A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

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Metaheuristics: From Design to Implementation

http://romisatriawahono.net/lecture/rm/survey/softcomputing/Boussaid%20-%20Optimization%20Metaheuristics%20-%202013.pdf

A survey on optimization metaheuristics

https://ira.lib.polyu.edu.hk/bitstream/10397/1269/1/SetupSurvey-2006%28June-19%29.pdf

A survey of scheduling problems with setup times or costs

http://www.upv.es/deioac/Investigacion/revision.pdf

The hybrid flow shop scheduling problem

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

We provide a unified framework for the total tardiness problem by surveying the related literature in the single-machine, parallel machine, flowshop and jobshop settings. We focus on critically evaluating the heuristic algorithms; we also propose new heuristics for both the single-machine and the parallel-machine tardiness problems. Finally, we identify the areas where further research is needed and we give directions for future research.

The total tardiness problem: review and extensions

This paper surveys the application of simulated annealing (SA) to operations research (OR) problems It is concluded that SA has been applied to both traditional (like single machine, flowshop and jobshop scheduling, lot sizing, traveling salesman problems) and non-traditional (like graph coloring, number partitioning) OR areas It is also concluded that SA is quite appropriate when the alternative solution method is based on enumeration SA usually requires more computational resources in exchange for not being trapped in local optima

Christos Koulamas

Papers

The total tardiness problem: review and extensions

A survey of simulated annealing applications to operations research problems

Single-machine and two-machine flowshop scheduling with general learning functions

A new constructive heuristic for the flowshop scheduling problem

Single-machine scheduling problems with past-sequence-dependent setup times