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Recently, much attention has been given to Stochastic demand due to uncertainty in the real -world. In the literature, decision-making models and suppliers\' selection do not often consider inventory management as part of shopping problems. On the other hand, the environmental sustainability of a supply chain depends on the shopping strategy of the supply chain members. The supplier selection plays an important role in the green chain. In this paper, a multi-objective nonlinear integer programming model for selecting a set of supplier considering Stochastic demand is proposed. while the cost of purchasing include the total cost, holding and stock out costs, rejected units, units have been delivered sooner, and total green house gas emissions are minimized, while the obtained total score from the supplier assessment process is maximized. It is assumed, the purchaser provides the different products from the number predetermined supplier to a with Stochastic demand and the uniform probability distribution function. The product price depends on the order quantity for each product line is intended. Multi-objective models using known methods, such as Lp-metric has become an objective function and then uses genetic algorithms and simulated annealing meta-heuristic is solved.

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In the emerging supply chain environment, green supply chain risk management plays a significant role than ever. Risk is an inherent uncertainty and has tendency to disrupt the typical green supply chain management (GSCM) operations and eventually reduce the success rate of industries. In order to mitigate the consequences, a fuzzy multi-criteria group decision making modeling (FMCGDM) which could evaluate the potential risks in the context of (GSCM) is needed from the industrial point of view. Therefore, this research proposes a combined fuzzy analytical hierarchy process (AHP) to calculate the weight of each risk criterion and sub-criterion and technique for order performance by similarity to ideal solution (TOPSIS) methodology to rank and assess the risks associated with implementation of (GSCM) practices under the fuzzy environment. The proposed fuzzy risk-oriented evaluation model is applied to a practical case of textile manufacturing industry. Finally, the proposed model helps the researchers and practitioners to understand the importance of conducting appropriate risk assessment when implementing green supply chain initiatives.

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This paper considers the multi-depot vehicle routing problem with time window in which each vehicle starts from a depot and there is no need to return to its primary depot after serving customers. The mathematical model which is developed by new approach aims to minimizing the transportation cost including the travelled distance, the latest and the earliest arrival time penalties. Furthermore, in order to reduce the problem searching space, a novel GA clustering method is developed. Finally, Experiments are run on number problems of varying depots and time window, and customer sizes. The method is compared to two other clustering techniques, fuzzy C means (FCM) and K-means algorithm. Experimental results show the robustness and effectiveness of the proposed algorithm.

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Occurrence of natural disaster inflicts irreparable injuries and symptoms on humans. In such conditions, affected people are waiting for medical services and relief commodities. Thus, quick reaction of medical services and relief commodities supply play important roles in improving natural disaster management. In this paper, a multi-objective non-linear credibility-based fuzzy mathematical programming model under uncertainty conditions is presented, which considers two vital needs in disaster time including medical services and relief commodities through location of hospitals, transfer points, and location routing of relief depots. The proposed model approaches reality by considering time, cost, failures probability in routes, and parameters uncertainty. The problem is first linearized and then global criterion method is applied for solving the multi objective model. Moreover, to illustrate model efficiency, a case study is performed on region 1 of Tehran city for earthquake disaster. Results demonstrate that if Decision-makers want to meet uncertainty with lowered risk, they have to choose a high minimum constraint feasibility degree even though the objective function will be worse.