Fuzzy Stochastic Programming Approach Research Articles

Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach

Compromising allocation for optimising agri-food supply chain distribution network: a fuzzy stochastic programming approach

Sustainable inventory management in blood banks considering health equity using a combined metaheuristic-based robust fuzzy stochastic programming

This study examines the challenges of healthcare systems toward sustainable inventory management of blood products. In this regard, three main goals are pursued; First, promoting social equity in providing medical services to various patients and reducing the risk to lives. Second, the optimal management of medical products in a way that minimizes economic costs. Third, optimal management of biological waste due to inventory corruption and reduction of greenhouse gas emissions caused by transportation, to the least environmental pollution. To achieve these purposes, a practical demand-driven multi-objective inventory model is presented by utilizing hybrid policies in an uncertain environment. In the proposed model, the demands are in two types, including elective and non-elective. These demands are classified according to their medical urgency, substitution allowance, and product freshness. A hybrid robust fuzzy stochastic programming approach is applied to capture real-world uncertainties. The proposed model is implemented for blood platelet. The solution is obtained using a combined metaheuristic technique established on genetic algorithms and simulated annealing according to global and local search paradigms. To create a proper perspective for decision-makers, sensitivity analysis is performed. Besides, the performance of the model is proved by the realization. The results show that the performance of the proposed RFSP model is better than the Nominal model. Also, it performs well in minimizing the overall system costs and environmental degradation, besides reducing shortage and wastage. It is also effective for taking steps toward equity in health and suggests a proper strategy for dealing with emergencies.

Power distribution network design considering the distributed generations and differential and dynamic pricing

In this study, a power distribution network design model is developed considering voltage control, as well as differential and dynamic pricing schemes. The objective is to maximize power distribution network profits under uncertain demands and parameters, such as power generation capacity, investment and generation cost of renewable distributed generation units, and the penalty budget for unsatisfied demands. The model determines the percentage of the power supply to the customer groups, the amount of power supplied to each customer group, the technology and capacity of renewable distributed generation units, and the prices offered. The proposed pricing scheme is differential for different types of customers with allowable voltage limits. The price is dynamic according to the planning time, including low, medium, and peak times. A fuzzy stochastic programming approach is developed to solve this problem. By simultaneously using triangular crisp numbers and probabilistic variables, the fuzzy stochastic programming integrates the fuzzy and stochastic approaches to deal with the uncertainty of demand and parameters. The numerical analysis shows that, as the confidence level decreases, the capacity of renewable distributed generation units and the profit increase. The results also show that when the decision maker responds to the uncertainty in demand with a high confidence level, the optimum profit can be obtained; approximately 55.90% of the power is supplied to low-demand customers.

Design of control strategies to help prevent the spread of COVID-19 pandemic

This paper proposes control strategies to allocate COVID-19 patients to screening facilities, health facilities, and quarantine facilities for minimizing the spread of the virus by these patients. To calculate the transmission rate, we propose a function that accounts for contact rate, duration of the contact, age structure of the population, susceptibility to infection, and the number of transmission events per contact. Moreover, the COVID-19 cases are divided into different groups according to the severity of their disease and are allocated to appropriate health facilities that provide care tailored to their needs. The multi-stage fuzzy stochastic programming approach is applied to cope with uncertainty, in which the probability associated with nodes of the scenario tree is treated as fuzzy variables. To handle the probabilistic model, we use a more flexible measure, Me measure, which allows decision-makers to adopt varying attitudes by assigning the optimistic-pessimistic parameter. This measure does not force decision-makers to hold extreme views and obtain the interval solution that provides further information in the fuzzy environment. We apply the proposed model to the case of Tehran, Iran. The results of this study indicate that assigning patients to appropriate medical centers improves the performance of the healthcare system. The result analysis highlights the impact of the demographic differences on virus transmission, and the older population has a greater influence on virus transmission than other age groups. Besides, the results indicate that behavioral changes in the population and their vaccination play a key role in curbing COVID-19 transmission.

Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels

The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.

A novel two-stage fuzzy stochastic model for water supply management from a water-energy nexus perspective

Integrated water resources management from a water-energy nexus perspective is necessary to realize sustainable development. However, the inherent uncertainties and complexities in the nexus system have posed great challenges for decision makers. In this study, a novel two-stage fuzzy stochastic programming approach capable of addressing uncertainties with both possibility and probability distribution is developed for water resources management problems under water-energy nexus. Under this framework, the future uncertain events can be effectively defined as a scenario tree with fuzzy random information and fuzzy probability distribution. Then, the proposed method is examined through a real case of Tianjin, a coastal water-stressed city in northeast China. A series of decision alternatives are obtained under various water resource availability scenarios with the consideration of decision makers’ risk preferences. Results showed that the surface water and the imported water accounting for 70% of the total water supply would still be the main contributors to support local socioeconomic development and that the estimated electricity consumption embodied in per-unit water supply ranges from 0.56 kWh/m3 to 0.61 kWh/m3 through the system nexus relationship modeling. The results also disclosed that due to climate change, water scarcity would require more exploitation of groundwater and utilization of desalination water and recycle water, which would exacerbate environmental pressure and increase energy demand for water supply. These findings can provide managerial insights and suggestions for decision makers to achieve flexible water management with the consideration of system uncertainties, water-energy nexus, water shortage, and decision maker’s risk attitude.

A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data

Today, in many organizations, the debate about the difference in core capabilities has become an important factor for market competition. Companies, based on the field of activity, decide to strengthen some of their capabilities, capacities, and expertise. Therefore, the focus of an organization on the strengths and efforts to develop its sustainability will lead to a competitive advantage in the marketplace. Due to changes in environmental factors, organizations have focused on carbon emissions in procurement and transportation that have the highest carbon footprint. This paper proposes a multi-objective, eco-sustainability model for a supply chain. The objectives are to minimize overall costs, maximize the efficiency of transportation vehicles and minimize information fraud in the process of information sharing within supply chain elements. Big data is considered in the amount of information exchanged between customers and other elements of the proposed supply chain; since there are frauds in information sharing then using big data 5Vs the model is adapted to control the cost of information loss leading to customer dissatisfaction. Since uncertainty is inevitable in the real environments, in this research hybrid uncertainty is considered. Because two sources of uncertainty are considered in most of the parameters, thus it is necessary to robustify the decision-making process. The model is a mixed integer nonlinear program including big data for an optimal sustainable procurement and transportation decision. A heuristic method is used to solve the big data problem that makes use of a robust fuzzy stochastic programming approach. The proposed model can prevent disturbances by using a scenario-based stochastic programming approach. An effective hybrid robust fuzzy stochastic method is also employed for controlling uncertainty in parameters and risk taking out of outbound decisions. To solve the multi-objective model, augmented ε-constraint method is utilized. The model performance is investigated in a comprehensive computational study.

Multiobjective robust fuzzy stochastic approach for sustainable smart grid design

Smart grids are an effective solution for the rapidly increasing power demand. This study attempts to solve the sustainable smart grid design problem, where three indispensable sustainability dimensions, economy, environment, and society, are considered simultaneously. A multiobjective robust fuzzy stochastic programming approach is presented to minimize the economic, environmental and social costs of the network under uncertain scenarios and parameters. The objective is to determine the optimal number, location and capacity of renewable distributed generation units as well as the dynamic electricity pricing and energy resources scheduling in a sustainable smart grid. The proposed method is applied to a Vietnamese power company in the residential sector. The results indicate that demand response with dynamic pricing reduces environmental and social costs by 3%. Moreover, the proposed model efficiently tackles both uncertainties in scenarios and parameters with a total cost lower than other models.

Short‐term crude oil scheduling with preventive maintenance operations: a fuzzy stochastic programming approach

AbstractThis paper presents a fuzzy stochastic mathematical model for a short‐term crude oil scheduling problem with preventive maintenance for charging tanks. The objective function of the proposed model minimizes the supply chain total cost. Lost sale is also reflected in the objective function acting as a criterion for the supply chain responsiveness. This study is among the first attempts to consider preventive maintenance operations, unavailability of the charging tanks, and minimizing lost sale for crude oil scheduling problem. The model encounters two types of uncertainty, namely fuzzy and stochastic uncertainty caused by demand, cost, and time parameters. Fuzzy programming and chance constraint method are employed to formulate the nondeterministic model. The model's applicability is illustrated with a case study. Results indicate that in cases where demand is sizable compared to storage volumes and maximum flow rate is sufficiently large, a preventive maintenance system can be scheduled regularly. Furthermore, the performed sensitivity analyses reveal that the problem is more sensitive to demand uncertainty than to cost and time uncertainty.

A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty

In today's business environments, the high importance of economic benefits and environmental impacts of using scrapped products has caused most companies to move to designing the closed loop supply chain network. This paper considers the closed loop supply chain network design problem under hybrid uncertainty, while there are two sources of uncertainty for most parameters, thus require fortifying of the robustness of the decision. The first source is that some uncertain parameters may be based on the future scenarios which are considered according to the probability of their occurrence. The second source is that the values of these parameters in each scenario are usually imprecise and can be specified by possibilistic distributions. In this case, the best robust decision has some additional properties in terms of mean value and variability of the objective function. We introduced two types of the variability named scenario variability and possibilistic variability. Possibility theory is used to choose a solution in such a problem and a novel robust fuzzy stochastic programming approach is proposed that has significant advantages. The performance of the proposed model is also compared with that of other models in term of the mean cost and variability by simulation.