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Paper Citation Record · LEDGER

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses

As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2605.26763.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.26763 v1

Coverage vector

measured 63 of 63 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-29T19:50:06.390639Z

measured 63 of 63 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

63 of 63 outbound references displayed

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Outbound references

Observation 360e1325-e827-4ecd-acf8-1626bb139fc6 · outbound

This paper cites A maximum expected covering location model: formula- tion, properties and heuristic solution,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A maximum expected covering location model: formula- tion, properties and heuristic solution,

Reference 1

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Observation 423596d8-4e47-4add-9b40-03c2d1962c6b · outbound

This paper cites The maximal covering location problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses The maximal covering location problem,

Reference 2

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Observation 28dbcf8a-12d1-43f5-8ebf-5e8f8ed1e126 · outbound

This paper cites A review of covering problems in facility location,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A review of covering problems in facility location,

Reference 3

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Observation 185c1f10-565f-4348-8d56-e8ae496fb067 · outbound

This paper cites Disruption, protection, and resilience,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Disruption, protection, and resilience,

Reference 4

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Observation bcca6c33-6264-4975-826e-1c666de46499 · outbound

This paper cites Us risks national blackout from small-scale attack,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Us risks national blackout from small-scale attack,

Reference 5

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Observation 511ca101-3730-4a1c-b8b0-2804aa93c9b3 · outbound

This paper cites Facility reliability issues in network p-median problems: Strategic centralization and co-location effects,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Facility reliability issues in network p-median problems: Strategic centralization and co-location effects,

Reference 6

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Observation 82096c10-c446-464b-acdc-3e9ad7e83d1a · outbound

This paper cites Or/ms models for supply chain disruptions: A review,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Or/ms models for supply chain disruptions: A review,

Reference 7

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Observation a15ad82a-8116-47f2-a02e-4e521df793ad · outbound

This paper cites A survey of network interdiction models and algorithms,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A survey of network interdiction models and algorithms,

Reference 8

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Observation 075d0368-a780-48ca-8566-534e1a338fff · outbound

This paper cites Heuristic solution methods for two location problems with unreliable facilities,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Heuristic solution methods for two location problems with unreliable facilities,

Reference 9

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Observation e4920d9e-dddd-4008-9807-2f935efb8650 · outbound

This paper cites A facility relia- bility problem: Formulation, properties, and algorithm,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A facility relia- bility problem: Formulation, properties, and algorithm,

Reference 10

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:5bc7367bf0958b8260ce2ff476e0a7e815ff444405ec09616517ad3c8511f467

Observation 3da5f346-4b51-4186-b70c-0bcd80d7231e · outbound

This paper cites A general model and efficient algorithms for reliable facility location problem under uncertain disruptions,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A general model and efficient algorithms for reliable facility location problem under uncertain disruptions,

Reference 11

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Observation 4031077b-b7d4-42ac-a158-20cef33c5008 · outbound

This paper cites Concepts and applications of backup cover- age,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Concepts and applications of backup cover- age,

Reference 12

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Observation 3fccd25e-49cc-4486-9f99-5ea38b002213 · outbound

This paper cites An analysis of p-median location problem: Effects of backup service level and demand assignment policy,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses An analysis of p-median location problem: Effects of backup service level and demand assignment policy,

Reference 13

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Observation d4aadd41-e8a4-4d64-b935-71297872b0ec · outbound

This paper cites Location optimization of urban fire stations considering the backup coverage,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Location optimization of urban fire stations considering the backup coverage,

Reference 14

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Observation 9e681a62-e127-419b-8176-b1fcfccc7f38 · outbound

This paper cites Designing robust coverage networks to hedge against worst-case facility losses,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Designing robust coverage networks to hedge against worst-case facility losses,

Reference 15

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Observation 49914fde-32c3-44b9-8481-5201322cfa2f · outbound

This paper cites Identifying critical infrastructure: the median and covering facility interdiction problems,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Identifying critical infrastructure: the median and covering facility interdiction problems,

Reference 16

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Observation e593cbb3-70bc-4e9d-b5d9-ef727d3da212 · outbound

This paper cites IBM ILOG CPLEX Optimization Studio,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses IBM ILOG CPLEX Optimization Studio,

Reference 17

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Observation 52692bc0-1bc5-431e-91f6-d8f386e3c494 · outbound

This paper cites Gurobi Optimizer Reference Manual,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Gurobi Optimizer Reference Manual,

Reference 18

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Observation bceab852-cfd5-4002-b2f3-ab11dedb99d6 · outbound

This paper cites Available: https://www.gurobi.com.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Available: https://www.gurobi.com

Reference 19

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Observation 9a8c26c4-1c48-49f4-b50a-9657528b8326 · outbound

This paper cites Metaheuristics for bilevel optimization: A comprehensive review,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Metaheuristics for bilevel optimization: A comprehensive review,

Reference 20

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Observation 70d3c175-1f75-4474-a412-d22e88b357af · outbound

This paper cites Mathematical programs with optimization problems in the constraints,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Mathematical programs with optimization problems in the constraints,

Reference 21

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Observation 73a33bc8-d02a-4c9d-a472-a221f9ebb07e · outbound

This paper cites The polynomial hierarchy and a simple model for competitive analysis,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses The polynomial hierarchy and a simple model for competitive analysis,

Reference 22

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Observation da29fc65-e996-47a7-8104-00bb0cd629a0 · outbound

This paper cites Solving the bilevel facility location problem under preferences by a stackelberg-evolutionary algorithm,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Solving the bilevel facility location problem under preferences by a stackelberg-evolutionary algorithm,

Reference 23

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Observation 2e9fa5d8-0f77-4589-833e-993b4d627cbc · outbound

This paper cites A matheuristic for solving the bilevel approach of the facility location problem with cardinality constraints and preferences,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A matheuristic for solving the bilevel approach of the facility location problem with cardinality constraints and preferences,

Reference 24

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Observation 82cff39f-53be-42fc-9251-cf9ed306afce · outbound

This paper cites There’s no free lunch: on the hardness of choosing a correct big-m in bilevel optimization,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses There’s no free lunch: on the hardness of choosing a correct big-m in bilevel optimization,

Reference 25

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Observation 1251718e-a55d-4830-886c-9a265804ba37 · outbound

This paper cites The mixed integer linear bilevel program- ming problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses The mixed integer linear bilevel program- ming problem,

Reference 26

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Observation ad9c60bb-b813-45c5-9b13-35feeedd644f · outbound

This paper cites The solution of the linear bilevel programming problem by using the linear complementarity problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses The solution of the linear bilevel programming problem by using the linear complementarity problem,

Reference 27

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Observation 2a9a7dc2-5e78-4c78-8970-687db498c6bd · outbound

This paper cites A branch and bound algorithm for the bilevel programming problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A branch and bound algorithm for the bilevel programming problem,

Reference 28

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Observation 8aee5012-bd2e-4de9-9c72-d5f8fb368414 · outbound

This paper cites Exact solution methodologies for linear and (mixed) integer bilevel programming,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Exact solution methodologies for linear and (mixed) integer bilevel programming,

Reference 29

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Observation c2601be3-d2a7-4ecf-8d4d-fff52ad90dea · outbound

This paper cites A survey on mixed- integer programming techniques in bilevel optimization,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A survey on mixed- integer programming techniques in bilevel optimization,

Reference 30

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Observation cf0d3322-2868-4848-8aba-dbf1cc149637 · outbound

This paper cites A taxonomy of metaheuristics for bi-level optimization,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A taxonomy of metaheuristics for bi-level optimization,

Reference 31

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Observation f2c31822-8898-4a3b-b1bd-72ba7f7494aa · outbound

This paper cites Optimal pricing for bidirectional wireless charging lanes in coupled transportation and power networks,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Optimal pricing for bidirectional wireless charging lanes in coupled transportation and power networks,

Reference 32

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Observation 7471a086-b2f3-44ff-b6ce-7e56ea48de86 · outbound

This paper cites A hybrid heuristic approach with adaptive scalar- ization for linear semivectorial bilevel programming and its application,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A hybrid heuristic approach with adaptive scalar- ization for linear semivectorial bilevel programming and its application,

Reference 33

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Observation caec2de0-f3c2-4fc0-a31e-5aae093045b0 · outbound

This paper cites Bilevel optimization model for sizing of battery energy storage systems in a microgrid considering their economical operation,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Bilevel optimization model for sizing of battery energy storage systems in a microgrid considering their economical operation,

Reference 34

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Observation fa29c670-a43d-4dcd-870f-4e13bdb3e1f0 · outbound

This paper cites An efficient environmentally friendly transportation network design via dry ports: a bi-level programming approach,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses An efficient environmentally friendly transportation network design via dry ports: a bi-level programming approach,

Reference 35

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Observation 2f90f5e1-e286-45ee-b05d-19fed7458bda · outbound

This paper cites Research on location-routing problem of maritime emergency materials distribution based on bi-level programming,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Research on location-routing problem of maritime emergency materials distribution based on bi-level programming,

Reference 36

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:4064e1ce8a705655748d7b8f98ace455c5ae23992b37381ba94f4bfc14f93a1b

Observation 8fd0bdfa-c12a-4a9b-acf7-29da9879f4c8 · outbound

This paper cites A bilevel whale optimization algorithm for risk management scheduling of infor- mation technology projects considering outsourcing,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A bilevel whale optimization algorithm for risk management scheduling of infor- mation technology projects considering outsourcing,

Reference 37

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Observation b20c855c-01a4-42c2-afee-f2c78aeff9d3 · outbound

This paper cites Bilevel memetic search approach to the soft-clustered vehicle routing problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Bilevel memetic search approach to the soft-clustered vehicle routing problem,

Reference 38

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Observation a6890636-8157-4293-bfff-43e3979536d6 · outbound

This paper cites Discretization-based feature selection as a bilevel optimization prob- lem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Discretization-based feature selection as a bilevel optimization prob- lem,

Reference 39

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Observation afd5f545-83b5-4238-a200-f9ef25f3ecd9 · outbound

This paper cites Integrated optimization of transfer station selection and train timetables for road–rail intermodal transport network,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Integrated optimization of transfer station selection and train timetables for road–rail intermodal transport network,

Reference 40

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:abe4799fc953dd0960a98e4a808a9caf1ef944d0931a97d441d72093e1db03c2

Observation e8ec0dbe-d698-4f46-843e-ddf297131e15 · outbound

This paper cites Cobra: A coevolutionary metaheuristic for bi-level optimization,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Cobra: A coevolutionary metaheuristic for bi-level optimization,

Reference 41

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Observation 86f1c8f1-4396-4eea-a545-02bfd10f8da8 · outbound

This paper cites Bilevel com- petitive facility location and pricing problems,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Bilevel com- petitive facility location and pricing problems,

Reference 42

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Observation 7ec650a3-cd30-4c29-8c1b-07d894929253 · outbound

This paper cites A surrogate-assisted meta- heuristic for bilevel optimization,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A surrogate-assisted meta- heuristic for bilevel optimization,

Reference 43

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:74efdc72bd18bb3a31c8a09a5c4f3da5dd73c8db973b9d7b641a6bf1bfe0ee95

Observation 8b79807a-5f57-4932-8156-76d80fcb0b59 · outbound

This paper cites An attention model with multiple decoders for solving p-center problems,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses An attention model with multiple decoders for solving p-center problems,

Reference 44

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Observation 77284152-2a79-4aba-a37d-52c59423cf75 · outbound

This paper cites Deepmclp: Solving the mclp with deep reinforcement learning for urban spatial,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Deepmclp: Solving the mclp with deep reinforcement learning for urban spatial,

Reference 45

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:3ae1df36895c0540b517b0f94d1da2dfa024e3a8e4c0a846547056602422be45

Observation a36ac374-6fca-4053-9fcc-60a4bc64bcf4 · outbound

This paper cites Recovnet: Reinforcement learning with covering information for solving maximal coverage billboards location problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Recovnet: Reinforcement learning with covering information for solving maximal coverage billboards location problem,

Reference 46

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Observation 3dbda144-02d0-4c01-98b9-064792c8b31b · outbound

This paper cites Sponet: solve spatial optimization problem using deep reinforcement learning for urban spatial decision analysis,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Sponet: solve spatial optimization problem using deep reinforcement learning for urban spatial decision analysis,

Reference 47

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Observation 04a9078a-f669-4f02-944a-49857455ab57 · outbound

This paper cites Deep reinforcement learning for multi-period facility location: pk-median dynamic location problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Deep reinforcement learning for multi-period facility location: pk-median dynamic location problem,

Reference 48

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Observation 44b61374-3c65-46e0-8dfb-68d367ed053d · outbound

This paper cites An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems

Reference 49

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:7e09df57d8d0a8723bfe7a659a62f9f574ea0c36fe25c6b916107049ca022324

Observation 0b6bc1c3-61df-40d3-a3d3-38f779faa09e · outbound

This paper cites A deep reinforcement learning method for solving two-echelon location-routing problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A deep reinforcement learning method for solving two-echelon location-routing problem,

Reference 50

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Observation 6d9df973-9c52-42ae-8943-597aa9e425c4 · outbound

This paper cites On the stackelberg strategy in nonzero- sum games,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses On the stackelberg strategy in nonzero- sum games,

Reference 51

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Observation 55ec0ad8-9795-4e72-a5fa-c2362e4f3d21 · outbound

This paper cites Generative adversarial nets,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Generative adversarial nets,

Reference 52

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Observation fa587eb8-0035-40e3-9d3b-163354f51b04 · outbound

This paper cites A column generation approach for the maximal covering location problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A column generation approach for the maximal covering location problem,

Reference 53

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Observation 99d46bbd-dbe2-4016-9d95-2b146dbb15ca · outbound

This paper cites Attention, learn to solve routing problems!.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Attention, learn to solve routing problems!

Reference 54

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Observation 022380a0-2dde-4984-98a6-024530c47690 · outbound

This paper cites A heuristic program for locating warehouses,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A heuristic program for locating warehouses,

Reference 55

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Observation 70dc461d-f000-450e-ba0b-d69856a0b6e8 · outbound

This paper cites Warehouse location under con- tinuous economies of scale,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Warehouse location under con- tinuous economies of scale,

Reference 56

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Observation 765bea3f-6c65-4097-a4ed-730b56171607 · outbound

This paper cites A fast algorithm for the greedy interchange for large-scale clustering and median location problems,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A fast algorithm for the greedy interchange for large-scale clustering and median location problems,

Reference 57

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Observation 9528fed7-8ff2-4b57-a871-a5d9648ebb74 · outbound

This paper cites A more efficient heuristic for solving large p-median problems,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A more efficient heuristic for solving large p-median problems,

Reference 58

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Observation e99e62cf-144d-4c58-9f60-dda800675992 · outbound

This paper cites On the location of supply points to minimize transport costs,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses On the location of supply points to minimize transport costs,

Reference 59

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Observation 1ce5183e-131b-418f-b082-bede167f518f · outbound

This paper cites The large scale maximal covering location problem,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses The large scale maximal covering location problem,

Reference 60

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source=pdf_text observed=2026-06-29T19:50:06.390639Z digest=sha256:d6bd117275e02319da34efb1038f809b9a7fb3771e9edc336ce5c869b969cdbf

Observation da6f4e9f-7947-4a1c-89fc-ce9d07c8ab7c · outbound

This paper cites Maximal covering location problem (mclp) with fuzzy travel times,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses Maximal covering location problem (mclp) with fuzzy travel times,

Reference 61

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Observation a7f95570-89fb-4136-bc27-08aa6bfef69a · outbound

This paper cites The minimum weighted covering location problem with distance constraints,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses The minimum weighted covering location problem with distance constraints,

Reference 62

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Observation c1f2d95a-7e13-4292-971a-ddcc6791c2bf · outbound

This paper cites A variable neighborhood search for the budget-constrained maximal covering location problem with customer preference ordering,.

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses A variable neighborhood search for the budget-constrained maximal covering location problem with customer preference ordering,

Reference 63

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