Sharif Digital Repository

In this paper, the agent velocity in robotic swarm was determined by using particle swarm optimization (PSO) to maximize the robotic swarm coordination velocity. A swarm as supposed here is homogenous and includes at least two members. Motion and behavior of swarm members are mostly result of two different phenomena: interactive mutual forces and influence of the agent. Interactive mutual forces comprise both attraction and repulsion. To be more realistic the field of the swarm members' view is not infinity. So influence of the coordinator agent on the robotic swarm would be local. The objective here is to guide the robotic swarm with maximum possible velocity. According to equation motion... 

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

This paper addresses a real-world problem faced by the public healthcare sector. The problem consists of both the patients’ and service provider's requirements (i.e., accessibility vs. costs) for locating healthcare facilities, allocating service units to those facilities, and determining the facilities’ capacities. The main contribution of this study is capturing both short-term and long-term uncertainties at the modelling stage. The queuing theory is incorporated to consider stochastic demand and service time as a short-term uncertainty, as well as a service level measurement. The developed nonlinear model is then converted into a linear model after introducing a new set of decision... 

Growing demand for flexible ramp products (FRPs) to maintain the power balance in active distribution networks (ADNs) has expanded the use of costly fast-start generators in the upstream real-time market. In order to limit the power system's reliance on these units, it is necessary to establish new sources for providing ADNs with FRPs. In this context, the present paper examines the abilities of distributed energy resources (DERs) and active loads for supplying the ramp capacity demands of ADNs. Accordingly, a local real-time market was established in the ADN, empowering several DERs and active loads to provide the network's FRPs demands. The proposed market was formed in the context of a... 

Imbalanced international trade and the limited number of containers in the maritime transportation industry have resulted in considerable costs and decreased profits for shipping firms. To manage these issues, this study utilizes price management techniques to control empty container repositioning in a transportation system. In this system, there is a firm providing transportation services between two ports in both directions. Each direction has its own uncertain potential demand that can be affected by the prices and the quality set by the firm. We develop a mathematical formulation to set the quality and prices of the service in order to maximize the firm's profit. Due to demand... 

Restructuring and privatization in power systems have resulted in a fundamental transition of conventional distribution systems into modern multi-agent systems. In these structures, each agent of the distribution system would independently operate its local resources. In this regard, uncertainties associated with load demands and renewable energy sources could challenge the operational scheduling conducted by each agent. Therefore, this paper aims to develop a distributed operational management for multi-agent distribution systems taking into account the uncertainties of each agent. The developed framework relies on alternating direction method of multipliers (ADMM) to coordinate the... 

The huge availability of data in the last decade has raised the opportunity for the better use of data in decision-making processes. The idea of using the existing data to achieve a more coherent reality solution has led to a branch of optimization called data-driven optimization. On the one hand, the presence of uncertain variables in these datasets makes it crucial to design robust optimization methods in this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by the above points, in this paper a Gaussian process is used in a Bayesian optimization framework to design a... 

Growing interest in the intermittent renewable energy sources may jeopardize the flexibility of power systems. In order to improve the flexibility of modern power systems, the surplus electricity generated by renewable sources can be deployed into several carriers, such as natural gas and heating energy via power-to-gas (PtG) and power-to-heat (PtH) technologies. This paper proposes an optimal daily energy management model of residential energy hubs integrated with power-to-X technologies. The proposed energy hub is incorporated with PtG, PtH, combined heat and power (CHP) facilities, and thermal storage to meet the required electrical, gas, and heating demands. In order to capture the... 

Medical waste management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up, the amount of MWMs increases day by day. We present medical waste chain network design (MWCND) that contains health center (HC), waste segregation (WS), waste purchase contractor (WPC), and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and... 

Robust optimization (RO) approach is inherently a multi-objective paradigm. The proposed multi-objective optimization formulation would attempt to find the optimum - yet robust - water injection policies. Two multi-objective, Pareto-based robust optimization scenarios have been investigated to encounter the permeability uncertainties. These multi-objective RO scenarios have been done based on a small representative set of realizations but they have introduced optimum points that could be reliable for the original set of realizations either. In both scenarios, the desired objective functions are expected value and variance of Net Present Value (NPV). The underlying RO scenarios have been done...