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Here, scalarization techniques for multi-objective optimization problems are addressed. A new scalarization approach, called unified Pascoletti-Serafini approach, is utilized and a new algorithm to construct the Pareto front of a given bi-objective optimization problem is formulated. It is shown that we can restrict the parameters of the scalarized problem. The computed efficient points provide a nearly equidistant approximation of the whole Pareto front. The performance of the proposed algorithm is illustrated by various test problems and its effectiveness with respect to some existing methods is shown.

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Here, we work on the problem of point estimation of the parameters of the Poisson-exponential distribution through the Bayesian and maximum likelihood methods based on complete samples. The point Bayes estimates under the symmetric squared error loss (SEL) function are approximated using three methods, namely the Tierney Kadane approximation method, the importance sampling method and the Metropolis-Hastings within Gibbs algorithm. The interval estimators are also obtained. The performance of the point and interval estimators are compared with each other by means of a Monte Carlo simulation. Several conclusions are given at the end.

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Here, we investigate the classical p-median location problem on a network in which the vertex weights and the distances between vertices are uncertain. We propose a programming model for the uncertain p-median location problem with tail value at risk objective. Then, we show that it is NP-hard. Therefore, a novel hybrid modified binary particle swarm optimization algorithm is presented to obtain the approximate optimal solution of the proposed model. The algorithm contains the tail value at risk simulation and the expected value simulation. Finally, by computational experiments, the algorithm is illustrated to be efficient.

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Here, a new mathematical model for cellular manufacturing systems considering three important features of part priority, levels of machine’s technology, and the operator’s skill is developed. Simultaneous consideration of these features provides a more realistic analysis of the problems in cellular manufacturing systems. A model with multiple design features including cell formation, human resources flexibility with different skills, machines flexibility, operational sequence, processing time, and the capacity of machine and manpower is proposed in this article. Ourfocus is on the design of cells to implementtwo dissimilar goals. The first goal is the reduction of inter-cellular movements of parts and workers. The second goal is the creation of efficient cellsby making cell’s quality level identical for produced products so that the production of all the different parts have good quality. Two approaches of augmented ε-constraint and non-dominated sorting genetic algorithm II (NSGA-II) are used to solve this model. By comparison of these two approaches, we realizethat the multi-objective evolutionary optimization algorithm creates a Pareto-optimal front in a reasonable amount of time forlarge-scale problems

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Cost efficiency models evaluate the ability of decision-making units (DMUs) to produce current
outputs at minimal cost. In real applications, the observed values of the input-output data and
their corresponding input prices are imprecise and vague. This paper employs a fuzzy data
envelopment analysis (Fuzzy DEA) method to study cost efficiency of DMUs. In previous studies
on the cost efficiency, no attention has been paid to the issue of ranking problem in fuzzy
environment. In addition, adequate accuracy is ignored in regards to appropriate range of fuzzy
cost efficiency scores. In this study, the proposed method is applied to assess fuzzy cost efficiency
in accordance with the  -level based approach. In this method, data information is considered
as triangular fuzzy numbers. The main idea is to convert the fuzzy DEA model into a family of
parametric crisp models to estimate the lower and upper bounds of the a-cut of the membership
functions of the cost efficiency measures. Moreover, the problem of ranking DMUs is investigated
based on the fuzzy cost efficiency, using a new method. Finally, the proposed method is illustrated
applying a numerical example, and then comparisons between the proposed method and previous
approaches are carried out.
 

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Using the experiences of successful and unsuccessful companies can be a criterion for predicting the situation of emerging companies. Each company can have a vector include both financial and non-financial characteristics. Accordingly, for an active or emerging company, it is possible to determine the characteristic vector and predict which group it is likely to belong to. The techniques used in this research are discriminant analysis and data envelopment analysis. Based on this technique, discriminant functions are designed to separate known sets. The main idea for finding discriminant functions is from data envelopment analysis, which makes a limit of efficiency for separating efficient units from inefficient ones. The discriminant functions of this method are used to predict the state of the company. Hyper planes are obtained as discriminant functions to separate companies. These hyper planes are based on multiple indicators. Each of these indicators can also apply in certain situations. The modeling used in this paper was used on oil companies listed on the Iran Stock Exchange. 15 indicators and criteria have been defined for each company. The data were for 2015 and 2016, and the number of oil companies was 18, of which 9 were successful and 9 were bankrupt. In this paper, with the help of data envelopment analysis and discriminant analysis, a new modeling was designed to find hyper planes for separating two sets. Modeling has been performed based on the different criteria that have existed, and each one applies in certain circumstances. In the following, the properties of the designed model are expressed and proved. The specific conditions of the criteria have become limitations that have been added to the multiplicative form of the designed model.

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In this paper, two non-linear technologies are proposed based on weak disposability definitions: weak disposability with non-uniform abatement factors and new weak disposability. Both technologies are applied to Spanish airport systems and the existing technologies are modified. To remove the computational complexity of non-linear approaches, the linearization methods are proposed. Then, in order to evaluate the efficiency measure of decision making units (DMUs), a directional distance function (DDF) is applied to the linear technologies and the analysis of the results is presented.

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The present study aims to identify the factors affecting the behavior of customers’ use of ebanking services of Tejarat Bank in Tehran. A qualitative method and an in-depth interview have been applied to achieve the research goal. The information and data collected from the interviews have been analyzed using open coding and axial coding. Then, the initial indicators of the model of using e-banking services were identified. In the next step, e-banking experts and specialists were asked to comment on the indicators obtained from the interviews, using the Delphi technique. The final results demonstrated that trust in banks, perceived security, ease of use, perceived utility, the impact of society, and perceived risk are considered as indicators affecting users’ behavioral intentions and ultimately their use behavior.
 

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Ant colony optimization (ACOR) is a meta-heuristic algorithm for solving continuous optimization
problems (MOPs). In the last decades, some improved versions of ACOR have been proposed.
The UACOR is a unified version of ACOR that is designed for continuous domains. By adjusting
some specified components of the UACOR, some new versions of ACOR can be deduced. By doing
that, it becomes more practical for different types of MOPs. Based on the nature of meta-heuristic
algorithms, the performance of meta-heuristic algorithms are depends on the exploitation and
exploration, which are known as the two useful factors to generate solutions with different
qualities. Since all the meta-heuristic algorithms with random parameters use the probability
functions to generate the random numbers and as a result, there is no any control over the
amount of diversity; hence in this paper, by using the best parameters of UACOR and making
some other changes, we propose a new version of ACOR to increase the efficiency of UACOR.
These changes include using chaotic sequences to generate various random sequences and also
using a new local search to increase the quality of the solution. The proposed algorithm, the two
standard versions of UACOR and the genetic algorithm are tested on the CEC05 benchmark
functions, and then numerical results are reported. Furthermore, we apply these four algorithms
to solve the utilization of complex multi-reservoir systems, the three-reservoir system of Karkheh
dam, as a case study. The numerical results confirm the superiority of proposed algorithm over
the three other algorithms.
 

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A branch of researches is devoted to semiparametric and nonparametric estimation
of stochastic frontier models to employ the advantages in the operations research
technique of data envelopment analysis. The stochastic frontier model is the
parametric competition of data envelopment technique. This paper focused on a
nonlinear autoregressive stochastic frontier production model that covers dynamic
technical inefficiency. We consider a semiparametric method for the model ‎by
combining a parametric regression estimator with a nonparametric adjustment‎. The
unknown parameters are estimated using the full maximum likelihood and pairwise
composite likelihood methods‎. After the parameters are estimated by parametric
methods‎ , ‎ the obtained regression function is adjusted by a nonparametric factor‎, ‎and
the nonparametric factor is obtained through a natural consideration of the local -
fitting criterion‎. ‎Some asymptotic and simulation results for the semiparametric
method are discussed
 

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The hub location and revenue management problem are two research topics in the field of network design and transportation. The hub location model designs the structure of the transportation network, while the revenue management model allocates network capacity to different customer categories according to their price sensitivity. Revenue management determines which products to sell to which customers and at what price. On the other hand, due to the limited number of aircraft seats, the revenue management problem has been widely used in the aviation industry. In this study, a robust optimization model is developed for the hub location and revenue management problem. For this purpose, a real-world case study with a central hub and six airports is presented and solved using CPLEX solver in GAMS software. Finally, a sensitivity analysis was performed on the key parameters of the problem, and their effect on the objective functions of the problem was investigated. Results show that the proposed model achieved the feasible solution in reasonable time for real case problem by exact method.

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Predictive analytics is an area of statistics that deals with extracting information from data and using
that to predict trends and behavioral patterns. Many mathematical models have been developed and
used for prediction, and in some cases, they have been found to be very strong and reliable. This
paper studies different mathematical and statistical approaches for events prediction. The main goal
of this research is to design and construct a hybrid prediction method for events prediction, based on
Logistic Regression (LR) method and Data Envelopment Analysis (DEA) technique. In this study, a
novel hybrid algorithm was developed, and considering the kind of collected data, LR method was
applied for input selection, and the capability of the additive (ADD) model of DEA was examined to
predict the occurrence or non-occurrence of the events. To apply the proposed approach, the selected
disease for the case study was a stroke. The results showed that any patient who was placed on the
frontier has had a stroke by one or more risk factors. On the other hand, the observations that were
not on the frontier had not suffered from a stroke. The overall accuracy of 88.5 percentages was
obtained for the developed method
 

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Brand personality has always been considered by researchers as an important factor in branding
studies. Therefore, the present study has been conducted to evaluate the effect of brand
personality on factors related to the brand-customer relationship (brand commitment, attachment
and trust). The research model is based on the data collected from 400 questionnaires that have
been distributed and collected among the costumers of Irtoya brand - Toyota representative in
Iran - in Tehran by sampling method. Its validity been confirmed by various methods including
factor validity, content validity, and face validity, and its reliability by Cronbach's alpha method,
test-retest, and split half method. It has been tested based on structural equation modeling (SEM).
The results of this study show that brand personality affects brand trust and attachment directly
and affects loyalty indirectly. Also, the results show that brand attachment and brand trust
directly affect brand loyalty. However, in this study, the effect of brand trust on brand
commitment, as well as the effect of brand commitment on brand loyalty, was not confirmed. The
factors studied in this study are known as the main factors of customer relationship with the
brand, so if marketers need to create this relationship, it is better to use these factors in a great
way
 

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This study was conducted with the aim of designing and explaining a content marketing pattern with a brand reinforcement approach in the country's banking industry. This research is applied in terms of objective, exploratory in terms of approach, and mixed in terms of data analysis. In the qualitative phase of the research and in order to  design a model based on methodology of data foundation theory, a group of experts including senior managers of the banking industry, university professors in the field of marketing, and marketing consultants familiar with the banking industry were considered as the statistical population. Snowball sampling method was used in this phase, and this process continued until reaching the theoretical saturation. 9 interviews were conducted in total. Also in quantitative phase, the customers of the banking industry in the city of Tehran were considered as population and 450 people were selected among them as the statistical sample based on equal size cluster sampling. In the qualitative phase of the research, due to using the data foundation theory, the main data collection tool was unstructured in-depth interviews with experts. In the quantitative phase of the research, the main data collection tool was a closed-ended researcher-made questionnaire consisting of 37 items that were designed based on the initial conceptual model. The SPSS, LISREL, and smart-PLS pieces of software were used to perform descriptive and inferential analyzes in the quantitative phase of the research. Finally, the research results led to designing a content marketing pattern with a brand reinforcement approach in the country's banking industry with 11 main variables and the hypothetical relationships of the model were tested and approved in a large population.

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New concepts of -feasibility and -efficiency of solutions for fuzzy mathematical programming problems are used, where  is a vector of distinct satisfaction degrees. Recently, a special kind of fuzzy mathematical programming entitled Fuzzy Flexible Linear programming (FFLP) is attracted many interests. Using the mentioned concepts, we propose a two-phase approach to solve FFLP. In the first phase, the original FFLP problem converts it to a Multi-Parametric Linear Programing (MPLP) problem, and then in phase II using the convenient optimal solution with the higher feasibility degree is concluded. Using this concept, we have solved the problem of the animal diet. In the process of milk production, the highest cost relates to animal feed. Based on reports provided by the experts, around seventy percent of dairy livestock costs included feed costs. In order to minimize the total price of livestock feed, according to the limits of feed sources in each region or season, and also the transportation and maintenance costs and ultimately milk price reduction, optimization of the livestock nutrition program is an essential issue. Because of the uncertainty and lack of precision in the optimal food ration done with existing methods based on linear programming, there is a need to use appropriate methods to meet this purpose. Therefore, in this study formulation of completely mixed nutrient diets of dairy cows is done by using a fuzzy linear programming in early lactation. Application of fuzzy optimization method and floating price make it possible to formulate and change the completely mixed diets with adequate safety margins. Therefore, applications of fuzzy methods in feed rations of dairy cattle are recommended to optimize the diets. Obviously, it would be useful to design suitable software, which provides the possibility of using floating prices to set feed rations by the use of fuzzy optimization method.

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Selecting resilient-sustainable suppliers can improve sustainability status and reduce supply chain disruption. This study aims to design a model for selecting resilient-sustainable suppliers in the supply chain of the Shahid Ghandi Corporation Complex. For this purpose, after reviewing the theoretical literature, 76 and 50 indicators were identified for evaluating sustainable suppliers and resilient suppliers, respectively. These indicators were investigated by supply chain experts in Shahid Ghandi Corporation Complex and, then, 15 indicators were determined to be suitable for each of the sustainable and resilient suppliers. A questionnaire was distributed among the supply chain experts of Shahid Ghandi Corporation Complex and the resilient-sustainable supplier selection model was confirmed using confirmatory factor analysis (CFA) based on the 136 questionnaires gathered from the participants. Sustainability indicators were classified into three economic, social, and environmental dimensions, and resilience indicators were divided into three categories of absorptive capacity, adaptive capacity, and restorative capacity. The results showed that the economic dimension had the first rank, the environmental dimension the second rank, and the indices of adaptation capacity, restorative capacity, social capacity and absorption capacity in choosing the sustainable-resilient supplier model were the next priorities, respectively.

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Today, new technologies have changed the global financial panorama and communities. Due to the advancement of new technologies and the competitiveness of the company, the nature of innovation has changed. In order to take full advantage of the potential of technological transformation, companies must incorporate platforms into their operations. The purpose of this have a look at became to designing and explaining the model of technological platforms capabilities within the cosmetics enterprise. The prevailing take a look at is carried out in terms of motive and descriptive in phrases of the way to acquire information. The information evaluation method locations this study within the discipline of qualitative research of interpretive type. The study population in the present study consists of all university professors in the field of business management and information technology management. In order to design the model of the present study, interviews were conducted based on purposive and theoretical sampling methods to the extent of theoretical saturation. In this study, in order to evaluate the validity of the interview from the approach of credibility or credibility criteria including the use of negative case strategies, triangulation, rich explanation and reliability approach including the use of third parties and also repetition of the coding process based on the validity model. Qualitative research by Lincoln and Guba (1982) was used. In evaluating the reliability of the interview, two strategies of using third party as well as repetition of coding were used (Lincoln and Guba, 1985). The analysis of the interview data using the data-based method was based on the systematic approach of Strauss and Corbin (1998), based on three stages of open, axial and selective coding. Data coding showed the extraction of 16 selected codes, 60 axial codes and 248 open codes which were classified based on causal conditions, central phenomenon, interfering factors, contextual factors, strategies and consequences.

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The present study aims at designing a cold multi-cycle supply chain based on a multi cross-dock system taking into account uncertainty. In the first step, we identified the factors and variables of the model. In the second, by selecting the study period through designing data collection forms and using the documents reviewing methodologies, the raw data required to measure the final indicators were collected and processed in the project model. Then, they were analyzed considering the research topic and using the techniques of genetic algorithm and particle swarm optimization. The primary objective function is minimizing the cost of transportation and warehousing throughout the supply chain, the second minimizing the total operation time and the number of vehicles within the supply chain, and the third maximizing the product freshness time. Also meta-heuristic optimization methods (strongly adjustable) were adopted to deal with the travel time of suburban vehicles. We also provide an example of the performance of optimization models for a small-sized sample. The computational results showed that longer travel time and further distance do not necessarily increase costs. In fact, it is possible to distribute the products with the right number of trucks at an optimal cost at the right time.

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In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods  and the accuracy is increased by an average of 12%.

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Hubs are facilities that can decrease the cost of many-to-many distribution systems by acting as an interconnector between the demand and supply nodes. This type of facility can reduce the number of direct links needed in a logistics network. Hub location problems (HLP) have been discussed by many authors for more than four decades, and different approaches have been developed for modeling and solving this problem. We propose a fuzzy type I and II programming approach for a new model presented in the literature, i.e., the single allocation ordered median problem. The level of flow among the nodes will be considered as a fuzzy parameter. In the fuzzy type I approach, a linear programming problem with fuzzy parameters is used, while for the fuzzy type II approach, the rules of interval arithmetic are developed to simplify the problem to the fuzzy type I case. Finally, we apply our method on Kalleh Dairy Co. data of transportation as a case study and compare crisp and fuzzy situations. We show that the results of the fuzzy approach could be 2% better than the crisp approach and also discuss the pros and cons of fuzzy type I and type II approaches.