Nowadays, the selection and management of the optimal portfolio are the most primary fields of financial decision-making. Thereby, selecting a portfolio capable of providing the highest efficiency and, at the same time, the lowest investment risk has been turned into one of the most critical concerns among financial activists. However, in this selection, the two factors above are not the only determining ones. Various factors are affecting financial markets' behavior under different possible scenarios, which should be identified. In this paper, we examine the high sensitivity of the Iranian capital market to the exchange rate fluctuations in the different scenarios due to the lack of a unified view of the value of that rate among experts as one of the mentioned factors and obtain its value using Dempster–Shafer theory (DST). Then, a portfolio selection model that prefers stocks with higher ranks is proposed. Representative results of the real-life case study reveal that the submitted approach is productive and practically applicable.

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

This paper investigates various techniques used to solve the differential equations governing the free vibration of columns. The present work focuses on the study of the free vibration of Euler’s Bernoulli column of equal strength in compression, considering its own weight and the axial load in compression and tension while subjected to symmetrical boundary conditions. The investigation utilizes the differential quadrature method to examine the fifth natural frequency parameters of the column in different states of column boundary conditions and varying geometric section shapes, including pin-pin and clamp-clamp configurations. The results of this work contribute valuable insights for informed decisions on selecting the cross-section types and appropriate boundary conditions for ensuring the stability of such columns in civil constructions.

Today, improving machine availability is vital for industries to compete in the global market. Spare parts play an essential role in the maintenance and repair of equipment, but planning an extensive network in strategic industries with various spare parts can be very challenging due to the existence of different decision factors. The spare parts supply chain deals with inventory management issues, which necessitates considering the related decisions such as determining the stock level and order quantity. Moreover, demand uncertainty and long supply time make decision-making more complex. This paper presents a repair and supply planning model for repairable spare parts while considering a modified formulation of demand uncertainty to minimize costs. The model determines the optimal stock level, lateral transshipment, assignment of spare part orders to suppliers, equipment to repair centers, and the number of intervals over the planning horizon used in demand estimation. This research contributes to the literature by integrating recent decisions, using demand approximation by piecewise linearization, and considering backorder in warehouses evaluated by queuing models. A hybrid approach, including heuristic and genetic algorithms, is used to optimize the model using data from an oil company. The results show that using piecewise linearization and integrated repair and supply planning decisions optimizes costs and improves performance. Also, the availability is affected by the demand estimation, which necessitates precision prediction.

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.

The dynamic behavior of the cylindrical shell can be predicted by more simplified beam models for a wide range of applications. The present paper deals with finding design conditions in which the cylindrical shell performs like a beam. Employing the Hamilton’s principle, the governing equations are obtained using both Donnell-Mushtari and Flugge shell theories and the analytical solution is obtained for long cylinders with simply-supported boundary conditions at both ends. Then, by equalizing the shell and beam vibration frequencies, the shell-to-beam transition conditions are obtained for both theories. To account for the effects of shear distortion and rotatory inertia of the shell, the finite element method is applied to find the best transition conditions with less approximating assumptions. Finally, the effects of boundary conditions on the transition parameters as well as the frequency response are studied. The obtained conditions simply define that if the shell can be assumed as a beam in any specific geometrical and material conditions.

This paper presents a mathematical model for a single depot, time-dependent vehicle routing problem with backhaul considering the first in first out (FIFO) assumption. As the nature of the problem is NP-hard, variable neighborhood search (VNS) meta-heuristic and mat-heuristic algorithms have been designed. For test problems with large scales, obtained results highlight the superior performance of the mat-heuristic algorithm compared with that of the other algorithm. Finally a case study at the post office of Khomeini-Shahr town, Iran, was considered. Study results show a reduction of roughly 19% (almost 45 min) in the travel time of the vehicle.

In reality, concrete structures are normally under various loadings, and results of different studies have shown that cracks in these structures and their materials, due to their nature as well as the loading type, do not develop along the crack plane (pure mode I); rather, they expand under mixed modes, making the crack growth studies under these modes a very important issue. In the crack growth phenomenon, the fracture toughness is a very effective parameter usually calculated by ENDB samples because they are easy to handle. In this study, several samples were made by changing the maximum aggregates size (dmax = 9.5, 12.5 & 19 mm) and the amount of hooked-end steel fibers (SF = 0.1, 0.3 & 0.5%), and tested under different loading modes (pure/mixed modes I and III) using the strain control jack device. According to the results, the lowest fracture toughness belonged to pure mode III, aggregates with dmax = 12.5 mm performed better in the self-compacting concrete reinforced with steel fiber, Also, the results show that the increasing trend of steel fibers does not have a positive effect on the fracture toughness performance.

Quality profiles representing the quality of a process or product as the functional relationship between one or more dependent variables and one or more explanatory variables are nowadays widely recognized in statistical process control (SPC) applications by both researchers and practitioners. On the other hand, in many real-world cases, evaluation of process or product characteristics is carried out with ambiguity or conducted using linguistic values. The theory of fuzzy sets provides an appropriate approach to deal with uncertainty due to ambiguity in human subjective evaluations or vagueness in linguistic variables. The purpose of this study is to introduce two novel methods based on fuzzy regression modeling for monitoring fuzzy linear profiles in phase II of SPC. To accomplish this, fuzzified Hoteling’s T2 statistic and fuzzy hypothesis testing are used. Moreover, a simulation study is used to compare the performance of the proposed methods compared with previous methods, based on the average run length (ARL) criterion in order to assess the detectability of charts with regard to the step shifts in profile parameters. Finally, the results of a real-world example in the tile and ceramic industry are presented.

In this article, the application of the Multi-Skilled Resource-Constrained Flow Shop Scheduling Problem (MSRC-FSSP) in preventive maintenance as a case study has been investigated. In other words, to complete each maintenance order at each stage, in addition to the machine, a set of required human resources with different skills must be available. According to human resources skills, each of them can perform at least one order or at most N orders, and each maintenance order must be done by a set of human resources with different skills. To carry out a maintenance order, different human resources must be in communication and cooperation so that a preventive maintenance order can be completed. In this article, these resources are considered as technical supervisors, repairmen and maintenance managers who complete all maintenance orders in a flow shop environment as a job. For this problem, a new Mixed Integer Linear Programming (MILP) model has been formulated with the two-objective functions, minimizing total orders completion time and the human resources idle time. To solve the model on a small scale, CPLEX is used, and to solve it on a large scale, due to the fact that this problem is NP-Hard, a meta-heuristic algorithm named Genetic Algorithm (GA) is presented. Finally, the computational results have been done to validate the model, along with the analysis of the human resources idle time.