The last decade has seen a numerous studies focusing on the closed-loop supply chain. Accordingly, the uncertainty conditions as well as the green emissions of facilities are still open issues. In this paper, a new fuzzy multi-objective programming approach is to present for a production-distribution model in order to develop a multi-product, multi-period and multi-level green closed-loop supply chain network problem, which this model is formulated as multi-objective mixed linear integer programming (MOMILP). In regards to offered fuzzy multi-objective model, three conflicting goals are exited, simultaneously. The objective functions are to minimizing the total cost, minimizing the gas emissions costs due to vehicle movements between centers, and maximizing the reliability of delivery demand due to the reliability of the suppliers. To get closer to real-world applications, the parameters of model are considered by fuzzy numbers. Another novelty of proposed model is in the solution methodology. To solve the model, this study not only uses a well-known Imperialist Competitive Algorithm (ICA) but a number of new modifications of ICA (MICA) also have been provided to address the proposed problem, which is to demonstrate the efficiency and performance of the proposed algorithm with other algorithms included: SA, ICA, ACO, GA, and PSO are compare. Finally, different analyses with a variety of problem complexity in different sizes are performed to assess the performance of algorithms as well as some sensitivity analyses on the efficiency of model are studied.

The global population growth has led to a considerable rise in demand for wheat. Today, the amount of energy consumption in agriculture has also increased due to the need for sufficient food for the growing population. Thus, agricultural policymakers in most countries rely on prediction models to influence food security policies. This research aims to predict and reduce the amount of energy consumption in wheat production. Data were collected from the farms of Estahban city in Fars province of Iran by the Jihad Agricultural Department’s experts for 20 years from 1994 to 2013. In this study, a novel prediction method based on consumed energy in the production period is proposed. The model is developed based on artificial intelligence to forecast the output energy in wheat production and uses extreme learning machine (ELM) and support vector regression (SVR). In the experimental stage, the value of elevation metrics for the EVM and ELM was reported to be equal to 0.000000409 and 0.9531, respectively. Total input energy (consumed) is found to be 1,460,503.1 Mega Joules (MJ), and output energy (produced wheat) is 1,401,011.945 MJ for the Estahban. The result indicates the superiority of the ELM model to enhance the decisions of the agricultural policymakers.