Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

A. I. Utilization in the Construction Business: A review on present state and potential for Elenia Oy

The thesis examines the present applications of artificial intelligence in the construction busi-ness domain. Nowadays, businesses are focusing on the safety of an operating environment. In a project-based business, managing projects and portfolios with safety management is significantly important. Lack of knowledge is rarely a root cause of undesired deviations. More often, the deviations in processes are related to an irregularity in compliance with the instructions and rules. With the assistance of AI-based tools, such as machine learning, one can improve efficiency on safety and project management tasks. The thesis provides a gen-eral view of artificial intelligence and a review of present approaches on AI utilization in the construction domain. Also, the thesis suggests the next steps for the utilization of AI in Elenia’s construction business. The first section of the thesis gives an overall view of artificial intelligence. In the second and third sections, a review of the present utilization approaches is examined. In the second section, the utilization is examined in the construction site safety domain. In the third section the examined field is related to the project management do-main. The most common way to utilize AI were to exploit existing data for risk prediction and relationship detection. The risks differ from the examined domain. Thus, building a machine learning model is use-case related. There are various ways to utilize different models to achieve the benefits of machine learning. In Elenia Oy’s activities managing projects have a key role for achieving company’s mission: Electrifying life. The electric grids demand continu-ous maintenance and consistent development. One part of the development is replacement of components that have reached end of the technical lifecycle. For example, replacement can be executed in Elenia’s Säävarma projects. The development of occupational safety Elenia together with its partners has committed for safety manifesto. The key theme of safe-ty manifesto is to render everyone related to Elenia’s work field to return home in good health. The key approach of thesis was to find widely different approaches to utilize an AI for the development of safety and project objectives.Tässä diplomityössä selvitettiin rakentamisliiketoimintoihin liittyviä tekoälyn käyttökohteita. Nykyisin liiketoiminnoissa keskitytään operatiivisten toimintojen turvallisuuteen. Projektiliike-toiminnassa projektien ja portfolioiden johtaminen yhdessä turvallisuusjohtamisen kanssa on huomattavan tärkeää. Tiedon puute on harvoin juurisyy ei-toivotuille poikkeamille. Useammin poikkeamat prosesseissa johtuvat epäsäännöllisyydestä ohjeistuksien ja sääntöjen noudatta-misen suhteen. Tekoälyyn pohjautuvien työkalujen, kuten koneoppiminen, avulla on mahdol-lista kehittää turvallisuuteen ja projektijohtamiseen liittyvien tehtävien tehokkuutta. Tutkielma sisältää yleisen katsauksen tekoälyyn ja tarkastelun nykyisistä lähestymistavoista tekoälyn hyödyntämiseen rakentamisliiketoimintoihin liittyen. Lisäksi työssä muodostetaan ehdotukset tuleville vaiheille tekoälyn hyödyntämiseen Elenian rakentamisliiketoiminnassa. Ensimmäisessä osassa käydään läpi yleiskatsaus tekoälyyn liittyen. Toisessa ja kolmannessa osassa työtä tar-kastellaan nykyisiä tekoälyn käyttökohteita. Toisessa osassa tarkastellaan rakentamistöiden turvallisuuteen liittyviä hyödyntämiskohteita. Kolmannessa osassa vastaava tarkastelu keskit-tyy projekti ja portfoliojohtamisen toimintaympäristöön. Yleisin tapa hyödyntää tekoälyä on selvittää ja tunnistaa toimintaympäristön riskeihin liittyvien tekijöiden suhteita toisiinsa. Erilai-sissa toimintaympäristöissä on erilaisia riskejä, joiden esiintymisen todennäköisyyttä on syytä pienentää. Koneoppimismallien rakentamisen toteutus on käyttökohde sidonnainen, joten on monia tapoja hyödyntää koneoppimista. Elenia Oy:n toiminnassa projektit ja niiden hallinta ovat keskeisessä osassa mahdollistamassa yhtiön missiota: Elämää sähköistämässä. Sähköver-kot vaativat jatkuvaa kunnossapitoa ja johdonmukaista kehittämistä. Osa tätä kehittämistä on teknisen käyttöiän saavuttaneiden komponenttien uusinta, esimerkiksi Elenian Säävarma-hankkeissa. Työturvallisuuden edistämiseksi Elenia on yhdessä kumppaniensa kanssa allekir-joittanut Turvallisuusmanifestin, jonka keskeinen teema on mahdollistaa kaikkien Elenian töissä olevien henkilöiden turvallisen palaamisen terveenä kotiin. Tutkielman keskeisenä lähestymis-tapana oli etsiä laajasti erilaisia tapoja hyödyntää tekoälyä liittyen turvallisuus- ja projektita-voitteiden kehittämiseen

Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Data Mining with Supervised Instance Selection Improves Artificial Neural Network Classification Accuracy

IDSs may monitor intrusion logs, traffic control packets, and assaults. Nets create large amounts of data. IDS log characteristics are used to detect whether a record or connection was attacked or regular network activity. Reduced feature size aids machine learning classification. This paper describes a standardised and systematic intrusion detection classification approach. Using dataset signatures, the Naive Bayes Algorithm, Random Tree, and Neural Network classifiers are assessed. We examine the feature reduction efficacy of PCA and the fisheries score in this study. The first round of testing uses a reduced dataset without decreasing the components set, and the second uses principal components analysis. PCA boosts classification accuracy by 1.66 percent. Artificial immune systems, inspired by the human immune system, use learning, long-term memory, and association to recognise and v-classify. Introduces the Artificial Neural Network (ANN) classifier model and its development issues. Iris and Wine data from the UCI learning repository proves the ANN approach works. Determine the role of dimension reduction in ANN-based classifiers. Detailed mutual information-based feature selection methods are provided. Simulations from the KDD Cup'99 demonstrate the method's efficacy. Classifying big data is important to tackle most engineering, health, science, and business challenges. Labelled data samples train a classifier model, which classifies unlabeled data samples into numerous categories. Fuzzy logic and artificial neural networks (ANNs) are used to classify data in this dissertation

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure