This tutorial will review some of the basic concepts related to evolutionary multiobjective optimization (i.e., the use of evolutionary algorithms to handle more than one objective function at a time). The most commonly used evolutionary multiobjective optimization techniques will be described and criticized, including some of their applications. Theory, test functions and metrics will be also discussed. Finally, we will provide some possible paths of future research in this area.

/pdf/a-short-tutorial-on-evolutionary-multiobjective-optimization-4g4duwtzfp.pdf

A Short Tutorial on Evolutionary Multiobjective Optimization

In recent years, scheduling research has had an increasing impact on practical problems, and a range of scheduling techniques have made their way into real-world application development Constraint-based models now couple rich representational flexibility with highly scalable constraint management and search procedures Similarly, mathematical programming tools are now capable of addressing problems of unprecedented scale, and meta-heuristics provide robust capabilities for schedule optimisation With these mounting successes and advances, it might be tempting to conclude that the chief technical hurdles underlying the scheduling problem have been overcome However, such a conclusion (at best) presumes a rather narrow and specialised interpretation of scheduling, and (at worst) ignores much of the process and broader context of scheduling in most practical environments In this note, I argue against this conclusion and outline several outstanding challenges for scheduling research

http://www.mistaconference.org/2003/papers/page011.pdf

Is Scheduling a Solved Problem

A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices

The embedded systems domain represents a class of systems that have high requirements on cost efficiency as well as run-time properties such as timeliness and dependability. The research on component-based systems has produced component technologies for guaranteeing real-time properties. However, the issue of saving resources by allocating several components to real-time tasks has gained little focus. Trade-offs when allocating components to tasks are, e.g., CPU-overhead, footprint and integrity. In this paper we present a general approach for allocating components to real-time tasks, while utilizing existing real-time analysis to ensure a feasible allocation. We demonstrate that CPU-overhead and memory consumption can be reduced by as much as 48% and 32% respectively for industrially representative systems.

/pdf/optimizing-resource-usage-in-component-based-real-time-54c309plc7.pdf

Optimizing resource usage in component-based real-time systems

Since scheduling process is an important and complicated process, many programmers have been searching and working on this issue for years Still many researchers in the academic institutes are trying to find the best solution As time is money, time optimization is the most important point, which makes the researchers develop a system for scheduling at the best way by applying the best solution Once look at the production line of a factory or the number of classes and classrooms in a university, shows that having a time table in these places not only helps regulate things, but also it helps optimize consumption of resources such as time and energy within the constraints and limitations This paper explains and reviews the three techniques, which have previously been applied on scheduling domain by researchers and developers among several artificial intelligence techniques These three techniques ie Genetic Algorithm, Neural Network and Fuzzy Logic will be defined, discussed and compared in terms of some measures

A Comparative Study of Three Artificial Intelligence Techniques: Genetic Algorithm, Neural Network, and Fuzzy Logic, on Scheduling Problem

Real-time scheduling of large-scale problems in complex domains presents a number of difficulties for search and optimization techniques, including: large and complex search spaces; dynamically changing problems; and a variety of problem-dependent constraints and preferences. Genetic algorithms are well suited to such problems due to their adaptability and their effectiveness at searching large spaces. We have used genetic algorithms to solve real-world problems in areas such as field service scheduling, air crew scheduling and transportation scheduling. We discuss key aspects of our approach including: domain-specific chromosome representation and genetic operators; multi-objective evaluation function; heuristic initialization of the population; dynamic rescheduling; and cooperative interaction with human operators.

http://www.davidmontana.net/papers/smc98.pdf

Genetic algorithms for complex, real-time scheduling

Applications that require real-time scheduling of large-scale problems in complex domains present a number of difficulties for search and optimization techniques. These difficulties include: (i) a search space whose size grows exponentially with the size of the problem, (ii) a problem that is constantly changing due to a changing environment and user interaction, and (iii) the need to trade off between a variety of different criteria measuring the relative fitness of a particular schedule. BBN has used genetic algorithms to solve a variety of real-world scheduling problems, including applications in areas such as field service scheduling, job shop scheduling, transportation scheduling, and laboratory experiment scheduling. With the recent acquisition of BBN by GTE, we expect that our scheduling technology will soon be deployed to solve hard scheduling problems in network operations management. Our genetic algorithm technology addresses the issues above and provides a domain-independent infrastructure upon which we have rapidly developed customized software solutions for various customers. Because the infrastructure is domain independent, adding network operations management to the list of domains in which our scheduling technology has been successfully applied should be straightforward.

Using genetic algorithms for complex, real-time scheduling applications

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Genetic algorithms for complex, real-time scheduling

Using genetic algorithms for complex, real-time scheduling applications