Scientia Iranica
Volume:27 Issue: 2, Mar-Apr 2020

The multi-period portfolio optimization models were introduced to overcome the weaknesses of the single-period models via considering a dynamic optimization system. However, due to the nonlinear nature of the problem and rapid growth of the size complexity with increasing the number of periods and scenarios, this study is devoted to developing a novel league championship algorithm (LCA) to maximize the portfolio’s mean-variance function subject to different constraints. A Vector Auto Regression model is also developed to estimate the return on risky assets in different time periods and to simulate different scenarios of the rate of return accordingly. Besides, we proved a valid upper bound of the objective function based on the idea of using surrogate relaxation of constraints. Our computational results based on sample data collected from S&P 500 and 10-year T. Bond indices indicate that the quality of portfolios, in terms of the mean-variance measure, obtained by LCA is 10 to 20 percent better than those of the commercial software. This sounds promising that our method can be a suitable tool for solving a variety of portfolio optimization problems.

In this paper, we focus on solving the integrated energy and flexiramp procurement problem in the day-ahead market. The problem of energy and ramp procurement could be perfectly analyzed through Stackelberg concept, because of its hierarchical nature of the decision-making process. Such a circumstance is modeled via a bi-level programming, in which suppliers act as leaders and the ISO appear as the follower. The ISO intends to minimize the energy and spinning reserve procurement cost, and the suppliers aim to maximize their profit. To solve the proposed model, a fuzzy max-min approach is applied to maximize the players’ utilities. The objectives and suppliers’ dynamic offers, determined regarding the market clearing prices, are reformulated through fuzzy utility functions. The proposed approach is an effective and simple alternative to the KKT method, especially for problems with non-convex lower-level.

Remarkable efforts are made to develop the job shop scheduling problem up to now. As a novel generalization, the stage shop can be defined as an environment, in which each job is composed of some stages and each stage may include one operation or more. A stage can be defined a subset of operations of a job, such that these operations can be done in any arbitrary relative order while the stages should be processed in a predetermined order. In other words, the operations of a stage cannot be initiated until all operations of the prior stage are completed. In this paper, an innovative lower bound based on solving the preemptive open shop (using a linear programming model in polynomial time) is devised for the makespan in a stage shop problem. In addition, three metaheuristics, including firefly, harmony search and water wave optimization algorithms are applied to the problem. The results of the algorithms are compared with each other, the proposed lower bound, and a commercial solver.

It is becoming increasingly difficult for enterprises to survive under competitive conditions. Enterprises with high levels of institutionalization are able to survive and benefit more advantages than their competitors. Excellence models are widespread tools for measuring the degree of institutionalization of enterprises. The importance of institutionalization has been increasingly considered in excellence models. EFQM (European Foundation for Quality Management) is a suitable tool to show how successful organizations are in terms of institutionalization. In this study, EFQM criteria are evaluated with fuzzy multi-criteria decision-making techniques. The fuzzy DEMATEL method is used to determine the interactions amongst main EFQM criteria. According to the relationship diagram obtained from the Fuzzy DEMATEL method, the weights of the sub criteria are calculated according to the expert evaluations using Fuzzy Analytic Network Process (FANP) method. The criterion “Business Results” has been determined to be the most important criteria. The criteria weights are taken as input for the VIKOR method. The institutional scores obtained by the proposed method, and the scores given by the EFQM evaluators to the institutions, are statistically analyzed to demonstrate that the proposed method has produced meaningful results.

Due to the competitiveness of banking industry and increasing bargaining power of customers, evaluation of the banks’ performance is crucial to better serve the classified customers in a universal system In this paper, with consideration of segmenting the customers into personal and business ones, methods such as confirmatory factor analysis (CFA) and structural equation model (SEM) have been used in selecting appropriate variables of the network data envelopment analysis (NDEA) model based on network slacks-based measure and consideration of the undesirable variables and shared resources. The SEM model has been used to establish a proper connection between the different dimension of the NDEA model and CFA model has been used to identify the importance of each dimension. Also, the proposed model has been used to measure the Operational and decomposed universal efficiency of one of the Iranian bank branches (Day Bank). The results show that the extracted model provides managers with a suitable perspective in adopting appropriate policies to promote their performance in the different sectors, including deposit attraction, financial serving personal and business banking customers, and profit generation, and also in comparing them in the different dimensions of the model.

Behavioral economic has received much attention recently. Learning and fatigue are two typical behavioral phenomena in industrial production operation processes. The existence of learning and fatigue result in a dynamic change in productivity. In this paper, a classical economic production quantity model is extended to consider the behavioral economic value of learning and fatigue. Based on a real case study, each production cycle is divided into five phases, i.e, the learning phase, stable phase, fatigue phase, fatigue recovery (rest) phase, and the relearning phase. The new production inventory decision model is incorporated with dynamic productivity and learning-stable-fatigue-recovery effect. Numerical simulation and sensitivity analysis show that appropriate rest alleviates employees fatigue and increases productivity, resulting in a lower average production cost. On the other hand, when the rest time is too high, exceeding a certain value, it leads to the decline of the actual labor productivity, resulting in an increase in the average cost of the system.

Today’s competitive business environment has resulted in increasing attention to social responsibilities and customer’s attitudes. Buying and returning have become a common practice for different reasons, including incompleteness or immature failure of the product or its failure to meet the customer’s satisfaction. Before the buying and returning cycle can be handled appropriately, companies need a proper logistics network designed following a proper design strategic. In the present research, a forward and reverse logistics network is proposed for product distribution and collection. The contribution of this paper to the literature is the proposal of a multi-period, multi-echelon, integrated forward and reverse supply chain network design problem with transportation mode selection considered. Different kinds of decisions including the determination of optimum number and locations of facilities, facilities opening time and transportation mode selection among different facilities have been considered in this paper. Due to multi-period nature of the problem, the problem is flexible for future periods. A new mixed integer nonlinear programming model was proposed for the introduced problem considering different levels of facility capacities with the maximum profit objective function. As another contribution, a genetic algorithm was developed to cope with problem’s complexity when the problem size goes large.

After its rapid economic growth, China is facing a very serious problem of atmospheric pollution with major long-term atmospheric problems appearing in large cities. Air pollution not only affects people’s normal lives, but also has a greater negative impact on their bodies, causing diseases, impacting productivity, and influencing people’s creativity. Due to past articles, the discussion on the efficiency of innovation and research has not been considered the impact of environmental variables. This study combines energy consumption, economics, environmental variables and innovative research and development capabilities to analyze and explore the relationship between consumption, environment, economy, and innovative R&D capabilities, this is the feature of this article. This study employ the Dynamic Data Envelopment Analysis (DEA) model to calculate energy consumption efficiency, R&D input efficiency, innovation patent output efficiency, carbon dioxide emission efficiency, and AQI efficiency of each city and further compare each city to find their space for improvement. The results of the study show that 10 cities have a total efficiency score of 1, implying the improvement space is already 0, whereas the total efficiency scores of the other 21 cities mean there is still much room for improvement, and there are big differences among the cities.

Redundancy Allocation Problem (RAP) is one of the most important problems in the field of reliability. This problem aims to increase system reliability, under constraints such as cost, weight, etc. In this paper, we work on a system with series-parallel configuration and multi-state components. To draw the problem nearer to real condition, we merge this problem with discount levels in purchasing components. For calculating sub-systems reliability, we used recursive algorithm. Because RAP belongs to Np. Hard problems, for optimizing the presented model a new Genetic algorithm (GA) was used. The algorithm parameters tuned using Response surface methodology (RSM) and for validation of GA an enumeration method was used.

In recent years, the implementation of safety management has been increased in construction projects by institutions, and many companies have recognized environmental and social effects of injuries at project work systems. In this regard, a novel decision model is presented based on a new version of complex proportional assessment method with last aggregation under a hesitant fuzzy environment. The decision makers (DMs) assign their opinions by hesitant linguistic variables that are converted to the hesitant fuzzy elements. Also, the DMs’ judgments are aggregated in last step of decision making to decrease information loss. Since weights of the DMs or professional safety experts and evaluation criteria are not equal in practice, a new version of hesitant fuzzy compromise solution method is proposed to compute these weights. In addition, the criteria weights are determined based on proposed hesitant fuzzy entropy method. A real case study in developing countries about the safety of construction projects is considered to indicate the suitability and applicability of the proposed new hesitant fuzzy decision model with last aggregation approach. In addition, an illustrative example is prepared to show that the proposed approach is suitable and reliable in larger size safety problems