The sixth edition provides expanded Discussion and Comments and References sections at the end of each chapter, creating a spotlight on practical applications of the theory presented in that chapter. New topics include rules for stochastic parallel machine scheduling and for stochastic online scheduling, models of flow shops with reentry, fixed parameter tractability, and new designs and implementations of scheduling systems.
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The IEEE Transactions on Industrial Informatics is a multidisciplinary journal publishing technical papers that bridge the gap between theory and application practice of informatics in industrial environments. Its scope encompasses the use of information in intelligent, distributed, agile industrial automation and control systems. Included are knowledge-based and AI enhanced automation; intelligent computer control systems; flexible and collaborative manufacturing; industrial informatics aspects in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems; real-time and networked embedded systems; security in industrial processes; industrial communications; systems interoperability and human machine interaction.
Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity.
The areas in which evolutionary algorithms are practically used are almost unlimited and range from industry, engineering, complex scheduling, agriculture, robot movement planning and finance to research and art. The application of an evolutionary algorithm requires some rethinking from the inexperienced user, as the approach to a task using an EA is different from conventional exact methods and this is usually not part of the curriculum of engineers or other disciplines. For example, the fitness calculation must not only formulate the goal but also support the evolutionary search process towards it, e.g. by rewarding improvements that do not yet lead to a better evaluation of the original quality criteria. For example, if peak utilisation of resources such as personnel deployment or energy consumption is to be avoided in a scheduling task, it is not sufficient to assess the maximum utilisation. Rather, the number and duration of exceedances of a still acceptable level should also be recorded in order to reward reductions below the actual maximum peak value. There are therefore some publications that are aimed at the beginner and want to help avoiding beginner's mistakes as well as leading an application project to success. This includes clarifying the fundamental question of when an EA should be used to solve a problem and when it is better not to. 2b1af7f3a8