Search published articles


Showing 9 results for Optimization Model

Saffar Zadeh M., Karbasi Zadeh B.,
Volume 2, Issue 1 (3-2004)
Abstract

In this paper, optimal bridge management system models have been presented. These optimization models are capable of allocating limited resources to the bridge preservation schemes in order to establish the optimal time of completing the activities. Bridge-based activities are divided into two main groups: repair projects, and maintenance activities and both models are presented in this paper. Particular attention has been made to optimize the management of the two system activities. The dynamic programming approach was utilized to formulate and analyze the two models. The developed models are found to be more accurate and faster than the previous ones.
M.h. Afshar, R. Rajabpour,
Volume 5, Issue 4 (12-2007)
Abstract

This paper presents a relatively new management model for the optimal design and operation of irrigation water pumping systems. The model makes use of the newly introduced particle swarm optimization algorithm. A two step optimization model is developed and solved with the particle swarm optimization method. The model first carries out an exhaustive enumeration search for all feasible sets of pump combinations able to cope with a given demand curve over the required period. The particle swarm optimization algorithm is then called in to search for optimal operation of each set. Having solved the operation problem of all feasible sets, one can calculate the total cost of operation and depreciation of initial investment for all the sets and the optimal set and the corresponding operating policy is determined. The proposed model is applied to the design and operation of a real-world irrigation pumping system and the results are presented and compared with those of a genetic algorithm. The results indicate that the proposed mode in conjunction with the particle swarm optimization algorithm is a versatile management model for the design and operation of real-world irrigation pumping systems.
K. Behzadian, M. Alimohammadnejad, A. Ardeshir, H. Vasheghani, F. Jalilsani,
Volume 10, Issue 1 (3-2012)
Abstract

Compared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost

solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points

of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic

disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase

approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized

in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the

location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate

levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified

number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the

second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to

minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels

of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of

chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective

genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different

numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can

explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the tradeoff

between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels.


A. Shariat Mohaymany, M. Babaei,
Volume 11, Issue 1 (3-2013)
Abstract

Since the 1990’s, network reliability has been considered as a new index for evaluating transportation networks under uncertainty. A large number of studies have been revealed in the literature in this field, which are mostly dedicated to developing relevant measures that can be utilized for the evaluation of vulnerable networks under different sources of uncertainty, such as daily traffic flow fluctuations, natural disasters, weather conditions, and so fourth. This paper addresses the resource allocation problem in vulnerable transportation networks, in which multiple performance reliability measures should be met at their desired levels, while the overall cost of upgrading links’ performances should be minimized simultaneously. For this purpose, a new approach has been considered to formulate the two well-known performance measures, connectivity and capacity reliability, along with their application in a bi-objective nonlinear mixed integer goal programming model. In order to take into account the uncertain conditions of supply, links’ capacities have been assumed to be random variables and follow normal distribution functions. A computationally efficient method has been developed that allows calculating the network-wise performance indices simply by means of a set of functions of links’ performance reliabilities. Using this approach, as the performance reliability of links are themselves functions of the random links’ capacities, they can be simply calculated through numerical integration. To achieve desirable levels for both connectivity reliability and capacity reliability (as network-wise performance reliability measures) two distinct objectives have been considered. One of the objectives seeks to maximize each of the measures regardless of what is happening to the other objective function which minimizes the budget. Since optimization models with two conflicting objectives cannot be solved directly, the well-known goal attainment multi-objective decision-making (MODM) approach has been adapted to formulate the model as a single objective model. Then the resultant single objective model has been solved through the generalized gradient method, which is a straightforward solution algorithm coded in existing commercial software such as MATLAB programming software. To show the applicability of the proposed model, numerical results are provided for a simple network. Also, to show the sensitiveness of the model to decision maker’s direction weights, the results of sensitivity analysis are presented..
Zh. Zhang, J. Xu,
Volume 11, Issue 1 (3-2013)
Abstract

To improve the construction efficiency of the Longtan Hydropower Project, this paper studies the multi-mode resourceconstrained project scheduling problem in its Drilling Grouting Construction Project. A multiple objective decision making model with bi-random coefficients is first proposed for this practical problem to cope with hybrid uncertain environment where twofold randomness exists. Subsequently, to deal with the uncertainties, the chance constraint operator is introduced and the equivalent crisp model is derived. Furthermore, the particular nature of our model motivates us to develop particle swarm ptimization algorithm for the equivalent crisp model. Finally, the results generated by computer highlight the performances of the proposed model and algorithm in solving large-scale practical problems.
S. Soudmand, M. Ghatee, S. M. Hashemi,
Volume 11, Issue 4 (12-2013)
Abstract

This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming problem. The objective function of the upper level problem is to minimize the difference between current LOS and desired LOS of links. In this level, to find optimal toll, a simulated annealing algorithm is used. The lower level problem is a fuzzy flow estimator model with fuzzy link costs. Applying a famous defuzzification function, a real-valued multi-commodity flow problem can be obtained. Then a polynomial time interior point algorithm is proposed to find the optimal solution regarding to the estimated flows. In pricing process, by imposing cost on some links with LOS F or E, users incline to use other links with better LOS and less cost. During the iteration of SA algorithm, the LOS of a lot of links gradually closes to their desired values and so the algorithm decreases the number of links with LOS worse than desirable LOS. Sioux Falls network is considered to illustrate the performance of SA-IP method on congestion pricing based on different LOS. In this pilot, after toll pricing, the number of links with LOS D, E and F are reduced and LOS of a great number of links becomes C. Also the value of objective function improves 65.97% after toll pricing process. It is shown optimal toll for considerable network is 5 dollar and by imposing higher toll, objective function will be worse.
Mohammad Tamannaei, Mahmoud Saffarzadeh, Amin Jamili, Seyedehsan Seyedabrishami,
Volume 14, Issue 3 (4-2016)
Abstract

This paper presents a novel approach to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach restricts the effects of an incident to a specific time, based on which the trains are divided into rescheduled and unchanged ones, so that the latter retain their original time-table after the incident. The main contribution of this approach is the simultaneous consideration of three rescheduling policies: cancelling, delaying and re-ordering. A mixed-integer optimization model is developed to find optimal conflict-free time-table compatible with the proposed approach. The objective function minimizes two cost parts: the cost of deviation from the primary time-table and the cost of train cancellation. The model is solved by CPLEX 11 software which automatically generates the optimal solution of a problem. Also, a meta-heuristic solution method based on simulated annealing algorithm is proposed for tackling the large-scale problems. The results of an experimental analysis on two double-track railways of the Iranian network show an appropriate capability of the model and solution method for handling the simultaneous train rescheduling. The results indicate that the proposed solution method can provide good solutions in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software.


Xiaoling Song, Jiuping Xu, Charles Shen, Feniosky Peña-Mora,
Volume 15, Issue 2 (3-2017)
Abstract

The construction temporary facilities layout planning (CTFLP) requires an identification of necessary construction temporary facilities (CTFs), an identification of candidate locations and a layout of CTFs at candidate locations. The CTFLP is particularly difficult and complex in large-scale construction projects as it affects the overall operation safety and effectiveness. This study proposes a decision making system to decide on an appropriate CTFLP in large-scale construction projects (e.g. dams and power plants) in a comprehensive way. The system is composed of the input, CTF identification, candidate location identification, layout optimization, evaluation and selection, as well as output stages. The fuzzy logic is employed to address the uncertain factors in real-world situations. In the input stage, the knowledge bases for identifying CTFs and candidate locations are determined. Then, CTFs and candidate locations are identified in the following two stages. In the mathematical optimization stage, a multiobjective mathematical optimization model with fuzzy parameters is established and fuzzy simulation-based Genetic Algorithm is proposed to obtain alternative CTFLPs. The intuitionistic fuzzy TOPSIS method is used to evaluate and select the most satisfactory CTFLP, which is output in the last stage. To demonstrate the effectiveness and efficacy of the proposed method, the CTFLP for the construction of a large-scale hydropower dam project is used as a practical application. The results show that the proposed system can assist the contractor to obtain an appropriate CTFLP in a more efficient and effective manner.


Laemthong Laokhongthavorn, Chalida U-Tapao,
Volume 15, Issue 2 (3-2017)
Abstract

This paper has applied operation research to solid waste disposal by which two objective functions are optimized to minimize the expected operational costs (maximize revenues) and the expected net carbon dioxide equivalent (CDE) emissions. Types and uncertain amounts of solid wastes as well as costs of electricity were factored into the selection decision of solid waste disposal, i.e. landfill, incineration, composting and recycling. An optimization model was applied to the solid waste disposal of Bangkok, Thailand. In addition, a multi-objective optimization technique was proposed for a tradeoff decision-making between minimum operational costs and CDE emissions. Composting and landfill are effective alternatives for Bangkok’s solid waste disposal system. The operational costs and net CDE emissions are highly correlated with the quantity of solid waste. Policy-makers and plant operators could adopt the proposed optimization model under uncertainty in the selection of an optimal solid waste disposal.



Page 1 from 1     

© 2020 All Rights Reserved | International Journal of Civil Engineering

Designed & Developed by : Yektaweb