{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:377WO6M7BYYTHJV4KSM6LI4CFV","short_pith_number":"pith:377WO6M7","canonical_record":{"source":{"id":"2309.15517","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-09-27T09:33:56Z","cross_cats_sorted":[],"title_canon_sha256":"2dc335a3eda3ba5f4facfd6e9f2f8d94f7806949ecda73f4809ac3e6689d726c","abstract_canon_sha256":"a49ca50df5369c75266f4bc16cc956c04e03149b79d7a9e815649d02c3d39e95"},"schema_version":"1.0"},"canonical_sha256":"dfff67799f0e3133a6bc5499e5a3822d488769d29dd2bf1e2229065e68377e4f","source":{"kind":"arxiv","id":"2309.15517","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.15517","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"arxiv_version","alias_value":"2309.15517v2","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15517","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_12","alias_value":"377WO6M7BYYT","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_16","alias_value":"377WO6M7BYYTHJV4","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_8","alias_value":"377WO6M7","created_at":"2026-07-05T06:56:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:377WO6M7BYYTHJV4KSM6LI4CFV","target":"record","payload":{"canonical_record":{"source":{"id":"2309.15517","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-09-27T09:33:56Z","cross_cats_sorted":[],"title_canon_sha256":"2dc335a3eda3ba5f4facfd6e9f2f8d94f7806949ecda73f4809ac3e6689d726c","abstract_canon_sha256":"a49ca50df5369c75266f4bc16cc956c04e03149b79d7a9e815649d02c3d39e95"},"schema_version":"1.0"},"canonical_sha256":"dfff67799f0e3133a6bc5499e5a3822d488769d29dd2bf1e2229065e68377e4f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:39.131925Z","signature_b64":"uWx0eoPn5I5/fjyO2o5EvDxY00AJdMH7EGPGCN6VfWZjn4/5eUuZ/mIieXrZZrmYvaVwpYvURB8u9DbvDcXRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dfff67799f0e3133a6bc5499e5a3822d488769d29dd2bf1e2229065e68377e4f","last_reissued_at":"2026-07-05T06:56:39.131540Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:39.131540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.15517","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:56:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J9w3fB2+9JZBD6tV4B2Pgm774/w6xBzF5m6TeUILZ8Y58jslDLm+6Pri/fvIhbvPfVO9wtbsQuqdYFW2EQKpAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:16:37.753180Z"},"content_sha256":"e97bc474f65b86423d5f230e5df0144cf981589e4422667d15c397c15bf3d061","schema_version":"1.0","event_id":"sha256:e97bc474f65b86423d5f230e5df0144cf981589e4422667d15c397c15bf3d061"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:377WO6M7BYYTHJV4KSM6LI4CFV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fan Chiang, I-Chen Wu, Ji-Han Wu, Kuo-Hao Ho, Ruei-Yu Jheng, Yen-Chi Chen, Yuan-Yu Wu","submitted_at":"2023-09-27T09:33:56Z","abstract_excerpt":"Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes important to develop efficient heuristics to solve JSP/FJSP. A kind of method of solving scheduling problems is construction heuristics, which constructs scheduling solutions via heuristics. Recently, many methods for construction heuristics leverage deep reinforcement learning (DRL) with graph neural n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15517","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.15517/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:56:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lVfKpoomhd8F+pFZp6OlGoFtUFthvCaIKKT2t0ehMie7cDrNL2AAJsthbo0OX97oU6c0KUyr/nfAyveEE2kIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:16:37.753737Z"},"content_sha256":"5887f86a6e916f68efedeb85b901dfdfabdac3e6f8b485cbb8ce35e0ccae159c","schema_version":"1.0","event_id":"sha256:5887f86a6e916f68efedeb85b901dfdfabdac3e6f8b485cbb8ce35e0ccae159c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/377WO6M7BYYTHJV4KSM6LI4CFV/bundle.json","state_url":"https://pith.science/pith/377WO6M7BYYTHJV4KSM6LI4CFV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/377WO6M7BYYTHJV4KSM6LI4CFV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T19:16:37Z","links":{"resolver":"https://pith.science/pith/377WO6M7BYYTHJV4KSM6LI4CFV","bundle":"https://pith.science/pith/377WO6M7BYYTHJV4KSM6LI4CFV/bundle.json","state":"https://pith.science/pith/377WO6M7BYYTHJV4KSM6LI4CFV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/377WO6M7BYYTHJV4KSM6LI4CFV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:377WO6M7BYYTHJV4KSM6LI4CFV","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":"a49ca50df5369c75266f4bc16cc956c04e03149b79d7a9e815649d02c3d39e95","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-09-27T09:33:56Z","title_canon_sha256":"2dc335a3eda3ba5f4facfd6e9f2f8d94f7806949ecda73f4809ac3e6689d726c"},"schema_version":"1.0","source":{"id":"2309.15517","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.15517","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"arxiv_version","alias_value":"2309.15517v2","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.15517","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_12","alias_value":"377WO6M7BYYT","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_16","alias_value":"377WO6M7BYYTHJV4","created_at":"2026-07-05T06:56:39Z"},{"alias_kind":"pith_short_8","alias_value":"377WO6M7","created_at":"2026-07-05T06:56:39Z"}],"graph_snapshots":[{"event_id":"sha256:5887f86a6e916f68efedeb85b901dfdfabdac3e6f8b485cbb8ce35e0ccae159c","target":"graph","created_at":"2026-07-05T06:56:39Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.15517/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes important to develop efficient heuristics to solve JSP/FJSP. A kind of method of solving scheduling problems is construction heuristics, which constructs scheduling solutions via heuristics. Recently, many methods for construction heuristics leverage deep reinforcement learning (DRL) with graph neural n","authors_text":"Fan Chiang, I-Chen Wu, Ji-Han Wu, Kuo-Hao Ho, Ruei-Yu Jheng, Yen-Chi Chen, Yuan-Yu Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-09-27T09:33:56Z","title":"Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.15517","kind":"arxiv","version":2},"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:e97bc474f65b86423d5f230e5df0144cf981589e4422667d15c397c15bf3d061","target":"record","created_at":"2026-07-05T06:56:39Z","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":"a49ca50df5369c75266f4bc16cc956c04e03149b79d7a9e815649d02c3d39e95","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-09-27T09:33:56Z","title_canon_sha256":"2dc335a3eda3ba5f4facfd6e9f2f8d94f7806949ecda73f4809ac3e6689d726c"},"schema_version":"1.0","source":{"id":"2309.15517","kind":"arxiv","version":2}},"canonical_sha256":"dfff67799f0e3133a6bc5499e5a3822d488769d29dd2bf1e2229065e68377e4f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dfff67799f0e3133a6bc5499e5a3822d488769d29dd2bf1e2229065e68377e4f","first_computed_at":"2026-07-05T06:56:39.131540Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:56:39.131540Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uWx0eoPn5I5/fjyO2o5EvDxY00AJdMH7EGPGCN6VfWZjn4/5eUuZ/mIieXrZZrmYvaVwpYvURB8u9DbvDcXRDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:56:39.131925Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.15517","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e97bc474f65b86423d5f230e5df0144cf981589e4422667d15c397c15bf3d061","sha256:5887f86a6e916f68efedeb85b901dfdfabdac3e6f8b485cbb8ce35e0ccae159c"],"state_sha256":"5f3c37ee82414b1655ffa81e8e088cccf37e5edb3cb38fae172aaeae189c03b2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"du4ZAYUBzkc5yXuGydW2XCPvdnfNgyAstb5teSxjv6wYraPnWUaLbVyaeHYsv2ZV+81kA6xduNcfoSbL1aA3AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:16:37.757633Z","bundle_sha256":"4f02192f56bd905dc14397a01d922151f7b01f26a55c4e414fb18437e65554a9"}}