Computational Intelligence Systems in Industrial Engineering With Recent Theory and Applications

Computational Intelligent Systems in Industrial Engineering.- A Comparison of Methods For Selecting Preferred Solutions in Multiobjective Decision Making.- Using a RS to Help TTO Staff to Disseminate Selective Information.- Power of Linguistic Data Summaries and their Protoforms.- Can Fuzzy Logic Formalism Bring Complex Environmental Issues into Focus? TOPSIS and its Extensions: Applications for Road Safety Performance Evaluation.- Computational Intelligence Applications in Industrial Engineering.- A Fuzzy Analytic Network Process Approach.- Swarm Intelligence Optimization.- A Review of Genetic Algorithm Applications in Supply Chain Network Design.- Fuzzy PROMETHEE for the Environmental Quality Assessment of Energy Dedicated Crops.- Forecasting Energy Demand Using Fuzzy Seasonal Time Series.- Multi-objective Scheduling Problems for Re-entrant Shops.- An Introduction to Self-Organizing Maps.- Fuzzy Multi-Objective Programming for Energy Resource Planning.- Fuzzy Simulation Based Chance Constrained Programming.- A Cooperative Intelligent Approach for Job-shop Scheduling.- Using Fuzzy Cognitive Maps as a Modeling Tool for Traveler Satisfaction in Public Transit Systems.- Genetic Algorithm to Solve Container Storage Problem for a Single and Various Types.- Fuzzy Geometric Programming Model with Applications in Industrial Engineering.- An Artificial Intelligent Classification System for Thermoplastic Recycling Using Sensor Fusion.- Linear Programming for Distribution Network Design under Fuzzy Environment.- GSA–Based Training of Convolutional Neural Networks for OCR Applications.- Fuzzy Logic Applications in Cellular Manufacturing System Design.- The Shape of Crisis Lessons from Self Organizing Maps.- Evolutions in Particle Swarm Optimization: Benchmark on Continuous Cases.- Active Distribution Networks and SmartGrids.- Re-Mining Association Mining Results.- A Multi-Objective Evolutionary Algorithm.-

Human Fatigue: Evaluation with the Usage of Bayesian Networks.