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This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
Methodology of economics --- Operational research. Game theory --- Mathematical statistics --- Engineering sciences. Technology --- Planning (firm) --- Production management --- Business management --- Business economics --- Programming --- Computer. Automation --- financieel management --- KBS (knowledge based system) --- mathematische modellen --- programmeren (informatica) --- productie --- econometrie --- operationeel onderzoek --- ingenieurswetenschappen
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Methodology of economics --- Operational research. Game theory --- Mathematical statistics --- Engineering sciences. Technology --- Planning (firm) --- Production management --- Business management --- Business economics --- Programming --- Computer. Automation --- financieel management --- KBS (knowledge based system) --- mathematische modellen --- programmeren (informatica) --- productie --- econometrie --- operationeel onderzoek --- ingenieurswetenschappen --- Computer scheduling. --- Genetic programming (Computer science) --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Computer programming --- Genetic algorithms --- Electronic data processing --- Processor scheduling (Electronic data processing) --- Scheduling of electronic data processing --- Production scheduling --- Time-sharing computer systems --- Scheduling
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Mozi (470-390), appelé aussi maître Mô, a vécu un peu après Confucius (570-479) et une génération avant celle d'Aristote (384-322). Figure mal connue, son œuvre offre une impression très inhabituelle dans le cadre de la tradition philosophique chinoise. Il s'est opposé à Confucius, qui a finalement triomphé, mais sa logique constitue un pont entre la pensée chinoise et la philosophie grecque. Inclassable, son œuvre a provoqué l'hostilité du courant confucéen dominant, la sympathie des savants missionnaires chrétiens pour son monothéisme et l'intérêt de Sun-Yat Sen, sans oublier les communistes, pour sa portée sociale. Ce qui est le plus important et le plus original chez Mozi est son idée de 1'"amour universel" qui n'a guère son équivalent dans l'Antiquité aussi bien en Occident qu'en Orient, les prophètes d'Israël exceptés et avant le christianisme.
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