Injecting problem-dependent knowledge to improve evolutionary optimization search ability

Izquierdo, Joaquín; Campbell, Enrique; Montalvo, Idel; Pérez-García, Rafael

doi:10.1016/j.cam.2015.03.019

Cited by 6 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An order line specifies the quantity to which a product is ordered. The class AssociationRule defines association rules derived by the Apriori algorithm [36]. The confidence ranges from 0 to 1 and expresses the strength of the correlation between the left-sided and the right-sided set of products.…”

Section: Meta-model Of Considered Mezzanine Warehousesmentioning

confidence: 99%

A Case Study on Optimization of Warehouses

Müller¹,

Krämer²,

Kounev³

et al. 2021

Preprint

View full text Add to dashboard Cite

In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance. Hence, many approaches exist, that optimize the order picking process based on diverse economic criteria. However, most of these approaches focus on a single economic objective at once and disregard ergonomic criteria in their optimization. Further, the influence of the placement of the items to be picked is underestimated and accordingly, too little attention is paid to the interdependence of these two problems. In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence. We propose a customized version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the storage assignment problem as well as an Ant Colony Optimization (ACO) algorithm for optimizing the order picking problem. Both algorithms incorporate multiple economic and ergonomic constraints simultaneously. Furthermore, the algorithms incorporate knowledge about the interdependence between both problems, aiming to improve the overall warehouse performance. Our evaluation results show that our proposed algorithms return better storage assignments and order pick routes compared to commonly used techniques for the following quality indicators for comparing Pareto fronts: Coverage, Generational Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational Distance. Additionally, the evaluation regarding the interaction of both algorithms shows a better performance when combining both proposed algorithms.

show abstract

Section: Meta-model Of Considered Mezzanine Warehousesmentioning

confidence: 99%

A Case Study on Optimization of Warehouses

Müller¹,

Krämer²,

Kounev³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…There are two popular swarm-inspired methods in computational intelligence: ACO (ant colony optimization) (Dorigo et al, 1996), inspired by the foraging behavior of ants, and PSO (particle swarm optimization) (Kennedy and Eberhart, 1995), inspired by the social behavior of flocks of birds or schools of fish. Hybrid platforms that use several metaheuristics (Montalvo et al, 2014), with self-adaptive abilities (Izquierdo et al, 2016a) and able to exploit knowledge injected to the model (Izquierdo et al, 2016b) both from the expert know-how in the field and from mining tasks performed during the evolution process itself, have also shown great interest, because of their improved search abilities.…”

Section: Evolutionary Computationmentioning

confidence: 99%

Which method to use? An assessment of data mining methods in Environmental Data Science

Gibert

Izquierdo

Sànchez–Marrè

et al. 2018

Environmental Modelling & Software

Self Cite

View full text Add to dashboard Cite

Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge library of DM algorithms has been developed to tackle a variety of problems in fields such as medical imaging and traffic analysis. Many DM techniques are far more flexible than more classical numerial simulation or statistical modelling approaches. These could be usefully applied to data-rich environmental problems. Certain techniques such as artificial neural networks, clustering, case-based reasoning or Bayesian networks have been applied in environmental modelling, while other methods, like support vector machines among others, have yet to be taken up on a wide scale. There is greater scope for many lesser known techniques to be applied in environmental research, with the potential to contribute to addressing some of the current open environmental challenges. However, selecting the best DM technique for a given environmental problem is not a simple decision, and there is a lack of guidelines and criteria that helps the data scientist and environmental scientists to ensure effective knowledge extraction from data. This paper provides a broad introduction to the use of DM in Data Science processes for environmental researchers. Data Science contains three main steps (pre-processing, data mining and post-processing). This paper provides a conceptualization of Environmental Systems and a conceptualization of DM methods, which are in the core step of the Data Science process. These two elements define a conceptual framework that is on the basis of a new methodology proposed for relating the characteristics of a given environmental problem with a family of Data Mining methods. The paper provides a general overview and guidelines of DM techniques to a non-expert user, who can decide with this support which is the more suitable technique to solve their problem at hand. The decision is related to the bidimensional relationship between the type of environmental system and the type of DM method. An illustrative two way table containing references for each pair Environmental System-Data Mining method is presented and discussed. Some examples of how the proposed methodology is used to support DM method selection are also presented, and challenges and future trends are identified.

show abstract

“…Kohonen et al (2000) highlights the use of SOMs as a clustering tool for database operation. Izquierdo et al (2016) uses SOMs for early data labeling for the application of classification tools. Kalteh et al (2008) highlight its extensive use in water resources problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid SOM+k-Means clustering to improve planning, operation and management in water distribution systems

Brentan

Lima

Luvizotto

et al. 2018

Environmental Modelling & Software

View full text Add to dashboard Cite

With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results.

show abstract

Injecting problem-dependent knowledge to improve evolutionary optimization search ability

Cited by 6 publications

References 36 publications

A Case Study on Optimization of Warehouses

A Case Study on Optimization of Warehouses

Which method to use? An assessment of data mining methods in Environmental Data Science

Hybrid SOM+k-Means clustering to improve planning, operation and management in water distribution systems

Contact Info

Product

Resources

About