Airline Disruption Management:A Review of Models and Solution Methods

The normal operation of aircraft and flights can be affected by various unpredictable factors,such as severe weather,airport closure,and corrective maintenance,leading to disruption of the planned schedule.When a disruption occurs,the airline operation control center performs various operations to reassign resources(e.g.,flights,aircraft,and crews)and redistribute passengers to restore the schedule while minimizing costs.We introduce different sources of disruption and corresponding operations.Then,basic models and recently proposed extensions for aircraft recovery,crew recovery,and integrated recovery are reviewed,with the aim of providing models and methods for different disruption scenarios in the practical implementation of airlines.In addition,we provide suggestions for future research directions in these topics.

Dynamic grouping of heterogeneous agents for exploration and strike missions

The ever-changing environment and complex combat missions create new demands for the formation of mission groups of unmanned combat agents.This study aims to address the problem of dynamic construction of mission groups under new requirements.Agents are heterogeneous,and a group formation method must dynamically form new groups in circumstances where missions are constantly being explored.In our method,a group formation strategy that combines heuristic rules and response threshold models is proposed to dynamically adjust the members of the mission group and adapt to the needs of new missions.The degree of matching between the mission requirements and the group’s capabilities,and the communication cost of group formation are used as indicators to evaluate the quality of the group.The response threshold method and the ant colony algorithm are selected as the comparison algorithms in the simulations.The results show that the grouping scheme obtained by the proposed method is superior to those of the comparison methods.

A discussion of objective function representation methods in global optimization

Non-convex optimization can be found in several smart manufacturing systems. This paper presents a short review on global optimization(GO) methods. We examine decomposition techniques and classify GO problems on the basis of objective function representation and decomposition techniques. We then explain Kolmogorov's superposition and its application in GO. Finally,we conclude the paper by exploring the importance of objective function representation in integrated artificial intelligence, optimization, and decision support systems in smart manufacturing and Industry 4.0.

A Novel Method of Finance Market Regulation Based on Control Overshoot

In the finance market, risk happened in two pattern. In one case, extreme volatility together with a short balance time leads to a great panic to the market. On the contrary, if the volatility is smaller, the time period will usually be longer. It will bring many infections to various related fields,which causes wider range influences to the economy. Both cases hurt financial market and the economy itself deeply. In this paper, we developed a novel market regulation method in which the conflict of fluctuation time and volatility will be balanced. It describes a way to compute a portfolio of relatively short time period together with smaller fluctuation volatility by using a general prediction algorithm based on overshoot in cybernetics. It can also give explanation to counter-cyclical supervision theory and macro-prudential regulation. Furthermore, it can provide numerical operation guide for countercyclical supervision theory and macro-prudential regulation.

Special issue:Manufacturing engineering management

Numerous manufacturing engineering management problems have emerged and attracted great attention in academia and practice,especially in high-end equipment manufacturing field with the development of the Internet and big data technology in recent years.Therefore,investigations on manufacturing engineering management technology,theoretical methods,and opportunities and challenges in the new technological environment are essential.The increasing number of relevant studies from theoretical researchers and practical engineers attempted to construct a scientific and reasonable system of manufacturing engineering management based on the Internet and big data technology.

Yield performance estimation of corn hybrids using machine learning algorithms

Estimation of yield performance for crop products is a topic of interest in agriculture.In breeding programs,we cannot test all possible hybrids created by crossing two parents(inbred and tester)since it would be too time consuming and costly.In this paper,we exploit different machine learning algorithms including decision tree,gradient boosting machine,random forest,adaptive boosting,XGBoost and neural network to predict the yield of corn hybrids using data provided in the 2020 Syngenta Crop Challenge.The participants were asked to predict the yield of missing hybrids which were not tested before.Our results show that the prediction obtained by XGBoost is more accurate than other models with a root mean square error equal to 0.0524.Therefore,we use XGBoost model to estimate the yield performance for untested combinations of inbreds and testers.Using this approach,we identify hybrids with high predicted yield that can be bred to increase corn production.