stochastic programming

Based on financial statements of 2013 to 2021 for Civil Servants Pension Fund, fiscal sustainability index (ratio of resources to expenditures) was 40% in average while this index should be equal or more than 100% so that the Fund would be able to fulfill its liabilities; Due to lack of financial ability, about 90% of the credits needed to pay pension salaries were provided by the government aid. In this paper, analyzing of asset - liability management of Civil Servants Pension Fund is carried out using a model based on multi-stage stochastic programming and suggestions for managing the assets and liabilities of the Fund with the aim of fiscal sustainability and gradually reducing dependence on government aid to fulfill annual liabilities have been presented.

Autoregressive Moving-Average Model was used for predicting interest rate of different asset classes in next 20 years (2022 to 2041) and for modeling Asset - Liability Management for the Fund, multi-stage stochastic programming was applied based on predicted data for next 20 years and generating 300 scenarios with a 95% confidence interval, and the results have been analyzed. The model was implemented in GAMS software and results of the model were evaluated with application of conditional value at risk index. Data used for the study are based on financial statements of 2007 to 2021 for Civil Servants Pension Fund.

The implementation of the multi-stage stochastic programming model during years of 2011 to 2021 has shown a rational behavior based on compliance with investment policies and defined limits and an acceptable allocation of the fund's capital resources to all types of asset classes. Implementation of the model during years of 2022 to 2041, taking into account the investment and management policies of the Fund (case study) and restrictions (including a ceiling of 65% for stocks, a ceiling of 15% for real estate, a minimum share of 19% for bonds and bank deposits and a minimum share of five percent for cash of the total assets of the fund) was able to achieve a feasible solution in all scenarios, and the yearly value at risk of the portfolio with a probability of 5% was averagely about 2.3% of total value of the portfolio.

Results of the model and comparison with results of traditional method used for asset - liability management of the case study showed that if the proposed model is used for a period of 20 years in the future, the value of the Fund's assets will grow more than the value resulted from traditional method and the need for receiving government aid to fulfill Fund obligations will be reduced.

Attention to environmental issues in supply chain activities has been taken into consideration due to the increase in public awareness and strict laws related to environmental protection. Initially, only the economic aspects of the supply chain were considered in the network configuration, but with increasing concerns about environmental issues, reverse logistics and closed-loop supply chains were developed. Designing a closed-loop supply chain network plays an important role in reducing costs, improving service levels, and responding to environmental issues. Therefore, the purpose of this study is to design a closed-loop supply chain network taking into account hybrid uncertainties and flexibility in constraints.

In most of the conducted studies about supply chain network design, the types of cognitive and random uncertainties, as well as the flexibility of soft constraints, have not been investigated simultaneously, while the conducted modeling is not able to consider hybrid uncertainty in supply chain parameters in the real world. In this study, to simultaneously consider the hybrid uncertainties and flexibility in constraints, a novel model of robust stochastic, possibilistic, and flexible programming based on Me measurement was developed. In this model, the convex combination of optimistic and pessimistic attitudes of decision-makers was considered in the form of the Me measure, and the modeling was more flexible and realistic.

In the proposed approach, a convex combination of optimistic and pessimistic spectra was considered in the model. The need for subjective and repetitive reviews by decision-makers was eliminated in the model and the level of satisfaction was calculated optimally after solving the problem. On the other hand, due to the robustness of the model, possible deviations, scenario deviations, non-fulfillment of demand and capacity, and deviations of soft constraints were minimized. In the proposed approach based on the Me measure, the problem-solving approach was reduced and there was no need for a two-step solution to find solutions.

A case study was conducted in the supply chain of stone paper production to evaluate the efficiency of the proposed model. The results of sensitivity analysis, robustness analysis, and simulation with the realization model showed that the proposed model was able to provide robust and realistic solutions. The proposal of a realistic and flexible solution for designing problems of the supply chain network by creating a trade-off between the objective function and the risk-taking level of decision-makers and managers through changing the justified space in the Me criterion in the proposed approach was one of the achievements of the present study. As its other achievement, the present study could provide a combination of different viewpoints of decision-makers’ risk-taking through changing the justified space based on different values of the parameter λ in measuring Me and propose flexible and realistic solutions according to the results of numerical simulation in the proposed approach.

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.

Considering the cognitive, random, uncertain, and flexible constraints, a robust, stochastic, possibilistic, and flexible chance-constrained model was developed based on credibility measurement. The ultimate aim was closed-loop supply chain network design. Different attitudes of decision-makers were answered by more flexible measurements of optimistic and pessimistic parameters in the form of credibility measurement. The model has been able to reduce the possible deviation, stochastic deviations, non-fulfillment of demand and capacity constraints, and violation of flexible constraints, which simultaneously include cognitive and random uncertainties and flexibility of constraints in the model. To apply the model, a case study was conducted to design the closed-loop supply chain network of multi-product and multi-period stone paper. The results of implementing the model showed that in different situations and according to the importance of decision makers' opinions, using the range of optimism and pessimism, the number, location of facilities, optimal flow of products and materials between centers in the stone paper supply chain network can be determined. The proposed model was evaluated using robustness and sensitivity analysis, and its performance was evaluated using nominal data in the realization model, which results showed the appropriate performance of the model.

In the pharmaceutical supply chain, pharmaceutical products must be distributed among consumers with good quality at the right time and in the right place. Medicineis a product which affects the health of society and its timely delivery to consumers is of great importance. Therefore, it requires proper planning for its production and distribution. In this paper, we developed a model that minimize the cost of production, inventory, delivery, earliness and tardiness. We also assumed the uncertainty of demand and solved the linear mathematical model using stochastic programming and we solved the problem with stochastic programming. Also, due to the fact that the model with the objective function of earliness and tardiness with different delivery times of NP-hard problem for this problem, a hybrid genetic and variable neighborhood search algorithm were presented. Here, five scenarios were considered, the expected value of perfect information (EVPI) was measured and the obtained results were compared with the two-stage random-scheduling model. The computational results showed the efficiency of the developed model. Also, the results of the proposed hybrid algorithm were compared with the genetic algorithm, and the results showed that in terms of objective function, the hybrid algorithm has a much better performance compared to the genetic algorithm.

Suppliers, as one of the vital partners of the supply chain, contribute to creating a sustainable supply chain. Also, resilient supplier selection can help to reduce the severity of the disruptions' effects. Thus, the simultaneous application of the sustainability and resilience principles in the process of supplier selection and order allocation plays a significant role in achieving sustainability and continuity of business processes in the occurrence of disruptions. This study aims to select a set of sustainable and resilient suppliers and to allocate demand in an automotive supply chain under disruption risks.

In this research, a hybrid approach has been presented for evaluating and ranking suppliers based on sustainability criteria and modeling the problem of selecting a sustainable-resilient supplier and order allocation under disruption risks. VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje which means multi-criteria optimization and compromise solution, in Serbian) as a multi-criteria decision-making technique has been used to determine the sustainability score of suppliers and the results have been considered as the input parameter for the mathematical model. The proposed mathematical model is two-stage stochastic programming to minimize total cost and maximizing sustainability performance in the case of disruptions, which is solved using the Ɛ-constraint method. Multiple sourcing and flexibility in the production capacity of suppliers have been considered as effective strategies to reduce the severity of disruptions. Iran-Khodro supply chain data has been also used to validate the research model. Finally, sensitivity analysis has been performed to investigate the effects of parameters' change on the final results.

The results indicated that by increasing the importance coefficient of sustainability performance in the supply chain, the overall value of the supply process increases during disruptions, and multiple sourcing and the use of suppliers' flexible capacity are effective strategies in reducing the severity of disruptions. Also, examining the effect of reliability on the expected cost indicates that the occurrence probability of disruption risk in suppliers can affect the total cost of the procurement process and can also reduce the sustainability goals.

In this research, the effect of disruption risk has been examined. As a subject of future study, the operational risk is suggested to be recognized in the supplier selection problem. Besides, unique events have been considered in the model of this research. In the future, semi-super and super events can be also studied. Finally, using other resilience strategies in supplier selection problem such as adopting backup suppliers, protecting suppliers, and pre-positioning inventory can be a potential direction for future research.

The company under study can consider the results of the presented model and focus on utilizing suppliers' flexible capacity, multiple sourcing instead of single sourcing of parts, partially. Given the importance of sustainable development, particularly in automotive supply chains, it is essential to consider the sustainability performance of suppliers in sourcing decisions with disruption risk in addition to cost and reliability criteria. Thus, the company can evaluate the suppliers' sustainable performance in the occurrence of disruption to achieve sustainable development and business continuity and to meet the needs of stakeholders.

Due to the subject of this study, i.e. sustainable supplier selection in the resilient supply chain, the performance of each supplier has been evaluated based on social and environmental criteria to consider social responsibility and environmental issues in the sourcing decisions under disruption risk. Originality/value: A few studies have taken into account the dimensions of sustainability and supplier risk simultaneously in the field of supplier selection and demand allocation. Flexibility in the supply capacity of the supplier, as an effective resilience strategy, and economic criteria such as C/100 index, PPM production line, payday resistance, communication system, etc. have not been studied in the literature of sustainable and resilient supplier selection.

Environmental concerns have spurred an interest in studying green supply chain. Nowadays, governmental and non-governmental organizations consider environmental management as a strategic requirement having numerous benefits. Therefore, they effort to increase customers' satisfactory and market share considering external factors like environmental consequences in addition to internal factors. In this paper, a bi-objective mixed integer programming model is developed to identify the optimal location for manufacturers and disassembly sites in a green supply chain network design. This paper addresses the role of the reliability of facilities and vehicles to ensure effective stream among supply chain network, the objective functions are defined as total cost minimization, and total co2 emissions minimization. Besides, uncertainties on the network design are investigated through two-stage stochastic programming. with respect to the fact that the model is non-linear and bi-objective, at first, an approach is presented to linearize it and then the proposed bi-objective mathematical model is solved as a single-objective one by compromise programming method. The effectiveness of the proposed model is demonstrated by using of a numerical example derived from a real case.

This research involves the optimization tool for use by supply chain managers in the design and operation of manufacturing- distribution networks under uncertain demand conditions. The problem under consideration consists of determining the supply chain infrastructure; raw material purchases, shipments, and inventories; and finished product production quantities, inventories, and shipments needed to achieve maximum profit while fulfilling demand and minimizing profit variability and unsatisfied demand. This research presented model to supply chain infrastructure design. In this research, a multi-period,multi objective mixed integer robust optimization formulation of the strategic model is presented to account for the probabilistic demand data. In order to demonstrate, numerical examples are presented and solved by LINDO software.

Hub networks are among the important variations of transportation networks that have applications in air transportation, post, and telecommunication. An important feature of these networks is the possibility of disruption in the network performance due to failure of hub facilities or access links. In this study, the hub arc covering location problem is studied under disruption of hub arcs. First a new mathematical model is introduced based on the chance-constrained programming technique in stochastic programming. Then computational results of solving the proposed model for problem instance (based on Iran Aviation Data and Australian Post) are presented. The computational results confirmed the importance of disruption consideration before designing hub networks and changes in the optimal configuration of the network under disruption.

The purpose of this paper is to present a model for gas allocation to different sub-sectors in Iran by means of Non-Linear Programming method. Stochastic programming approach is applied to obtain the optimal system configuration meeting the gas demand in different subsectors based on available gas produced. The model considers possible threats would occur in natural gas supply. The solutions provide gas allocation to different sub-sectors at macro level taking the possible threats in to consideration. Research results show the significant percentage share of gas should be assigned to residential-commercial, power plants, industries, natural gas injection, exportation, petrochemical, rich gas injection, transportation and agriculture respectively. The novelty of this work is its ability to quantitate possible threats using stochastic programming approach in order to provide more holistic point of view on energy security factors impacting gas allocation in Iran.