جستجوی مقالات مرتبط با کلیدواژه « بهینه سازی چند هدفه » در نشریات گروه « عمران »

Transportation issues are categorized into three strategic, tactical, and operational levels, each of which has a different level of influence, required budget, decision makers, and time period. The issue of developing the rail transportation network is one of the key issues at the strategic level. In short, network design deals with the solution of allocating a limited budget to a feasible subset of the set of projects, in such a way that specific goals: such as minimizing the total travel time in the network, the developing costs of the network, maximizing revenue from freight transportation, or maximizing the attraction of freight demand to the rail mode should be taken into account. In this issue, two stakeholders are considered. On one side, the operators make the macro decisions to meet the criteria; such as maximization of benefit, maximization of travel coverage, minimization of development costs, minimization of casualties and minimization of total travel time. On the other side users who try to maximize their benefits such as finding the shortest route through the network.The general form of the network design problem is a two-level problem in the category of NP-hard problems, which is difficult to solve in even small scales. To solve this problem, the solution algorithms are classified into two general categories: exact and approximate. The exact solution algorithm give the best global solution among the possible solutions, they are so-called intractable in terms of memory usage and solution time with the increase in the size of the problem. Therefore, the second category of so-called approximate algorithms was presented to solve network design problem. Greedy algorithms are classified in the category of approximate algorithms. In the greedy algorithm, reaching the goal in each step is independent of the previous step. That is, at each step to reach the solution, regardless of what choices was made in the previous stages.In this article, the greedy algorithm is presented to solve the problem of network design trying to reduce network development costs. The proposed algorithm is designed to develop the blocks with priority of the lowest cost, and this process continues until the entire level of incoming demand can be transferred through the network. This algorithm is implemented with Java language and the railway of Iran is used as a case study. Considering the nature of two objectives in the problem, freight demand passing through and development cost in the network, "pseudo-pareto" solutions with different percentages of the importance of two mentioned objectives are discussed. The analysis has shown that with the increasing importance of the development cost, fewer blocks are developed and as a result, less demand is passed through the network. Also, with the increasing importance of freight demand, the algorithm leads to solutions that have caused extensive development in the network. The proposed greedy algorithm has a light computational load, and it achieves its solutions in less than 1 hour. Also, the algorithm is implemented for two demand levels of 70 million tons per year and 110 million tons per year and the results are analyzed.

Buckling-Restrained Braces (BRBs) are one of the new seismic resistant systems. The cross-sectional area and length of the BRB brace is one of the most important characteristics of these braces that directly affects the cost of BRB frames. Since beams, columns, and connections are designed for the maximum forces developed in BRB, the decrease in cross-sectional area of the BRBs decreases the steel consumption in the whole structure.The main purpose of this study is to optimize the weight of the structure, BRBs weight while uniforming the drift profile by changing the cross-sectional area and the length of the BRBs using genetic algorithms and other multi-objective optimization algorithms. Optimization is based on the results of nonlinear time history analysis under seven earthquake records using OpenSEES software. For this purpose, the objective function and constraints were defined in the genetic algorithm NSGA_II, MOPSO, MOEA_D, PESA_II, SPEA_II, and the initial population produced was entered as the initial cross-sectional area and length of the braces in the OpenSEES software. The optimization results show that for all three objective functions, the optimization values with high percentages of structural performance were optimized in such a way that the weight of BRB can be decreased up to about 50%.

Power distribution networks as the interface between the power transmission networks and micro/macro consumers, are among the most important components of electrical energy systems. The main task of these networks can be considered as reducing the voltage level and preparing electrical energy for delivery to consumers, which is done according to different structures and voltage levels. An instance of extensive and important research in this area is the rearrangement of distribution networks. One of the effective optimization methods in network rearrangement is the use of heuristic algorithms in order to find the best arrangement for the distribution network by considering various network parameters and constraints. In this paper, while modeling the problem of optimal rearrangement of power distribution networks with the aim of improving power quality, the solution is also presented using both the single and multi-objective variants of the particle swarm optimization (PSO) algorithm. The simulation results show successful performance and suitable efficiency for both methods of single-objective and multi-objective PSO optimization.

Population growth and development of economic relations have led to increase utilization of transportation infrastructures. The bridge is one of the important components of road construction that always needs special attention during the whole period of design, execution and operation. To ensure that the stairs are regularly inspected, evaluated and operated, management is needed to meet this need in the stairs by performing appropriate maintenance and repairs. Therefore, in this study, a three-objective optimization model includes minimization of maintenance costs, maximization of bridge performance index and reliability of bridges. For this purpose, a complex integer mathematical programming model has been developed. In order to solve the problem, a multi-objective particle mass optimization algorithm has been developed. From the solution of the proposed algorithm, different Pareto answers are obtained so that the decision makers can choose the answer from the obtained Pareto answers that is their priority.Also, after performing a sensitivity analysis on the parameters of failure rate and failure rate during the effect, it has been observed that by increasing the scenario number in two cases, which indicates an increase in failure rate and failure rate during the effect, the amount of costs increases and The overall performance of the system has been declining, which seems natural and correct.

The hub location is one of the most challenging subject in urban transportation issue that plays an essential role in decreasing both urban traffic and transportation costs. Since there is a cost variance and irregular changes among different places in a metropolis, transportation mangers must to choose one of two conflicting options: installing hubs on central place of a metropolis that leads into more expensive setup cost, less access time to other nodes, and probably more expensive outer shipment cost and vice versa. This study presents a bi-objective approach for capacitated planar hub location being composed of two conflicting objective function: the first including installing fix cost, inner and outer shipment cost and the second including summation of inner and outer travel time among nodes. Regarding continuous nature of this problem, a non-dominate solution set, cost and travel time, obtained by presented bi-objective approach thanks to which mangers are able to locate their hub network under installing cost related to urban coordinates in most sufficient manner is one of the most outstanding innovations of this research.  Capacitated hub as well as budget constraint for installing cost are other contributions. To evaluate solutions’ quality, we have used three algorithms, named epsilon-constraint, multi-objective genetic, and multi-objective particle swarms. Comparing pareto solutions to ones obtained through Gams-software in small size; demonstrating pareto solutions with the calculating properties; displaying and analyzing the topologies related to a certain pareto solution are other subject discussed by this research.

In this paper, we introduce the sustainable routing problem in a network with forward and reverse flows, in which different economic, environmental and social factors are considered in a bi-objective mixed integer linear programming mathematical model. The purpose of the problem is to design the service routes and determine the optimal speed of vehicles in such a way that, on the one hand, the amount of fuel consumed and, consequently, pollution caused by the transportation process are minimized, and on the other hand, in order to create satisfaction among drivers, the workload of different vehicles in terms of the duration of tour is balanced. A comprehensive function is used to estimate the amount of fuel consumed, in which the amount of fuel consumed is a function of the distance traveled as well as the speed, load, and technical characteristics of the vehicle. In order to solve the problem optimally, the augmented epsilon constraint method is used. Also, for solving large-scale instances, two multi-objective meta-heuristic algorithms based on genetic algorithm and fireworks algorithm have been developed. In order to increase the efficiency of these algorithms, a local search method is also used in their structure. The results of solving various examples represent a better performance of the fireworks algorithm. Also analysis of the pareto-front shows that with a one percent increase in fuel cost, the longest tour can be reduced by more than 20% and the difference between the running times of different machines is reduced by 15%. This difference can also be reduced by up to 25% by increasing fuel consumption by 3%.

Time-cost trade-off analysis is one of the most challenging tasks of construction project planners. Project planners face complicated multivariate, Time-Cost Optimization (TCO) problems, which require simultaneous minimization of total project duration and total project cost, while considering issues related to the optimal present value of profit. Also, the complexity of construction projects in recent years has risen up the importance of cleverly management in cases of project financing and scheduling. There are choices and limits that make it difficult to project planners to develop a proper financing plan considering project time status. Therefore, the methods of financing affect the project plan. Therefore, a skilled planner should consider various effective parameters for scheduling projects. This study presents a hybrid meta-heuristic algorithms to solve a multi-objective optimization problem in construction project planning and finance. First, the model is compared with common meta-heuristic algorithms in a simple case study. Then it is applied to a complex case study and it shows the optimal solutions which have time, cost and net present value of profit. It is shown that the proposed model is superior to the existing optimization algorithms to find better project planning solutions with less total project duration, less total project cost, and optimal profit in the construction project problems. The cumulative results are shown in a three-dimensional Pareto front. Also, the proposed model improves the solutions through generations and provide optimal solutions in an acceptable processing time.

With entering container into transportation area, this area experienced a huge change; however, the imbalance problem between input and output container in ports has been standing until now. This imbalance between import and export has made a problem called Empty Container, as the import and export ports are facing surplus and shortage of empty container. In this paper, to solve the desired problem, the empty containers transportation planning model with two goals, minimizing total costs including transportation, storage, shortage, loading and unloading cost of empty containers, and maximizing availability of empty container formulated by minimizing sum of the maximum shortage of empty containers at the export ports in all periods, has been developed. Then the proposed model was linearized, and with using weighting method was converted to a single objective of the minimizing costs. Finally, to verify the proposed model, a numerical example by considering a time-space network of Iranian maritime ports has been solved applying the Lp-metrics method, using GAMs software. The computational results indicates the efficiency of the proposed model in making the problem balance, reducing the total cost with 66% compared to the real world, and optimality of set targets.

With globalization and the massive growth in consumption worldwide, supply chain networks have changed to very large networks and the components of these large networks caused serious environmental problems. In traditional supply chain network design, optimization models are mostly single-objective and the aim is to minimize the cost of the network. In this paper, a new two-objective mixed integer linear model for closed-loop supply chain network design has been proposed that consider both forward and reverse flow to avoid sub- optimality. The proposed model takes all aspects of possible carbon dioxide emissions in the proposed supply chain network and considers a fair and reasonable balance between economic and environmental objectives are established. To illustrate the trade-off between the goals, epsilon constraint method has been used. To demonstrate the effectiveness and applicability of the proposed model, this study utilizes data owned from a case study of a copying industry closed loop supply chain. This paper can help managers improve the environmental performance of green logistics in the supply chain as a supplement strategy for the benefit of sustainable competitive advantage.

The use of Pareto dominance for evaluating the solutions has been the mainstream in evolutionary multi objective optimization for the last two decades. An alternative is multi objective evolutionary algorithm based on decomposition (MOEA/D) which uses scalarizing the objective functions. In this paper, decomposition strategies are developed for water distribution network (WDN) design problems by integrating the concepts of genetic algorithm (GA) within the MOEA/D framework. The proposed algorithms are then compared with the two well-known non-dominance based MOEAs: NSGA2, and SPEA2. This comparison is made by plotting the Pareto fronts and evaluating the hyper-volume and two-set coverage metrics across two large scale WDN design problems. Experimental results show that MOEA/D outperform the Pareto dominance methods in terms of both non-domination and diversity criteria. It suggests that decomposition based multi objective evolutionary algorithms are very promising in dealing with real-world water engineering optimization problems.

In the rail fleet Sizing problem, it is aimed to determine the optimal number of each vehicle type in transportation fleet, while optimizing system objectives. This issue has been investigated as a single-objective problem in the context of rail transportation with the assumption of homogeneity in the fleet, despite the existence of different goals in real systems. In this paper, a dual objective function is adopted after studying real-world consideration. According to the experts at the IslamicRepublic of Iran Railway Company (IRIR), reduction of the number of delays in response to requests during the course of planning is an important additional objective function, which is included in our model. The solved problem is dynamic and demands for wagons and the transportation times are assumed to be deterministic. In this paper, after presenting the mathematical model, the importance factor of each objective function is calculated taking into account theprescriptions of the experts at the IRIR Research Center and based on aggregate weighting method. In order to solve the model and find the solutions on the Pareto front, three methods based on Genetic Algorithms, Simulated Annealing, and a hybrid of those are designed and their parameters tuned, by which the rail transportation system of the IRIR is solved and the results are discussed.

Nowadays, rapid growth of cities and other factors like climate change motivate to apply adaptive water resource management. In this paper, the case of Tabriz urban water is studied. Tabriz, one of the large cities of Iran, needs a reliable water supply. Two transfer lines from Zarrinehroud and Nahand reservoirs and groundwater are the main resources for the city. The leak detection and network rehabilitation is also assumed as a potential water supply resource. In this research, water supplying for the city is studied up to the year 2023 considering population growth and change in consumption pattern. Three conflicting objectives are considered as minimizing the cost of water supply, minimizing the environmental externalities, and maximizing the water supply per capita. In the first function, the cost of supplying one cubic meter is the sum of the multiplication of the amount of water from the groundwater, surface waters and leak detection. In environmental objective, the problem of Urmia Lake is considered. This important ecosystem is now drying because of various factors among them water diversion from recharging rivers. Then water transfers to city should be reduced. In the case of third objective, the social attitude on having sufficient water for now and next generations is considered. Then three main objectives of integrated water resources management are considered in this approach. To aggregate the three different objectives, the compromise programming approach is used. In this method, firstly the inputs are normalized. Then they are weighted based on the importance of the objectives and finally they are scaled based on their amounts. If the risk is important for the decision maker larger, values get more importance and smaller values find less magnitude. This approach has found high attention in multi objective optimization especially in water resources management. Reliability is proposed as a system performance measure and then it is evaluated as a performance function. Improved Particle Swarm Optimization is used for solving the model and its performance is then compared with the basic PSO. Outcomes show that adding the mutation operator to the basic algorithm has improved its performance. Existing resources will be enough just up to 2021, according to the system reliability level and after this year new resources will be needed. In addition, the sensitivity of system to the failure of water supply resources is analyzed, showing high sensitivity for transfers from Zarrinehroud. As suggestions for further studies, the improved PSO approach could be compared with other evolutionary multi objective methods like genetic algorithms. In addition, rather than obtaining aggregated multi objective, the Pareto frontier may be attained which provides a space in which trade off among the objectives is visible. This helps the decision maker to select more preferred option. It is desired for selecting better actions which is urgently needed for robust water management. In addition, using stochastic simulation on the system to find the reliability of water resources in this city is under study.