Learning crew scheduling constraints from historical schedules

Suraweera, Pramuditha; Webb, Geoffrey I.; Evans, Ian; Wallace, Mark

doi:10.1016/j.trc.2012.08.002

Cited by 14 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the NMaximize (see Wolfram Research Inc., 2021) function available in Mathematica 11.3 Student Edition . The prevalent application of the NMaximize (NMinimize) function for solving NLPs can be found in operations literature (Suraweera et al., 2013; Reed and Zhang, 2017).…”

Section: Numerical Illustrationmentioning

confidence: 99%

A socially responsible decision‐making model for firms contracting with constrained farmers

Dutta

Kaur

2022

Int Trans Operational Res

View full text Add to dashboard Cite

In emerging economies, farming contracts with collateral‐free credits are essential in linking the poor producers to the mainstream agri‐supply chains (SCs). Thus, we frame a decision‐making model for a firm procuring through advance‐payment contracts (APCs) from numerous small farmers. These farmers are decision‐biased and have varying needs for credit. Hence, composed of a fraction of the per unit guaranteed price, the advance payment uniquely affects the farmers' realized yields. We model the contracting scenario assuming the farmers as reference‐dependent loss‐averse newsvendors facing random yields who are allowed to commit their supply quantities; and the firm as a risk‐neutral profit‐maximizer, encountering demand stochasticity. We then analyze the association of the farmer's optimal commitment and the firm's optimal number of contracted farmers with the contract's parameters. Subsequently, given the information asymmetry on farmers' cost and behavioral attributes, we design a menu of contracts based on the contract theory. The menu simultaneously aims to maximize the firm's expected profit while meeting the welfare needs of the smallholders having the highest requirement for an advance. Finally, we use existing data on the model's parameters value to numerically illustrate its applicability. The illustration could demonstrate the customarily observed practice of excluding smallholders from contractual arrangements featuring no‐APCs. It also exhibited a menu's dominance over a single contract in meeting the SC entities' mutual needs and its usability in promoting socially responsible operations. We believe that firms and policy makers would find these analyses helpful in designing socioeconomically viable agri‐SCs contracts.

show abstract

Section: Numerical Illustrationmentioning

confidence: 99%

A socially responsible decision‐making model for firms contracting with constrained farmers

Dutta

Kaur

2022

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…Campigotto et al [18] and Kolb [22] study the acquisition of the utility function of the optimization model. Suraweera et al [31] propose a system that learns parameters for template constraints previously defined from historical schedules. The quality of a parameter set is the distance to the real set of parameters that created the examples.…”

Section: Constraint Acquisitionmentioning

confidence: 99%

Learning the Parameters of Global Constraints Using Branch-and-Bound

Picard-Cantin

Bouchard²,

Quimper

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Precise constraint satisfaction modeling requires specific knowledge acquired from multiple past cases. We address this issue with a general branch-and-bound algorithm that learns the parameters of a given global constraint from a small set of positive solutions. The idea is to cleverly explore the possible combinations taken by the constraint's parameters without explicitly enumerating all combinations. We apply our method to learn parameters of global constraints used in timetabling problems such as Sequence and SubsetFocus. The later constraint is our adaptation of the constraint Focus to timetabling problems.

show abstract

“…With the rapid development of the air transport industry in aircraft design, manufacturing, maintenance, and other aspects, the reliability and safety of aircraft have been significantly improved. However, the proportion of flight accidents caused by human factors in the overall number of accidents has not decreased [1][2][3]; these are especially caused by pilot fatigue [4,5]. The noise and vibration of the cabin, air pressure changes, long-haul flights, high-load work, circadian rhythm disturbance, and lack of sleep lead pilots to often be in a state of fatigue [6].…”

Section: Introductionmentioning

confidence: 99%

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Pan

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

show abstract

Learning crew scheduling constraints from historical schedules

Cited by 14 publications

References 11 publications

A socially responsible decision‐making model for firms contracting with constrained farmers

A socially responsible decision‐making model for firms contracting with constrained farmers

Learning the Parameters of Global Constraints Using Branch-and-Bound

Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Contact Info

Product

Resources

About