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.
