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.
Author(s): Carlos Cotta, Antonio J. Fernà ndez (auth.), Dr. Keshav P. Dahal, Prof. Kay Chen Tan, Professor Peter I. Cowling (eds.)
Series: Studies in Computational Intelligence 49
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2007
Language: English
Pages: 628
City: Berlin; London
Tags: Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)
Front Matter....Pages I-XI
Memetic Algorithms in Planning, Scheduling, and Timetabling....Pages 1-30
Landscapes, Embedded Paths and Evolutionary Scheduling....Pages 31-48
Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes....Pages 49-99
Designing Dispatching Rules to Minimize Total Tardiness....Pages 101-124
A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling....Pages 125-142
Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study....Pages 143-164
An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem....Pages 165-195
Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem....Pages 197-236
Metaheuristics for University Course Timetabling....Pages 237-272
Optimum Oil Production Planning using an Evolutionary Approach....Pages 273-292
A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems....Pages 293-311
Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch....Pages 313-347
Evolutionary Generator Maintenance Scheduling in Power Systems....Pages 349-382
Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network....Pages 383-403
A Multi-Objective Evolutionary Algorithm for Channel Routing Problems....Pages 405-436
Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles....Pages 437-464
Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's....Pages 465-483
A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities....Pages 485-511
Evolutionary Optimization of Business Process Designs....Pages 513-541
Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling....Pages 543-576
A Genetic-Algorithm-Based Reconfigurable Scheduler....Pages 577-611
Evolutionary Algorithm for an Inventory Location Problem....Pages 613-628
