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Since lean concept has appeared many works have been done on decreasing or even eliminating of wastes such as extra inventory. Although these studies have not taken into account expected availability seriously. So in this paper, a Multiple Objective Decision Making (MODM) model has been developed by viewpoint of these subjects. They are decreasing wastes and increasing system availability. Wastes that have been minimized are maintenance requirements (i.e. labor, spare parts, reserve system and productive maintenance activities) and maintenance system availability that has been maximized is a function of maintenance requirements. A set of Society Anonym Iranian Production Automobile (SAIPA) data has been used to run the case study and test the effectiveness and the efficiency of the proposed model.

The increasing opportunities for cost savings and customer satisfaction have propelled thirdparty logistics providers (3PLs) to get involved in the forward and reverse logistics operations. The forward−reverse supply chain network design (FRSCND) for 3PLs have been somehow studied under various conditions, so far. However, some very common business configurations and real-world concerns such as pricing and uncertainty are less investigated in the literature. Accordingly, this paper proposes a novel robust model for the design of a 3PL's logistics network and pricing decisions. Since the value of uncertainty budget parameter in the robust model is an epistemic uncertain one, the fuzzy-robust model regarding the uncertainty budget parameter as a fuzzy number is also developed. The results of numerical examples show that the proposed models outperform the deterministic model regarding solution robustness and computational time. Considering the robust sensitivity analysis, the effects of uncertain parameters on total cost, based on their conservatism levels are analyzed. In addition, the conducted sensitivity analysis over penalty values for constraints violation reveal that for the medium-and large-size problems, the proposed models are more cost-effective for high penalty values, while the models' performance is related to the problem size, for their low amounts.