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ABSTRACT An important goal in many planning contexts is maximizing primary and secondary (or backup) coverage while locating a specified number of service facilities. In general, we are interested in providing the greatest level of coverage to demand that is continuously distributed across space. A critical issue is how to represent continuous demand in coverage analysis, reducing or eliminating error and uncertainty. This paper evaluates representation issues in primary and secondary coverage location modeling. To overcome representational limitations, enhancements for spatial coverage abstraction are introduced and incorporated in a mathematical optimization model. In addition to model improvements, this paper introduces a new and novel error assessment approach arising due to the existence of multiple objectives. Surveillance sensor siting in an urban area is utilized to assess enhanced modeling capabilities.
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Stored only when both papers are in the corpus — 24 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.