{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NXR3LNJGMP7IA7QU3MQTEQ3I77","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"54440f45ac5f80577affdea26ba2f1ab56e629ce5d2ce2e068ba63cd1eac2ecd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-01T04:35:55Z","title_canon_sha256":"0d61a13adff1e6f587f730946927460ecce6e095e1023f8cb33999ae52c8f3f9"},"schema_version":"1.0","source":{"id":"1901.00090","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.00090","created_at":"2026-05-17T23:57:06Z"},{"alias_kind":"arxiv_version","alias_value":"1901.00090v1","created_at":"2026-05-17T23:57:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00090","created_at":"2026-05-17T23:57:06Z"},{"alias_kind":"pith_short_12","alias_value":"NXR3LNJGMP7I","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NXR3LNJGMP7IA7QU","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NXR3LNJG","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:20092d91556f9bff5e5cff1364434a6ac33a3a5f3d71140b3c71893c46eee5b5","target":"graph","created_at":"2026-05-17T23:57:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Modeling and optimization of multi-echelon supply chain systems is challenging as it requires a holistic approach that exploits synergies and interactions between echelons while accurately accounting for variability observed by these systems. We develop a simulation-optimization framework that minimizes average inventory while maintaining desired average $\\beta$ service level at stocking locations. We use a discrete-event simulation framework to accurately capture system interactions. Instead of a parametric estimation approach, the demand and the lead time variability are quantified by bootst","authors_text":"Anshul Agarwal","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-01T04:35:55Z","title":"Multi-echelon Supply Chain Inventory Planning using Simulation-Optimization with Data Resampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00090","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fbc95119fd4bdfffadb19e6ac137d21e5436ad0210ed8feae8781d7ddc203791","target":"record","created_at":"2026-05-17T23:57:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"54440f45ac5f80577affdea26ba2f1ab56e629ce5d2ce2e068ba63cd1eac2ecd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-01T04:35:55Z","title_canon_sha256":"0d61a13adff1e6f587f730946927460ecce6e095e1023f8cb33999ae52c8f3f9"},"schema_version":"1.0","source":{"id":"1901.00090","kind":"arxiv","version":1}},"canonical_sha256":"6de3b5b52663fe807e14db21324368ffdc2c61f6e1e783b0aa86c5efcb1d57c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6de3b5b52663fe807e14db21324368ffdc2c61f6e1e783b0aa86c5efcb1d57c1","first_computed_at":"2026-05-17T23:57:06.951423Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:06.951423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1vvdzaJJpvvrmtoQw0GmPV2orv0mwDKkvsZ9BEGnKLnyHc4uHLtAudNIJAOuHK9ZPdKEy1ZnQJKzLOjdjAzqBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:06.951810Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.00090","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbc95119fd4bdfffadb19e6ac137d21e5436ad0210ed8feae8781d7ddc203791","sha256:20092d91556f9bff5e5cff1364434a6ac33a3a5f3d71140b3c71893c46eee5b5"],"state_sha256":"d237d09afc15b0db27bb4a12f5ed96b82772d6e6f462f916106d4a2a1465b406"}