Intercluster interaction of district regions is optimized in the following sequence: 1) logistic, 2) informational, and 3) economic and financial. At the first two stages, the problem is solved as a quadratic assignment problem using an ant colony optimization algorithm. The third stage is...

Should automation be regulated? This paper studies optimal tax of robot and regulation of automation. A job assignment model is embedded into a Mirrleesian tax problem. A task may either be assigned to the robot or one type of labors, which naturally determines the automation rate. The robot can...

This paper proposes a branch-and-bound algorithm (B&B) for the proactive scheduling problem, which incorporates a degree of anticipation for variability in practical projects. First, the resource-constrained proactive scheduling model is formulated. Second, solving its resource-unconstrained...

Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive...

Surrogate-based optimization is commonly used in engineering design problems to determine optimal performance parameters for computationally expensive simulations. The sampling phase of surrogate optimization plays a critical role in identifying the most promising input data points for...

Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to deep learning models to provide robustness and reliability. Due to the growth...

As water transportation undergoes a new technological revolution, automated terminal technology has become a key issue in reducing costs and improving efficiency. However, the operation theory limitation makes it problematic for machines to cooperate effectively and operate profitably, which is...

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed...