Sharif Digital Repository

Decision on the location of new wells through infill drilling projects is a complex problem that depends on the reservoir rock and fluid properties, well and surface facilities specifications, and economic measures. Conventional approach to address this is a direct optimization that uses the numerical flow simulation. However, this is computationally very extensive. In this study the authors use a hybrid genetic algorithm (HGA) optimization technique based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. This hybridization introduces hill-climbing into the stochastic search and makes use of proxies created and calibrated iteratively... 

A novel bi-objective multi-server location-allocation (LA) model is developed in this paper, in which the facilities are modeled as an M/M/m queuing system. Further, capacity and budget limitations are provided to make the LA problem more realistic. The two objective functions include (1) minimizing aggregate waiting times and (2) minimizing the maximum idle time of all facilities. Since the proposed model is NP-hard, a meta-heuristic algorithm called multi-objective harmony search algorithm (MOHA) is developed to solve it. In this algorithm, a new presentation scheme that satisfies most of the model constraints is proposed. Since there is no benchmark available in the literature to validate... 

The practicality of robust model predictive control of systems with model uncertainties depends on the time consumed for solving a defined optimization problem. This paper presents a method for the computational complexity reduction in a robust model predictive control. First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities. The simulation results show that the number of iterations to solve the problem... 

A novel and robust approach is proposed to find the optimum shape of concrete arch dams located on any unsymmetrical shape of a valley. The approach is capable of finding the optimum shape for any given valley type in suitable time, based on abutment stability analysis, against thrust forces from an arch dam. The behavior and stability of a concrete arch dam is strongly dependent on the bedrock on which the dam rests. The stability of the abutment is considered a constraint in the proposed approach. In addition, a new objective function is introduced to decrease the final volume of the arch dam. Furthermore, a computer program was developed, which takes the effect of the dam foundation... 

In this work a microgrid is established within the geographical perspective of interest. The characteristics of the grid and its urban are employed to determine the candidate points for establishment of EV charging stations. The investment costs and operation costs are calculated for each station realizing the required constraints. This process resulted in optimum selection of EV stations. The optimization is achieved based on two different types of preliminary assumptions. In the first one, it is assumed to have only one station, while in the second one possibility of having multi-stations is considered. Using the total investment and operation costs as the objective function of... 

A dictionary learning problem is a matrix factorization in which the goal is to factorize a training data matrix, Y, as the product of a dictionary, D, and a sparse coefficient matrix, X, as follows, Y ≈ DX. Current dictionary learning algorithms minimize the representation error subject to a constraint on D (usually having unit column-norms) and sparseness of X. The resulting problem is not convex with respect to the pair (D, X). In this letter, we derive a first order series expansion formula for the factorization, DX. The resulting objective function is jointly convex with respect to D and X. We simply solve the resulting problem using alternating minimization and apply some of the... 

We propose novel techniques to find the optimal location, size, and power factor of distributed generation (DG) to achieve the maximum loss reduction for distribution networks. Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation. Based on the network's total load demand, four DG sizes are selected. They are used to form energy loss curves for each bus and then for determining the optimal DG options. The study shows that by defining the energy loss minimization as the objective function, the time-varying load demand significantly affects the sizing of DG resources in... 

This paper presents a new Compressive Sensing (CS) scheme for detecting network congested links. We focus on decreasing the required number of measurements to detect all congested links in the context of network tomography. We have expanded the LASSO objective function by adding a new term corresponding to the prior knowledge based on the relationship between the congested links and the corresponding link Betweenness Centrality (BC). The accuracy of the proposed model is verified by simulations on two real datasets. The results demonstrate that our model outperformed the state-of-the-art CS based method with significant improvements in terms of F-Score  

The objective of this study is to design sub-optimal signature matrices for binary inputs for an overloaded code division multiple access (CDMA) system as developed by this author group. In this study, the authors propose to use the sum capacity, the bit error rate and distance criteria as objective functions for signature matrix optimisation. Three optimisation techniques, the genetic algorithm, the particle swarm optimisation and the conjugate gradient (CG) are exploited in this work. Since the optimisation computational complexity increases by matrix dimensions, it is practically impossible to directly optimise the large signature matrices. In order to address this problem, a method is... 

This paper presents a biomechanical model with seven degrees of freedom as a seated human exposed to vertical vibration. Experimental data is used to find the stiffness and damping parameters of the model. The data includes values of seat to head transmissibility, driving point mechanical impedance and apparent mass. The multi-objective function is used to obtain theoretical results similar to three different experimental cases. The unknown coefficients are calculated by a genetic algorithm. Improved results, in comparison with previous models, are achieved from the presented model. Next, this modified model is assembled on a quarter car and parameters of the cushion and the suspension... 

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic... 

A genetic-based pore network extraction method from micro-computed tomography (micro-CT) images is proposed in this paper. Several variables such as the number, radius and location of pores, the coordination number, as well as the radius and length of the throats are used herein as the optimization parameters. Two approaches to generate the pore network structure are presented. Unlike previous algorithms, the presented approaches are directly based on minimizing the error between the extracted network and the real porous medium. This leads to the generation of more accurate results while reducing required computational memories. Two different objective functions are used in building the... 

The bulk of the worldwide annual commercial production of ethylene is based on thermal cracking of petroleum hydrocarbons with steam. In this research, the effect of coil outlet temperature on the reactor yield has been studied. In order to investigate a reliable mathematical correlation, several data points were obtained by adjusting coil outlet temperature in a real plant. In order to investigate best coil outlet temperature, an objective function was represented. Based on the obtained mathematical correlation and the assumed objective function, it was found that best coil outlet temperature for investigating maximum income is 1,128 K  

Decision on the location of new wells through infill drilling projects is a complex problem that depends on the reservoir rock and fluid properties, well and surface facilities specifications, and economic measures. Conventional approach to address this is a direct optimization that uses the numerical flow simulation. However, this is computationally very extensive. In this study the authors use a hybrid genetic algorithm (HGA) optimization technique based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. This hybridization introduces hill-climbing into the stochastic search and makes use of proxies created and calibrated iteratively... 

This research deals with a flexible flowshop scheduling problem with the arrival and delivery of jobs in groups and processing them individually. Each group of jobs has a specific release time. Due to the special characteristics of each job, only a specific group of machines in each stage are eligible to process that job. All jobs in a group should be delivered at the same time after processing. The objectives of the problem are the minimization of the sum of the completion time of groups on one hand and the minimization of sum of the differences between the completion time of jobs and the delivery time of the group containing that job (waiting period) on the other hand. The problem can be... 

This paper proposes a new algorithm to determine the best connection of multiple distributed generations (DGs) on distribution networks according to type of customers. The considered DG technology is combined heat and power (CHP) in various sizes which can be extended to other technologies. Different types of demand, such as industrial, residential and commercial are defined by the load model that is voltage dependent and the load patterns from real life hourly demand data. This method which is based on multi-year multi-period power flow is useful for Distribution Network Operators (DNOs) when they are faced with voltage dependent load models in their networks. However it can be used in... 

In this study, a Discrete Particle Swarm Optimization (DPSO) algorithm is assimilated to solve the Transit Network Design Problem (TNDP). First, A Mixed Integer Model is developed for the TNDP. The solution methodology utilized here is made of two major elements. A route generation module is firstly developed to generate all the feasible transit lines. Through the second part, a DPSO algorithm is utilized to select the optimal set of lines from the constructed ones. The objective function is to maximize coverage index while satisfying the operator cost upper level constraints. The efficacy and accuracy of the implemented algorithms is compared with ones obtained by an enumeration process as... 

Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near-optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet... 

Purpose - The purpose of this paper is to analyze the effects of supply chain coordination on inventory management while the retailer inventory cycle consists of a shortage period and the backorder rate linearly decreases as a function of shortage duration. It is intended to consider how on-hand inventory and shortage durations are altered when the decisions are centralized. Design/methodology/approach - Mathematical modelling of inventory costs for the retailer and the vendor is used to formulate objective functions. The vendor sets his inventory period as an integer multiple (n) of the retailer inventory cycle in which the integer multiple is a decision variable. Solution spaces of models... 

This paper presents a dynamic multi objective model for distribution network expansion, considering the distributed generators (DGs) and network reinforcements. The proposed model simultaneously optimizes three objective functions namely, total cost, emission cost and technical satisfaction (voltage profile) by finding the optimal schemes of timing, sizing, placement and DG technologies in a long term planning period (dynamic planning). The importance of each objective function can be changed in the interactive steps. The calculation algorithm is based on Chaotic Local Search with Modified Honey Bee Mating Optimization (CLSMHBMO). The effectiveness of the proposed model and the calculation...