Bagchi T.P. Multiobjective Scheduling by Genetic Algorithms

Springer, 1999. — 369 p.This book describes methods for developing multiobjective solutions to common production scheduling situations modeled in the literature as flowshops, job shops and open shops. The methodology is metaheuristic, one inspired by how nature has evolved a multitude of coexisting species of living beings on earth.
Multiobjective scheduling situations are ubiquitous. Rarely is a shop manager interested only in "getting the orders out the fastest way possible." Typically, he/she attempts also to minimize tardiness, maximize the utilization of expensive capital equipment and human resources, minimize the mean flow time of the jobs to be done, etc. etc. In this book we demonstrate a framework for representing such challenging problems and then finding their solutions efficiently. The method uses enhancements of "genetic algorithms" (GAs)--search methods that use the "survival of the fittest" rule and cross-breeding, mutation and niche-formation, processes that nature is believed to have used to create well-adapted and co-habitant life forms.
In precise terms, we use enhancements of the Nondominated Sorting Genetic Algorithm (NSGA), a metaheuristic method recently proposed, which produces Pareto-optimal solutions to numerical multiobjective problems. One such important enhancement introduced in this text is called the Elitist Nondominated Sorting Genetic Algorithm (ENGA). The object of such methods is singular: solve a variety of multiobjective optimization problems, and do it efficiently. The final solutions evolved are all Pareto-optimal or "efficient." In this regard, these methods may be easily extended to other multiobjective decision situations such as configuring an FMS, operating an airport, or providing a multiplicity in patient care. Thus this book is intended for students of industrial engineering, operations research, operations management and computer science, as well as practitioners. It may also assist in the development of efficient shop management software tools for schedulers and production planners who face multiple planning and operating objectives as a matter of course.Shop Scheduling: An Overview
What are Genetic Algorithms?
Calibration of GA Parameters
Flowshop Scheduling
Job Shop Scheduling
Multiobjective Optimization
Niche Formation and Speciation: Foundations of Multiobjective GAs
The Nondominated Sorting Genetic Algorithm: NSGA
Multiobjective Flowshop Scheduling
A New Genetic Algorithm for Sequencing the Multiobjective Flowshop
A Comparison of Multiobjective Flowshop Sequencing by NSGA and ENGA
Multiobjective Job Shop Scheduling
Multiobjective Open Shop Scheduling
Epilog and Directions for Further Work
A: C++ Codes for a Hybridized GA to Sequence the Single-Objective Flowshop