Evolutionary Scheduling

Summary

Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems. The intended readers of this book are engineers, researchers, practitioners, senior undergraduates, and graduate students who are interested in the field of evolutionary scheduling.

Memetic algorithms in planning, scheduling, and timetabling	p. 1
Landscapes, embedded paths and evolutionary scheduling	p. 31
Scheduling of flow-shop, job-shop, and combined scheduling problems using MOEAs with fixed and variable length chromosomes	p. 49
Designing dispatching rules to minimize total tardiness	p. 101
A robust meta-hyper-heuristic approach to hybrid flow-shop scheduling	p. 125
Hybrid particle swarm optimizers in the single machine scheduling problem : an experimental study	p. 143
An evolutionary approach for solving the multi-objective job-shop scheduling problem	p. 165
Multi-objective evolutionary algorithm for university class timetabling problem	p. 197
Metaheuristics for university course timetabling	p. 237
Optimum oil production planning using an evolutionary approach	p. 273
A hybrid evolutionary algorithm for service restoration in power distribution systems	p. 293
Particle swarm optimisation for operational planning : unit commitment and economic dispatch	p. 313
Evolutionary generator maintenance scheduling in power systems	p. 349
Evolvable fuzzy scheduling scheme for multiple-channel packet switching network	p. 383
A multi-objective evolutionary algorithm for channel routing problems	p. 405
Simultaneous planning and scheduling for multi-autonomous vehicles	p. 437
Scheduling production and distribution of rapidly perishable materials with hybrid GA's	p. 465
A scenario-based evolutionary scheduling approach for assessing future supply chain fleet capabilities	p. 485
Evolutionary optimization of business process designs	p. 513
Using a large set of low level heuristics in a hyperheuristic approach to personnel scheduling	p. 543
A genetic-algorithm-based reconfigurable scheduler	p. 577
Evolutionary algorithm for an inventory location problem	p. 613
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