GAA-Based Decision Approach for Hospital Building Renovation Management

2011 ◽  
Vol 403-408 ◽  
pp. 5265-5272
Author(s):  
Yi Kai Juan ◽  
Yu Ching Cheng ◽  
Yeng Horng Perng ◽  
Guang Bin Wang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Use ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Developed Countries ◽  
Sustainable Technologies ◽  
Trade Offs ◽  
Hospital Building

More and more attention has been paid to hospital facilities since modern pandemics have emerged such as SARS and avian influenza. Energy consumption by buildings accounts for 20-40% of energy use in developed countries, so many global organizations make efforts to develop sustainable technologies or materials to create a sustainable environment, and to reduce energy consumption when renovating building. Therefore, maintaining high standards of hygiene and reducing energy consumption has become the major task for hospital buildings. This study develops an integrated decision support system to assess existing hospital building conditions and to recommend an optimal scheme of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines the A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A simulated hospital renovation project is established to demonstrate the system. The result reveals the system can solve complicated and large-scale combinational, discrete and determinate problems such as the hospital renovation project, and also improve traditional building condition assessment to be more effective and efficient.

scholarly journals Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1221
Author(s):  
Tao Ren ◽  
Yan Zhang ◽  
Shuenn-Ren Cheng ◽  
Chin-Chia Wu ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Differential Evolution Algorithm ◽  
Developed Countries ◽  
Productivity Level ◽  
Jobshop Scheduling

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

scholarly journals Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
I. Hameem Shanavas ◽  
R. K. Gnanamurthy
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Design Automation ◽  
Large Scale ◽  
Hybrid Approach ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Physical Design ◽  
Silicon Area ◽  
Vlsi Physical Design

In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

Economic Load Dispatch Using Oppositional Backtracking Search Algorithm

2017 ◽  
Vol 6 (2) ◽  
pp. 79-97 ◽  
Author(s):  
Moumita Pradhan ◽  
Provas Kumar Roy ◽  
Tandra Pal
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Economic Load Dispatch ◽  
Invasive Weed ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Test Systems ◽  
Generation Cost ◽  
Feasible Solutions

In this paper, an oppositional backtracking search algorithm (OBSA) is proposed to solve the large scale economic load dispatch (ELD) problem. The main drawback of the conventional backtracking search algorithm (BSA) is that it produces a local optimal solution rather than the global optimal solution. The proposed OBSA methodology is a highly-constrained optimization problem has to minimize the total generation cost by satisfying several constraints involving load demand, generation limits, prohibited operating zone, ramp rate limits and valve point loading effect. The proposed method is applied for three test systems and provides the unique and fast solutions. The new heuristic OBSA approach is successfully applied in three test systems consisting of 13 and 140 thermal generators. The test results are judged against various methods. The simulation results show the effectiveness and accuracy of the proposed OBSA algorithm over other methods like conventional BSA, oppositional invasive weed optimization (OIWO), Shuffled differential evolution (SDE) and oppositional real coded chemical reaction optimization (ORCCRO). This clearly suggests that the new OBSA method can achieve effective and feasible solutions of nonlinear ELD problems.

scholarly journals Energy and performance costs evaluation for BLE mesh links

2018 ◽  
Author(s):  
Henrique Carvalho Silva ◽  
Cíntia Borges Margi
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Mesh Networks ◽  
Lower Boundary ◽  
Low Energy ◽  
Trade Offs ◽  
Main Challenge ◽  
And Performance ◽  
Large Scale Networks ◽  
The Internet Of Things

Bluetooth Low Energy (BLE for short) is among the favorites to become a de facto standard in the context of the Internet of Things (IoT). However, its main challenge is the lack of standards for efficient mesh networking. Furthermore, the literature lacks works analyzing energy consumption trade-offs for BLE mesh networks. We address this issue by experimentally evaluating three minimal topologies for linking separate BLE star networks. We aim to determine a lower boundary in terms of energy and performance costs using the metrics of energy consumption, delivery rate, and goodput. We perform our experiments using a testbed comprised of TI CC1350 nodes running Contiki OS. Our results enable us to estimate similar costs for large scale networks.

scholarly journals AN EFFICIENT Hybrid Swarm Intelligence Technique for Solving Integer Programming

2013 ◽  
Vol 10 (4) ◽  
pp. 1531-1538
Author(s):  
Mahmoud M. Ismail ◽  
Ibrahim M. El-henawy
Keyword(s):  
Integer Programming ◽  
Large Scale ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Research Area ◽  
Optimization Techniques ◽  
Computational Time ◽  
Hybrid Swarm ◽  
Integer Programming Problems ◽  
Scale Optimization

In this paper, a hybridization of two different swarm intelligent approaches, stochastic diffusion search, and particle swarm optimization techniques is presented  for solving integer programming problems. The hybrid implementation allows us to avoid certain drawbacks and weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. Our hybrid implementation allows the IP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the PSO with SDS approach for solving IP problems appears to be an interesting research area in combinatorial optimization. 

A New Sensor-Based Spatial OLAP Architecture Centered on an Agricultural Farm Energy-Use Diagnosis Tool

2013 ◽  
Vol 5 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Sandro Bimonte ◽  
Marilys Pradel ◽  
Daniel Boffety ◽  
Aurelie Tailleur ◽  
Géraldine André ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Spatial Data ◽  
Large Scale ◽  
Energy Use ◽  
Ad Hoc ◽  
Sensor Data ◽  
Loosely Coupled ◽  
Diagnosis Tool ◽  
Agricultural Farm

Agricultural energy consumption is an important environmental and social issue. Several diagnosis tools have been proposed to define indicators for analyzing the large-scale energy consumption of agricultural farm activities (year, farm, production activity, etc.). In Bimonte, Boulil, Chanet and Pradel (2012), the authors define (i) new appropriate indicators to analyze agricultural farm energy-use performance on a detailed scale and (ii) show how Spatial Data Warehouse (SDW) and Spatial OnLine Analytical Processing (SOLAP) GeoBusiness Intelligence (GeoBI) technologies can be used to represent, store, and analyze these indicators by simultaneously producing graphical and cartographic reports. These GeoBI technologies allow for the analysis of huge volumes of georeferenced data by providing aggregated numerical values visualized by means of interactive tabular, graphical, and cartographic displays. However, existing data collection systems based on sensors are not well adapted for agricultural data. In this paper, the authors show the global architecture of our GeoBI solution and highlight the data collection process based on agricultural ad hoc sensor networks, the associated transformation and cleaning operations performed by means of Spatial Extract Transform Load (ETL) tools, and a new implementation of the system using a web-services-based loosely coupled SOLAP architecture to provide interoperability and reusability of the complex multi-tier GeoBI architecture. Moreover, the authors detail how the energy-use diagnosis tool proposed in Bimonte, Boulil, Chanet and Pradel (2012) theoretically fits with the sensor data and the SOLAP approach.

scholarly journals A Hybrid Approach Using an Artificial Bee Algorithm with Mixed Integer Programming Applied to a Large-Scale Capacitated Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Guillermo Cabrera G. ◽  
Enrique Cabrera ◽  
Ricardo Soto ◽  
L. Jose Miguel Rubio ◽  
Broderick Crawford ◽  
...  
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Location Problem ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

We present a hybridization of two different approaches applied to the well-known Capacitated Facility Location Problem (CFLP). The Artificial Bee algorithm (BA) is used to select a promising subset of locations (warehouses) which are solely included in the Mixed Integer Programming (MIP) model. Next, the algorithm solves the subproblem by considering the entire set of customers. The hybrid implementation allows us to bypass certain inherited weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. In this paper we demonstrate that BA can be significantly improved by use of the MIP algorithm. At the same time, our hybrid implementation allows the MIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the BA with a mathematical programming approach appears to be an interesting research area in combinatorial optimization.

IMPROVED TABU SEARCH RECURSIVE FUZZY METHOD FOR CRUDE OIL INDUSTRY

2012 ◽  
Vol 03 (01) ◽  
pp. 1150002 ◽  
Author(s):  
P. VASANT ◽  
T. GANESAN ◽  
I. ELAMVAZUTHI
Keyword(s):  
Tabu Search ◽  
Crude Oil ◽  
Large Scale ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Oil Field ◽  
Fuzzy Linear Programming ◽  
Oil Refinery ◽  
Profit Function ◽  
Decision Variables

The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry. In this paper, a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry. This problem involves 44 variables, 36 constraints, and four decision variables which represent four types of crude oil types. The decision variables have been modeled in the form of fuzzy linear programming problem. The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function. A recursive improved tabu search has been used to solve this fuzzy optimization problem. Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil. The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.

scholarly journals AN INVESTIGATION ON RELATIONSHIP BETWEEN ENERGY CONSUMPTION OF HIGH RISE INSTITUTIONAL BUILDINGS AND THE CLIMATE OF DHAKA CITY

2012 ◽  
Vol 12 (1) ◽  
pp. 39-48
Author(s):  
Md. Yousuf Reja ◽  
Amreen Shajahan
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Use ◽  
Meteorological Data ◽  
Building Design ◽  
Building Energy ◽  
Tropical Region ◽  
Dhaka City ◽  
Building Energy Consumption ◽  
Public Buildings

Growth in population, mounting demand for building services and comfort levels, along with the rise in time spent inside buildings, assure the upward trend in energy consumption of large scale public buildings in Dhaka city. For this reason, energy efficiency in buildings is a prime objective today for energy policy at regional, national and international levels. This paper devotes to discuss the holistic utility bills analysis method for investigating and analyzing whole building energy consumption of public buildings with special emphasis on private sector institutions in a tropical region like Dhaka city. Correlations between operational records of energy consumption of three institutional buildings and the meteorological data including monthly mean outdoor dry-bulb temperature (To), and relative humidity (RH) of Dhaka city have been derived. The findings of the study reveals that the overall building energy consumption is highly dependent on climate, building design characteristics including internal layout, orientation, fenestration and site configurations, and ownership. The analysis of such kind of model is especially useful for building managers and owners to track energy use during preretrofit and post-retrofit periods and to reduce building operational costs in the tropical region. Keywords: Energy consumption, Institutional buildings, Utility bills, Heat gain, Meteorological data.

scholarly journals Solar energy in the context of energy use, energy transportation and energy storage

2013 ◽  
Vol 371 (1996) ◽  
pp. 20110431 ◽  
Author(s):  
David J. C. MacKay
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Population Density ◽  
Power Density ◽  
Large Scale ◽  
Energy Use ◽  
Solar Power ◽  
Unit Area ◽  
Solar Photovoltaic ◽  
The Uk

Taking the UK as a case study, this paper describes current energy use and a range of sustainable energy options for the future, including solar power and other renewables. I focus on the area involved in collecting, converting and delivering sustainable energy, looking in particular detail at the potential role of solar power. Britain consumes energy at a rate of about 5000 watts per person, and its population density is about 250 people per square kilometre. If we multiply the per capita energy consumption by the population density, then we obtain the average primary energy consumption per unit area, which for the UK is 1.25 watts per square metre. This areal power density is uncomfortably similar to the average power density that could be supplied by many renewables: the gravitational potential energy of rainfall in the Scottish highlands has a raw power per unit area of roughly 0.24 watts per square metre; energy crops in Europe deliver about 0.5 watts per square metre; wind farms deliver roughly 2.5 watts per square metre; solar photovoltaic farms in Bavaria, Germany, and Vermont, USA, deliver 4 watts per square metre; in sunnier locations, solar photovoltaic farms can deliver 10 watts per square metre; concentrating solar power stations in deserts might deliver 20 watts per square metre. In a decarbonized world that is renewable-powered, the land area required to maintain today's British energy consumption would have to be similar to the area of Britain. Several other high-density, high-consuming countries are in the same boat as Britain, and many other countries are rushing to join us. Decarbonizing such countries will only be possible through some combination of the following options: the embracing of country-sized renewable power-generation facilities; large-scale energy imports from country-sized renewable facilities in other countries; population reduction; radical efficiency improvements and lifestyle changes; and the growth of non-renewable low-carbon sources, namely ‘clean’ coal, ‘clean’ gas and nuclear power. If solar is to play a large role in the future energy system, then we need new methods for energy storage; very-large-scale solar either would need to be combined with electricity stores or it would need to serve a large flexible demand for energy that effectively stores useful energy in the form of chemicals, heat, or cold.

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