Sharif Digital Repository

The world around us changes almost every second, so the economy in this world feels it more by every single minute. When we look at the organizational structures, they experience lots of things for keeping the companies competitive and the human resources have the main role among the other stimulating factors.The human resource management and its improvements have done a lot in this century to organize and manage the human problems in organizations. After that those companies that had more effective strategies become on top of this important management system. By changing fast the work models companies experience more and immediate changes that may not happen even through the years. In this... 

There are several types of uncertainty in supply chains that cause some difficulties in the planning of a supply chain. Uncertainty on the supply side can cause interruptions in the quality, quantity, or delivery time of orders. To avoid such interruptions, retailers usually contract with two different suppliers for a common good. In this setting, one of the suppliers has higher reliability and higher price, while the other supplier offers a lower price but has a lower level of reliability. In other words, to ensure a lower level of uncertainty, retailers have to pay more. This is one of the main tradeoffs in the dual sourcing strategy. In this thesis, we consider a two-echelon supply chain... 

Pavement maintenance and rehabilitation is an important problem in road pavement programming. Pavements experience significant distress over its life due to passage of vehicle (particularly heavy trucks), as well as abrupt variation in temperature (freeze and thaw). This would deteriorate pavements and reduces their serviceability. Rise of pavement maintenance costs places higher importance upon the need for maintenance programming. This study employs an evolutionary model to optimize the long-run benefit of the maintenance program. The model is stochastic in nature, and imbeds a Markovian decision procedure in the model to take into account such variabilities. The model is applied on... 

Rail transport system plays an important role in the development of the economies of the countries. This system is worn out over time by operating and weather conditions and requires maintenance and repair. One of the objectives of maintenance operations is to maintain tracks in an acceptable level and prevent their excessive deviation from the optimal situation. This deviation is called geometric irregular deviation. If these irregularities go away, they can cause traffic accidents and derailment in addition to losing travel convenience. So avoiding the deviation of the tracks is a very important. Maintenance management systems in the past few decades have been considered by the experts in... 

The main objective of this research is to provide a framework by which decision-makers can evaluate and compare alternatives for sustainable development planning under uncertain dynamic future considering the risk and uncertainty associated with human judgment as well as the uncertain future. This approach combines the Delphi method, fuzzy set theory, and a discrete multi-criteria method based on prospect theory (TODIM). TODIM (an acronym in Portuguese for iterative MCDM) method is a particular multi-criteria approach based on the prospect theory. A qualitative Delphi technique is used to identify a set of qualitative sustainability criteria and to rate the alternatives accordingly. The... 

In recent years, environmental issues has been gaining a great deal of attention in the field of Supply Chain and Supplier Selection. In this research, we first review the current literature and then we try to find the research gap in this area. According to the studies done in this area, no research on supplier selection and optimal order allocation to the suppliers have simultaneously investigated environmental issues, the possibility of storage, green routing of vehicles, purchasing discount, multi-product, and multi-period state in a green supply chain. Therefore, in this study, a multi-objective mathematical model is proposed for supplier selection and optimal order allocation in a... 

In this study, given the increasing challenges urban and intercity transportation face in the modern world, including population growth, urban development, and environmental concerns, an innovative approach to optimize the use of car-sharing vehicles is presented. The research aims to develop a multi-objective mathematical programming model for the optimal allocation of car-sharing vehicles, considering key factors such as transportation costs, travel time, and environmental pollution. This seeks to reduce costs, increase efficiency, shorten travel times, and contribute to environmental sustainability. Initially, a thorough literature review was conducted to provide a comprehensive... 

Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and economic criteria. Various approaches have been proposed for this problem. Among those, direct optimization using the simulator as the evaluation function, although accurate, is in most cases infeasible due to the number of simulations required. This study proposes a hybrid optimization technique (HGA) based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. Hybridization of the GA with these helper methods introduces hill-climbing into the stochastic search and also makes use... 

The aim of this research is to develop and implement a deep reinforcement learning algorithm for portfolio management in the Tehran stock market, which is considered an emerging market with distinct patterns compared to the stock markets of developed countries. In this study, in addition to the market price data extensively used in previous research, we leverage fundamental ratio data extracted from company financial reports, which have received less attention. Furthermore, the research scope is limited to stocks in the mining and metal industries to enable the utilization of specific industry features, such as susceptibility to global prices of a key commodity. The portfolio management... 

Nowadays, living in information era has forced us to face with a great deal of problems of which the input data is received like a nonstop endless stream. Intrusion detection in networks or filtering spam emails out of legal ones are instances of such problems. In such areas, traditional classification algorithms show function improperly, thus it is necessary to make use of novel algorithms that can tackle these problems. Among classification algorithms, decision trees have significant advantages such as being independent of any parameter and acting robust against outliers or unrelated attributes. Moreover, results of a decision tree are quite easy to interpret and extract rules from.... 

Inverse reinforcement learning (IRL) is one of the machine learning frameworks based on learning from humans; That is, instead of producing a decision process maximizing a predefined reward function, seeks to find the reward function based on the observed behavior of an agent. The biggest motivation of IRL is that, usually, determining a reward function for a problem is very difficult. We consider IRL in Markov decision processes; that is, the problem of extracting a reward function with the assumption of knowing the optimal behavior. IRL could be useful for apprenticeship learning to obtain skilled behavior, and for optimizing a reward function by a natural system. We first, determine a set... 

Reinforcement learning (RL) is a subfield of machine learning that expresses how to learn optimal actions in a wide range of unknown environments. Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs). However, being restricted to Markov environments to solve problems with limited state space is not an unreasonable assumption, but the main challenge is to consider these problems in as large a class of environments as possible, which includes any challenges that an agent may face in real world. Such agents are able to learn to play chess, wash dishes, invest in financial markets, and do many tasks that an intelligent human being can learn and do. In... 

The main goal of this research is to reduce aircraft design cycle period, by reducing the necessary number of design cycle iterations. The Design cycle period is one of the main characteristics of the design process and design cycle iterations play a major role in the design cycle period. To achieve the above mentioned goal, we present a mathematical model of iterations for the aircraft design process. This model describes the design coupled tasks as a discrete-linear time invariant dynamic system. This model also helps identify tasks which are the most important for generating iterations. This new method basically helps break information cycles that create iterations among important tasks.... 

The ability of blockchain to record transactions and automatic execution feature in smart contracts, increase the speed of the supply chain and reduce unnecessary delays. A blockchain-enabled supply chain is more dynamic and responds to changes faster. This makes many processes more efficient in the supply chain. Supplier performance evaluation is an essential field in Supply chain management. At any time, by recording real-time data through smart contracts in the blockchain, it is possible to see the supplier’s performance in different aspects according to all the information recorded up to that moment. This research investigates the use of smart contracts and blockchain in solving supply... 

Due to the numerous climate fluctuations and inefficient management of water resources during recent decades in Iran, the condition of some wetlands has become critical and the need for rehabilitation has increased. The importance of the wetlands and plains ecosystem have led to initiate some extensive projects to preserve and revive them. While implementing the projects some questions have been raised by the scientists and public whether the impact and success of these projects are in direct with objectives of the restoration program or not.For instance, Urmia Lake as one of the most precious natural habitats of Iran and as one of the biological reservoirs of the world is selected for this... 

One of the most important matters beside the shortage and requirement of water in big cities is how to dispose of wastewater. Also concerns with the continued use of centralized facilities include the capacity limitation, increased population growth, sustainable use of water, water shortages due to changes in global cycles and homeland security and disaster mitigation. Therefore the use of decentralized and satellite treatment systems is essential for big cities such as Tehran. One important matter pertaining to this issue is site selection of decentralized wastewater treatment plant. Multicriteria decision making is one of the methods that is used for site selection. By multicriteria... 

Nowadays, data is the most important asset for health organizations in which the process of collecting, storing and analyzing of data leads to success of health organizations. Many companies have turned to data mining for the beneficial use of these data. The main purpose of data mining is to obtain useful knowledge from existing data. One of the diseases that is very significant for data miners is cardiovascular disease. Cardiovascular disease is the most important cause of death in the world. Therefore, it is necessary to improve the diagnostic and predictive measures of these patients. In this study, a database containing of characteristics of patients with chest pain who referred to... 

As the size and the scope of infrastructure projects have expanded, so has the array of stakeholders, either involved or affected. Such projects frequently take years to come together and develop over time through the efforts of project promoters and the participation of various stakeholders. Stakeholders through participation and input will help to legitimize and develop large scale project initiatives.This study used a longitudinal multiple case study approach that analyzed two tolled fixed crossing megaprojects. Activity research hits benchmarks before and after developments and shifts in participation strategies and stakeholders. Subsequently, we identify the main and key issues related... 

In this thesis, the Conditional Value-at-Risk measure (CVaR) is used in two basic decision making problems under uncertainty. This measure is used previously in the field of risk management. The first problem is decision tree problem which is a multi-stage stochastic decision problem. In this problem, the risk of a decision maker is estimated by CVaR measure and a linear programming model is presented by using disjunctive programming theory. The importance of the developed model is in its generality for modeling all problems with decision tree structure. Then, its computational performance is compared to intuitive nonlinear programming models for the problem. Moreover, the presented model... 

A qualitative study has been conducted in order to identify evidence of bounded rationality in software engineers' career decision making. Tversky and Kahneman's heuristics and biases approach has been used as a theoretical framework of the study. The theoretical framework comprised four decision-making and judgment heuristics, i.e. availability, representativeness, anchoring and adjustment, and affect heuristic. The data were gathered through semi-structured interviews and analyzed using thematic analysis method. The final results revealed that software engineers did make use of heuristics in making career decisions. Several incidents of the use of availability, representativeness, and...