scholarly journals Designing a bi-objective decision support model for the disaster management

2021 ◽  
Author(s):  
Sina Nayeri ◽  
Ebrahim Asadi-Gangraj ◽  
Saeed Emami ◽  
Javad Rezaeian
Keyword(s):  
Objective Function ◽  
Disaster Management ◽  
Performance Metrics ◽  
Pareto Frontier ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Test Problems ◽  
Decision Support Model ◽  
Nsga Ii ◽  
The Impact

This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a Bi-Objective Mixed Integer Programming (BOMIP) model is proposed to assign and schedule of the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents and the second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to more similar to the real-world, time-windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position on the corresponding relief team and fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the optimal solution in the reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of  the proposed algorithms are compared with respect to different performance metrics in large size test problems. Finally, in order to investigate the impact of some parameters on the Pareto frontier, the sensitivity analysis and the managerial suggestions are provided.

scholarly journals Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

scholarly journals Improvement of Daylight Factor Model for Window Size Optimization and Energy Efficient Building Envelope Designs

2021 ◽  
Vol 8 (2) ◽  
pp. 204-221
Author(s):  
Chahrazed Mebarki ◽  
◽  
Essaid Djakab ◽  
Abderrahmane Mejedoub Mokhtari ◽  
Youssef Amrane ◽  
...  
Keyword(s):  
Energy Demand ◽  
Research Work ◽  
Optimal Solution ◽  
Window Size ◽  
Building Envelope ◽  
Nsga Ii ◽  
New Approach ◽  
Building Model ◽  
Heating And Cooling ◽  
The Impact

Based on a new approach for the prediction of the Daylight Factor (DF), using existing empirical models, this research work presents an optimization of window size and daylight provided by the glazed apertures component for a building located in a hot and dry climate. The new approach aims to improve the DF model, considering new parameters for daylight prediction such as the orientation, sky conditions, daytime, and the geographic location of the building to fill in all the missing points that the standard DF, defined for an overcast sky, presents. The enhanced DF model is considered for the optimization of window size based on Non dominated Sorting Genetic Algorithm (NSGA II), for heating and cooling season, taking into account the impact of glazing type, space reflectance and artificial lighting installation. Results of heating and cooling demand are compared to a recommended building model for hot and dry climate with 10% Window to Wall Ratio (WWR) for single glazing. The optimal building model is then validated using a dynamic convective heat transfer simulation. As a result, a reduction of 48% in energy demand and 21.5% in CO2 emissions can be achieved. The present approach provides architects and engineers with a more accurate daylight prediction model considering the effect of several parameters simultaneously. The new proposed approach, via the improved DF model, gives an optimal solution for window design to minimize building energy demand while improving the indoor comfort parameters.

scholarly journals Channel Optimization of Marketing Based on Users’ Social Network Information

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chaolin Peng
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Information Technologies ◽  
Information Dissemination ◽  
Product Information ◽  
Main Idea ◽  
Optimal Solution ◽  
User Interest ◽  
Nsga Ii ◽  
The Impact

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence (MOIM) model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions.

scholarly journals A COMPARISON BETWEEN MILP AND MINLP APPROACHES TO OPTIMAL SOLUTION OF NONLINEAR DISCRETE TRANSPORTATION PROBLEM

Transport ◽  
2014 ◽  
Vol 30 (2) ◽  
pp. 135-144 ◽  
Author(s):  
Uroš Klanšek
Keyword(s):  
Transportation Problem ◽  
Optimal Solution ◽  
Optimization Method ◽  
Mixed Integer ◽  
Test Problems ◽  
Model Formulation ◽  
Reference Test ◽  
Adequate Model ◽  
Integer Nonlinear Programming ◽  
Selection Of

Finding an exact optimal solution of the Nonlinear Discrete Transportation Problem (NDTP) represents a challenging task in transportation science. Development of an adequate model formulation and selection of an appropriate optimization method are thus significant for attaining valuable solution of the NDTP. When nonlinearities appear within the criterion of optimization, the NDTP can be formulated directly as a Mixed-Integer Nonlinear Programming (MINLP) task or it can be linearized and converted into a Mixed-Integer Linear Programming (MILP) problem. This paper presents a comparison between MILP and MINLP approaches to exact optimal solution of the NDTP. The comparison is based on obtained results of experiments executed on a set of reference test problems. The paper discusses advantages and limitations of both optimization approaches.

scholarly journals MOEA/D with Adaptive Weight Adjustment

2014 ◽  
Vol 22 (2) ◽  
pp. 231-264 ◽  
Author(s):  
Yutao Qi ◽  
Xiaoliang Ma ◽  
Fang Liu ◽  
Licheng Jiao ◽  
Jianyong Sun ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Weight Vector ◽  
Test Problems ◽  
Great Success ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decomposition Scheme ◽  
Adaptive Weight

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

scholarly journals Sizing and Management of an Energy System for a Metropolitan Station with Storage and Related District Energy Community

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5997
Author(s):  
Mircea Stefan Simoiu ◽  
Ioana Fagarasan ◽  
Stephane Ploix ◽  
Vasile Calofir
Keyword(s):  
Energy Storage ◽  
Optimal Solution ◽  
Energy System ◽  
District Level ◽  
Mixed Integer ◽  
Programming Approach ◽  
Net Energy ◽  
Self Sufficiency ◽  
Photovoltaic Plant ◽  
The Impact

Future renewable energy communities will reshape the paradigm in which we design and control efficient power systems at the district level. In this manner, the focus will be fundamentally shifted towards sustainable related concepts such as self-consumption, self-sufficiency and net energy exchanged with the grid. In this context, the paper presents a novel approach for optimally designing and controlling the photovoltaic plant and energy storage systems for a metro station in order to increase collective self-consumption and self-sufficiency at the district level. The methodology considers a community of several households connected to a subway station and focuses on the interaction between energy sources and consumers. Furthermore, the optimal solution is determined by using a Mixed Integer Linear Programming Approach, and the impact of different configurations on the overall district benefit is investigated by using several simulation scenarios. The work presents a detailed case study to underline the benefits and flexibility offered by the energy storage system in comparison with a scenario involving only a photovoltaic plant.

scholarly journals Transportation Optimization Models for Intermodal Networks with Fuzzy Node Capacity, Detour Factor, and Vehicle Utilization Constraints

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2109
Author(s):  
Chia-Nan Wang ◽  
Thanh-Tuan Dang ◽  
Tran Quynh Le ◽  
Panitan Kewcharoenwong
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Deterministic Model ◽  
Mixed Integer ◽  
Model Parameters ◽  
Total Cost ◽  
The Impact ◽  
Node Capacity

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize the total cost, which consists of fixed costs, transportation costs, intermodal transfer costs, and CO2 emission costs. It presents a mixed integer linear programming (MILP) model that minimizes total costs, and a fuzzy mixed integer linear programming (FMILP) model that minimizes imprecise total costs under conditions of uncertain data. In the models, node capacity, detour, and vehicle utilization are incorporated to estimate the performance impact. Additionally, a computational experiment is carried out to evaluate the impact of each constraint and to analyze the characteristics of the models under different scenarios. Developed models are tested using real data from a case study in Southern Vietnam in order to demonstrate their effectiveness. The results indicate that, although the objective function (total cost) increased by 20%, the problem became more realistic to address when the model was utilized to solve the constraints of node capacity, detour, and vehicle utilization. In addition, on the basis of the FMILP model, fuzziness is considered in order to investigate the impact of uncertainty in important model parameters. The optimal robust solution shows that the total cost of the FMILP model is enhanced by 4% compared with the total cost of the deterministic model. Another key measurement related to the achievement of global sustainable development goals is considered, reducing the additional intermodal transfer cost and the cost of CO2 emissions in the objective function.

scholarly journals Bicriteria Optimization in Wireless Sensor Networks: Link Scheduling and Energy Consumption

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jian Chen ◽  
Jie Jia ◽  
Enliang Dai ◽  
Yingyou Wen ◽  
Dazhe Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Performance Metrics ◽  
Data Communication ◽  
Wireless Sensor ◽  
Link Scheduling ◽  
Nsga Ii ◽  
Routing Tree ◽  
The Impact

Link scheduling is important for reliable data communication in wireless sensor networks. Previous works mainly focus on how to find the minimum scheduling length but ignore the impact of energy consumption. In this paper, we integrate them together and solve them by multiobjective genetic algorithms. As a contribution, by jointly modeling the route selection and interference-free link scheduling problem, we give a systematical analysis on the relationship between link scheduling and energy consumption. Considering the specific many-to-one communication nature of WSNs, we propose a novel link scheduling scheme based on NSGA-II (Non-dominated Sorting Genetic Algorithm II). Our approach aims to search the optimal routing tree which satisfies the minimum scheduling length and energy consumption for wireless sensor networks. To achieve this goal, the solution representation based on the routing tree, the genetic operations including tree based recombination and mutation, and the fitness evaluation based on heuristic link scheduling algorithm are well designed. Extensive simulations demonstrate that our algorithm can quickly converge to the Pareto optimal solution between the two performance metrics.

scholarly journals Hybrid model to design a distribution network in contract farming

DYNA ◽  
2019 ◽  
Vol 86 (208) ◽  
pp. 102-109 ◽  
Author(s):  
Rafael Granillo-Macias ◽  
Isidro Jesus Gonzalez Hernandez ◽  
Jose Luis Martinez-Flores ◽  
Santiago Omar Caballero-Morales ◽  
Elias Olivarez-Benitez
Keyword(s):  
Hybrid Model ◽  
Programming Model ◽  
Distribution Network ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Distribution Problem ◽  
Contract Farming ◽  
The Impact ◽  
Distribution Cost

This paper suggests a hybrid model to solve a distribution problem incorporating the impact of uncertainty in the solution. The model combines the deterministic approach and the simulation including stochastic variables such as harvest yield, loss risk and penalties/benefits to design a distribution network with the minimal cost. Through a case study that includes farmers, hubs and malt producers in the supplying chain of barley in Mexico, nine possible scenarios were analyzed to plan and distribute the harvested grain based on contract farming. This approach gets an optimal solution through an iterative process simulating the suggested solution by a mixed-integer linear programming model under uncertain conditions. The results show the convenience of maintaining the operation between four and five hubs depending on the possible scenario; besides, the variation of the levels of the barley producers’ capacities are key elements in the planning to minimize the distribution cost throughout the suggested chain

scholarly journals Incorporating Location Aspects in Process Integration Methodology

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3338 ◽  
Author(s):  
Hür Bütün ◽  
Ivan Kantor ◽  
François Maréchal
Keyword(s):  
Large Scale ◽  
Process Integration ◽  
Optimal Solution ◽  
Economic Feasibility ◽  
Mixed Integer ◽  
Industrial Plants ◽  
Pressure Steam ◽  
The Cost ◽  
Business As Usual ◽  
The Impact

The large potential for waste resource and heat recovery in industry has been motivating research toward increasing efficiency. Process integration methods have proven to be effective tools in improving industrial sites while decreasing their resource and energy consumption; however, location aspects and their impact are generally overlooked. This paper presents a method based on process integration, which considers the location of plants. The impact of the locations is included within the mixed integer linear programming framework in the form of heat losses, temperature and pressure drop, and piping cost. The objective function is selected as minimisation of the total cost of the system excluding piping cost and ϵ -constraints are applied on the piping cost to systematically generate multiple solutions. The method is applied to a case study with industrial plants from different sectors. First, the interaction between two plants and their utility integration are illustrated, depending on the piping cost limit which results in the heat pump and boiler on one site being gradually replaced by excess heat recovered from the other plant. Then, the optimisation of the whole system is carried out, as a large-scale application. At low piping cost allowances, heat is shared through high pressure steam in above-ground pipes, while at higher piping cost limits the system switches toward lower pressure steam sharing in underground pipes. Compared to the business-as-usual operation of the sites, the optimal solution obtained with the proposed method leads to 20% reduction in the overall cost of the system, including the piping cost. Further reduction in the cost is possible using a state of the art method but the technical and economic feasibility is not guaranteed. Thus, the present work provides a tool to find optimal industrial symbiosis solutions under different investment limits on the infrastructure between plants.

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