Nowadays, in order to increase construction of tall structures, the importance of choosing optimum systems, with a huge energy absorption capacity against wind and earthquake loads, has been widely considered. Since four decades ago, steel shear walls had been used as a stiff and high performance lateral system. This study is about the effect of concrete filled steel tubes (CFT) columns as vertical boundary elements of steel shear wall on seismic behavior of steel structures. Due to do this, three 10-storey steel structures, with similar plans and lateral load career systems of steel shear wall, coinciding X-bracing, and moderate steel frame were analyzed by means of non-linear, time-history method through SAP2000 software, and the results of roof displacement of them were compared with each other. Also after validating a two-storey, single-span frame sample with steel shear walls and CFT columns, 3 single-storey structures were analyzed by means of hysteresis and pushover, through ABAQUS software. The results of this study showed that a shear wall system presents suitable stiffness, resistance and ductility in comparison with other lateral bearing systems.

Due to the important environmental effects on human life and the conflict between the mining process and environmental factors, the enactment of protectionist environmental laws in the mineral industry has received extensive attention especially in countries with high mining potential. In this regard, using an extended fuzzy TOPSIS method, this study develops a novel conceptual framework to identify the key protection laws in Iran by considering the mineral successful laws and regulations in mineral-developed countries such as Australia, Chile, India, Turkey, Canada, South Africa, and China. This helps to apply the experiences of the abovementioned countries in resolving similar conflicts in Iran’s mineral industry. The key protection laws are selected based on their national and international environmental treaties, mines’ environmental protection laws, conflict solution methods, social responsibilities in mining activity, and laws for the abandoned mines. The proposed framework demonstrates that Iran’s mineral laws require revision and more transparency to avoid ambiguity and conflict with mineral property laws and governmental rights.

This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS).

Rotating shafts have a vast application in various industries especially in the aerospace industry such as engines, compressors and turbines. The researchers have performed considerable efforts on the rotating shafts’ dynamic behavior because of their sensitivity to the rotor specifications and different parameters such as supports. In this paper by employing a pipe elbow element, an especial finite element formulation is derived to investigate dynamic behavior of rotating shaft in the presence of support clearance. The proposed element consists of four nodes with twenty-four degrees of freedom, which also accounts for the shear and gyroscopic effects. Within a finite element analysis framework, the focus of the paper is proposing a formulation to account for the dynamic behavior of a rotating shaft with much less number of elements. The element is implemented in a finite element code and then is used to model and analyze some rotating shaft examples. In order to verify the developed formulation, results are compared with those obtained from other schemes reported in the literature.

Aggregate production planning (APP) is concerned with determining the optimum production and workforce levels for each period over the medium term planning horizon. It aims to set overall production levels for each product family to meet fluctuating demand in the near future. APP is one of the most critical areas of production planning systems. After the state-of-the-art summaries in 1992 by Nam and Logendran [ Nam, S. J., & Logendran, R. (1992). Aggregate production planning—a survey of models and methodologies. European Journal of Operational Research, 61(3), 255-272. ], which specifically summarized the various existing techniques from 1950 to 1990 into a framework depending on their abilities to either produce an exact optimal or near-optimal solution, there has not been any systematic survey in the literature. This paper reviews the literature on APP models to meet two main purposes. First, a systematic structure for classifying APP models is proposed. Second, the existing gaps in the literature are demonstrated in order to extract future directions of this research area. This paper covers a variety of APP models’ characteristics including modeling structures, important issues, and solving approaches, in contrast to other literature reviews in this field which focused on methodologies in APP models. Finally some directions for future research in this research area are suggested.

The Resource-Constrained Project Scheduling Problem (RCPSP) remains a critical area of study in project management, focusing on optimizing project schedules under constraints such as limited resources and task interdependencies. This review synthesizes advancements from 2016 to 2024, encompassing problem variants, optimization techniques, objectives, and real-world applications. Key developments include the evolution of hybrid metaheuristics, multi-objective optimization approaches, and the integration of stochastic models to enhance robustness against uncertainties. Furthermore, the application of machine learning and sustainability-driven models has expanded the practical scope of RCPSP in dynamic and complex environments. Challenges such as scalability, uncertainty management, and the need for practical implementations are addressed, with future directions emphasizing AI integration, decentralized scheduling, and real-time adaptive solutions. This study provides a comprehensive perspective on RCPSP, bridging theoretical research with practical implications for diverse industries.

Bidding strategy is an important part of a negotiation strategy in automated multi-issue negotiations. In order to present good offers, which help maximize the agent’s utility, we need to search the outcome space and find appropriate bids. Bid search can become challenging in large outcome spaces with more than ten thousands of possible bids. The traditional search methods such as exhaustive or binary search are not efficient enough to find the right bids in a large space. This is mostly due to the high number of issues, high number of possible values for each issue, and increased time complexity of usual search methods. In this paper, we investigate the potential of using meta-heuristic methods for optimizing bid search in large outcome spaces. We apply some of the most popular meta-heuristic algorithms for bid search in bidding strategy of baseline negotiating agents and evaluate their impacts on negotiation performance in different negotiation domains. The evaluation results obtained through comprehensive experiments show how meta-heuristic algorithms can help improve bid search capability and consequently negotiation performance of the agents on different performance criteria. In addition, we show which search algorithm is most suitable for improving any particular performance criterion.

Indole is a hazardous substance existing in the curl oil, which released to the environment by various industrial activities; thus, decreasing its ratio from nature seems to be essential. In this study, a facial pathway for loading Cu(II) particles on MCM-41 mesoporous material was introduced as an efficient catalyst for indole oxidation. The obtained catalyst was characterized by XRD, FESEM, EDS, TEM and BET/BJH techniques. Based on Taguchi method, indole oxidation via prepared catalyst at mild condition was evaluated. By applying this method, the operating factors including Indole initial concentration (mg/L), pH value and the mass of catalyst (g) were optimized. It was found that pH plays the most crucial role in indole oxidation, on the other word, in alkaline environment; indole oxidation was happened with higher yield.

Machine vision systems are among the novel tools proven to be useful in different applications, among which monitoring and controlling manufacturing processes is one of the most important ones. However, due to the complexity resulted from high-dimensional image data and their inherent correlations, the acquisition of traditional statistical process control tools seems inapplicable. To overcome the shortcomings of the traditional methods in this regard, a statistical model is proposed in this paper which utilizes the concepts of both the PCA-based T2 control chart and the classification methods to develop a tool capable of controlling an image-based process. By defining the warning zones, collected data taken from an image-based process are classified into more than the two classes related to in-control and out-of-control processes. This helps practitioners to define rules to make it easier to realize when the process is getting out of control. Through simulation, the accuracy performance and the speed of four different types of classifiers including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kth nearest neighbors (KNN), and support vector machine (SVM) are assessed in different scenarios, based on which the functionality of the proposed approach is evaluated in in-control and out-of-control conditions.

Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects and also consider project risks in their decisions. If project risks are not addressed during the risk management process, issues such as schedule delays, cost overruns, and even project failure may occur. This paper aims to introduce a Markov method to integrate project risk analysis and risk response planning. This method is applied to forecast the following status of the project when limited information about the project is available. Moreover, earned value management (EVM) methods were used to include various types of project risks through the project lifecycle. The model also offers the capability to choose the most effective risk response for managing project risks through the application of the Markov decision process (MDP). Eventually, we introduce a case study to demonstrate functionality and effectiveness of the presented approach. Solving the model allows for identifying the best set of risk response strategies tailored to each specific project state. The computational results illustrate that the current state of the project has a significant impact in the process of risk response planning. Since uncertainty is the inherent characteristic of projects, the use of the project’s current state is more reliable than the previous status of projects, and the Markov method is applied in this research because it uses the current state for its modelling. Using this method, managers can predict the future state of projects and find the best response in each status of projects.