Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Issues in environmentally conscious manufacturing and product recovery: a survey
Computers and Industrial Engineering - Special issue on o/perational issues in environmentally conscious manufacturing
Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions
Evolutionary Computation
Risk Modeling, Assessment, and Management
Risk Modeling, Assessment, and Management
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Selection of green production strategy is a critical but difficult task due to the fact that it affects not only green benefits, but also production economy. The problem is essentially multi-objective and involves dynamic and uncertain conditions. This study focused on an integrated approach to improve the analysis and facilitate decision-making process. Discrete-event simulation model was developed to capture production flow and decision logic under real world conditions. A multi-objective genetic algorithm (MOGA), combined with improving heuristics, was developed to search the best solutions (Pareto optimums). The two modules are integrated to work in evolutionary cycles to achieve the optimization. Experiments were designed and carried out via a prototype system developed to verify and validate proposed concepts, including sensitivity analysis of related model parameters.