{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:O3ODVTDV3GRTPPIUESTZGW6W66","short_pith_number":"pith:O3ODVTDV","schema_version":"1.0","canonical_sha256":"76dc3acc75d9a337bd1424a7935bd6f7b04b2bc54c4f47497d9a91216a003b66","source":{"kind":"arxiv","id":"1803.01451","version":2},"attestation_state":"computed","paper":{"title":"Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.SY"],"primary_cat":"math.OC","authors_text":"Bruce Ellingwood, Edwin K. P. Chong, Hussam Mahmoud, Saeed Nozhati, Yugandhar Sarkale","submitted_at":"2018-03-05T01:22:22Z","abstract_excerpt":"The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along wi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1803.01451","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-03-05T01:22:22Z","cross_cats_sorted":["cs.CE","cs.SY"],"title_canon_sha256":"1fe460caadd27dc8b6682ed04e51e0e62fd0fe46b59d48d195234687febb57cc","abstract_canon_sha256":"e9f735e2d1a4adca593b5845bda2d2ca005d6cc6c5e471b26c7705e0edc36fea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:30.259558Z","signature_b64":"b+vBR72BvisRoZ/5pXZzpgUl9pYMYj8gWdpp+OEydT+d2Wlqcu8OZIrWdqnJzxtCcqeSQKnCZSWK4y6tgjnWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76dc3acc75d9a337bd1424a7935bd6f7b04b2bc54c4f47497d9a91216a003b66","last_reissued_at":"2026-05-18T00:04:30.258463Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:30.258463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Near-optimal planning using approximate dynamic programming to enhance post-hazard community resilience management","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.SY"],"primary_cat":"math.OC","authors_text":"Bruce Ellingwood, Edwin K. P. Chong, Hussam Mahmoud, Saeed Nozhati, Yugandhar Sarkale","submitted_at":"2018-03-05T01:22:22Z","abstract_excerpt":"The lack of a comprehensive decision-making approach at the community level is an important problem that warrants immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01451","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1803.01451","created_at":"2026-05-18T00:04:30.259001+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.01451v2","created_at":"2026-05-18T00:04:30.259001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01451","created_at":"2026-05-18T00:04:30.259001+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3ODVTDV3GRT","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3ODVTDV3GRTPPIU","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3ODVTDV","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66","json":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66.json","graph_json":"https://pith.science/api/pith-number/O3ODVTDV3GRTPPIUESTZGW6W66/graph.json","events_json":"https://pith.science/api/pith-number/O3ODVTDV3GRTPPIUESTZGW6W66/events.json","paper":"https://pith.science/paper/O3ODVTDV"},"agent_actions":{"view_html":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66","download_json":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66.json","view_paper":"https://pith.science/paper/O3ODVTDV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.01451&json=true","fetch_graph":"https://pith.science/api/pith-number/O3ODVTDV3GRTPPIUESTZGW6W66/graph.json","fetch_events":"https://pith.science/api/pith-number/O3ODVTDV3GRTPPIUESTZGW6W66/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66/action/storage_attestation","attest_author":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66/action/author_attestation","sign_citation":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66/action/citation_signature","submit_replication":"https://pith.science/pith/O3ODVTDV3GRTPPIUESTZGW6W66/action/replication_record"}},"created_at":"2026-05-18T00:04:30.259001+00:00","updated_at":"2026-05-18T00:04:30.259001+00:00"}