Optimal Allocation Designs for a Multi-Arm Multi-Objective Clinical Trial

2015 ◽  
pp. 219-238
Keyword(s):  
Clinical Trial ◽  
Optimal Allocation ◽  
Multi Objective

A Multi-Objective Programming Model of Dynamic Adjusting Flight Traffic

2014 ◽  
Vol 538 ◽  
pp. 127-133 ◽  
Author(s):  
Zhao Ning Zhang ◽  
Zhong Zhou Hao ◽  
Zheng Gao
Keyword(s):  
Load Balance ◽  
Optimal Allocation ◽  
Least Squares Method ◽  
Programming Model ◽  
Limiting Factors ◽  
Flight Delays ◽  
Traffic Demand ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

To alleviate the conflicts between the current flight traffic demand and the resource constraints of airspace, we need to improve the restrictions of flow allocation caused by the static air traffic flow allocation mode. The author analyzes the optimal allocation problem of dynamic adjusting flight flow and draws the conclusion that the problem should satisfy multiple targets, such as low flight delays, low flight cost and balancing the load of the route. Then consider a variety of limiting factors, such as the capacity of the route, flight planning, emergency situations, etc. Then establish multi-objective programming model of dynamic adjusting flight traffic. The objective function is determined by the flight cost, the flight delays and the value of the load balance. And the value of the load balance was first proposed according to the idea of least squares method. Then solve the model based on linear weighted technique. Finally the numerical result shows that the model can satisfy the multiple objectives and dynamic adjust the flight traffic optimally, that proves the rationality and validity of the model and the algorithm.

scholarly journals Multi-Objective Optimal Model for Task Scheduling and Allocation in a Two Machines Robotic Cell Considering Breakdowns

2020 ◽  
Keyword(s):  
Optimal Allocation ◽  
Solution Procedure ◽  
Optimal Solutions ◽  
Operational Cost ◽  
Optimal Model ◽  
Robotic Cell ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Processing Times ◽  
Two Machines

This paper aimed to demonstrate a metaheuristic as a solution procedure to schedule a two-machine, identical parts robotic cell under breakdown. The proposed previous model enabled one to determine optimal allocation of operations to the machines and corresponding processing times of each machine. For the proposed mathematical model to minimize cycle time and operational cost, multi-objective particle swarm optimization (MOPSO) algorithm was provided. Through some numerical examples, the optimal solutions were compared with the previous results. MOPSO algorithm could find the solutions for problems embeds up to 50 operations in a rationale time.

Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China

2020 ◽  
Vol 14 (2) ◽  
pp. 241-253
Author(s):  
Pei Li ◽  
Guotian Cai ◽  
Yuntao Zhang ◽  
Shangjun Ke ◽  
Peng Wang ◽  
...  
Keyword(s):  
Optimal Allocation ◽  
Allocation Strategy ◽  
Energy Internet ◽  
Multi Objective

A multi-objective optimal allocation of treated wastewater in urban areas using leader-follower game

2020 ◽  
Vol 267 ◽  
pp. 122189 ◽  
Author(s):  
Mehrdad Ghorbani Mooselu ◽  
Mohammad Reza Nikoo ◽  
Morvarid Latifi ◽  
Mojtaba Sadegh ◽  
Malik Al-Wardy ◽  
...  
Keyword(s):  
Urban Areas ◽  
Optimal Allocation ◽  
Treated Wastewater ◽  
Multi Objective

scholarly journals Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2961
Author(s):  
Anders Clausen ◽  
Aisha Umair ◽  
Yves Demazeau ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Resource Allocation ◽  
Social Welfare ◽  
Energy Demand ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Multi Objective Optimization ◽  
Multi Agent Systems ◽  
Multi Objective ◽  
Pareto Sets ◽  
The Impact

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

scholarly journals Evaluation of a Multi-Objective Genetic Algorithm for Low Impact Development in an Overcrowded City

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2010 ◽  
Author(s):  
Hao-Che Ho ◽  
Shih-Wei Lin ◽  
Hong-Yuan Lee ◽  
Cheng-Chia Huang
Keyword(s):  
Genetic Algorithm ◽  
Urban Areas ◽  
Optimal Allocation ◽  
Low Impact Development ◽  
Return Periods ◽  
Detention Ponds ◽  
Study Region ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Study Results

Sustainability and resilience are up-to-date considerations for urban developments in terms of flood mitigation. These considerations usually pose a new challenge to the urban planner because the achievement of a sustainable design through low impact development (LID) practices would be affected by the selection and the distribution of them. This study proposed a means to optimize the distribution of LIDs with the concept of considering the reduction of the flood peak and the hydrologic footprint residence (HFR). The study region is a densely populated place located in New Taipei City. This place has been developing for more than 40 years with completive sewer systems; therefore, the design must consider the space limitations. The flood reduction induced by each LID component under different rainfall return periods was estimated, and then the detention ponds were also conducted to compare the improvements. The results showed that the performance of LIDs dramatically decreased when the return periods were larger than ten years. A multi-objective genetic algorithm (MOGA) was then applied to optimize the spatial distribution of LIDs under different budget scenarios, and to decide the priority of locations for the LID configuration. Finally, the Monte Carlo test was used to test the relationship between the optimal space configuration of LIDs and the impermeability of the study region. A positive correlation was uncovered between the optimal allocation ratio and the impermeable rate of the partition. The study results can provide general guidelines for urban planners to design LIDs in urban areas.

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