Parallel Empirical Stochastic Branch and Bound for large-scale discrete optimization via Simulation

Rosen, Scott; Schillebeeckx, P.; Brian, Wickham; Ashley, Williams; Christine, Harvey; Catlett, Erin; Taghiyeh, Sajjad; Xu, Jie

doi:10.1109/wsc.2016.7822127

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…However, research was done by Giesecke et al (2011) has shown that it may not be an important factor in forecasting the charge-off rate. Stochastic and fuzzy optimization algorithms can also be used in the financial industry to improve the efficiency of the algorithms ( Mokhtarimousavi et al, 2019 , Mokhtarimousavi et al, 2018 , Rosen et al, 2016 , Taghiyeh et al, 2020b , Taghiyeh and Xu, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry

Taghiyeh

Lengacher

Handfield

2021

Expert Systems with Applications

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A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals’ behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts’ opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework. We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E−03 and 1.04E−03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss.

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Section: Introductionmentioning

confidence: 99%

Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry

Taghiyeh

Lengacher

Handfield

2021

Expert Systems with Applications

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“…One is the accurate algorithms, the other is the heuristic algorithms. Accurate algorithms mainly include dynamic programming (DP) [2,3], integer linear programming (ILP) [4], branch and bound (B&B) [5] and so on. The advantage of accurate algorithms is that it can precisely find the absolute optimal solution of the hardware/software partitioning problem.…”

Section: Introductionmentioning

confidence: 99%

Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

2019

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Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.

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An Architecture for Low Overhead Grid, HPC, and Cloud-Based Simulation as a Service

McMahon

Wickham

Page

2020

Simulation for Cyber-Physical Systems Engineering

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Parallel Empirical Stochastic Branch and Bound for large-scale discrete optimization via Simulation

Cited by 8 publications

References 9 publications

Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry

Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry

Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning

An Architecture for Low Overhead Grid, HPC, and Cloud-Based Simulation as a Service

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