Sharif Digital Repository

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... 

The vast nationwide COVID-19 vaccination programs are implemented in many countries worldwide. Mass vaccination is causing a rapid increase in infectious and non-infectious vaccine wastes, potentially posing a severe threat if there is no well-organized management plan. This paper develops a mixed-integer mathematical programming model to design a COVID-19 vaccine waste reverse supply chain (CVWRSC) for the first time. The presented problem is based on minimizing the system's total cost and carbon emission. The uncertainty in the tendency rate of vaccination is considered, and a robust optimization approach is used to deal with it, where an interactive fuzzy approach converts the model into... 

One of the most critical issues in manufacturing systems is the operator management. In this paper, the operator assignment problem is studied within a cellular manufacturing system. The most important novelty of this research is the consideration of operator learning and forgetting effects simultaneously. The skill level of an operator can be increased/decreased based on the time spent on a machine. Moreover, the issues related to operators like hiring, firing, and salaries are considered in the proposed model. The parameters are considered to be uncertain in this model, and a robust optimization approach is developed to handle it. Using this approach, the model solution remains feasible... 

A new robust continuous-time optimization algorithm for distributed problems is presented which guarantees fixed-time convergence. The algorithm is based on a Lyapunov function technique and applied to a class of problems with coupled local cost functions. The algorithm applies a methodology with no expansion of the local variables. This reduces the computation complexities of the solution and improves scalability. Using an integral sliding mode strategy we incorporate effective disturbances rejection on the decision variables as experienced in a wide range of industrial applications. It is shown that the algorithm can easily be modified to a finite-time solution when evaluations of the... 

Using Renewable Energy (RE) is growing day by day. We need to locate RE in the best place to maximize energy production and supplier profit. As a result, we propose a novel method for RE location (REL). This model suggests a Data-Driven Robust Optimization (DDRO) for multi-objective REL by considering Risk (DDROMORELR). We consider risk by adding min function in energy and profit objectives (government and supplier objectives). A DDRO approach is added to the model to tackle uncertainty and be close to the real world. We utilize an improved Augmented ε-constraint (AUGEPS2) to solve objectives and produce a Pareto front. We compare problems with DDRO and without considering DDRO, and the... 

Improving the discharge process of phase change materials (PCMs) is of great importance and can be effective for thermal energy storage (TES) systems. In this research, the direct-contact method for acetone droplet on molten paraffin is optimized to enhance the efficiency of the discharge process of PCMs and TES systems working with intermediate boiling fluid (IBF). In order to improve the storage rate and increase the freezing rate in the system, the NSGA-II algorithm is used. When the acetone droplet hits, owing to its low boiling point relative to the temperature of molten paraffin, the acetone evaporates, causing the creation of solidified paraffin area. The main goal of the current... 

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)... 

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... 

Nowadays, using Blockchain Technology (BCT) is growing faster in each country. It is essential to apply BCT in Supply Chain Network Design (SCND) and is considered by the designer and manager of SC. This research indicates Viable Supply Chain Network Design (VSCND) by applying BCT. A new form of two-stage robust optimization is suggested. Facility locations and activation BCT for VSCND is the first stage of decisions; finally, we determine flow transshipment between components in the next stage. The GAMS-CPLEX is used for solving the model. The results show that running BCT will decrease 0.99% in costs. There is an economic justification for using BCT when demand is high. A fix-and-optimize... 

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... 

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... 

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment.... 

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... 

In this paper, a new approach for multi-objective robust optimization of flutter velocity and maximum displacement of the wing tip are investigated. The wing is under the influence of bending–torsion coupling and its design variables have different levels of uncertainty. In designing and optimizing wings with a high aspect ratio, the optimization process can be done in such a way to increase the flutter velocity, but this can increase the amplitude of the wing tip displacement to a point that leads to the wings damage and structural failure. Therefore, single-objective design optimization may lead to infeasible designs. Thus, for multi-objective optimization, modeling is based on the... 

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... 

One of the basic requirements of the companies to survive in real-world competitive environments is to make their supply chains as efficient as possible. Due to recent governmental regulations, environmental issues, and the development of the concept of social responsibility, the closed-loop supply chain management has been focused by many researchers. A closed-loop supply chain includes both forward and reverse supply chain networks with the purpose of combining environmental considerations with the traditional supply chain network designs through the collection of used products and activities related to their reuse. In this paper, a bi-objective, multi-period, multi-product, closed-loop... 

The production routing problem (PRP) merges the lot-sizing problem and the vehicle routing problem, two classical problems that have been the focus of comprehensive studies for over half a century. Solving the PRP is an effort to optimize decisions about the production, inventory, distribution, and routing in an integrated manner. In the literature of the recent decade, due to economic changes and regulatory issues, reverse logistics has become a focal point. Subsequently, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) has drawn more and more attention for its considerable effect on the reverse logistics problem. In addition, one of the major arguments in supply... 

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...