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Objective: One of the important issues in project management is project scheduling. Since projects are faced with high uncertainty, project scheduling is of high importance under uncertain conditions. The purpose of this research is to present a robust multi-objective model to optimize project scheduling with limited resources by considering uncertainty, in which activities have several execution modes with uncertain duration, costs, and resources. Methods: After reviewing the extant theoretical literature on project scheduling, the assumptions, parameters, and variables of the mathematical model were determined. Then, considering the goals and limitations of the project scheduling problem in deterministic conditions, a mathematical model was developed. This model was transformed into a single-objective model by the epsilon constraint method. To consider the uncertainty in the parameters of the problem, robust optimization, and Bertsimas and Sim's approach were used. Also, Robust optimization of the single-objective model was developed to consider the uncertainty. Results: According to the obtained results, while the duration of the project increases with the increase in the tolerance of non-deterministic parameters; the percentage of project duration changes decreases for higher values ​​of robust parameters. Conclusion: The robust parameters are with negative coefficient in the objective function, so their increase leads to a decrease in the objective function value. The highest reduction of the objective function is when the robust parameter is changed from two to one. This coefficient decreases with the increasing value of the corresponding parameter i.e., as the value of stability parameters increases, its effect on changing the value of the objective function decreases. The cost parameter was changed between -40% to +40% for the value of gamma 10 (the state where 10 activities of the project are non-deterministic) for different variation values of parameters of non-deterministic activities. Its effect on the values of the objective function indicated that the variation of the cost parameter in the range of 0 to +40% turns it into an unnecessary constraint and that its change has no effect on the value of the objective function (project duration). Also, in the range of 0 to -40%, the cost reduction caused a decrease in the value of the objective function (increasing the project duration) and the maximum impact of the reduction of the project budget related to the situation where the uncertain parameters of time and cost change by 40% and 50%.

The impact of the COVID-19 pandemic and the introduction of artificial intelligence-based tools created significant job losses across various sectors in all countries around the world. A large portion of these job losses is permanent. Furthermore, the hidden unemployment numbers are higher than currently reported and the impact of Generative Pretrained Transformer (GPT) based tools will further increase the unemployed population in the coming years. Most businesses are likely to experience significant disruptions to their business-as-usual operations and will face business underperformance for long periods. To ensure business continuity and a smooth recovery process following severe disruptions, it is crucial to establish a recovery strategy. To provide enough workforce for the recovery strategy of various businesses, a large-scale rapid re-education of the workforce is required. Intelligent and virtual workplaces will replace traditional offices in various sectors in the upcoming years and many low-skilled jobs are in danger of being permanently lost. In this paper, an artificial intelligence-based framework for rapid work-skill re-education for evolving markets named Career-gAIde is presented. The proposed framework uses automatic analysis of the job resume of the workers for recommendations of a suitable new job with a higher salary and the best rapid re-education path toward that job. Custom build deep neural networks based on CNN-Random along with customized natural language processing tools are designed for large-scale automatic recommendation of a personalized education and career path to each job seeker. The proposed work is focused on software engineering job search and resume upgrades. There is also a book recommendation module for obtaining the knowledge of job seekers. Precision criteria were used to evaluate the job offer recommendations and the proposed framework achieves 67% in this measure. The Recall criteria were used to assess the required skills, with results of 84% and 79%, respectively. The experimental results show that the proposed framework can provide a solution for rapid work-skill re-adjustment for large-scale workforces.

Proper management and optimal allocation of financial resources will increase gross national product and growth, create jobs and increase public welfare. The purpose of this study is to present an investment strategy that has tried to pave the way for the development of the investing company in the financial markets. Therefore, the forthcoming research can be considered as applied in terms of purpose. Also, considering that in the present research, mathematical modeling, modeling, artificial intelligence, etc. are used and the optimization of the investor company's portfolio is evaluated with the proposed model, so it is a quantitative and descriptive research. This study evaluated the performance of the proposed model in three modes: prudent, moderate and risky investor company. The results showed that for all three cases, the proposed strategy performs significantly better than the market index and other previous strategies. At the end of the investment period, the risky portfolio was more valuable than other portfolios. On the other hand, a prudent portfolio has achieved a more stable and stable return. These results revealed that the proposed fuzzy programming is able to reflect the characteristics and desires of the investor company in the portfolio composition.

Objective: Energy saving regarding its high share in energy consumption of industries has a significant impact on the growth and development of countries. This study aims to evaluate the performance of Iran's electricity generation, transmission, and distribution processes. Methods: By using a network data envelopment analysis (DEA) model, the overall efficiency scores, and efficiency scores of production, transmission, and distribution processes are calculated. The network structure considers the main and surplus inputs (fuel consumption costs, internal consumption, transmission substation capacity, power transmission lines length, transformers capacity, low and medium voltage network length), intermediate sizes (net power generation, gross power generation, and delivered energy), desirable (nominal power, actual power, and delivered energy) and undesirable outputs (environmental pollutants, and energy losses). Results: An algorithm based on a multi-objective programming model is presented to evaluate network performance and simultaneously to evaluate processes efficiency. The proposed algorithm is used to evaluate 16 electricity areas in Iran. Conclusion: The results showed that Tehran, Khorasan, Khuzestan, and Zanjan are the most efficient areas.

Este estudio emplea un estudio de mapeo sistemático para analizar publicaciones sobre evaluación del desempeño de la biblioteca con análisis por envoltura de datos (DEA, por sus siglas en inglés). La revisión se basa en una búsqueda exhaustiva en la literatura académica que incluye bases de datos científicas de Scopus y Web of Science. Se seleccionaron 44 artículos publicados entre 1997 y 2018 como el núcleo del estudio. Los resultados muestran que los estudios publicados son realizados principalmente por especialistas en economía, administración, ingeniería y apenas por especialistas en bibliotecología y ciencias de la información. Curiosamente, una gran cantidad de los estudios se llevan a cabo en Taiwán y los Estados Unidos con un enfoque específico en la evaluación del desempeño de las bibliotecas universitarias. Los resultados de este estudio identifican lagunas en términos de técnicas, métodos y procesos utilizados para evaluar el rendimiento de las bibliotecas. Identificar estas brechas puede ayudar a los investigadores de las Ciencias de la Información y Bibliotecología a prestar más atención a la investigación sobre la evaluación del desempeño de las bibliotecas. Además, el estudio identifica las variables clave de entrada y salida en la evaluación del desempeño de las bibliotecas.

Nowadays, the importance of supply chain management and its effect on business performance is undeniable. Boosting competitive environment makes every single firm adopt an assignable supply chain strategy. This study is one of the rare practical researches that recognize key factors related to the application of a successful and efficient supply chain strategy. So far, many researchers have conducted studies on responsive supply chain strategy; but in this study, it is sought to focus on efficient supply chain strategies due to increasing need for organizations to enhance efficiency and reduce costs. Structural equation modelling using SmartPLS software is used to examine the research assumptions. Analysis of the structural model showed that there is a positive relationship between implementation of efficient supply chain strategy with supply chain performance; therefore the main research hypothesis is confirmed. Research revealed internal integration, top management support and information technology as efficient supply chain characteristics that have positive effects on supply chain performance. To reduce costs of implementation of efficient supply chain strategy, it is necessary to invest in factors that influence supply chain performance positively.

a lot of studies have been done about customer credit scoring, considering importance of the topic on credit institutions decision making. As an evolutionary computation method, Genetic algorithm is one of the methods used in this field. A variety of papers are published on comparing the performance of genetic algorithms with other scoring method but there is little information regard to fitness functions while these fitness functions play a vital role in overall performance of the model. To further investigation of the problem, three different fitness functions are proposed in the current paper and their performance is compared with other scoring methods including logistic regression and data envelopment analysis. The obtained results have shown that genetic algorithms quadratic function totally outperformed other methods based on accuracy, detection and sensitivity criteria.

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.

Prominent influence of transportation costs on supply chain overall profit indicates the importance and emergence of transportation optimization models. Regarding this issue and in view of realistic situation consisting of non-deterministic information, in this research optimizing inbound and outbound transportation costs of a multi echelon supply chain has been considered. To deal with uncertain time deliveries and pricing strategies adopted by different members of supply chain, in conjunction with unpredictable demand rate, fuzzy logic and specifically Trapezoidal Fuzzy Numbers (TrFNs) are included. After designing a fuzzy binary multi objective model based upon structural assumptions, the solving approach is proposed and the model is employed on Iranian steel supply network to illustrate the potential and advantages of our scheduled model. The bi-objective mixed integer fuzzy programming model presents and encompasses many realistic circumstances making the model applicable in network transportation cases.

Multi criteria decision aid (MCDA) deals with the problem of evaluating a set of finite alternatives regard to a set of finite criteria. A remarkable volume of qualitative and quantitative researches are done on decision making methods and situations, indicating its important role for managers at different organizational levels. These types of problems are applied in many different fields of human life. A challenging feature of these problems is non-existence of an optimal solution due to considering multiple criteria and the proposed methods seeking to find a satisfactory solution called efficient of Pareto-optimal. In consideration of MCDA problem, in this paper a new method is proposed for solving DM problems, consisting three fundamental steps of initialization, orthogonalization, and comparison. Thus, a new MCDA method called total area based on orthogonal vectors (TAOV) is introduced. This method is constructed on orthogonality of decision criteria. Application of TAOV method is illustrated in a decision problem and its performance is evaluated regard to other MCDA methods. Furthermore, its features are explained around the features of a desirable MCDA method. The obtained results indicate that the TAOV method can be considered as an acceptable method of handling multi-criteria decision making problems.

Decision makers usually prefer to express their preferences by linguistic variables. Classic fuzzy sets allowed expressing these preferences using a single linguistic value. Considering inevitable hesitancy of decision makers, hesitant fuzzy linguistic term sets allowed them to express individual evaluation using several linguistic values. Therefore, these sets improve the ability of humans to determine believes using their own language. Considering this feature, in this paper a method upon linear assignment method is proposed to solve group decision making problems using this kind of information, when criteria weights are known or unknown. The performance of the proposed method is illustrated in a numerical example and the results are compared with other methods to delineate the models efficiency. Following a logical and well-known mathematical logic along with simplicity of execution are the main advantages of the proposed method.

Master production scheduling is a midterm phase in planning which translates the long term aggregate production planning to a plan which determines the scheduling and magnitude of different products production. This problem requires investigating a wide range of parameters about demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty where demands are considered as stochastic variables, while cost and utilization parameters are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a numerical example.

Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteristic which optimizes the performance of units in one hand. This flexibility on the other hand threats the comparability of different units because different weighting schemes are used for different DMUs. This paper proposes a unified model for determination of a common set of weights to calculate DMUs efficiency. This model is developed based on a multi objective fractional linear programming model that considers the original DEA's results as ideal solution and seeks a set of common weights that rank the DMUs and increase the model's discrimination power. Comparison of the proposed method with some of the previously presented models has shown its advantages as a DMUs ranking model.

Inventory control is an important subject in supply chain management. In this paper, a three echelon production, distribution, inventory system composed of one producer, two wholesalers and a set of retailers has been considered. Costumers' demands follow a compound Poisson process and the inventory policy is a kind of continuous review (R, Q). In this paper, regarding the standard cost structure in an inventory model, the cost function of system has been approximated using Response Surface Methodology as a combination of designed experiments, simulation, regression analysis and optimization. The proposed methodology in this paper can be applied as a novel method in optimization of inventory policy of supply chains. Also, the joint optimization of inventory parameters, including reorder point and batch order size, is another advantage of the proposed methodology.

The development of Multiple Criteria Decision Making Techniques is to provide the possibility of using multiple criteria in decision making problems. Multi-criteria decision making problems primarily relate to choosing the best option among several available options and according to some indicators. One of the major issues in this regard is to determine the importance weights of decision criteria. Various methods such as Entropy, LINMAP and Saaty’s method have been presented for determining the weights of the indicators. In this paper, a non-linear model based on the logic of TOPSIS method is proposed to estimate the ideal weights. Simplicity, ‌ Reduction of received data from the decision makers in the evaluation process, and the possibility of considering his comments about the preference of indicators to each other can be cited as the advantages of this method

Public transport is a critical part of civilization in this decade. The amount of money invested and the criticality of transferring people in an acceptable time and without any conflict made it a challenging problem for managers, especially in metropolises. Absolutely, making effective decisions in this area requires considering different aspects. Waiting time is a key criterion in apprising quality of public transport. In this paper, a real world case study of ranking causes of delay in Tehran (Iran) metro system is solved by developing multi attribute group decision-making VIšeKriterijumska Optimizacija I KOmpromisno Rešenje (in Serbian, VIKOR) method under uncertainty, where this uncertainty is captured by Fuzzy Belief Structures (FBS). The obtained results are then compared with a previously proposed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method with FBSs. The results show that human related issues, along with the problems related to line and transportation system are the most important causes of delay. The obtained results of the problem seem acceptable for decision makers.

Inventory control is one of the most important issues in supply chain management. In this paper, a three-echelon production, distribution, inventory system composed of one producer, a set of wholesalers and retailers is considered. Costumers' demands can be approximated by a normal distribution and the inventory policy is a kind of continuous review (R, Q). In this paper, a model based on standard cost structure of inventory systems is developed and a heuristic algorithm is designed to optimize the developed model. The application of model is examined in a series of designed experiments that are compared with simulation results. These comparisons verify the validity of the model. Regarding to real complexities in three-echelon systems analysis, the proposed method can have a wide application in practical problems with the same considerations and assumptions. In addition, this method can be used to approximate those systems that follow a Poisson demand.

An important objective of a group decision-making problem is to determine the weights of attributes that are given by experts participating in the decision-making process. Since different decision-makers have unequal importance in decision-making, a series of studies focused on finding a set of appropriate weights for experts participating in a decision problem. In this paper, the problem of weight determination among decision-makers is investigated by extending an algorithm taken from the technique for order preference by similarity-to-ideal solution. In this case, a pair of most compromising and least compromising solutions is derived from individual judgments of decision-makers and then, these solutions are applied as the bases for determining the magnitude of individual alignment with the group opinion by using a closeness coefficient approach. Determining the weights of decision-makers, the group decision-making problem is then solved. Application of the proposed method is illustrated by a numerical example for the selection of a maintenance strategy.

Supply chains have become the major and dominant paradigm of business and competition. The main challenge is how to act in multi-echelon supply chains considering the levels involved. Making a choice independently or integrating with some or all levels will be a critical decision, and therefore affects the overall profit of the chain. This article proposes a non-cooperative game theory approach to helping in making a better decision in the supply chain and gaining the most accessible benefit. Our research considers unlimited three-echelon supply chains with S suppliers, M manufacturers and K retailers. The Nash equilibrium and definition are used bearing in mind inventory and pricing and marketing cost as decision variables for this matter. This paper studies a three-echelon supply chain network and focuses on the value of integrating a pair of partners in the chain. In the decentralized case, the supplier sets its own price, the manufacturer points out order quantity, wholesale price and backorder quantity, and the retailer charges the final retail price of the product and marketing product. Though there are multiple players at a single echelon level, each manufacturer supplies only a specific product to a given retailer. In addition to the decentralized case, two integration scenarios have been taken into account: manufacturer-retailer and supplier-manufacturer. As for manufacturer-retailer integration, inventory/holding cost issues diminish to a single warehouse and the retailer does not have to enforce marketing effort any more. Supplier-manufacturer integration brings similar benefits. Under each scenario, all parties involved simultaneously set their strategies. Through a numerical experiment, 17 design cases (through designing experiments) have been developed and the total profit of the supply chain under each scenario has been evaluated. Statistical tests on the above introduced 17 experiments have found that the decentralized system performs significantly worse than the integration of the supplier with the manufacturer, whereas no significant difference can be observed regarding other combinations.

Export promotion programs are incentives to increase the participation of companies in international markets. On the other hand, governments try to help exporting companies with developing their goal markets. Therefore, for this purpose, many different programs have been created. To show the effectiveness of these programs, the paper refers to stepwise DEA and grey incidence analysis. Finally, the article determines a unified ranking of the applied programs that can be used by decision makers for resource allocation considering different types of programs based on their effectiveness.

Multi-criteria decision making is an implicational field that concerns with selecting or designing the best scenarios among a finite set of scenarios based on a finite set of criteria. Different methods and techniques for handling this issue have been proposed. Complex proportional assessment is an analytical tool for solving multi-criteria decision making problems. Originally, the COPRAS method has been developed for decision making under a deterministic environment. Since uncertainty is an unavoidable property of decision making due to a lack of knowledge, this paper suggests an extended form of the COPRAS method used for group decision making problems in an uncertain environment where such uncertainty is captured through a generalized form of fuzzy sets - the so called interval valued intuitionistic fuzzy sets. An algorithmic scheme for the COPRAS-IVIF method has been introduced thus examining its application with reference to two numerical examples. It seems that the recommended framework of COPRAS-IVIF can be satisfactorily implemented in decision making problems under ambiguous and ill-defined conditions.

The linear programming technique for multidimensional analysis of preference, known as LINMAP is one of the existing well-known ideal seeking methods for multi attribute decision making problems. This method originally is proposed under crisp and deterministic circumstances. However, uncertainty is an indubitable property of decision making problems. In this paper, a new version of LINMAP-G is proposed where the decision maker's judgments are expressed as grey numbers. Like original LINMAP method, the grey ideal solution and attributes weight vector is determined and alternatives are ranked according to their weighted distance from determined ideal point. Application of the proposed method is illustrated in two numerical examples.