multi-objective optimization

The use of rubber powder and carbon powder additives in pavement, despite its technical and environmental advantages, faces limitations that require optimization of their usage percentage. The aim of this research is to optimize the mechanical properties of asphalt in order to increase the lifespan and quality of the pavement and reduce maintenance costs based on laboratory results. The input variables include the percentage of carbon powder and rubber powder, and the output variables include the modulus of elasticity, moisture resistance, fracture energy, rutting depth, and asphalt cumulative strains. The mathematical process model for each asphalt mechanical characteristic was determined using response surface method (RSM) and its accuracy was checked by variance analysis. The results showed that the statistical analysis of RSM is highly accurate and the linear variables of CRM and ACP have a significant effect on the mechanical and functional behavior of asphalt. The optimal conditions for maximizing the mechanical characteristics of asphalt were proposed as ACP equal to 44.4% and CRM equal to 14.9%. The comparison of the optimized and minimum laboratory conditions showed that the use of the optimal combination of rubber powder and carbon powder, respectively 9%, 53% and 69% improvement in moisture resistance, fracture energy and modulus of elasticity, as well as 48% and 44% reduction in the depth of rutting. and creates plastic strain. In general, the results of this research show that the optimization of fracture energy, modulus of elasticity and moisture resistance, along with the reduction of rutting, can lead to an increase in the quality and lifespan of the pavement and a reduction in maintenance costs.

Considering the variety of urban buildings from the point of view of regularity and irregularity, it is important to examine their life cycle cost (LCC); this issue has not yet been fully explored in previous research. On the one hand, the lack of utilization of building information modeling (BIM) in previous structural design-based LCC research is evident. This research aims to highlight the impact of irregularity on the LCC of structures by providing an integrated framework based on the seismic design optimization of structures by using LCC and BIM capacities. For this, a shared environment is created in MATLAB software, information is exchanged between Revit, Etabs, and Excel software, and optimization is done using NSGA-II for establishing a trade-off between initial cost and LCC. BIM tools can greatly reduce the limitations of LCC analysis, such as information exchange time, and increase the accuracy and speed of calculations. By modeling six models in two regular and irregular types, the framework of the research and the difference in the behavior of the structures are examined. The results showed that the indirect costs of irregular structures are more than the regular ones. In addition, the findings show that reducing the LCC of irregular structures compared to regular ones requires a higher initial cost percentage. For example, for regular and irregular 13-story structures, a 17% increase in the initial cost leads to a 48% and 40% reduction in their LCC, respectively.

Considering that dams are one of the most important water storage resources in water projects, therefore, proper construction and operation management of them is one of the most important responsibilities of water resource managers. One of the important parts in the construction of any dam is the design of the dam spillway, which is responsible for discharging excess water from the reservoir to downstream in case of floods. Destruction or inefficiency of spillways causes severe damage to the dam or even causes its complete destruction, that is why the design and construction of this part of the dam is very important. Among different types of spillways, stepped spillways, in addition to passing excess water from the reservoir of dams, are also a structure that consumes flow energy. Until now, in the studies carried out in this field, the optimization of stepped spillways has been considered as a single objective, which usually aims to optimize, minimize construction costs or maximize energy consumption in these spillways, and rarely optimize the dimensions of spillways with Taking into account the two factors of cost and energy consumption has been considered simultaneously. Therefore, in this study, for the first time, a multi-objective optimization model has been developed by benefiting from the capabilities of the improved version of the dragonfly optimization algorithm for the design of stair overflows with the aim of minimizing the volume of concrete used and increasing energy consumption.

In the design of the stepped spillway, the discharge parameters (Q) and the horizontal length of the overflow (L) are among the most important input parameters, which are determined based on the initial studies of topography and hydrology and are considered constant during the design. According to the purpose of the present research, an optimal combination of effective parameters in the design of a stepped spillway, including slope, width, height of the spillway, water pressure on the spillway, and step height, should be determined in a way that leads to the highest energy consumption and the lowest design costs with By-law restrictions should be taken into account. In the current research, the objective function, which consists of decision variables, should simultaneously provide the maximum amount of energy consumption and the minimum volume of consumed concrete, while satisfying the constraints and hydraulic conditions of the design problem. For this purpose, the dragonfly optimization algorithm, which is one of the powerful algorithms of collective intelligence methods, and was first proposed by Mirjalili.This algorithm is inspired by the group and social behavior of dragonflies in nature. Dragonflies usually live alone in the wild and only act in groups for hunting and migration. The basis of the dragonfly optimization algorithm to find the optimal answer is their two crowding behaviors. In this way, based on the static behavior, the members of the algorithm population are divided into smaller groups and search the problem space, then based on their dynamic behavior (exploitation phase), they determine the optimal answer with more members.

The spillway of Sarouq Dam, located in West Azarbaijan province, was selected as a case study in this research to evaluate the developed model. In the first part, the problem of optimal design of the stepped spillway of Sarouk Dam was investigated as a single-objective problem with the aim of maximizing the amount of energy consumption. The purpose of redesigning Sarouk dam spillway was to provide an optimal design for a stepped spillway instead of the existing flat spillway with the aim of maximizing energy consumption downstream of the dam. In all the optimal models provided by IDA, the consumption of water energy has increased significantly. According to the report of Hosseini, 2019, the amount of energy consumption at the foot of the existing spillway of Sarouk Dam is equal to 23.41, which the use of the stepped spillway in the present research increases it for the flow rates of 560.2, 776.9, and 422 cubic meters per second, respectively. 71.32, 65.72 and 74.74 percent. According to the main goal of this article, in this section, the new and improved algorithm based on the dragonfly algorithm has been used to optimize the multi-objective overflow of Sarouk Dam by considering the amount of concrete pouring and the amount of energy consumption as objective functions. The results shown that, using the multi-objective optimization approach for the design of stair overflows leads to the presentation of different types of designs with different implementation costs and energy consumption rates. Therefore, using the multi-objective optimization approach has led to the production of many answers, which allows the employer to design the overflow according to the budget he has.

In this research, using the improvement of a new metaheuristic algorithm called the dragonfly algorithm, a single-objective model was developed with the aim of maximizing energy consumption, and another multi-objective model was developed with the objectives of minimizing the construction costs of the stair overflow. The developed models were used for the optimal design of Sarouk Dam spillway. The use of the improved Dragonfly multi-objective optimization algorithm to minimize construction costs and increase energy consumption showed that the multi-objective optimization approach can provide a set of answers that is a great help to the employer in order to achieve an economic plan based on the budget. available

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%.

The main purpose of this paper was to provide a mathematical model of the production routing problem by applying environmental protection policies. The target company consists of two independent departments of production and distribution. These subsets are managed locally, and the two-way communication between them forms a Stackelberg game. The first subset (distribution company) is the first level decision maker and determines the route for vehicles and the amount of product transfer to each customer. The distribution company pursues two goals, minimizing costs of distribution, maintenance, and vehicle emissions. As the limited production capacity prevents the manufacturer from meeting all the demands of the distribution sector, the distribution company can procure products from the subsidiary manufacturing company or provide alternative products by paying more from other manufacturers, so in this case, it will receive compensation from the manufacturer. At a lower level, the second subset (manufacturer) schedules production with the aim of minimizing production and maintenance costs. Finally, the multi-objective bi-level problem-solving algorithm is described and developed based on the bi-level fuzzy goal programming approach. Based on numerical analysis results, the utility of final decisions regarding both the distributor and manufacturer perspective is sensitive to the cost of alternative products and also compensation. In order to improve the agreement between the first and second level decision-makers, the proposed algorithm considers tolerances that can be adjusted to achieve more executive plans. Numerical results show that the answers of the bi-level fuzzy goal programming method are equal or very close to the worst answer.

Rutting is one of the major deteriorations of asphalt pavement significantly impact on road safety and service quality. Forecasting models are necessary to prevent and control the damage caused by this deterioration in the pavement management system. In this study, using the artificial neural network algorithm, models have been developed to predict the amount of rutting deterioration using the long-term pavement performance (LTPP) database. These models have been developed for wet freeze, dry freeze, and dry no freeze climates. Since proper accuracy and simplicity are the most important features of a prediction model, using the NSGA ІІ-MLP multi-objective optimization method, the more important variables in predicting rutting deterioration are identified and selected as the model input. Then, using traffic, climatic and structural variables selected from the genetic algorithm, rutting deterioration prediction models were developed. The coefficient of determination and the mean squared error for the model made in the wet freeze zone and the common model of dry freeze and dry no freeze zones are equal to 0. 96, 2.05, 0.94 and 3.45, respectively. Also, by performing sensitivity analysis, the effect of input data of each model on rutting deterioration was determined. Among the variables that have the greatest impact on rutting deterioration are the cumulative maximum and minimum daily temperature difference per year, pavement age, asphalt layer thickness, annual equivalent single axle loads, and bitumen penetration.

In the oscillation traffic, various factors could affect a driver’s decision to choose a different speed and decrease or increase acceleration based on different driver’s patterns of behavior. In the present study, different behavioral patterns (aggressive and cautious) is analyzed in the oscillation traffic, with regard to three mental behavior of driver (safety, comfort, and travel time). In this regard, a structured methodology was proposed to quantify the indices’ weights including a multi-objective optimization problem in which the indices were the model’s objective functions. Then, a driver’s behavior was evaluated and analyzed using a neural network. The results of evaluating the driver's behavioral performance showed that the aggressive behavioral performance in the deceleration (acceleration) phase with respect to comfort index based on changes, increasing (increasing) acceleration and safety index based on changes, a decreasing-increasing (decreasing) acceleration and travel time index based on changes is determined by decreasing (increasing) acceleration. Finding also revealed The results of evaluating the timid behavioral performance show that the timid driver behavior in the deceleration (acceleration) phase with respect to comfort index based on changes, increasing - decreasing (increasing) acceleration and safety index based on changes, increasing ( decreasing-increasing) acceleration and travel time index based on changes is determined by decreasing (increasing) acceleration.

One of the important issues in supply chain management, vehicle routing and scheduling problem has always been which has so far attracted considerable attention from researchers. This paper attempts to optimize routing and scheduling for a heterogeneous transportation system with considering stochastic supply and demand, various traffic conditions, maximum continuous driving time constraint and soft time window constraint of each customer delivery. The consideration of pollution in routing decisions gives rise to a new routing framework where measures of the environmental implications are traded off with business performance measures. Also, the aim of this study is to minimize energy consumption and to maximize customer satisfaction, simultaneously. At first, this problem is presented in the form of a multi-objective linear programming model and then a novel integrated approach is proposed based on combination of chance constraint method and goal programming. Finally, the results of numerical example are analyzed with sensitivity analysis.
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Today, information and knowledge are so intertwined with human life that the present age has been called the information era. In this situation, the role of human in the organization and the way of looking at him/her will have a significant contribution to the success or failure of the organization. The role of human resources is becoming more and more prominent and it is now mentioned as the only factor in gaining a sustainable competitive advantage. Furthermore, due to dramatic developments in the field of human resource management, competency-based human resource planning has found a special place. In this study, a project scheduling problem considering multi-skilled resource and manpower competency is investigated. To do this end, an integrated multi-objective optimization model of project scheduling and human resource assignment including total completion time and execution cost minimization and project quality maximization is presented. To solve problem exactly, a mixed-integer programming model is developed and then solved by ε-constraint method in GAMS software. As the proposed model is NP-hard, a non-dominated sorting genetic (NSGA-II) algorithm is developed to solve the large scale problem. Furthermore, in order to validate the proposed algorithm, its results compare with exact method for instance problem in small scale. Results indicate that the proposed algorithm is outperformed and it can be used for real case problems.

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.