{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:QZJNPEFSIUUEASIRW67SBEZTZR","short_pith_number":"pith:QZJNPEFS","schema_version":"1.0","canonical_sha256":"8652d790b24528404911b7bf209333cc59d26ed154496a3314ea6c79ccdc31d5","source":{"kind":"arxiv","id":"1005.5525","version":4},"attestation_state":"computed","paper":{"title":"Efficient Local Search Algorithms for Known and New Neighborhoods for the Generalized Traveling Salesman Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Daniel Karapetyan, Gregory Gutin","submitted_at":"2010-05-30T13:13:09Z","abstract_excerpt":"The Generalized Traveling Salesman Problem (GTSP) is a well-known combinatorial optimization problem with a host of applications. It is an extension of the Traveling Salesman Problem (TSP) where the set of cities is partitioned into so-called clusters, and the salesman has to visit every cluster exactly once.\n  While the GTSP is a very important combinatorial optimization problem and is well studied in many aspects, the local search algorithms used in the literature are mostly basic adaptations of simple TSP heuristics. Hence, a thorough and deep research of the neighborhoods and local search "},"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":"1005.5525","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2010-05-30T13:13:09Z","cross_cats_sorted":[],"title_canon_sha256":"30be6f367b100a46d99cb5d7abbebf816c172cf023138fc9bbd339d34cb50456","abstract_canon_sha256":"53d3b07571fc61e95f0a36f52a0a76759c0adb874574dd30ce9745f36ac1dc36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:02:25.354176Z","signature_b64":"wLb1dhTD3XuHDJQ3bGd4vitZsYqoU7t+pwYsfotEOK8L3O7WjXFFjSi6qe2akG0j7IOhnN5T8wwyG1deHFdRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8652d790b24528404911b7bf209333cc59d26ed154496a3314ea6c79ccdc31d5","last_reissued_at":"2026-05-18T04:02:25.353721Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:02:25.353721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Local Search Algorithms for Known and New Neighborhoods for the Generalized Traveling Salesman Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Daniel Karapetyan, Gregory Gutin","submitted_at":"2010-05-30T13:13:09Z","abstract_excerpt":"The Generalized Traveling Salesman Problem (GTSP) is a well-known combinatorial optimization problem with a host of applications. It is an extension of the Traveling Salesman Problem (TSP) where the set of cities is partitioned into so-called clusters, and the salesman has to visit every cluster exactly once.\n  While the GTSP is a very important combinatorial optimization problem and is well studied in many aspects, the local search algorithms used in the literature are mostly basic adaptations of simple TSP heuristics. Hence, a thorough and deep research of the neighborhoods and local search "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1005.5525","kind":"arxiv","version":4},"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":"1005.5525","created_at":"2026-05-18T04:02:25.353787+00:00"},{"alias_kind":"arxiv_version","alias_value":"1005.5525v4","created_at":"2026-05-18T04:02:25.353787+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1005.5525","created_at":"2026-05-18T04:02:25.353787+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZJNPEFSIUUE","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZJNPEFSIUUEASIR","created_at":"2026-05-18T12:26:13.927090+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZJNPEFS","created_at":"2026-05-18T12:26:13.927090+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/QZJNPEFSIUUEASIRW67SBEZTZR","json":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR.json","graph_json":"https://pith.science/api/pith-number/QZJNPEFSIUUEASIRW67SBEZTZR/graph.json","events_json":"https://pith.science/api/pith-number/QZJNPEFSIUUEASIRW67SBEZTZR/events.json","paper":"https://pith.science/paper/QZJNPEFS"},"agent_actions":{"view_html":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR","download_json":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR.json","view_paper":"https://pith.science/paper/QZJNPEFS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1005.5525&json=true","fetch_graph":"https://pith.science/api/pith-number/QZJNPEFSIUUEASIRW67SBEZTZR/graph.json","fetch_events":"https://pith.science/api/pith-number/QZJNPEFSIUUEASIRW67SBEZTZR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR/action/storage_attestation","attest_author":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR/action/author_attestation","sign_citation":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR/action/citation_signature","submit_replication":"https://pith.science/pith/QZJNPEFSIUUEASIRW67SBEZTZR/action/replication_record"}},"created_at":"2026-05-18T04:02:25.353787+00:00","updated_at":"2026-05-18T04:02:25.353787+00:00"}