fuzzy and random fuzzy variables is incorporated into the model. Appropriate methods are then tailored to convert the original uncertain model into a deterministic counterpart. To demonstrate the applicability of the developed model, an illustrative example is provided. Sensitivity analyses are performed on the critical parameters of the developed model and its solution methodology to provide useful managerial insights. Also, a novel performance comparison method is devised to compare the original random fuzzy model against its fuzzy and deterministic counterparts. The results demonstrate the superiority of the proposed random fuzzy model over its counterparts.

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ERIM Top-Core Articles
Information Sciences
Erasmus Research Institute of Management

Arani, H.V. (H. Vafa), & Torabi, S.A. (S. A.). (2018). Integrated material-financial supply chain master planning under mixed uncertainty. Information Sciences, 423, 96–114. doi:10.1016/j.ins.2017.09.045