robust programming

In recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously, the potential for profit arising from product recycling has further emphasized the importance of these systems. This research focuses on developing a four-stage closed-loop network model for the supply chain, taking into account the uncertainty of product recycling rates. The primary objective of this study is to provide an integer linear programming model aimed at minimizing supply chain costs and customer service time under uncertain conditions. The supply chain model is designed by integrating queuing theory and product recycling system optimization. A critical aspect of this research involves modeling the uncertainty in the return rate of consumer products in the closed-loop supply chain, with the aim of developing a robust approach to address this issue. Additionally, the performance of the proposed model in the paper industry is evaluated, and a sensitivity analysis is conducted with respect to the decision variables using two metaheuristic algorithms: the Multiple Objective Harmony Search and the Non-dominated Sorting Genetic Algorithm.

In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.

In this study, a multi-objective mathematical model is developed to optimize the distribution network in a three level supply chain based on the inventory location- models with the aim of locating distribution centers. also, appropriate allocation of customers in order to maximize coverage of the demand, increasing sales through discount policies and ultimately improving the transportation system by selling the package of products agaainst individual sales is considered.On the other hand, the policy of selling pakages, Increases the total sales and their profit margin by creating appropriate price for the customrers. Due to the circumstances, each distribution center has specific reliability level which the proposed model try to maximize it. In order to make the model more realistic, some parameters have been considered under conditions of uncertainty and robust planning techniques have been used to solve it, finally, to validate the presented model, and instance according to real-world problems were solved by GAMS software. The Results are presented in details