Resource Allocation and Time-Cost Trade-Off Using Colliding Bodies Optimization

2015 ◽  
pp. 261-277
Author(s):  
A. Kaveh ◽  
V. R. Mahdavi
2015 ◽  
Vol 59 (3) ◽  
pp. 361-371 ◽  
Author(s):  
Ali Kaveh ◽  
Mostafa Khanzadi ◽  
Majid Alipour ◽  
Mohammad Rajabi Naraki

2021 ◽  
Vol 13 (13) ◽  
pp. 7318
Author(s):  
Wei He ◽  
Wenjing Li ◽  
Wei Wang

In the construction industry, it is of great importance for project managers (PM) to consider the resource allocation arrangement problem based on different perspectives. In this situation, the management of resources in construction becomes a challenge. Generally speaking, there are many objectives that need to be optimized in construction that are in conflict with each other, including time, cost, and energy consumption (EC). This paper proposed a multi-objective optimization framework based on the quantum genetic algorithm (QGA) to obtain the best trade-off relationship among these goals. The construction resources allocated in each construction activity would eventually determine its execution time, cost, and EC, and a complexed time-cost-energy consumption trade-off framework of the project is finally generated due to correlations between construction activities. QGA was performed to find the best combination among time, cost, and EC and the optimal scheme of resource arrangement under this state. The construction process is simulated in BIM to check the rationality of this resource allocation mode. An industrial plant office building in China is presented as an example to illustrate the implementation of the proposed model. The results show that the presented method could effectively reduce 7% of cost, 17% of time, and 21% of energy consumption. This developed model is expected to help PMs to solve the problem of multi-objective optimization with limited resource allocation.


1982 ◽  
Vol 14 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Suleyman Tufekci
Keyword(s):  

1998 ◽  
Vol 49 (11) ◽  
pp. 1153 ◽  
Author(s):  
E. Demeulemeester ◽  
B. De Reyck ◽  
B. Foubert ◽  
W. Herroelen ◽  
M. Vanhoucke

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 190
Author(s):  
Peter Nghiem

Considering the recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for more energy-efficient computing. We previously proposed the Best Trade-off Point (BToP) method, which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce. The BToP method is expected to work for any application or system which relies on a trade-off elbow curve, non-inverted or inverted, for making good decisions. In this paper, we apply the BToP method to the emerging cluster computing framework, Apache Spark, and show that its performance and energy consumption are better than Spark with its built-in dynamic resource allocation enabled. Our Spark-Bench tests confirm the effectiveness of using the BToP method with Spark to determine the optimal number of executors for any workload in production environments where job profiling for behavioral replication will lead to the most efficient resource provisioning.


1997 ◽  
Vol 14 (4) ◽  
pp. 291-311 ◽  
Author(s):  
D. K. H. CHUA ◽  
W. T. CHAN ◽  
K. GOVINDAN

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