scholarly journals Research on Multi-objective Optimization of Substation Project Management Based on Resource Constraint

2021 ◽  
Vol 261 ◽  
pp. 01055
Author(s):  
Xichu Zhou ◽  
Li Sun ◽  
Sixia Fan ◽  
Bin Wu
Keyword(s):  
Project Management ◽  
Resource Constraints ◽  
Rapid Development ◽  
Multi Objective Optimization ◽  
Gradual Improvement ◽  
Project Duration ◽  
Multi Objective ◽  
Optimal Resource ◽  
Electricity Load ◽  
Engineering Projects

With the rapid development of China’s economy, the electricity load continues to increase, the national demand for power engineering projects is also increasing.In the context of the gradual improvement of national requirements for substation projects, decision makers no longer only consider the optimal standards of project duration, cost and quality, but also the resources consumed in the construction and the degree of impact on the environment are important criteria for judging projects.However, in order to be environment-friendly and based on resource constraints, the project management of substation projects is becoming more and more complicated.In this paper, resource factors are added on the basis of the three classical indexes, and the optimal resource allocation of the project is realized under the condition of minimizing the adverse impact on the environment. The feasibility scheme of multi-objective optimization is obtained through particle swarm optimization algorithm.

Mobile edge server placement based on meta-heuristic algorithm

2021 ◽  
Vol 40 (5) ◽  
pp. 8883-8897
Author(s):  
Feiyan Guo ◽  
Bing Tang ◽  
Jiaming Zhang
Keyword(s):  
Heuristic Algorithm ◽  
Ant Colony Algorithm ◽  
Resource Constraints ◽  
Rapid Development ◽  
Ant Colony ◽  
Optimal Placement ◽  
Low Latency ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Server Placement

The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through minimizing both communication delay and load difference as the optimization goal. Then, an Edge Server placement based on meta-Heuristic alGorithM (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom’s real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches.

Multi-objective optimization for repetitive scheduling under uncertainty

2019 ◽  
Vol 26 (7) ◽  
pp. 1294-1320 ◽  
Author(s):  
Tarek Salama ◽  
Osama Moselhi
Keyword(s):  
Cost Optimization ◽  
Indirect Costs ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Software Module ◽  
Content Type ◽  
Project Duration ◽  
Multi Objective ◽  
Work Interruptions ◽  
Cost Calculations

Purpose The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters. Design/methodology/approach The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module. Findings For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules. Originality/value Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

Application of Multi-Objective Optimization Model in Design of Photovoltaic Architecture

2013 ◽  
Vol 347-350 ◽  
pp. 1525-1529
Author(s):  
Jie Shi ◽  
Jun Shi ◽  
Lin Lin Yang ◽  
Xiu Ying Tang ◽  
Yi Jie Zhang
Keyword(s):  
Power Generation ◽  
Computer Aided Design ◽  
Rapid Development ◽  
Unit Cost ◽  
Current Trend ◽  
Ideal Point ◽  
Optimization Theory ◽  
Shanxi Province ◽  
Multi Objective Optimization ◽  
Multi Objective

With the increasingly serious crisis of the energy problem, all around the world, the application of solar energy has become an approach of solving the problem undoubtedly. In recent years, due to the rapid development of Chinese photovoltaic (PV) industry, the emergence of PV buildings is more and more widely in the domestic. The current trend is to apply Computer-Aided Design (CAD) software to realize the design of Building Integrated Photovoltaic (BIPV). The core problem of CAD is to obtain the best solution to a laying scheme of PV cell. In this paper, the design problem of PV building is concerned about. Through the research on the multi-objective optimization theory, it is to establish the model of multi-objective optimization that can meet these two objectives of the largest total annual solar photovoltaic power generation, yet the smallest possible the unit cost of power generation. The model can be solved by MATLAB using ideal point method. In order to detail explain the application of model and verify it, the design of PV cabin, in Datong area of Shanxi Province, is as an example. The study has laid the foundation for the development of CAD in the future.

scholarly journals Genetic Algorithm for the Multi-Objective Optimization of Product Gene

2012 ◽  
Vol 6-7 ◽  
pp. 116-121
Author(s):  
Qing Song Ai ◽  
Zhou Liu ◽  
Yan Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Engineering ◽  
Manufacturing Industry ◽  
Optimization Problems ◽  
Rapid Development ◽  
Optimal Solution ◽  
Adaptive Mutation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

scholarly journals Application of the integrated decision support system for scheduling of development projects

2018 ◽  
Vol 251 ◽  
pp. 05033
Author(s):  
Vitaly Berezka
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Decision Support Systems ◽  
Construction Industry ◽  
System Integration ◽  
Support Systems ◽  
Development Projects ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Joint Activities

Introduction: A higher level of specialization in various disciplines and technologies typical for the participants of modern investment and construction (development) projects creates the need for the advanced project management models. Decision support systems and tools for communication and organization of joint activities are the mandatory components for the efficient project management. Purpose-designed information systems allow to computerize such project management function as scheduling. However, the optimum decision is still found on the basis of personal assessment by decision makers. At the same time, as competition in the construction industry increases, the need for decision support systems to optimize investment and construction activities in the environment of multi-objective optimization becomes evident. Methods: The findings of DSS development projects in the construction industry have been used. The studies have been based on system integration approach to engineering, method of successive concession and the procedure for a search of the satisfactory values meeting STEM criteria under given weights. Results: Principles of development and functioning, architecture and organization and process aspects of DSS for scheduling under multi-objective optimization. Discussion: Integrated DSS capable for multi-objective optimization of the schedules has been proposed.

scholarly journals Multi-objective optimization of container-based microservice scheduling in edge computing

2020 ◽  
pp. 41-41
Author(s):  
Guisheng Fan ◽  
Liang Chen ◽  
Huiqun Yu ◽  
Wei Qi
Keyword(s):  
Load Balancing ◽  
Fitness Function ◽  
Rapid Development ◽  
Edge Computing ◽  
Multi Objective Optimization ◽  
Simulation Experiments ◽  
Multi Objective ◽  
Effectiveness And Efficiency ◽  
Container Orchestration ◽  
Microservice Architecture

Edge computing provides physical resources closer to end users, becoming a good complement to cloud computing.With the rapid development of container technology and microservice architecture, container orchestration has become a hot issue. However, the container-based microservice scheduling problem in edge computing is still urgent to be solved. In this paper, we first formulate the containerbased microservice scheduling as a multi-objective optimization problem, aiming to optimize network latency among microservices, reliability of microservice applications and load balancing of the cluster. We further propose a latency, reliability and load balancing aware scheduling (LRLBAS) algorithm to determine the container-based microservice deployment in edge computing. Our proposed algorithm is based on particle swarm optimization (PSO). In addition, we give a handling strategy to separate the fitness function from constraints, so that each particle has two fitness values. In the proposed algorithm, a new particle comparison criterion is introduced and a certain proportion of infeasible particles are reserved adaptively. Extensive simulation experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm compared with other related algorithms.

scholarly journals PROJECT MANAGEMENT BY MULTIMOORA AS AN INSTRUMENT FOR TRANSITION ECONOMIES / PROJEKTŲ VADYBA SU MULTIMOORA KAIP PRIEMONĖ PEREINAMOJO LAIKOTARPIO ŪKIAMS

2010 ◽  
Vol 16 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Willem Karel M. Brauers ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Project Management ◽  
Transition Economy ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Nominal Group ◽  
Benefit Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Delphi Techniques ◽  
Specific Project

The countries of Central and Eastern Europe moved from a previously centrally planned economy to a modern transition economy with strong market aspects. This paper proposes project management as an answer to this transition. Traditional Cost‐Benefit analysis does not respond to this purpose. Indeed Cost‐Benefit analysis is only interested in one specific project and not in a competition between projects. In addition all goals (objectives) have to be translated into money terms, leading sometimes to immoral consequences. On the contrary Multi‐Objective Optimization takes care of different objectives, whereas the objectives keep their own units. However different methods exist for the application of Multi‐Objective Optimization. The authors tested them after their robustness resulting in seven necessary conditions. MOORA (Multi‐Objective Optimization by Ratio analysis) and MULTIMOORA (MOORA plus Full Multiplicative Form), assisted by Ameliorated Nominal Group and Delphi Techniques, satisfy the seven conditions, although in a theoretical way. A simulation exercise illustrates the use of these methods, ideals to be strived for as much as possible. Santrauka Centrinės ir rytų Europos šalys perėjo iš anksčiau centralizuotai planuojamos ūkinės sistemos į šiuolaikinę pereinamąją ūkinę sistemą, kuriai būdingi ryškūs rinkos požymiai. Šiame straipsnyje siūloma projektų vadyba kaip atsakas į perėjimą. Įprastinė kainos ir naudos analizė tam tikslui yra netinkama. Be viso to, kainos ir naudos analizėje neatsižvelgiama į kiekvieną atskirai paimtą projektą bei į konkurenciją tarp tų projektų. Visi tikslai turi būti pakeisti piniginėmis vertėmis. Del to kartais kyla nepageidaujamų pasėkmių. Priešingai tam daugiatikslė optimizacija atsižvelgia į skirtingus tikslus, išsaugant tikslams būdingus mato vienetus. Yra daug įvairių daugiatikslės optimizacijos metodų. Autoriai patikrino jų stipriasias savybęs pagal septynias būtinasias sąlygas. MOORA (daugiatikslė optimizacija santykių dydžių analizės pagrindu) ir MULTIMOORA (MOORA plius pilnoji sandaugos forma), apimanti patobulintą normaliųjų grupių ir Delphi būdus, geriausiai atitinka septynias būtinąsiąs sąlygas taip pat ir teoriniu lygmeniu. Pavyzdžio modelis iliustruoja šiu metodu taikymą, idealai buvo pasiekti tiek, kiek galima.

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