In this article, a sustainable network of distribution of agricultural items with suppliers, distribution centers, and retailers is considered. The main purpose of presenting the mathematical model in this article is to determine the optimal number and location of suppliers, assigning suppliers to distribution centers and optimal routing for the distribution of agricultural items to retailers in a predefined time window. Also, determining the optimal amount of inventory and the reorder point in retailers and distribution centers is another problem decision. To model the problem, some parameters of the model were considered non-deterministic and were controlled by the probabilistic fuzzy method. The results of solving numerical examples in different sizes showed that with the increase of the total costs of the distribution network of agricultural items, the amount of greenhouse gas emissions decreases, and the employment rate increases. Also, with the increase of the uncertainty rate, due to the increase of the real demand and the change in the optimal amount of production, distribution, storage and reorder point, the values of all the objective functions also increase. It was also observed by solving different numerical examples with NSGA II and MOGWO algorithms, these algorithms have been able to solve the problem in a much shorter period than the epsilon constraint method, and comparison indicators such as NPF, MSI, SM, and computing time show These algorithms have a high efficiency in solving numerical examples of the problem of the distribution network of agricultural items. تفاصيل المقالة

Today, with the development of Information Technology (IT) and economic globalization, the suppliers’ selection has been emphasized in supply chain systems. Accordingly, artificial intelligence-based methods have attracted much attention. Hence, in this research, the selection of appropriate suppliers with respect to the multi-resource supply policy, and the implementation of lateral transshipment have been studied, and meta-heuristic algorithms have been employed to solve the problem. In the proposed method, the supply chain network is improved by minimizing the inventory shortages through utilizing lateral transshipment between different factories. In order toefficiently solve the problem, a hybrid meta-heuristic algorithm based on population-based genetic algorithm (GA) and single-solution simulated annealing (SA), named GASA, is propose, in order to simultaneously gain with the advantages of both algorithms, i.e., global search ability of GA and local search ability of SA. In order to compare the results of the proposed GASA, it is compared with GA and SA, to find the best solution. Given the parameters optimization and conducted analyses and comparisons of primary and hybrid algorithms performance, the hybrid GASA algorithm has been identified as the most efficient algorithm to solve the problem,compared to the other algorithms, emphasizing cost reduction and shortage volume. تفاصيل المقالة

The problem of allocation of financial resources in projects is one of the most important problems of mathematical optimization. Incorrect allocation of financial resources can lead to project failure, increased costs, and reduced profitability. The importance of this issue has led to the modeling of a financial resource allocation problem for sustainable projects under uncertainty in this article. A fuzzy programming method was used to control model parameters and GSSA, GA, and SSA algorithms were used to solve the model. In the mathematical model, the goal was to optimize the objective function consisting of predicted return, investment risk, and project sustainability. Mathematical calculation results showed that meta-heuristic algorithms have high efficiency in achieving optimal solutions in a short time. so that the average time to solve them was less than 10 seconds. Also, the calculation results showed that increasing the uncertainty rate leads to increasing the value of the objective function and creating a distance from the optimal point. This is due to increasing costs and decreasing profits in sustainable projects. Finally, usage the TOPSIS method, the ranking of solving algorithms was done, and the GSSA algorithm was the most efficient algorithm among other algorithms with a desirability weight of 0.846. تفاصيل المقالة