20,269 research outputs found
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄnĂ prĂĄce je zamÄĆena na optimalizaci prĆŻbÄhu pracovnĂch operacĂ v logistickĂœch skladech a distribuÄnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plĂĄnovĂĄnĂ, rozvrhovĂĄnĂ a odbavovĂĄnĂ. JelikoĆŸ jde o problĂ©m patĆĂcĂ do tĆĂdy sloĆŸitosti NP-teĆŸkĂœ, je vĂœpoÄetnÄ velmi nĂĄroÄnĂ© nalĂ©zt optimĂĄlnĂ ĆeĆĄenĂ. MotivacĂ pro ĆeĆĄenĂ tĂ©to prĂĄce je vyplnÄnĂ pomyslnĂ© mezery mezi metodami zkoumanĂœmi na vÄdeckĂ© a akademickĂ© pĆŻdÄ a metodami pouĆŸĂvanĂœmi v produkÄnĂch komerÄnĂch prostĆedĂch. JĂĄdro optimalizaÄnĂho algoritmu je zaloĆŸeno na zĂĄkladÄ genetickĂ©ho programovĂĄnĂ ĆĂzenĂ©ho bezkontextovou gramatikou. HlavnĂm pĆĂnosem tĂ©to prĂĄce je a) navrhnout novĂœ optimalizaÄnĂ algoritmus, kterĂœ respektuje nĂĄsledujĂcĂ optimalizaÄnĂ podmĂnky: celkovĂœ Äas zpracovĂĄnĂ, vyuĆŸitĂ zdrojĆŻ, a zahlcenĂ skladovĂœch uliÄek, kterĂ© mĆŻĆŸe nastat bÄhem zpracovĂĄnĂ ĂșkolĆŻ, b) analyzovat historickĂĄ data z provozu skladu a vyvinout sadu testovacĂch pĆĂkladĆŻ, kterĂ© mohou slouĆŸit jako referenÄnĂ vĂœsledky pro dalĆĄĂ vĂœzkum, a dĂĄle c) pokusit se pĆedÄit stanovenĂ© referenÄnĂ vĂœsledky dosaĆŸenĂ© kvalifikovanĂœm a trĂ©novanĂœm operaÄnĂm manaĆŸerem jednoho z nejvÄtĆĄĂch skladĆŻ ve stĆednĂ EvropÄ.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
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Decision support for build-to-order supply chain management through multiobjective optimization
This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF
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Decision support for build-to-order supply chain management through multiobjective optimization
This paper aims to identify the gaps in decision-making support based on
multiobjective optimization for build-to-order supply chain management (BTOSCM).
To this end, it reviews the literature available on modelling build-to-order
supply chains (BTO-SC) with the focus on adopting multiobjective optimization
(MOO) techniques as a decision support tool. The literature has been classified based
on the nature of the decisions in different part of the supply chain, and the key
decision areas across a typical BTO-SC are discussed in detail. Available software
packages suitable for supporting decision making in BTO supply chains are also
identified and their related solutions are outlined. The gap between the modelling and
optimization techniques developed in the literature and the decision support needed in
practice are highlighted and future research directions to better exploit the decision
support capabilities of MOO are proposed
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in todayâs business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of todayâs research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
Computer-Aided Warehouse Engineering (CAWE): Leveraging MDA and ADM for the Development of Data Warehouses
During the last decade, data warehousing has reached a high maturity and is a well-accepted technology in decision support systems. Nevertheless, development and maintenance are still tedious tasks since the systems grow over time and complex architectures have been established. The paper at hand adopts the concepts of Model Driven Architecture (MDA) and Architecture Driven Modernization (ADM) taken from the software engineering discipline to the data warehousing discipline. We show the works already available, outline further research directions and give hints for implementation of Computer-Aided Warehouse Engineering systems
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