Order picking is typically the most costly operation in a warehouse and traveling is typically the most time consuming task within order picking. In this study we focus on the layout design for a rectangular warehouse, a warehouse with parallel storage blocks with main aisles separating them. We specifically analyze the impact of adding cross aisles that cut storage blocks perpendicularly, which can reduce travel times during order picking by introducing flexibility in going from one main aisle to the next. We consider two types of cross aisles, those that are equally spaced (Case 1) and those that are unequally spaced, which respectively have equal and unequal distances among them. For Case 2, we extend an earlier model and present a heuristic algorithm for finding the best distances among cross aisles. We carry out extensive computational experiments for a variety of warehouse designs. Our findings suggest that warehouse planners can obtain great travel time savings through establishing equally spaced cross aisles, but little additional savings in unequally-spaced cross isles. We present a look-up table that provides the best number of equally spaced cross aisles when the number of cross aisles (N) and the length of the warehouse (T) are given. Finally, when the values of N and T are not known, we suggest establishing three cross aisles in a warehouse.

Ertek, G., Incel, B. and Arslan, M. C. (2007). “Impact of Crossaisles in a rectangular warehouse: A computational study,” in Facility Logistics: Approaches and Solutions to Next Generation Challenges, Editor: Maher Lahmar. Auerbach.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Teaching Warehousing Concepts through Interactive Animations and 3-D Models

Dr. Gürdal Ertek recommends the following related books:

Facility Logistics: Approaches and Solutions to Next Generation Challenges (Resource Management) 1st Edition

World-Class Warehousing and Material Handling 1st Edition

 

 

 

In this paper we present a study where we visually analyzed science citation data to investigate the competitiveness of world countries in selected categories of science. The data set that we worked on in our study includes the number of papers published and the number of citations made in the ESI (Essential Science Indicators) database in 2004. The data set lists these values for practically every country in the world. In analyzing the data, we employ methods and software tools developed and used in the data mining and information visualization fields of the Computer Science. Some of the questions for which we look for answers in this study are the following: (a) Which countries are most competitive in the selected categories of science? (i.e. Engineering, Computer Science, Economics & Business) (b) What type of correlations exist between different categories of science? For example, do countries with many published papers in the field of Engineering science also have many papers published on Computer Science or Economics & Business? (c) Which countries produce the most influential papers? This analysis is needed since a country may have many papers published but these papers may be cited very rarely. (d) Can we gain useful and actionable insights by combining science citation data with socioeconomic and geographical data?

Arslan, M. C. and Ertek, G. (2007). “Visual mining of science citation data for benchmarking scientific and technological competitiveness of world countries.” 2. International Conference on Technology and Economic Development, Izmir, Turkey.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Visual Mining of Science Citation Data for Benchmarking Scientific and Technological Competitiveness of World Countries

Dr. Gürdal Ertek recommends the following related books:

Information Visualization: Design for Interaction (2nd Edition)