THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

https://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2011-004.pdf

The irace package: Iterated racing for automatic algorithm configuration

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

On the truck dispatching problem

This is a survey of "Iterated Local Search", a general purpose metaheuristic for finding good solutions of combinatorial optimization problems. It is based on building a sequence of (locally optimal) solutions by: (1) perturbing the current solution; (2) applying local search to that modified solution. At a high level, the method is simple, yet it allows for a detailed use of problem-specific properties. After giving a general framework, we cover the uses of Iterated Local Search on a number of well studied problems.

/pdf/iterated-local-search-geqjq72fho.pdf

Iterated local search

A comprehensive review and evaluation of permutation flowshop heuristics

A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility

The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan ( C max ). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.

Two new robust genetic algorithms for the flowshop scheduling problem

Genetic algorithms have been applied to many different optimization problems and they are one of the most promising metaheuristics However, there are few published studies concerning the design of efficient genetic algorithms for resource allocation in project scheduling In this work we present a robust genetic algorithm for the single-mode resource constrained project scheduling problem We propose a new representation for the solutions, based on the standard activity list representation and develop new crossover techniques with good performance in a wide sample of projects Through an extensive computational experiment, using standard sets of project instances, we evaluate our genetic algorithm and demonstrate that our approach outperforms the best algorithms appearing in the literature

A Robust Genetic Algorithm for Resource Allocation in Project Scheduling

In this paper we consider the Multi-Mode Resource-Constrained Project Scheduling Problem with makespan minimisation as the objective. We have developed new genetic algorithms, extending the representation and operators previously designed for the single-mode version of the problem. Moreover, we have defined a new fitness function for the individuals who are infeasible. We have tested different variants of the algorithm and chosen the best to be compared to different heuristics previously published, using standard sets of instances included in PSPLIB. Results illustrate the good performance of our algorithm.

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Papers

A comprehensive review and evaluation of permutation flowshop heuristics

A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility

Two new robust genetic algorithms for the flowshop scheduling problem

A Robust Genetic Algorithm for Resource Allocation in Project Scheduling

Solving the Multi-Mode Resource-Constrained Project Scheduling Problem with genetic algorithms