Robust optimality analysis for linear programming problems with uncertain objective function coefficients: an outer approximation approach

summary:Linear programming (LP) problems with uncertain objective function coefficients (OFCs) are treated in this paper. In such problems, the decision-maker would be interested in an optimal solution that has robustness against uncertainty. A solution optimal for all conceivable OFCs can be considered a robust optimal solution. Then we investigate an efficient method for checking whether a given non-degenerate basic feasible (NBF) solution is optimal for all OFC vectors in a specified range. When the specified range of the OFC vectors is a hyper-box, i. e., the marginal range of each OFC is given by an interval, it has been shown that the tolerance approach can efficiently solve the robust optimality test problem of an NBF solution. However, the hyper-box case is a particular case where the marginal ranges of some OFCs are the same no matter what values the remaining OFCs take. In real life, we come across cases where some OFCs' marginal ranges depend on the remaining OFCs' values. For example, the prices of products rise together in tandem with raw materials, the gross profit of exported products increases while that of imported products decreases because they depend on the currency exchange rates, and so on. Considering those dependencies, we consider a case where the range of the OFC vector is specified by a convex polytope. In this case, the tolerance approach to the robust optimality test problem of an NBF solution becomes in vain. To treat the problem, we propose an algorithm based on the outer approximation approach. By numerical experiments, we demonstrate how the proposed algorithm efficiently solves the robust optimality test problems of NBF solutions compared to a conventional vertex-listing method

Water filtration by using apple and banana peels as activated carbon

Water filter is an important devices for reducing the contaminants in raw water. Activated from charcoal is used to absorb the contaminants. Fruit peels are some of the suitable alternative carbon to substitute the charcoal. Determining the role of fruit peels which were apple and banana peels powder as activated carbon in water filter is the main goal. Drying and blending the peels till they become powder is the way to allow them to absorb the contaminants. Comparing the results for raw water before and after filtering is the observation. After filtering the raw water, the reading for pH was 6.8 which is in normal pH and turbidity reading recorded was 658 NTU. As for the colour, the water becomes more clear compared to the raw water. This study has found that fruit peels such as banana and apple are an effective substitute to charcoal as natural absorbent

Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017

Dieser Tagungsband enthält die Beiträge des 27. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

A Novel Probability-based Data Clustering Application for Detecting Elongated Clusters with Application to the Line Detection Problem

Ένα σημαντικό πρόβλημα που απαντάται σε διάφορα πεδία εφαρμογών, όπως η ανάλυση γεωχωρικών δεδομένων (geospatial data analysis), η συμπίεση εικόνας (image compression) και η εξαγωγή δρόμων (road extraction), μεταξύ άλλων παραδειγμάτων, είναι αυτό της ανίχνευσης ευθείων γρμμών ή ευθυγράμμων τμημάτων σε μια δεδομένη εικόνα. Στη διατριβή αυτή, προτείνεται μια νέα προσέγγιση στο παραπάνω πρόβλημα, η οποία βασίζεται στην πιθανοτική ομαδοποίηση (probabilistic clustering). Πιο συγκεκριμένα, ορίζεται μια νέα κατανομή πυκνότητας πιθανότητας η οποία αποτελεί παραλλαγή της Γκαουσσιανής κατανομής πιθανοτήτων (Gaussian probability distribution), στην οποία το κέντρο της δεν είναι πλέον σημείο, αλλά ένα ευθύγραμμο τμήμα, με στόχο τη μοντελοποίηση ευθυγράμμων τμημάτων. Στη συνέχεια, το σύνολο των δεδομένων σημείων θεωρείται ότι προέρχεται από μία κατανομή που εκφράζεται ως ένα σταθμισμένο άθροισμα (μίξη)επιμέρους κατανομών και ο στόχος είναι ο προσδιορισμός αυτών των κατανομών, κάθε μία από τις οποίες μοντελοποιεί και μια (γραμμική) συστάδα (linear cluster). Προτείνεται ένας αγόριθμος, ο οποίος ονομάζεται Αγλόριθμος Πιθανοτικής Συσταδοποίησης Ευθυγράμμων Τμημάτων (Probabilistic Line Segment Clustering algorithm – PLSC) και ακολουθεί τη λογική της Αποδόμησης Μίξης (Mixture Decomposition). Η διαδικασία εύρεσης βέλτιστων τοποθετήσεων των ευθυγράμμων τμημάτων (κέντρων των κατανομών πιθανοτήτων) φέρεται εις πέρας μέσω μιας επαναληπτικής διαδικασίας παρόμοιας του αλγορίθμου Αναμενόμενης Τιμής Βελτιστοποίησης (Expectation Maximization), κατά την οποία, τα τμήματα μετακινούνται σταδιακά με σκοπό να ταιριάξουν στις γραμμικές ομάδες που σχηματίζονται από τα δεδομένα, βάσει ενός ευρετικού κανόνα (heuristic rule). Ο αλγόριθμος δεν απαιτεί εκ των προτέρων γνώση του αριθμού των συστάδων. Αντί αυτού, ξεκινά κάνοντας μiα υπερεκτίμηση του πλήθους τους και σταδιακά τις μειώνει μέσω κατάλληλων μηχανισμών απαλοιφής και συνένωσης. Με σκοπό την τεκμηρίωση της αξίας της προτεινώμενης μεθόδου, διεξήχθησαν αρκετά πειράματα, τα αποτελέσματα των οποίων δείχνουν ότι η τρέχουσα μέθοδος είναι ικανή να αναγνωρίσει σε πολύ ικανοποιητικό βαθμό συστάδες τόσο σε απλούστερες όσο και σε πολυπλοκότερες περιπτώσεις. Ο αλγόριθμος μπορεί να αποδώσει παρόμοια και, σε μερικές περιπτώσεις, καλύτερα απολέσματα συγκρινόμενος με ένα επιλεγμένο πλήθος σχετικών δημοσιευμένων μεθόδων.Line detection is the process of identifying straight lines or line segments in a given image. Potential applications are commonly found in a variety of fields, such as analysis of geospatial data, image compression and road extraction, to name a few. In this dissertation an approach to the above problem based on probabilistic clustering is explored. A variation of the Gaussian probability distribution centered around a line segment is defined accordingly for the two dimensional space in order to model the line segments in the image under study and an algorithm, called Probabilistic Line Segment Clustering (PLSC) that follows the Mixture Decomposition approach is proposed. The process of finding the optimal positioning of the line segments is carried out by an iterative ExpectationMaximizationlike procedure in which the segments are gradually moved in order to fit the actual edges of the image using a heuristic rule. In order to find the appropriate number of segments/clusters, the algorithm starts with an overestimation of it and progressively reduces it via appropriate elimination and unification mechanisms. Toward supporting the value of the proposed method, experimental results have been carried out and discussed in which it is shown that the current method is able to appropriately identify clusters in multiple scenarios. The algorithm can perform mostly comparably and in some cases, even favorably with regard to a selection of relevant published methods

Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed

Optimization of broaching tool design /

Broaching is a commonly used machining operation in manufacturing of variety of internal or external complex features. High quality surfaces can be generated with high productivity if proper conditions are used. The main disadvantage of broaching is that it is not possible to change any of the cutting parameters but the cutting speed during production. That is because all machining parameters, except cutting speed, are built into broaching tools which makes tool design the most important aspect of broaching. In this thesis, a procedure for the optimization of broaching tools is presented. First, the mechanics of the broaching process and general properties of the broach tools are explained. Important design parameters and the effects of them on the broaching process are demonstrated. Most broaching tools have several tool segments with different profiles. One of the critical factors in the design of these tools is the assignment of segment profiles which determine the relative amounts of material removal rate in each section. Several alternatives are tried for optimization of section geometries and their effects are demonstrated by simulations. The objective function of the optimization problem and the constraints due to machine, tool and part limitations are presented. A heuristic optimization algorithm is developed, and demonstrated by examples. It is also shown that by using the algorithm developed the production time can be reduced due to shortened tool length. The simulation program developed is also explained and demonstrated

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic