two-stage stochastic programming

In this research, using the double-objective mixed integer linear programming method, an optimal supply chain network for the collection and recycling of urban waste has been presented in terms of source separation and the uncertainty of per capita waste generation by citizens. Due to the uncertainty in the parameters of the problem, the two-stage stochastic programming method has been used to model the problem. The objective functions include an economic function to minimize investment costs and a social objective function to maximize the amount of recycling. In order to accurately solve the problem on a large scale, the Lagrange release method has been used. To validate and confirm the effectiveness of the model presented in this research, the model was implemented on a case study in the city of Karaj. According to the obtained results, to increase the amount of recycling in the waste supply chain network, more infrastructural and operational investments are needed. By increasing recycling, the harmful environmental and destructive effects of burying and burning waste will be reduced. The Lagrange release solution method can be used as a suitable method to reduce problem-solving time. In this research, it was observed that the Lagrange release method can solve large-scale problems with appropriate accuracy and in less time compared to the commercial CPLEX solver.

In today’s competitive world, uncertainty is an integral part of all optimization problems. One of the cases where uncertainty has the greatest impact on optimization issues is SCN design. In most of the conducted studies, parameters such as demand, transportation cost and capacity of tehsils have been published in an uncertain manner. In this type of problem, various methods have been used to control these uncertainty parameters, which can be referred to as fuzzy programming, robust optimization, two-stage stochastic programming, multi-stage stochastic programming, multi-stage fuzzy stochastic programming, fuzzy robust optimization. Each of the mentioned methods has limitations in terms of its implementation. in the fuzzy programming method, there is no deviation from the data collected by experts’ opinions. In probabilistic methods, it is very difficult to determine the exact type of distribution function. Therefore, many researchers have investigated the strengths and weaknesses of each method in their studies. Therefore, in this paper we try to review the strength and weakness of these methods to apply the best approaches for different situations. In this paper, a real-world case study of a natural gas supply chain is investigated. By using concepts related to natural gas industry and the relations among the components of transmission and distribution network, a Five-level supply chain has been introduced and presented schematically.

The oil industry is one of the biggest and most effective industries in the world. Duo to complex structure of petroleum supply chains and price and demand uncertainties, supply chain design is a suitable method for improving this industry. The complexity of supply chains causes increase vulnerability to disruption. Therefore, in order to design this supply chain, attention to resiliency that enables the supply chain to perform well in face of disruption is necessary. Duo to lack of resiliency and disruption concept in the petroleum supply chain literature, a two stage stochastic model has been proposed. Additionally, to deal with disruptions, the strategy of developing refineries and replacement of bioethanol that affect oil exports and octane import of gasoline, is also studied. Results show that disruption in octane imports reduces the current net present value and in the oil transfer sector to export terminals causes a decrease of 2 % of the objective function. Additionally, using strategies such as increasing the capacity of the refineries cause a 1 % improvement in the objective function. The model has been solved using augmented epsilon constraint method for Iran as a case study for an 8 years time horizon and sensitivity analysis has been conducted.

In recent years, optimal use of fossil fuels, reduction of environmental pollution and exploitation of renewable energy is one of the most important issues in the policies of different countries, and as you know, Iranchr('39')s electricity industry is dependent on fossil fuel consumption strongly, which causes environmental pollution. The aim of this study is to obtain the optimal combination of electricity production plants in despite the uncertainties in the required components in decision making, in order to minimize environmental pollution and production costs. In this study, two-stage stochastic programming and interval parameter linear programming have been used to minimize uncertainty in electricity generation planning and fossil energy consumption. From one side, stochastic programming with considering different scenarios and from another side interval parameter instead of pointwise data minimize uncertainty in this study. Also, in obtaining the results, the data of the electricity industry report of West Azerbaijan province in 1394, 1395 and 1396 have been used. The results of this study show that the amount of production of combined cycle power plants and hydropower plant in this province is less than the optimal amount, And the amount of gas power plantchr('39')s production in this province is more than the optimal amount. It is also necessary to plan for the development of capacity in the Khoi Combined Cycle Power Plant.

This paper presents an integrated model as a combination of fuzzy analytical hierarchy process (FAHP), scenario-based two-stage stochastic programming and robust optimization approaches for the problem of supplier selection and order allocation under supply risk conditions. Uncertainties in the supply of materials are considered under three scenarios: increased sanctions, stability of sanctions, and sanctions removal. In the first step, the main qualitative factors for the selection of suppliers are identified and a specific weight is assigned to each supplier through FAHP. In the second step, these weights are introduced as inputs to a two-stage stochastic programming model and affect the second-stage variables. In the third step, we use the Mulvey formulation and then linearize the resulted robust two-stage stochastic model. The model is a integer linear programming model solved by CPLEX for a case study and the results are discussed. Finally, a sensitivity analysis is performed on the parameters of the robust model and the balance between the total cost and the unfulfilled demand is shown.

The increasing trend of supply chain disruptions in the recent decades has increased the importance of considering resilience strategies in supply chain network design/redesign problems. The present study provides a scenario-based mixed integer two-stage stochastic programming model for dealing with operational and disruption risks in a retail supply chain network redesign problem. The mode covers both pre-disruption and post-disruption decisions jointly. Five resilience strategies, including proactive, reactive, and chain design quality aspects (i.e. facility fortification, safety stock inventory holding, using excess capacity at some critical nodes of the network, using the direct to store delivery as the shipping strategy and multiple covering of retail stores), are considered. The model is applied for a case study in the retail industry. Several sensitivity analyses are carried out from which useful managerial insights are drawn to be used by top managers. The results show the positive effect of applying resilience strategies on the total cost reduction.