robust fuzzy programming

In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with LCA, PSO, and SSA methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. Because by reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the OBF averages among the solution methods, they were prioritized and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the MTOSC problem.

Given the importance of supply chain and environmental issues, this paper presents a new mathematical model for a green closed-loop supply chain (GCLSC) network with the objectives of maximizing profits, maximizing the number of jobs created, and maximizing reliability. Due to the uncertainty on some parameters such as demand and transportation costs, the new method of robust fuzzy programming model was utilized. Multi-objective Grey Wolf Optimizer (MOGWO) and Non-dominated Sorting Genetic Algorithm II (NSGA II) were used to tackle the problems for larger sizes. A number of instances of the problem in larger sizes were solved. The results from comparing the algorithms considering some criteria including means of objective functions, spacing index, distance index from ideal point, maximum amplitude index, Pareto response number index and computational time showed the fast convergence and high efficiency of MOGWO algorithm for this problem. Finally, the implementation of the model for a real case study in Iranian engine oil industry, showed the efficiency of the obtained solutions for this network.