20,598 research outputs found
Online Algorithms for Warehouse Management
As the prevalence of E-commerce continues to grow, the efficient operation of warehouses and fulfillment centers is becoming increasingly important. To this end, many such warehouses are adding automation in order to help streamline operations, drive down costs, and increase overall efficiency. The introduction of automation comes with the opportunity for new theoretical models and computational problems with which to better understand and optimize such systems.
These systems often maintain a warehouse of standardized portable storage units, which are stored and retrieved by robotic workers. In general, there are two principal issues in optimizing such a system: where in the warehouse each storage unit should be located and how best to retrieve them. These two concerns naturally go hand-in-hand, but are further complicated by the unknown request frequencies of stored products. Analogous to virtual-memory systems, the more popular and oft-requested an item is, the more efficient its retrieval should be. In this paper, we propose a theoretical model for organizing portable storage units in a warehouse subject to an online sequence of access requests. We consider two formulations, depending on whether there is a single access point or multiple access points. We present algorithms that are O(1)-competitive with respect to an optimal algorithm. In the case of a single access point, our solution is also asymptotically optimal with respect to density
Better Approximation Bounds for the Joint Replenishment Problem
The Joint Replenishment Problem (JRP) deals with optimizing shipments of
goods from a supplier to retailers through a shared warehouse. Each shipment
involves transporting goods from the supplier to the warehouse, at a fixed cost
C, followed by a redistribution of these goods from the warehouse to the
retailers that ordered them, where transporting goods to a retailer has
a fixed cost . In addition, retailers incur waiting costs for each
order. The objective is to minimize the overall cost of satisfying all orders,
namely the sum of all shipping and waiting costs.
JRP has been well studied in Operations Research and, more recently, in the
area of approximation algorithms. For arbitrary waiting cost functions, the
best known approximation ratio is 1.8. This ratio can be reduced to 1.574 for
the JRP-D model, where there is no cost for waiting but orders have deadlines.
As for hardness results, it is known that the problem is APX-hard and that the
natural linear program for JRP has integrality gap at least 1.245. Both results
hold even for JRP-D. In the online scenario, the best lower and upper bounds on
the competitive ratio are 2.64 and 3, respectively. The lower bound of 2.64
applies even to the restricted version of JRP, denoted JRP-L, where the waiting
cost function is linear.
We provide several new approximation results for JRP. In the offline case, we
give an algorithm with ratio 1.791, breaking the barrier of 1.8. In the online
case, we show a lower bound of 2.754 on the competitive ratio for JRP-L (and
thus JRP as well), improving the previous bound of 2.64. We also study the
online version of JRP-D, for which we prove that the optimal competitive ratio
is 2
Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies
Business Intelligence plays an important role in decision making. Based on
data warehouses and Online Analytical Processing, a business intelligence tool
can be used to analyze complex data. Still, summarizability issues in data
warehouses cause ineffective analyses that may become critical problems to
businesses. To settle this issue, many researchers have studied and proposed
various solutions, both in relational and XML data warehouses. However, they
find difficulty in evaluating the performance of their proposals since the
available benchmarks lack complex hierarchies. In order to contribute to
summarizability analysis, this paper proposes an extension to the XML warehouse
benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate
XML data warehouses with scalable complex hierarchies as well as
summarizability processing. We experimentally demonstrated that complex
hierarchies can definitely be included into a benchmark dataset, and that our
benchmark is able to compare two alternative approaches dealing with
summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP
2012), Maui : United States (2012
Data Management and Mining in Astrophysical Databases
We analyse the issues involved in the management and mining of astrophysical
data. The traditional approach to data management in the astrophysical field is
not able to keep up with the increasing size of the data gathered by modern
detectors. An essential role in the astrophysical research will be assumed by
automatic tools for information extraction from large datasets, i.e. data
mining techniques, such as clustering and classification algorithms. This asks
for an approach to data management based on data warehousing, emphasizing the
efficiency and simplicity of data access; efficiency is obtained using
multidimensional access methods and simplicity is achieved by properly handling
metadata. Clustering and classification techniques, on large datasets, pose
additional requirements: computational and memory scalability with respect to
the data size, interpretability and objectivity of clustering or classification
results. In this study we address some possible solutions.Comment: 10 pages, Late
A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
The overwhelmingly increasing amount of stored data has spurred researchers
seeking different methods in order to optimally take advantage of it which
mostly have faced a response time problem as a result of this enormous size of
data. Most of solutions have suggested materialization as a favourite solution.
However, such a solution cannot attain Real- Time answers anyhow. In this paper
we propose a framework illustrating the barriers and suggested solutions in the
way of achieving Real-Time OLAP answers that are significantly used in decision
support systems and data warehouses
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.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.
Perspects in astrophysical databases
Astrophysics has become a domain extremely rich of scientific data. Data
mining tools are needed for information extraction from such large datasets.
This asks for an approach to data management emphasizing the efficiency and
simplicity of data access; efficiency is obtained using multidimensional access
methods and simplicity is achieved by properly handling metadata. Moreover,
clustering and classification techniques on large datasets pose additional
requirements in terms of computation and memory scalability and
interpretability of results. In this study we review some possible solutions
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
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