davood shishebori

One of the most essential development factors in any country is the quality of electricity supply and distribution. Energy consumers seek electricity supply at a very high safety level. Considering the sensitivity of electronic devices and the dependence of most activities on electricity, providing sustainable energy in urban systems is critical. Unscheduled shutdowns are the leading cause of disruption in the continuity of electricity supply and reduce the quality of power delivered to customers. Operational risks are potential events that result in unplanned outages in the network. In the current research, the fuzzy cognitive map (FCM) method has been used to investigate the relationships between risks and to reach a comprehensive solution for the simultaneous control and management of several risks. Accordingly, extracting the effect of operational risks on each other and how to draw them in the form of a FCM is analyzed and represented. The results emphasize that adverse weather conditions are the most influential with the highest degree of output and equipment failure is the most influential with the highest degree of input in operational risks. The highest value of the sum of input and output degrees (centrality) is the breakdown in the transformer, which has the highest value. The analysis of managerial and practical perspectives shows that the operators, by focusing on forecasting weather conditions and retrofitting network structures and technical management of transformers, reach a convergent and sustainable solution to manage operational risks and ultimately reduce unplanned shutdowns.

Today, producing a product with high quality, according to the customer needs requires a clear strategy of the manufacturers in the market. To produce a good product, measures are taken to measure and control products at all production levels among, which the analysis of process capability indices is of great importance in the industry. In this context, the usability indicators can be effective when the data follow a normal distribution. On the other hand, if the data aren't standard normal, evaluation of the process's capability based on these indices will typically be confronted with the problem. In this paper, after investigating the behavior and characteristics of the median absolute deviation (MAD) and interquartile range (IQR) and (Q_n), their analysis is conducted for the Gamma distribution. Then, the bias errors and standard errors are obtained using the jackknife method. Three estimators are evaluated in three different modes according to the bootstrap methods and based on their confidence intervals. Finally, by analyzing the results of this research, the reliability, and performance of the estimators are evaluated in different states.

Generally, selecting an investment portfolio with appropriate returns that is also secure and auditable has been one of the issues raised in recent decades. For this purpose, the present research proposes an appropriate approach using ideal and anti-ideal values, ideal values, as well as maximum deviations of each objective, considering the sample in the examined market, fuzzy goals, interval fuzzy values for each asset, and their combination with satisfaction functions, fuzzy ideal planning, and weighting objectives using expert decision-makers' opinions, as well as the development of fuzzy basic weighting method. It seeks to select an investment portfolio in the digital currency market.

In this research, a new approach to selecting an investment portfolio based on uncertain data and multi-objective uncertain planning is proposed, and ultimately, the proposed approach is implemented in the digital currency market for portfolio selection.

The results of the present study show that the proposed model of investment portfolio compared to the base model not only led to higher returns but also had higher audibility and better risk control. In other words, the proposed model outperformed the base model in all the objectives under study.

Originality/Value:

 As distinguishing features of the proposed model of this research, one can mention: 1) constructing and using fuzzy distribution functions and calculating ideal values and expected ranges for all desired objectives considering the conditions of the examined market research using simple mathematical modeling, 2) utilizing the experience of financial market experts in planning model for selecting suitable investment portfolios in emerging financial markets, 3) presenting an approach to calculating portfolio risk in conditions of information scarcity in the problem environment using fuzzy theory, 4) development of the fuzzy benchmark-criterion method for weighting the objectives under study in the problem considering the expertise of financial market experts, and 5) simple modeling, considering interval fuzzy values in the model, and being usable for all individuals with different levels of investment knowledge.

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.

The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the advances in modelling capabilities. ARIMA is a popular model for describing the underlying stochastic structure of available data. Artificial Neural Networks (ANNs) can also be appropriate alternatives. In the literature, forecasting the temperature of Tehran using both techniques has not been presented so far. Therefore, this article focuses on modelling air temperatures in the Tehran metropolis and then forecasting for twelve months by comparing ANN with ARIMA. Particle Swarm Optimization (PSO) can help deal with complex problems. However, its potential for improving the performance of forecasting methods has been neglected in the literature. Thus, improving the accuracy of ANN using PSO is investigated as well. After evaluations, applying the seasonal ARIMA model is recommended. Moreover, the improved ANN by PSO outperforms the pure ANN in predicting air temperature.

Coronavirus disease 2019 is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to modeling, comparing the performance of models, and predict new cases and deaths efficiently in the future.

In this article nine popular forecasting techniques are tested on the data of Covid-19 in Bahabad city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. 

The results of the analysis showed that the best model according to the evaluation criteria for forecasting cumulative cases of hospitalization of Covid-19 is the cubic spline smoothing model, and cumulative cases of death, KNN regression model. Also, autoregressive neural network and theta models for hospitalization cases, and for death cases, autoregressive neural network model has the worst performance among other models.

This study can provide a proper understanding of the spread of covid-19 disease in this region so that by taking precautionary measures and formulating appropriate policies, this epidemic can be effectively overcome. Also, unlike other studies, this study uses 9 different techniques and their comparison, which in turn increases the confidence factor in decision making. Also, an important point is that the data should be updated in real time.

Coronavirus disease 2019 or COVID-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths today can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future.

In this article, 9 forecasting techniques were tested on the data of COVID-19 of Mehriz city, Iran as a case study from 2020/02/26 to 2021/12/19 and using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models were compared.

For cumulative cases of hospitalization, ARIMA, Exponential, Holt-Winters, and STL models performed better and autoregressive neural networks, Theta, and KNN regression showed poor performance. Also, for cumulative mortality cases, KNN regression, Exponential and Theta models have better performance in predicting cumulative mortality cases, and autoregressive neural networks, ARIMA, and cubic spline smoothing showed poor performance.

the best model according to the mentioned evaluation criteria for predicting cumulative cases of hospitalization of COVID-19 is STL model and for cumulative cases of death is the KNN regression model. Also, the autoregressive neural network model has the worst performance among other models, both for hospitalization and death cases. Also, the important point is that the data should be updated in real-time.

One of the most important factors of socio-economic development in any country is the quality of electricity sources. Considering the sensitivity of electronic devices and the dependence of most activities on electricity, providing sustainable energy in the urban system is very important. Therefore, a comprehensive view of the factors causing disturbances in the electricity distribution network is very valuable in order to prevent any electricity losses. The goal of the current research is to identify, evaluate and prioritize operational risks in the aerial electricity distribution network. Any operational risk is a potential cause of the incident that leads to an unplanned outage. In this study, by reviewing the research literature, incidents recorded in the electricity distribution network incident registration system (known as the 121 system), and conducting interviews, 21 operational risk cases have been listed and approved by experts. On the other hand, to solve the limitations of the FMEA method, by combining the BWM method and using the knowledge of experts (completion of the questionnaire), evaluation and prioritization were done with more differentiation. The results showed that from the point of view of experts, the intensity index is critical (0.475). Also, three operational risks with high priority in the electricity distribution network of Yazd province include; Failure in concrete foundations, the impact of foreign objects, and failure in transformers. Statistics emphasize that high-priority risks are responsible for 27% of unplanned outages in the last ten years. Operators and managers of electricity distribution companies can consider high-priority risks and provide solutions to reduce, eliminate or transfer risks. In this case, in addition to minimizing unplanned outages in the network and selling more electricity, customer satisfaction is achieved.

The main purpose is to analyze social entrepreneurship by presenting a multi-objective model of a multi-level supply chain of blood products. The present research is applied in terms of purpose, descriptive in nature, and quantitative in terms of gathering information. The studied community of new local bases, mobile bases as well as transportation equipment for the effective and timely transfer of blood products to the candidate provincial centers of the country in the time domain of 1400 is collected. In order to minimize the costs of the entire supply chain network and maximize the attractiveness of the transmission centers, the proposed mixed integer two-objective mathematical model was designed and carried out by epsilon constraint method with coding in GAMS24.1.2 software. The results showed that increasing the number of blood donation sites, facility capacity and attractiveness leads to improving the attractiveness and performance of the supply chain of blood products.

Electric power and power distribution are prominent infrastructures for economic development in any developing country like Iran. Also, the power distribution network is a very important supply chain that combines a variety of processes. Smart electrical energy distribution networks are one of the latest technologies in the world. The main goal of these networks is to provide reliable electricity, increase the reliability factor and network stability, and respond to the growing needs of customers with minimal damage to the environment, profit, and high efficiency. In the last three decades, the rapid evolution and prevalent adoption of information systems, distribution analysis tools, computational models, and more recently, the emergence of smart grid technologies have given utilities access to the data and tools required for improving these analyses and the possibility of increasing the efficiency of power distribution systems (by, for example, reducing losses and optimizing voltage profiles). Forecasting the future state of the network with the least error brings us closer to the smart network. Because electricity is a mortal product, a comprehensive approach to unplanned power extinction (outage) time is very valuable in preventing any power distribution losses. Various accidents disrupt (cause breakdowns in) the power distribution network, which can be repaired and restored without a hotline. One of the main reasons for customers' power outages is the blackouts in the distribution field, which are affected by technical and non-technical events in the electricity distribution networks. Forecasting these events and managing them can be effective in reducing unplanned power extinction (outage) time. The purpose of this article is to present a model for predicting the duration of unplanned power extinction (outage) and unsold energy based on the data recorded from 121 systems (controllers), the urban network of the three power distribution companies in Yazd province. The final result shows that the ARIMAX model(s) shows less error than the ARIMA model(s) and presents better prediction. Therefore, using exogenous variables in predictions and not being satisfied with the fluctuations of a variable can improve predictions. The model proposed for predicting unsold energy is ARIMAX(1,0,1)(0,0,0) considering the number of incidents and the time of unplanned outages as exogenous variables. The model also shows that in July 2022, the unplanned power extinction (outage) time of this network will be approximately ten hours and also the unsold power will be approximately 6 MWH. On the other hand, the community is without electricity and dissatisfaction has arisen, which lies in economic and social losses. Therefore, with this warning, managers should re-examine the factors of disruption and lack of electricity supply and think of measures to reduce these blackouts when planning for this month of the year

The pharmaceutical industry in Iran suffers from problems such as improper distribution and scheduling of drugs that have delayed the delivery of drugs to patients or on the other hand a huge volume of expired drugs. Also, paying attention to environmental and social issues along with economic ones is a serious approach to achieving sustainable development. Therefore, in this paper, economic, environmental, and social objective functions are considered for drug distribution in Iran according to the amount of demand. The purpose is to design a new model for the drug distribution network following the conditions of an epidemic (COVID-19) and to investigate its effect on the drug supply chain. Besides, in this paper, by adding backup suppliers, in addition to neutralizing the negative effects of Coronavirus, its effects are also examined and analyzed. To obtain the Pareto front, the improved version of augmented Ɛ-constraint (AUGMECON2) has been utilized. Using the proposed model, supply chain managers are able to make tactical decisions (product flow rate in the network) with the most profit, the most positive social impact, reduce greenhouse gasesو and reduce disease transmission. They can also greatly reduce or eliminate the serious problems of their supply chain network by creating resilience and sustainability in their network for the future, in critical situations such as the COVID-19 pandemic. The results showed that the suggested model has suitable efficiency for Coronavirus conditions, and can significantly reduce the destructive and harmful effects of this deadly epidemic

Nowadays, it is obvious that media has a dramatic role in people's lives. Among all types of media, TV can still be powerful if it tries to know its audience and uses creative management. A management that considers the interests of the media and the audience as one is effective.To achieve this goal, we should look for solutions to increase media influence by analyzing the audience and the programs together.In this article, we used innovative joint clustering of audiences based on demographic characteristics and characteristics of television programs. Solutions are provided for members of each cluster in order to increase media influence. The data was obtained from a researcher-made questionnaire and a sample of 390 related experts and people of Yazd.According to the demands of the audience during watching peaks, the managers of Yazd’s local TV channel have to review their broadcast schedule and use the solutions provided in this article on their agenda. Evaluating the quality of clustering shows its suitable structure. The proposed solutions have been validated according to the opinions of media experts and the degree of result’s compliance with the sources related to the research topic.In this article, due to a new technique called joint clustering, the audience is clustered hierarchically and simultaneously based on demographic characteristics and television programs. in addition, the solutions are provided for members in each cluster to increase media influence.

This study aims to evaluate and rank the performance of the currency units of the bank by using the integrated methods of the balanced scorecard, cross-efficiency data envelopment analysis, and game theory in a cooperative-competitive environment. In this regard, by studying the indices used to evaluate the efficacy of banks and with the help of experts in foreign exchange, seven indices are selected as inputs and outputs from four perspectives of the balanced scorecard approach. Then, by applying the proposed Nash bargaining game model in cross-efficiency in a competitive-cooperative environment, the efficiency of decision-making units is evaluated. In this way, the bank's branches compete in pairs. As a result, each branch tends to prioritize the other branch over the criteria in which they have a more significant advantage and allocate higher weight. This leads to the higher efficiency of the branch. Thus, the cross-performance matrix is ​​complemented by the performance of the bargaining model, rather than being filled by the performance of the conventional data envelopment analysis model. The proposed approach presents a new aspect of measuring performance based on the cross-efficiency model. The real case study of Isfahan Bank Melli branches is used to show the process of implementation of the model as well as the ability of the proposed approach.

Developing an aggregate production planning, as one of the most important manufacture tasks, can provide an efficient planning to optimize the companies’ objectives such as minimizing costs and maximizing profits. Also, community’s competitive pressures cause the need for considering green principles in production planning in order to balance environmental and economic performances. Hence, a multi-period, multi-product, multi-supplier, and multi-site aggregate production planning model is proposed to formulate a mathematical model of maximizing profit in green supply chain. Integer quadratic programming is used to solve the problem. Carbon dioxide emission from production and transportation modes are considered as green principle.  The feasibility and validity of the formulated model was tested using data from iron and steel industry as well as a sensitivity analysis on profit function. The results demonstrate the optimal amount of productions in order to maximize profit as well as developing green supply chain. Also, sensitivity analysis shows that profit objective fell steadily due to increase in total CO2 emissions from transportation and production processes. Consequently, some useful managerial insights were suggested regarding the consideration of green practices in aggregate production planning.

Consideration of environmental and social issues in addition to economic ones is a critical strategy that companies pay special attention to designing their supply chain. A resilient system prevents organizations from being surprised by catastrophic disruptions and critical conditions and eliminates high unwanted costs. In this study, a mixed-integer mathematical programming model is proposed to design a sustainable and resilient closed-loop supply chain network. Since suppliers are the most important external players, the slightest probability of disruptions can have a significant impact on chain performance. Accordingly, applying efficient strategies can be very helpful for coping with them. Also, because of the uncertain nature of some input parameters, the P-robust optimization method has been used to tackle them. An efficient algorithm has been carried out beside a heuristic method based on the strategic variables relaxation to solve the model. A case study of a lighting projectors industry has been conducted to evaluate the efficiency of the proposed approach. Finally, sensitivity analysis is performed on critical parameters of the problem. By solving the example, it is seen that 3 primary suppliers and 3 backups are selection, and 3 production centers, 2 collection centers and 1 repair, recycling and disposal centers have been established. The value of the economic objective function is equal to 565.857552 monetary units (MU). The CL-SCN environmental score is 658.07, while it is 608.93 in the social dimension. Eventually, the value of the final multi-objective function is equal to 0.658.

With 17 million annual deaths, cardiovascular diseases are the leading cause of mortality across the world with coronary artery disease (CAD) as the most prevalent one. CAD is the leading cause of death in industrial countries and at the same time is rapidly spreading in the developing world. Thus, the development and introduction of machine learning methods for the accurate diagnosis of heart diseases, especially CAD, have been an important debate in recent years in order to overcome relevant problems. The aim of this paper was to propose a model for enhancing CAD prediction accuracy. It sought a framework for predicting and diagnosing CAD using the features selection of Harris Hawks Optimization algorithm (HHO) and Support Vector Machine (SVM). The heart disease data set of Cleveland hospital available in the University of California Irvine (UCI) was used as the studied data set. It included 303 cases. Each case had 14 features with the final medical status of cases (CAD or normal case) as one of the features where 165 and 138 cases were diagnosed as CAD and normal, respectively. The results of this study revealed that HHO could enhance CAD diagnosis accuracy.

Process capability indices play a vital role in evaluating the conformity of the process properties to the required specifications. Process incapability indices are created by transformation in the process capability indices, leading to the separation of information related to the process accuracy and precision. This separation of information can be very beneficial to specify whether the process is capable or not and to detect deviations in the production processes that produce high-tech products, such as the electronics industry. The main goal of this study is to propose a process incapability index by considering the measurement error for processes with multivariate quality characteristics. The efficiency of this index is then examined by a numerical example using Monte Carlo simulation method. Moreover, the performance of proposed approach is compared with the case where there is no measurement error. In addition, as a practical example, this index is compared with a number of recently proposed indices in the literature, and sensitivity analysis is conducted, as well. The simulation results showed that the measurement error has a significant effect on process capability and incapability indices. Therefore, we strongly suggest that the measurement error has to be considered in the process analysis.

Nowadays, consumers tend to use more green and healthy products. Consumer awareness of environmental issues increases today. Declining natural resources, rising disease rates, and rising global temperatures due to industry-wide pollution have raised public concerns. The supply chain, as an important issue and involved with these problems, has always attracted the attention of researchers. A two-channel supply chain is considered in this study. The supply chain consists of one green producer and two retailers along with a Third-party Logistics Company (TPL). The government is also considered as the leader of the structure. The results show that the strategy adopted by the government has a major effect on other members’ decision of the supply chain and can therefore increase/decrease pollution. On the other hand, cooperating with a TPL companies also reduces environmental pollution, despite raising costs. The obtained results emphasize that by growing the value of subsidies allocated to the producer, the government can reduce the amount of pollution in the SC. Moreover, increasing the amount of subsidy will lead to an increase in the degree of product greenness. Also, increasing the degree of the greenness of the product does not always increase the profitability of SC members.

Process Capability Indices (PCI) show that the process conforms to the specification limits; when the product quality depends on more than one characteristic, Multivariate Process Capability Indices (MCPI) are used. By modifying in the process capability indices, the process incapability indices are created; these indices then provide information about the accuracy and precision of the process separately. In the real world, in most cases, the parameters cannot be specified precisely; therefore, the use of fuzzy sets can solve this problem in statistical quality control. The purpose of this paper is to present, for the first time, a Multivariate Process Incapability Index by considering the measurement error in a fuzzy environment. The presented index is shown for practical examples solved by considering Triangular Fuzzy Numbers; then the capability of the model is compared to the time when fuzzy logic is not used. The obtained results emphasize that ignoring the measurement error also leads to the incorrect calculation of process capability, causing a lot of damage to manufacturing industries, especially high-tech ones.

Increased pressure on natural resources, rising production costs, and multiple disposal challenges resulted in a growing global demand for integrated closed sustainable supply chain networks. In this paper, a bi-objective mixed integer linear programming model is developed to minimize the overall cost and maximize the use of eco-friendly materials and clean technology. The paper evaluates the exact, heuristic, and metaheuristic methods in solving the proposed model in both small and large sizes. The sensitivity analysis was conducted on LP-metric method as it outperformed the other two exact methods in solving the small size problems. The evaluation of LP-metric, modified ε-constraint, and TH as the exact methods, and Lagrange relaxation algorithm as the heuristic method in terms of solution value and CPU time revealed the inability of exact methods in solving the large size problems. The best combination of effective parameters for meta-heuristic algorithms were determined using the Taguchi method. The evaluation of MOPSO, NSGA-II, SPEA-II, and MOEA/D as the metaheuristic methods by means of Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), The Spread of Non-dominance Solutions (SNS), and CPU Time revealed the performance of these methods in solving the proposed model in a large size. The implementation of VIKOR technique identified the SPEA-II as the best method among the meta-heuristic methods. This study provides a holistic view regarding the importance of selecting an appropriate solution methodology based on the problem dimension to ensure obtaining the optimum and accurate solution within the reasonable processing time.

Natural and technological disasters threaten human life all around the world significantly and impose many damages and losses on them. The current study introduces a multi-objective three-stage location-routing problem in designing an efficient and timely distribution plan in the response phase of a possible earthquake. This problem considers uncertainty in parameters such as demands, access to routes, time and cost of travels, and the number of available vehicles. Accordingly, a three-stage stochastic programming approach is applied to deal with the uncertainties. The objective functions of the proposed problem include minimizing the unsatisfied demands, minimizing the arriving times, and minimizing the relief operations costs. A modified algorithm of the improved version of the augmented ε-constraint method, which finds Pareto-optimal solutions in less computational time, is presented to solve the proposed multi-objective mixed-integer linear programming model. To validate the model and evaluate the performance of the methods several test problems are generated and solved by them. The computational results show the satisfactory performance of the proposed methods and effectiveness of the proposed model for delivery of relief commodities in the affected areas.

One of the main challenges of the governments of each country is the increase in pollution caused by the production of products by manufacturers. Increased pollution leads to a significant growth in the number of patients with heart failure, respiratory failure and other diseases. One of the ways to deal with this situation is to produce green products with the least degree of pollution caused by the production. The green supply chain is one of the necessary solutions to improve the current situation. Pollution is not only due to the production of the product by the manufacturers, but also the transportation of products by non-standard means of transport is a factor in the production of pollution. In this study, a supply chain with a manufacturer and a third-party logistics company (3PL) that the government monitors the supply chain is considered. The government monitors on the supply chain by setting tariffs for the producer. The results show that the presence of a 3PL, although it increases the selling price of products, but reduces pollution and also increases the amount of demand.

Today, the simultaneous reduction of location costs and transportation costs in the establishment of urban facilities is of critical importance. This becomes extremely sensitive when the area under consideration is in crisis situations and the inevitable transit of vehicles and inland transportation is difficult in those circumstances and may cause financial and irreparable damages.
In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points. Regarding to the bi-objective mathematical model, the ε-constraint and GAMS software are applied. According to the application of the proposed model for the large-scale problems and The NP-Hard structure of the problem, the NSGA-II is applied and evaluated.  In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points.
According to the two-objective mathematical model, the ε-constraint method and the GAMS 24.1.2 software are used. Due to the possibility of using the model in the large dimensional applications as well as the NP-hardness of the model, the NSGA-II meta-algorithm is applied and the efficiency of the proposed approach is examined and evaluated.

This paper addresses a dynamic cell formation problem (DCFP) including a multi-period planning horizon in which demands for each product in each period are different and uncertain. Because the demand uncertainty is considered as stochastic data by discrete scenarios on a scenario tree, a multi-stage nonlinear mixed-integer stochastic programming is applied such that the objective function is minimizing of machine purchase costs, the operating costs, both inter and intra-cell material handling costs, and the machine relocation costs over the planning horizon. The main goal of the current study is to determine the optimal cell configuration in each period in order to achieve the minimum total expected costs under the given constraints. The nonlinear model is transformed into a linear form to this reason that GAMS can get to global optimal solutions in linear models. In order to find the optimal solutions, by using the GAMS for small and medium-sized problems, the optimal solutions are obtained. They applied in two bounds namely the Sum of Pairs Expected Values (SPEV) and the Expectation of Pairs Expected Value (EPEV). Also, according to the scenario-based model, the efficiency of two suggested bounds is shown in terms of the computational time. Finally, a practical case study is presented in detail to illustrate the application of the proposed model and it's solving method. The results show the efficiency of using SPEV and EPEV for several random examples as well as the proposed case study.

This study considers a multi-objective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.