The flowchart of Algorithm 2. by Jing Xu (15337)

“…Assuming that stochastic passenger demand follows a specific distribution and considering various constraints, including train capacity, demand, and denied boarding rate constraints, a nonlinear stochastic programming model for joint optimization of overbooking and seat allocation for HSR is constructed with the aim of maximizing railway expected revenue. …”

Simultaneous Estimation of Many Sparse Networks via Hierarchical Poisson Log-Normal Model by Changhao Ge (22410399)

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FIG. S2 by Bo Guo (1559695)

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FIG. S1 by Bo Guo (1559695)

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Objective values of both algorithms for m = 5. by Zhenzhong Zhang (806974)

“…Considering the carbon emission and the uncertainty of customer order arrivals in the actual transportation environment, a stochastic optimization model considering the cost of carbon emission is established with the objective of maximizing the expected profit from order transportation. …”

Objective values of both algorithms for m = 4. by Zhenzhong Zhang (806974)

“…Considering the carbon emission and the uncertainty of customer order arrivals in the actual transportation environment, a stochastic optimization model considering the cost of carbon emission is established with the objective of maximizing the expected profit from order transportation. …”

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm by Gelio Alves (51850)

“…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …”

Optimization flowchart of dung beetle optimization algorithm. by Xiangfei Tao (20775636)

BOPIM: Bayesian Optimization for Influence Maximization on Temporal Networks by Eric Yanchenko (3266514)

“…For this, we use the Expected Improvement function, suitably adjusting for noise in the observations, and optimize it using a greedy algorithm to account for the cardinality constraint. …”

Changes in railway expected revenue with passenger no-show rate. by Jing Xu (15337)

Variation of railway expected revenue with DB compensation multiplier. by Jing Xu (15337)

Variation of railway expected revenue with maximum DB rate. by Jing Xu (15337)

Comparison of key features of optimization algorithms. by Alireza Norouzpour Shahrbejari (21799964)

Execution time of the BMDD algorithm. by Huijuan Zhou (5745020)

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None - Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm by Naima Agouzoul (21743513)

HMM to infer selection. by Adam G. Fine (21763286)

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The railway expected revenue, revenue increase rate and calculation time under different numbers of scenarios. by Jing Xu (15337)

Variation of railway expected revenue with maximum overbooking rate under different passenger no-show rates. by Jing Xu (15337)

Description of algorithm parameters. by Naima Agouzoul (21743513)

“…In this regard, a modified Dandelion Optimizer (MDO) algorithm is introduced to optimize the SORPD solution with taking into consideration the stochastic fluctuations or the random variations of the load demand and the power produced by RERs. …”

Expected behavior system of ODEs. by Nicholas H. Vitale (20469289)