Estimation of distribution algorithms in logistics:Analysis, design, and application

Nowadays, managers are forced to excel competition on a global scale. Efficientbusiness processes have become a basic requirement to meet this goal. However,the design of efficient business processes and the improvement of existing ones is adifficult task that regularly overburdens human intuition. This is especially truein the field of logistics and supply chain management (SCM) where the globalintegration of economies, the advances in information and communication technologiesand increasing customer demands drive the complexity of information,material, and cash flows that require coordination.[...]

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Year of publication:	2008
Authors:	Grahl, Jörn
Institutions:	Betriebswirtschaftliches Institut <Mannheim>
Published in:	Johannes-Gutenberg-Universität Mainz - Lehrstuhl für Wirtschaftsinformatik und BWL: Books
Subject:	Supply Chain Management \| Monotone Funktion \| Sicherheitsbestand \| Losgröße \| batch size

1. Introduction<br<1.1. Motivation
1.2. Goal
1.3. Structure
2. Fundamentals and literature
2.1. Preliminaries and notation
2.2. Binary estimation of distribution algorithms
2.3. Continuous estimation of distribution algorithms
2.4. Ant colony optimization and EDA
2.5. Conclusions and outlook
I. Applications of discrete EDA and EDA-theory in lo-gistics and supply chain management
3. Decomposition of warehouse location problems and the linkageproblem
3.1. Introduction
3.2. Linkage in warehouse location problems
3.3. Experimental section
3.4. Summary and conclusion
4. Solving safety stock allocation problems with evolutionary algo-rithms
4.1. Introduction
4.2. The guaranteed service time safety stock allocation problem
4.3. Representation and (1+1)-EA
4.4. Decomposition of serial safety stock allocation problems
4.5. Experimental study
4.6. Summary and conclusion
5. Decomposition of single- and multi-product lot-sizing problems
5.1. Introduction
5.2. Lot-sizing
5.3. Decomposition of lot-sizing problems
5.4. Experimental results
5.5. Summary and conclusion
II. Analysis and design of continuous EDA
6. Convergence phases
6.1. Introduction
6.2. Notation and algorithm
7. UMDAc on monotonous functions
7.1. Monotonous fitness functions and truncation selection
7.2. UMDAc for monotonous fitness functions
7.3. Mean dynamics
7.4. Variance dynamics
7.5. Population statistics in generation t
7.6. Convergence of population statistics for t → ∞
8. Optimal sampling variances and runtime
8.1. Optimal sampling variances
8.2. Runtime bound
8.3. Summary and conclusion
9. Matching search bias and problem structure: CT-AVS-IDEA
9.1. Introduction
9.2. Adapting discrete EDA to continuous EDA
9.3. Adaptive variance scaling and correlation triggering
9.4. Experimental section
9.5. Summary and conclusion
10. CT-AVS-IDEA solves stochastic transportation problems
10.1. Introduction
10.2. Sto

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