Trucks Pooling and Allocation in TSE Concept Using GIS Spatial and Novel FFOA

Strategic system logistics business entails the importance of regulating truck pooling facilities and allocating the trucks for cost optimization goals. Regulators and investors must consider spatial constraints such as the supply-demand gap and service distance. Little attention has been paid to developing decision logistics models, particularly truck pooling and allocation decisions. In this study, the FFOA and GIS were used to determine the spatial component of truck pooling decisions, providing a scenario for origin pooling and delivery distance. The model evaluates truck allocation to each city, a distance vector, a spatial factor, and city demand are used for the cost optimization goal. The results show that the FFOA model successfully defines the optimal truck allocation for each truck pooling site with a cost. The managerial implication in developing a sharing economy concept for truck logistics is to use the study's framework model result to solve challenges in truck logistics

Railway Container Station Reselection Approach and Application: Based on Entropy-Cloud Model

Reasonable railway container freight stations layout means higher transportation efficiency and less transportation cost. To obtain more objective and accurate reselection results, a new entropy-cloud approach is formulated to solve the problem. The approach comprises three phases: Entropy Method is used to obtain the weight of each subcriterion during Phase  1, then cloud model is designed to form the evaluation cloud for each subcriterion during Phase  2, and finally during Phase  3 we use the weight during Phase  1 to multiply the initial evaluation cloud during Phase  2. MATLAB is applied to determine the evaluation figures and help us to make the final alternative decision. To test our approach, the railway container stations in Wuhan Railway Bureau were selected for our case study. The final evaluation result indicates only Xiangyang Station should be renovated and developed as a Special Transaction Station, five other stations should be kept and developed as Ordinary Stations, and the remaining 16 stations should be closed. Furthermore, the results show that, before the site reselection process, the average distance between two railway container stations was only 74.7 km but has improved to 182.6 km after using the approach formulated in this paper

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications