Multi-Objective Optimization Techniques for Task Scheduling Problem in Distributed Systems

2017 ◽  
Vol 61 (2) ◽  
pp. 248-263 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari
Keyword(s):  
Distributed Systems ◽  
Task Scheduling ◽  
Optimization Techniques ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Improved genetic algorithm for 3D printing multi-objective optimization task scheduling

2021 ◽  
Vol 336 ◽  
pp. 02020
Author(s):  
Haobin Zhao ◽  
Hongbin Yu
Keyword(s):  
3D Printing ◽  
Task Scheduling ◽  
Related Problem ◽  
Problem Definition ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Problem Model ◽  
Multi Objective ◽  
Optimization Task

For the 3D printing multi-objective optimization task scheduling problem, the problem model is established from the three aspects of related problem definition, constraint conditions, and objective function, which lays the foundation for subsequent research and solution.

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

scholarly journals Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale

2021 ◽  
Vol 13 (20) ◽  
pp. 11554
Author(s):  
Fahad Haneef ◽  
Giovanni Pernigotto ◽  
Andrea Gasparella ◽  
Jérôme Henri Kämpf
Keyword(s):  
Energy Efficiency ◽  
Building Energy ◽  
Energy Performance ◽  
Building Envelope ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Building Stock ◽  
Energy Efficiency Measures ◽  
Multi Objective ◽  
Efficiency Measures

Nearly-zero energy buildings are now a standard for new constructions. However, the real challenge for a decarbonized society relies in the renovation of the existing building stock, selecting energy efficiency measures considering not only the energy performance but also the economic and sustainability ones. Even if the literature is full of examples coupling building energy simulation with multi-objective optimization for the identification of the best measures, the adoption of such approaches is still limited for district and urban scale simulation, often because of lack of complete data inputs and high computational requirements. In this research, a new methodology is proposed, combining the detailed geometric characterization of urban simulation tools with the simplification provided by “building archetype” modeling, in order to ensure the development of robust models for the multi-objective optimization of retrofit interventions at district scale. Using CitySim as an urban scale energy modeling tool, a residential district built in the 1990s in Bolzano, Italy, was studied. Different sets of renovation measures for the building envelope and three objectives —i.e., energy, economic and sustainability performances, were compared. Despite energy savings from 29 to 46%, energy efficiency measures applied just to the building envelope were found insufficient to meet the carbon neutrality goals without interventions to the system, in particular considering mechanical ventilation with heat recovery. Furthermore, public subsidization has been revealed to be necessary, since none of the proposed measures is able to pay back the initial investment for this case study.

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