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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Related Experiment Video

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Algorithmic Study of Online Multi-Facility Location Problems.

Christine Markarian1, Abdul-Nasser Kassar2, Manal Yunis2

  • 1Department of Engineering and Information Technology, University of Dubai, Dubai, UAE.

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|May 25, 2022
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Summary
This summary is machine-generated.

This study introduces online algorithms for the multi-facility location problem, ensuring robust client service by connecting each to multiple facilities. It addresses both purchasing and leasing models in an online setting.

Keywords:
Competitive analysisLeasing frameworkMulti-facility locationOnline algorithmsRandomized roundingRobustness

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Area of Science:

  • Operations Research
  • Computer Science
  • Management Science

Background:

  • Facility Location (FL) problems optimize facility placement for cost minimization.
  • Traditional FL assumes clients connect to a single facility.
  • Robustness requires clients to connect to multiple facilities.

Purpose of the Study:

  • Investigate Multi-Facility Location (MFL) in the online setting (OMFL).
  • Develop the first online algorithms for metric and non-metric OMFL variants.
  • Analyze OMFL in a leasing setting with time-varying facility availability.

Main Methods:

  • Competitive analysis to compare online algorithms against optimal offline solutions.
  • Algorithm design for online client request arrivals.
  • Modeling facility leasing with duration and price considerations.

Main Results:

  • Proposed the first online algorithms for the metric and non-metric OMFL.
  • Evaluated algorithm performance using competitive analysis.
  • Introduced and analyzed OMFL in the leasing setting.

Conclusions:

  • The developed online algorithms provide robust solutions for OMFL.
  • Competitive analysis offers a worst-case performance guarantee.
  • The leasing model extends OMFL applicability to dynamic environments.