The growing trend of natural resources consumption has caused irreparable losses to the environment. The scientists believe that if environmental degradation continues at its current pace, the prospect of human life will be shrouded in mystery. One of the most effective ways to deal with the environmental adverse effects is by implementing green supply chains. In this study a multilevel mathematical model including supply, production, distribution and customer levels has been presented for routing-location-inventory in green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q) to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/ Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.

Nowadays, organisations have recognised the importance of integrated decision making to improve supply chain performance. Since organisations cooperate with each other as a network, any ineffectiveness and inefficiency will be getting more highlighted and integration has become more important. This research describes a four echelon supply chain including supplier, producer, distributor and customer levels. The considered problem is a location routing inventory problem with uncertain demand. To validate integrated mathematical model several problems have been generated and solved using GAMS software. Results show solving time increases exponentially as problems dimension increases, which represents problem's complexity. Therefore, a heuristic genetic algorithm base on NDEA selection method is proposed. To evaluate proposed algorithm's effectiveness, generated problems have been solved by proposed method and three famous selection methods. Obtained results are compared by Wilcoxon test which represents the proposed algorithm's effectiveness.

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