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Computer Science - Artificial Intelligence | Evolutionary Scheduling

Evolutionary Scheduling

Dahal, Keshav, Tan, Kay Chen, Cowling, Peter I. (Eds.)

2007, XI, 628 p.

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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.

Content Level » Research

Keywords » Computational Intelligence - Evolution - Evolutionary Algorithms - Fuzzy - Operations Research - algorithm - artificial intelligence - evolutionary algorithm - evolutionary strategies - genetic algorithms - heuristics - intelligence - metaheuristic - optimization - programming

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm for an Inventory Location Problem.

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