Andreas Fink

Solving the continuous flow-shop scheduling problem by metaheuristics

Abstract Continuous flow-shop scheduling problems circumscribe an important class of sequencing problems in the field of production planning. The problem considered here is to find a permutation of jobs to be processed sequentially on a number of machines under the restriction that the processing of each job has to be continuous with respect to the objective of minimizing the total processing time (flow-time). This problem is NP -hard. We consider the application of different kinds of metaheuristics from a practical point of view, examining the trade-off between running time and solution quality as well as the knowledge and efforts needed to implement and calibrate the algorithms. Computational results show that high quality results can be obtained in an efficient way by applying metaheuristics software components with neither the need to understand their inner working nor the necessity to manually tune parameters.

Looking Ahead with the Pilot Method

The pilot method as a meta-heuristic is a tempered greedy method aimed at obtaining better solutions while avoiding the greedy trap by looking ahead for each possible choice. Repeatedly a master solution is modified; each time in a minimal fashion to account for best choices, where choices are judged by means of a separate heuristic result, the pilot solution.The pilot method may be seen as a meta-heuristic enhancing the quality of (any) heuristic in a system for heuristic repetition. Experiments show that the pilot method as well as similar methods can behave quite competitively in comparison with well-known and accepted meta-heuristics. In this paper we review some less known results. As a higher time complexity is usually associated with repetition, we investigate a simple short-cut policy to reduce the running times, while retaining an enhanced solution quality. Furthermore, we report successful experiments that incorporate a distinguishing feature of the pilot method, which is the extension of neighborhoods into “local” search, creating tabu search hybrids.

Hotframe: A Heuristic Optimization Framework

In this paper we survey the design and application of HotFrame, a framework that provides reusable software components in the metaheuristics domain. After a brief introduction and overview we analyze and model metaheuristics with special emphasis on commonalities and variabilities. The resulting model constitutes the basis for the framework design. The framework architecture defines the collaboration among software components (in particular with respect to the interface between generic metaheuristic components and problem-specific complements). The framework is described with respect to its architecture, included components, implementation, and application.

Applications of modern heuristic search methods to pattern sequencing problems

Abstract This article describes applications of modern heuristic search methods to pattern sequencing problems i.e. problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two different objectives: minimization of the number of simultaneously open stacks and minimization of the average order spread. Both objectives require the adaptive evaluation of changed solutions to allow an efficient application of neighbourhood search techniques. We discuss the application of several modern heuristic search methods and present computational results. Scope and purpose Pattern sequencing problems have important applications, especially in the field of production planning. Those problems generally consist of finding a permutation of predetermined production patterns (groupings of some elementary order types) with respect to different objectives. These objectives may represent, e.g., handling costs or stock capacity restrictions, which usually leads to NP -hard problems. Thus, the use of heuristics to construct respective pattern sequences is generally assumed to be appropriate.

Advances in Data Analysis, Data Handling and Business Intelligence

Invited.- Semi-supervised Probabilistic Distance Clustering and the Uncertainty of Classification.- Strategies of Model Construction for the Analysis of Judgment Data.- Clustering of High-Dimensional Data via Finite Mixture Models.- Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data.- Kernel Methods for Detecting the Direction of Time Series.- Statistical Processes Under Change: Enhancing Data Quality with Pretests.- Clustering and Classification.- Evaluation Strategies for Learning Algorithms of Hierarchies.- Fuzzy Subspace Clustering.- Motif-Based Classification of Time Series with Bayesian Networks and SVMs.- A Novel Approach to Construct Discrete Support Vector Machine Classifiers.- Predictive Classification Trees.- Isolated Vertices in Random Intersection Graphs.- Strengths and Weaknesses of Ant Colony Clustering.- Variable Selection for Kernel Classifiers: A Feature-to-Input Space Approach.- Finite Mixture and Genetic Algorithm Segmentation in Partial Least Squares Path Modeling: Identification of Multiple Segments in Complex Path Models.- Cluster Ensemble Based on Co-occurrence Data.- Localized Logistic Regression for Categorical Influential Factors.- Clustering Association Rules with Fuzzy Concepts.- Clustering with Repulsive Prototypes.- Mixture Analysis.- Weakly Homoscedastic Constraints for Mixtures of -Distributions.- Bayesian Methods for Graph Clustering.- Determining the Number of Components in Mixture Models for Hierarchical Data.- Testing Mixed Distributions when the Mixing Distribution Is Known.- Classification with a Mixture Model Having an Increasing Number of Components.- Nonparametric Fine Tuning of Mixtures: Application to Non-Life Insurance Claims Distribution Estimation.- Linguistics and Text Analysis.- Classification of Text Processing Components: The Tesla Role System.- Nonparametric Distribution Analysis for Text Mining.- Linear Coding of Non-linear Hierarchies: Revitalization of an Ancient Classification Method.- Automatic Dictionary Expansion Using Non-parallel Corpora.- Multilingual Knowledge-Based Concept Recognition in Textual Data.- Pattern Recognition and Machine Learning.- A Diversified Investment Strategy Using Autonomous Agents.- Classification with Kernel Mahalanobis Distance Classifiers.- Identifying Influential Cases in Kernel Fisher Discriminant Analysis by Using the Smallest Enclosing Hypersphere.- Self-Organising Maps for Image Segmentation.- Image Based Mail Piece Identification Using Unsupervised Learning.- Statistical Musicology.- Statistical Analysis of Human Body Movement and Group Interactions in Response to Music.- Applying Statistical Models and Parametric Distance Measures for Music Similarity Search.- Finding Music Fads by Clustering Online Radio Data with Emergent Self Organizing Maps.- Analysis of Polyphonic Musical Time Series.- Banking and Finance.- Hedge Funds and Asset Allocation: Investor Confidence, Diversification Benefits, and a Change in Investment Style Composition.- Mixture Hidden Markov Models in Finance Research.- Multivariate Comparative Analysis of Stock Exchanges: The European Perspective.- Empirical Examination of Fundamental Indexation in the German Market.- The Analysis of Power For Some Chosen Backtesting Procedures: Simulation Approach.- Extreme Unconditional Dependence Vs. Multivariate GARCH Effect in the Analysis of Dependence Between High Losses on Polish and German Stock Indexes.- Is Log Ratio a Good Value for Measuring Return in Stock Investments?.- Marketing, Management Science and Economics.- Designing Products Using Quality Function Deployment and Conjoint Analysis: A Comparison in a Market for Elderly People.- Analyzing the Stability of Price Response Functions: Measuring the Influence of Different Parameters in a Monte Carlo Comparison.- Real Options in the Assessment of New Products.- Exploring the Interaction Structure of Weblogs.- Analyzing Preference Rankings when There Are Too Many Alternatives.- Considerations on the Impact of Ill-Conditioned Configurations in the CML Approach.- Dyadic Interactions in Service Encounter: Bayesian SEM Approach.- Archaeology and Spatial Planning.- Estimating the Number of Buildings in Germany.- Mapping Findspots of Roman Military Brickstamps in (Mainz) and Archaeometrical Analysis.- Analysis of Guarantor and Warrantee Relationships Among Government Officials in the Eighth Century in the Old Capital of Japan by Using Asymmetric Multidimensional Scaling.- Analysis of Massive Emigration from Poland: The Model-Based Clustering Approach.- Bio- and Health Sciences.- Systematics of Short-range Correlations in Eukaryotic Genomes.- On Classification of Molecules and Species of Representation Rings.- The Precise and Efficient Identification of Medical Order Forms Using Shape Trees.- On the Prognostic Value of Gene Expression Signatures for Censored Data.- Quality-Based Clustering of Functional Data: Applications to Time Course Microarray Data.- A Comparison of Algorithms to Find Differentially Expressed Genes in Microarray Data.- Exploratory Data Analysis, Modeling and Applications.- Data Compression and Regression Based on Local Principal Curves.- Optimization of Centrifugal Impeller Using Evolutionary Strategies and Artificial Neural Networks.- Efficient Media Exploitation Towards Collective Intelligence.- Multi-Class Extension of Verifiable Ensemble Models for Safety-Related Applications.- Dynamic Disturbances in BTA Deep-Hole Drilling: Modelling Chatter and Spiralling as Regenerative Effects.- Nonnegative Matrix Factorization for Binary Data to Extract Elementary Failure Maps from Wafer Test Images.- Collective Intelligence Generation from User Contributed Content.- Computation of the Molenaar Sijtsma Statistic.

Supply chain coordination by means of automated negotiations

The coordination within supply chains depends on appropriate forms of distributed decision making. Considering joint decisions as formal contracts, the coordination problem may be regarded as a search process in a corresponding contract space. Automated negotiations, with firms or decision making units represented as software agents, can provide an effective mechanism to determine mutually beneficial contracts. The generic negotiation approach examined in this paper is based on a formal specification of contracts that represent bilateral collaborations between two firms (agents) which aim for the coordination of their production sequences. Taking into account asymmetric information and opportunistic behavior, a mediator supports the negotiation process. This mediator repeatedly generates new candidate contracts, which are accepted or rejected by the agents according to particular strategies. We define an explicit mechanism for implementing a cooperative acceptance criterion, whereby agents conditionally agree on utility deteriorations according to a probabilistic criterion similar to that of simulated annealing. The proposed design enables the definition of negotiation rules to be verified by the mediator, forcing both agents to behave in a cooperative manner. The negotiation approach is validated for different supply chain sequencing scenarios. In spite of the simplicity and generality of the negotiation mechanism, the experimental results are very promising. Thus automated negotiations may constitute an effective means for coordinating decisions within supply chains.

Metaheuristics Comparison for the Minimum Labelling Spanning Tree Problem

We study the Minimum Labelling Spanning Tree Problem: Given a graph G with a color (label) assigned to each edge (not necessarily properly) we look for a spanning tree of G with the minimum number of different colors. The problem has several applications in telecommunication networks, electric networks, multimodal transportation networks, among others, where one aims to ensure connectivity by means of homogeneous connections. For this NP-hard problem very few heuristics are presented in the literature giving good quality solutions. In this paper we apply several metaheuristic approaches to solve the problem. These approaches are able to improve over existing heuristics presented in the literature. Furthermore, a comparison with the results provided by an exact approach existing in the literature shows that we may quite easily obtain optimal or close to optimal solutions.

Solving General Ring Network Design Problems by Meta-Heuristics

Ring network design problems have many important applications, especially in the field of telecommunications and vehicle routing. Those problems generally consist of constructing a ring network by selecting a node subset and corresponding direct links. Different requirements and objectives lead to various specific types of NP-hard ring network design problems reported in the literature, each with its own algorithms. We exploit the similarities in problems to produce a more general problem formulation and associated solution methods that apply to a broad range of problems. Computational results are reported for an implementation using a meta-heuristics framework with generic components for heuristic search.

On the Effectiveness of Particle Swarm Optimization and Variable Neighborhood Descent for the Continuous Flow-Shop Scheduling Problem

Recently population-based meta-heuristics under the cover of swarm intelligence have gained prominence. This includes particle swarm optimization (PSO), where the search strategy draws ideas from the social behavior of organisms. While PSO has been reported as an effective search method in several papers, we are interested in the critical success factors of PSO for solving combinatorial optimization problems. In particular, we examine the application of PSO with different crossover operators and hybridization with variable neighborhood descent as an embedded local search procedure. Computational results are reported for the continuous (nowait) flow-shop scheduling problem. The findings demonstrate the importance of local search as an element of the applied PSO procedures. We report new best solutions for a number of problem instances from the literature.

Generic metaheuristics application to industrial engineering problems

Metaheuristics subsume heuristic methods that are defined in a generic way with the key ideas independent from problem specific aspects. Accordingly, a nice way for problem solving would be to select and apply some appropriate method from a heuristics stockroom that provides ready-to-use software components incorporating respective methods. Thus, we are faced with the task to build reusable software components. In this paper we describe an object-oriented framework for heuristic search and discuss some experiences concerning the application of this framework.