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Optimizing warehouse network reliability under intentional disruption by increasing network ambiguity: a multi objective optimization model

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Abstract

In today’s world, intentional disruptions in networks are expanding and the impacts are seen in many parts of the world. An effective approach for reducing the impact of such disruptions is to confuse invaders. Increasing ambiguity in the network is one of the effective ways which may confuse the invaders. To attain this goal, dummy facilities are added to the network. Dummy facilities are the facilities which are exactly the same as the real ones thus making it hard for the invader to make the distinction. In this paper, a new multi-objective mathematical model is presented to suitably design a network consisting of real and dummy warehouses. One objective is to minimize the total cost and the other is set to maximize reliability. An index for assessing network reliability is also introduced and used. The model is solved using AUGMECON and NSGA-II. Results demonstrate that establishing dummy facilities in the network will increase reliability while no significant cost is imposed.

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Notes

  1. Non-dominated sorting chemical reaction optimization.

References

  1. Razmi J, Zahedi-Anaraki A and Zakerinia M 2013 A bi-objective stochastic optimization model for reliable warehouse network redesign. Math. Comput. Model.. 58:1804–1813.

    MATH  Google Scholar 

  2. Snyder L V, Atan Z, Peng P, Rong Y, Schmitt A J and Sinsoysal, B 2016 OR/MS models for supply chain disruptions: A review. IIE Trans.. 48:89–109

    Google Scholar 

  3. Thompson D J 2017 Southeastern state local law enforcement preparedness in domestic terrorism interdiction: A quantitative ex-post facto study. Doctor of Philosophy Degree, Capella University, Minneapolis, MN

    Google Scholar 

  4. Ouyang M, Xu M, Zhang C and Huang S 2017 Mitigating electric power system vulnerability to worst-case spatially localized attacks. Reliab. Eng. Syst. Saf. 165:144–154.

    Google Scholar 

  5. Tran T H, O’Hanley J R and Scaparra M P 2016 Reliable hub network design: Formulation and solution techniques. Transp. Sci. 51(1):1–19

    Google Scholar 

  6. DuHadway S, Carnovale S and Hazen B 2019 Understanding risk management for intentional supply chain disruptions: Risk detection, risk mitigation, and risk recovery. Ann. Oper. Res.. 283:179–198

    Google Scholar 

  7. Snyder L V 2003 Supply chain robustness and reliability: Models and algorithms. Doctor of Philosophy Degree, Northwestern University, Evanston, IL.

    Google Scholar 

  8. Snyder L V and Daskin M S 2005 Reliability models for facility location: the expected failure cost case. Transp. Sci. 39:400–416

    Google Scholar 

  9. Scaparra M P and Church R L 2015 Location Problems Under Disaster Events. Location Science. Switzerland: Springer, pp. 623–642

    Google Scholar 

  10. Smith J C and Song Y 2019 A Survey of Network Interdiction Models and Algorithms. Eur. J. Oper. Res. (in press)

  11. Church R L, Scaparra M P and Middleton R S 2004 Identifying critical infrastructure: the median and covering facility interdiction problems. Ann. Assoc. Amer. Geograph.. 94:491–502

    Google Scholar 

  12. Church R L and Scaparra M P 2007 Protecting critical assets: the r-interdiction median problem with fortification. Geograph. Anal. 39:129–146

    Google Scholar 

  13. Scaparra M P and Church R L 2008 An exact solution approach for the interdiction median problem with fortification. Eur. J. Oper. Res. 189:76–92

    MATH  Google Scholar 

  14. Khanduzi R and Maleki H 2018 A novel bilevel model and solution algorithms for multi-period interdiction problem with fortification. Appl. Intell. 48(9):2770–2791

    Google Scholar 

  15. Bao S, Zhang C, Ouyang M and Miao L 2019 An integrated tri-level model for enhancing the resilience of facilities against intentional attacks. Ann. Oper. Res. 283:87–117.

    MathSciNet  Google Scholar 

  16. Fathollahi-Fard A M and Hajiaghaei-Keshteli M 2018 A bi-objective partial interdiction problem considering different defensive systems with capacity expansion of facilities under imminent attacks. Appl. Soft Comput. 68: 343–359

    Google Scholar 

  17. Maiyar L M and Thakkar J J 2018 Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability. Int. J. Product. Econ. 217: 281–297

    Google Scholar 

  18. Li Q, Zeng B and Savachkin A 2013 Reliable facility location design under disruptions. Comput. Oper. Res. 40:901–909

    MathSciNet  MATH  Google Scholar 

  19. Losada C, Scaparra M P and O’Hanley J R 2012 Optimizing system resilience: a facility protection model with recovery time. Eur. J. Oper. Res. 217:519–530

    MathSciNet  MATH  Google Scholar 

  20. Aksen D, Akca S Ş and Aras N 2014 A bilevel partial interdiction problem with capacitated facilities and demand outsourcing. Comput. Oper. Res. 41:346–358

    MathSciNet  MATH  Google Scholar 

  21. Zhang C, Ramirez-Marquez J E and Li Q 2018 Locating and protecting facilities from intentional attacks using secrecy. Reliab. Eng. Syst. Saf. 169:51–62

    Google Scholar 

  22. Jiang J and Liu X 2018 Multi-objective Stackelberg game model for water supply networks against interdictions with incomplete information. Eur. J. Oper. Res.. 266:920–933

    MathSciNet  MATH  Google Scholar 

  23. Aksen D, Aras N and Piyade N 2013 A bilevel p-median model for the planning and protection of critical facilities. J. Heurist. 19:373–398

    Google Scholar 

  24. Aksen D and Aras N 2012 A bilevel fixed charge location model for facilities under imminent attack. Comput. Oper. Res. 39:1364–1381

    MathSciNet  MATH  Google Scholar 

  25. Shishebori D, Jabalameli M S and Jabbarzadeh A 2013 Facility location-network design problem: reliability and investment budget constraint. J. Urban Plan. Dev.. 140(3):04014005

    Google Scholar 

  26. Rocco C, Ramirez-Marquez J, Salazar D and Hernandez I 2010 Implementation of multi-objective optimization for vulnerability analysis of complex networks. Proc. Inst. Mech. Eng. O J. Risk Reliab. 224:87–95.

    Google Scholar 

  27. Royset J O and Wood R K 2007 Solving the bi-objective maximum-flow network-interdiction problem. IINFORMS J. Comput.. 19:175–184

    MathSciNet  MATH  Google Scholar 

  28. Ramirez-Marquez J E 2010 A bi-objective approach for shortest-path network interdiction. Comput. Ind. Eng. 59:232–240

    Google Scholar 

  29. Parvaresh F, Husseini S M, Golpayegany S H and Karimi B 2014 Hub network design problem in the presence of disruptions. J. Intell. Manuf. 25:755–774

    Google Scholar 

  30. Farmani R, Walters G A and Savic D A 2005 Trade-off between total cost and reliability for Anytown water distribution network. J. Water Resour. Plan. Manag. 131:161–171

    Google Scholar 

  31. Brown G, Carlyle M, Salmerón J and Wood K 2006 Defending critical infrastructure. Interfaces. 36:530–544

    Google Scholar 

  32. Brown G G, Carlyle W M, Salmeron J and Wood K 2005 Analyzing the vulnerability of critical infrastructure to attack and planning defenses. INFORMS TutORials Oper. Res., 102–123

  33. Wood R K 2010 Bilevel network interdiction models: Formulations and solutions. Wiley Encyclopedia of Operations Research and Management Science. New York: Wiley

    Google Scholar 

  34. Towle E and Luedtke J 2018 New solution approaches for the maximum-reliability stochastic network interdiction problem. Comput. Manag. Sci. 15:455–477

    MathSciNet  MATH  Google Scholar 

  35. Losada C, Scaparra M P, Church R L and Daskin M S 2012 The stochastic interdiction median problem with disruption intensity levels. Ann. Oper. Res. 201:345–365

    MathSciNet  MATH  Google Scholar 

  36. Li Q and Savachkin A 2016 Reliable distribution networks design with nonlinear fortification function. Int. J. Syst. Sci. 47:805–813

    MATH  Google Scholar 

  37. Berman O, Krass D and Menezes M B 2007 Facility reliability issues in network p-median problems: strategic centralization and co-location effects. Oper. Res. 55:332–350.

    MathSciNet  MATH  Google Scholar 

  38. Cui T, Ouyang Y and Shen Z -J M 2010 Reliable facility location design under the risk of disruptions. Oper. Res. 58:998–1011.

    MathSciNet  MATH  Google Scholar 

  39. Lei T L and Tong D 2013 Hedging against service disruptions: an expected median location problem with site-dependent failure probabilities. J. Geograph. Syst.. 15:491–512

    Google Scholar 

  40. Lim M, Daskin M S, Bassamboo A and Chopra S 2010 A facility reliability problem: formulation, properties, and algorithm. Naval Res. Log. 57:58–70

    MathSciNet  MATH  Google Scholar 

  41. Ukkusuri S and Yushimito W 2008 Location routing approach for the humanitarian prepositioning problem. Transp. Res. Rec.: J. Transp. Res. Board.. 2089(1):18-25

    Google Scholar 

  42. Shen Z -J M, Zhan R L and Zhang J 2011 The reliable facility location problem: Formulations, heuristics, and approximation algorithms. INFORMS J. Comput. 23:470–482.

    MathSciNet  MATH  Google Scholar 

  43. Hatefi S and Jolai F 2014 Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. v 38:2630–2647

    MathSciNet  MATH  Google Scholar 

  44. Akgün İ, Gümüşbuğa F and Tansel B 2015 Risk based facility location by using fault tree analysis in disaster management. Omega 52:168–179

    Google Scholar 

  45. An Y, Zeng B, Zhang Y and Zhao L 2014 Reliable p-median facility location problem: two-stage robust models and algorithms. Transp Res.B: Methodol. 64:54–72

    Google Scholar 

  46. Baghalian A, Rezapour S and Farahani R Z 2013 Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. Eur. J. Oper. Res. 227:199–215

    MathSciNet  MATH  Google Scholar 

  47. Vahdani B, Tavakkoli-Moghaddam R and Jolai F 2013 Reliable design of a logistics network under uncertainty: A fuzzy possibilistic-queuing model. Appl. Math. Model. 37:3254–3268

    MathSciNet  MATH  Google Scholar 

  48. Zarrinpoor N, Fallahnezhad M S and Pishvaee M S 2017 Design of a reliable hierarchical location-allocation model under disruptions for health service networks: A two-stage robust approach. Comput. Ind. Eng. 109:130–150

    Google Scholar 

  49. Azizi N, Chauhan S, Salhi S and Vidyarthi N 2016 The impact of hub failure in hub-and-spoke networks: Mathematical formulations and solution techniques. Comput. Oper. Res. 65:174-188

    MathSciNet  MATH  Google Scholar 

  50. Mohammadi M, Tavakkoli-Moghaddam R, Siadat A and Dantan J -Y 2016 Design of a reliable logistics network with hub disruption under uncertainty. Appl. Math. Model. 40:5621-5642

    MathSciNet  MATH  Google Scholar 

  51. Lei T L 2013 Identifying critical facilities in hub-and-spoke networks: a hub interdiction median problem. Geograph. Anal. 45:105–122

    Google Scholar 

  52. Azad N, Saharidis G K, Davoudpour H, Malekly H and Yektamaram S A 2013 Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Ann. Oper. Res. 210:125-163

    MathSciNet  MATH  Google Scholar 

  53. Peng P, Snyder L V, Lim A and Liu Z 2011 Reliable logistics networks design with facility disruptions. Transp Res. B: Methodol. 45:1190–1211

    Google Scholar 

  54. Yu R 2015 The Capacitated Reliable Fixed-charge Location Problem: Model and Algorithm. Master of Science Degree, Lehigh University, Bethlehem, PA

    Google Scholar 

  55. Salmerón J 2012 Deception tactics for network interdiction: A multiobjective approach. Networks: An International Journal 60:45–58

  56. Donald D and Herbig K 1981 Strategic military deception. New York:Pergamon Press

    Google Scholar 

  57. Vollmer T and Manic M 2014 Cyber-physical system security with deceptive virtual hosts for industrial control networks. IEEE Trans. Ind. Inf.. 10:1337–1347

    Google Scholar 

  58. . Jeon S, Yun J -H and Kim W -N 2014 Obfuscation of critical infrastructure network traffic using fake communication, In: Proceedings of the International Conference on Critical Information Infrastructures Security, pp. 268–274

  59. Teixeira A, Amin S, Sandberg H, Johansson K H and Sastry S S 2010 Cyber security analysis of state estimators in electric power systems, In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pp. 5991–5998

  60. Amoroso E G 2012 Cyber attacks: protecting national infrastructure. MA: Elsevier

    Google Scholar 

  61. Rauti S and Leppanen V 2017 A survey on fake entities as a method to detect and monitor malicious activity, In: Proceedings of the 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 386–390

  62. Hamidi M R, Gholamian M R and Shahanaghi K 2014 Developing prevention reliability in hub location models. Proc. Inst. Mech. Eng. O J. Risk Reliab.. 228:337–346

    Google Scholar 

  63. Hamidi M R, Gholamian M R, Shahanaghi K and Yavari A 2017 Reliable warehouse location-network design problem under intentional disruption. Comput. Ind. Eng. 113:123–134

    Google Scholar 

  64. Crowdy T 2011 Deceiving Hitler: Double-Cross and Deception in World War II. New York: Osprey Publishing

    Google Scholar 

  65. Chaturvedi S K 2016 Network reliability: measures and evaluation. New York: Wiley.

    MATH  Google Scholar 

  66. Ross S 2018 A first course in probability. 10th edn, New York: Pearson

    Google Scholar 

  67. Marler RT and Arora J S 2004 Survey of multi-objective optimization methods for engineering. Struct. Mmultidiscipl. Optim.. 26:369–395

    MathSciNet  MATH  Google Scholar 

  68. Deb K 2001 Multi-objective optimization using evolutionary algorithms. New York: Wiley.

    MATH  Google Scholar 

  69. Zavala G R, Nebro A J, Luna F and Coello C A C 2014 A survey of multi-objective metaheuristics applied to structural optimization. Struct. Mmultidiscipl. Optim. 49:537–558

    MathSciNet  Google Scholar 

  70. Mogale D, Kumar M, Kumar S K and Tiwari M K 2018 Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network. Transp. Res. E: Log. Transp. Rev.. 111:40–69

    Google Scholar 

  71. Mogale D, Kumar S K and Tiwari M K 2016 Two stage Indian food grain supply chain network transportation-allocation model. IFAC-PapersOnLine. 49(12):1767–1772

    Google Scholar 

  72. Bottani E, Murino T, Schiavo M and Akkerman R 2019 Resilient food supply chain design: Modelling framework and metaheuristic solution approach. Comput. Ind. Eng.. 135: 177–198

    Google Scholar 

  73. Mogale D, Dolgui A, Kandhway R, Kumar S K and Tiwari M K 2017 A multi-period inventory transportation model for tactical planning of food grain supply chain. Comput. Ind. Eng.. 110:379–394

    Google Scholar 

  74. Ghaffarinasab N and Motallebzadeh A 2018 Hub interdiction problem variants: Models and metaheuristic solution algorithms. Eur. J. Oper. Res.. 267:496–512.

    MathSciNet  MATH  Google Scholar 

  75. Kheirkhah A, Navidi H and Bidgoli M M 2016 An improved benders decomposition algorithm for an arc interdiction vehicle routing problem. IEEE Trans. Eng. Manag. 63:259–273

    Google Scholar 

  76. Mavrotas G 2009 Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput.. 213:455–465.

    MathSciNet  MATH  Google Scholar 

  77. Beasley J E 1988 An algorithm for solving large capacitated warehouse location problems. Eur. J. Oper. Res. 33:314–325

    MathSciNet  MATH  Google Scholar 

  78. Coello C A C, Lamont G B and Van Veldhuizen D A 2007 Evolutionary algorithms for solving multi-objective problems. New York: Springer

    MATH  Google Scholar 

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Correspondence to Mohammad Reza Gholamian.

Appendix A. Date set 1, small size data set

Appendix A. Date set 1, small size data set

 

Potential locations for warehouses

Demand

1

2

3

4

5

6

7

Demand nodes

1

7280

2920

8240

4420

3900

2574

6248

10000

2

2040

8510

3400

15360

7563

4228

23010

5000

3

6280

7540

2310

12300

10315

11598

12881

4000

4

5120

4110

11990

10630

14065

16506

18947

1000

5

3960

680

1670

8960

17815

6953

2501

3000

6

5500

1520

5965

6595

11066

5485

8922

6000

7

4600

821

7545

8711

4487

1410

7544

2000

Real fixed cost

3.5× 1010

5×1010

6×1010

8×1010

8.5×1010

9.5×1010

9.9×1010

 

Dummy fixed cost

3.5×108

5×108.

6×108

8×108

8.5×108

9.5×108

9.9×108

 

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Hamidi, M.R., Gholamian, M.R. Optimizing warehouse network reliability under intentional disruption by increasing network ambiguity: a multi objective optimization model. Sādhanā 45, 76 (2020). https://doi.org/10.1007/s12046-020-1295-6

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