July  2014, 10(3): 665-689. doi: 10.3934/jimo.2014.10.665

Humanitarian logistics planning for natural disaster response with Bayesian information updates

1. 

Department of Management Sciences and Engineering, School of Management, Zhejiang University, Hangzhou 310058, China

2. 

Academy of Financial Research, Wenzhou University, Wenzhou 325035, China

Received  May 2012 Revised  March 2013 Published  November 2013

The current study proposes a sequential approach for humanitarian logistics in natural disasters based on the Bayesian group information updates (GIU). First, a dynamic time-dependent nonlinear model without GIU is proposed. Then, two losses are addressed to explain the influence of a disaster on supply, demand, and humanitarian logistics. The two losses include losses caused by the mismatch between supply and demand in affected areas and the time losses caused by logistics processes under emergency conditions. Therefore, a multi-period humanitarian logistics planning model with GIU is established based on the model without GIU using Bayesian theory. Then, the model with GIU is revised into a single-objective model, and then a matrix-coding-based genetic algorithm is developed to solve the revised model. Finally, the proposed methodology is applied to the humanitarian logistics problems of emergency response encountered during the Wenchuan Earthquake in China. Computational results show that the proposed methodology can generate specific logistics plans for allocating relief resources according to updated information. Therefore, emergency planners can gain insights for humanitarian logistics planning in natural disaster response by inputting their own sets of data.
Citation: Nan Liu, Yong Ye. Humanitarian logistics planning for natural disaster response with Bayesian information updates. Journal of Industrial & Management Optimization, 2014, 10 (3) : 665-689. doi: 10.3934/jimo.2014.10.665
References:
[1]

C. Aggarwal, J. Orlin and R. Tai, An optimized crossover for the maximum independent set,, Operations Research, 45 (1997), 225. doi: 10.1287/opre.45.2.226. Google Scholar

[2]

K. Azoury, Bayes solution to dynamic inventory models under unknown demand distribution,, Management Science, 31 (1985), 1150. doi: 10.1287/mnsc.31.9.1150. Google Scholar

[3]

B. Beamon and S. Kotleba, Inventory modeling for complex emergencies in humanitarian relief operations,, International Journal of Logistics: Research and Applications, 9 (2006), 1. doi: 10.1080/13675560500453667. Google Scholar

[4]

J. Beasley and K. Jornsten, Enhancing an algorithm for set covering problems,, European Journal of Operational Research, 58 (1992), 293. doi: 10.1016/0377-2217(92)90215-U. Google Scholar

[5]

J. Beasley and P. Chu, A genetic algorithm for the set covering problem,, European Journal of Operational Research, 94 (1996), 392. doi: 10.1016/0377-2217(95)00159-X. Google Scholar

[6]

A. Ben-Tal, B. Chung, S. Mandala and T. Yao, Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains,, Transportation Research Part B, 45 (2011), 1177. doi: 10.1016/j.trb.2010.09.002. Google Scholar

[7]

J. Berger, Statistical Decision Theory and Bayesian Analysis,, 2nd edition, (1985). Google Scholar

[8]

T. Bui and S. Sankaran, Design considerations for a virtual information center for humanitarian assistance/disaster relief using workflow modeling,, Decision Support Systems, 31 (2001), 165. doi: 10.1016/S0167-9236(00)00129-9. Google Scholar

[9]

I. Burton, R. Kates and G. White, The Environment as Hazard,, 2nd edition, (1993). Google Scholar

[10]

S. Chaudhry and W. Luo, Application of genetic algorithms in production and operations management: A review,, International Journal of Production Research, 43 (2005), 4083. doi: 10.1080/00207540500143199. Google Scholar

[11]

D. Chen, Y. Miao and N. Liu, Emergency resource allocation model with emergency response cost constraints in emergency relief operations,, in ICCLTP2008, (2008). Google Scholar

[12]

H. Chen, Y. Chen, C. Chiu, T. Choi and S. Sethi, Coordination mechanism for supply chain with leadtime consideration and price-dependent demand,, European Journal of Operational Research, 203 (2010), 70. doi: 10.1016/j.ejor.2009.07.002. Google Scholar

[13]

T.-M. Choi, Quick response in fashion supply chains with dual information updating,, Journal of Industrial and Management Optimization, 2 (2006), 255. doi: 10.3934/jimo.2006.2.255. Google Scholar

[14]

T.-M. Choi, D. Li and H. Yan, Optimal single ordering policy with multiple delivery modes and Bayesian information updates,, Computers and Operations Research, 31 (2004), 1965. doi: 10.1016/S0305-0548(03)00157-6. Google Scholar

[15]

T.-M. Choi and S. Sethi, Innovative quick response programmes: A review,, International Journal of Production Economics, 127 (2010), 1. Google Scholar

[16]

CNCDR and MST of China, Comprehensive Analysis and Evaluation of Wenchuan Earthquake,, Chinese edition, (2008). Google Scholar

[17]

S. Doocy, A. Sirois, J. Anderson, M. Tileva, E. Biermannb, J. Storeya and G. Burnham, Food security and humanitarian assistance among displaced Iraqi populations in Jordan and Syria,, Social Science and Medicine, 72 (2011), 273. doi: 10.1016/j.socscimed.2010.10.023. Google Scholar

[18]

G. Dantzig and J. Ramser, The truck dispatching problem,, Management Science, 6 (): 80. doi: 10.1287/mnsc.6.1.80. Google Scholar

[19]

X. Ding, M. Puterman and A. Bisi, The censored newsvendor and the optimal acquisition of information,, Operations Research, 50 (2002), 517. doi: 10.1287/opre.50.3.517.7752. Google Scholar

[20]

M. Dror and P. Trudeau, Saving by split delivery routing,, Transportation Science, 23 (1989), 141. doi: 10.1287/trsc.23.2.141. Google Scholar

[21]

J. Emmett and S. Taskin, Supply chain planning for hurricane response with wind speed information updates,, Computers and Operations Research, 36 (2009), 2. Google Scholar

[22]

M. Fisher and P. Kedia, Optimal solution of set covering/partitioning problems using dual heuristics,, Management Science, 36 (1990), 674. doi: 10.1287/mnsc.36.6.674. Google Scholar

[23]

A. Haghani and S. Oh, Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations,, Transportation Research Part A, 30 (1996), 231. doi: 10.1016/0965-8564(95)00020-8. Google Scholar

[24]

J. Holland, Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence,, University of Michigan Press, (1975). Google Scholar

[25]

Z. Hu, A container multimodal transportation scheduling approach based on immune affinity model for emergency relief,, Expert Systems with Applications, 38 (2011), 2632. doi: 10.1016/j.eswa.2010.08.053. Google Scholar

[26]

R. Knott, The logistics of bulk relief supplies,, Disasters, 11 (1987), 113. doi: 10.1111/j.1467-7717.1987.tb00624.x. Google Scholar

[27]

D. Kostoulas, R. Aldunate, F. Mora and S. Lakhera, A nature-inspired decentralized trust model to reduce information unreliability in complex disaster relief operations,, Advanced Engineering Informatics, 22 (2008), 45. doi: 10.1016/j.aei.2007.09.001. Google Scholar

[28]

D. G. Kovács and K. Spens, Humanitarian logistics in disaster relief operations,, International Journal of Physical Distribution and Logistics Management, 37 (2007), 99. Google Scholar

[29]

M.-Y. Lai, C.-S. Liu and X.-J. Tong, A two-stage hybrid meta-heuristic for pickup and delivery vehicle routing problem with time windows,, Journal of Industrial and Management Optimization, 6 (2010), 435. doi: 10.3934/jimo.2010.6.435. Google Scholar

[30]

M.-Y. Lai and X.-J. Tong, A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search,, Journal of Industrial and Management Optimization, 8 (2012), 469. doi: 10.3934/jimo.2012.8.469. Google Scholar

[31]

L. Özdamar, E. Ekinci and B. Küçükyazici, Emergency logistics planning in natural disasters,, Annals of Operations Research, 129 (2004), 217. doi: 10.1023/B:ANOR.0000030690.27939.39. Google Scholar

[32]

J. Salmerón and A. Apte, Stochastic optimization for natural disaster asset prepositioning,, Production and Operations Management, 19 (2010), 561. doi: 10.1111/j.1937-5956.2009.01119.x. Google Scholar

[33]

J.-B. Sheu, Dynamic relief-demand management for emergency logistics operations under large-scale disasters,, Transportation Research Part E, 46 (2010), 1. doi: 10.1016/j.tre.2009.07.005. Google Scholar

[34]

S. Sethi, H. Yan and H. Zhang, Inventory and Supply Chain Management with Forecast Updates,, International Series in Operations Research and Management Science, (2005). Google Scholar

[35]

P. Tatham and G. Kovács, The application of "swift trust" to humanitarian logistics,, International Journal of Production Economics, 126 (2010), 35. doi: 10.1016/j.ijpe.2009.10.006. Google Scholar

[36]

C. Thévenaz and S. Resodihardjo, All the best laid plans conditions impeding proper emergency response,, International Journal of Production Economics, 126 (2010), 7. Google Scholar

[37]

A. Thomas and M. Mizushima, Logistics training: Necessity or luxury,, Forced Migration Review, 22 (2005), 60. Google Scholar

[38]

UNCRD, Reconnaissance Report of the 2008 Sichuan Earthquake,, Japanese Edition, (2009). Google Scholar

[39]

UNDAC, Handbook for Emergencies,, 2006., (). Google Scholar

[40]

P. Yi, Y. Zhang, Y. Tang and S. Li, An incident information management framework based on data integration, data mining, and multi-criteria decision making,, Decision Support Systems, 51 (2011), 316. Google Scholar

[41]

L. Yu and K. Lai, A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support,, Decision Support Systems, 51 (2011), 307. doi: 10.1016/j.dss.2010.11.024. Google Scholar

[42]

Y. Yuan and D. Wang, Path selection model and algorithm for emergency logistics management,, Computers and Industrial Engineering, 56 (2009), 1081. doi: 10.1016/j.cie.2008.09.033. Google Scholar

show all references

References:
[1]

C. Aggarwal, J. Orlin and R. Tai, An optimized crossover for the maximum independent set,, Operations Research, 45 (1997), 225. doi: 10.1287/opre.45.2.226. Google Scholar

[2]

K. Azoury, Bayes solution to dynamic inventory models under unknown demand distribution,, Management Science, 31 (1985), 1150. doi: 10.1287/mnsc.31.9.1150. Google Scholar

[3]

B. Beamon and S. Kotleba, Inventory modeling for complex emergencies in humanitarian relief operations,, International Journal of Logistics: Research and Applications, 9 (2006), 1. doi: 10.1080/13675560500453667. Google Scholar

[4]

J. Beasley and K. Jornsten, Enhancing an algorithm for set covering problems,, European Journal of Operational Research, 58 (1992), 293. doi: 10.1016/0377-2217(92)90215-U. Google Scholar

[5]

J. Beasley and P. Chu, A genetic algorithm for the set covering problem,, European Journal of Operational Research, 94 (1996), 392. doi: 10.1016/0377-2217(95)00159-X. Google Scholar

[6]

A. Ben-Tal, B. Chung, S. Mandala and T. Yao, Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains,, Transportation Research Part B, 45 (2011), 1177. doi: 10.1016/j.trb.2010.09.002. Google Scholar

[7]

J. Berger, Statistical Decision Theory and Bayesian Analysis,, 2nd edition, (1985). Google Scholar

[8]

T. Bui and S. Sankaran, Design considerations for a virtual information center for humanitarian assistance/disaster relief using workflow modeling,, Decision Support Systems, 31 (2001), 165. doi: 10.1016/S0167-9236(00)00129-9. Google Scholar

[9]

I. Burton, R. Kates and G. White, The Environment as Hazard,, 2nd edition, (1993). Google Scholar

[10]

S. Chaudhry and W. Luo, Application of genetic algorithms in production and operations management: A review,, International Journal of Production Research, 43 (2005), 4083. doi: 10.1080/00207540500143199. Google Scholar

[11]

D. Chen, Y. Miao and N. Liu, Emergency resource allocation model with emergency response cost constraints in emergency relief operations,, in ICCLTP2008, (2008). Google Scholar

[12]

H. Chen, Y. Chen, C. Chiu, T. Choi and S. Sethi, Coordination mechanism for supply chain with leadtime consideration and price-dependent demand,, European Journal of Operational Research, 203 (2010), 70. doi: 10.1016/j.ejor.2009.07.002. Google Scholar

[13]

T.-M. Choi, Quick response in fashion supply chains with dual information updating,, Journal of Industrial and Management Optimization, 2 (2006), 255. doi: 10.3934/jimo.2006.2.255. Google Scholar

[14]

T.-M. Choi, D. Li and H. Yan, Optimal single ordering policy with multiple delivery modes and Bayesian information updates,, Computers and Operations Research, 31 (2004), 1965. doi: 10.1016/S0305-0548(03)00157-6. Google Scholar

[15]

T.-M. Choi and S. Sethi, Innovative quick response programmes: A review,, International Journal of Production Economics, 127 (2010), 1. Google Scholar

[16]

CNCDR and MST of China, Comprehensive Analysis and Evaluation of Wenchuan Earthquake,, Chinese edition, (2008). Google Scholar

[17]

S. Doocy, A. Sirois, J. Anderson, M. Tileva, E. Biermannb, J. Storeya and G. Burnham, Food security and humanitarian assistance among displaced Iraqi populations in Jordan and Syria,, Social Science and Medicine, 72 (2011), 273. doi: 10.1016/j.socscimed.2010.10.023. Google Scholar

[18]

G. Dantzig and J. Ramser, The truck dispatching problem,, Management Science, 6 (): 80. doi: 10.1287/mnsc.6.1.80. Google Scholar

[19]

X. Ding, M. Puterman and A. Bisi, The censored newsvendor and the optimal acquisition of information,, Operations Research, 50 (2002), 517. doi: 10.1287/opre.50.3.517.7752. Google Scholar

[20]

M. Dror and P. Trudeau, Saving by split delivery routing,, Transportation Science, 23 (1989), 141. doi: 10.1287/trsc.23.2.141. Google Scholar

[21]

J. Emmett and S. Taskin, Supply chain planning for hurricane response with wind speed information updates,, Computers and Operations Research, 36 (2009), 2. Google Scholar

[22]

M. Fisher and P. Kedia, Optimal solution of set covering/partitioning problems using dual heuristics,, Management Science, 36 (1990), 674. doi: 10.1287/mnsc.36.6.674. Google Scholar

[23]

A. Haghani and S. Oh, Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations,, Transportation Research Part A, 30 (1996), 231. doi: 10.1016/0965-8564(95)00020-8. Google Scholar

[24]

J. Holland, Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence,, University of Michigan Press, (1975). Google Scholar

[25]

Z. Hu, A container multimodal transportation scheduling approach based on immune affinity model for emergency relief,, Expert Systems with Applications, 38 (2011), 2632. doi: 10.1016/j.eswa.2010.08.053. Google Scholar

[26]

R. Knott, The logistics of bulk relief supplies,, Disasters, 11 (1987), 113. doi: 10.1111/j.1467-7717.1987.tb00624.x. Google Scholar

[27]

D. Kostoulas, R. Aldunate, F. Mora and S. Lakhera, A nature-inspired decentralized trust model to reduce information unreliability in complex disaster relief operations,, Advanced Engineering Informatics, 22 (2008), 45. doi: 10.1016/j.aei.2007.09.001. Google Scholar

[28]

D. G. Kovács and K. Spens, Humanitarian logistics in disaster relief operations,, International Journal of Physical Distribution and Logistics Management, 37 (2007), 99. Google Scholar

[29]

M.-Y. Lai, C.-S. Liu and X.-J. Tong, A two-stage hybrid meta-heuristic for pickup and delivery vehicle routing problem with time windows,, Journal of Industrial and Management Optimization, 6 (2010), 435. doi: 10.3934/jimo.2010.6.435. Google Scholar

[30]

M.-Y. Lai and X.-J. Tong, A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search,, Journal of Industrial and Management Optimization, 8 (2012), 469. doi: 10.3934/jimo.2012.8.469. Google Scholar

[31]

L. Özdamar, E. Ekinci and B. Küçükyazici, Emergency logistics planning in natural disasters,, Annals of Operations Research, 129 (2004), 217. doi: 10.1023/B:ANOR.0000030690.27939.39. Google Scholar

[32]

J. Salmerón and A. Apte, Stochastic optimization for natural disaster asset prepositioning,, Production and Operations Management, 19 (2010), 561. doi: 10.1111/j.1937-5956.2009.01119.x. Google Scholar

[33]

J.-B. Sheu, Dynamic relief-demand management for emergency logistics operations under large-scale disasters,, Transportation Research Part E, 46 (2010), 1. doi: 10.1016/j.tre.2009.07.005. Google Scholar

[34]

S. Sethi, H. Yan and H. Zhang, Inventory and Supply Chain Management with Forecast Updates,, International Series in Operations Research and Management Science, (2005). Google Scholar

[35]

P. Tatham and G. Kovács, The application of "swift trust" to humanitarian logistics,, International Journal of Production Economics, 126 (2010), 35. doi: 10.1016/j.ijpe.2009.10.006. Google Scholar

[36]

C. Thévenaz and S. Resodihardjo, All the best laid plans conditions impeding proper emergency response,, International Journal of Production Economics, 126 (2010), 7. Google Scholar

[37]

A. Thomas and M. Mizushima, Logistics training: Necessity or luxury,, Forced Migration Review, 22 (2005), 60. Google Scholar

[38]

UNCRD, Reconnaissance Report of the 2008 Sichuan Earthquake,, Japanese Edition, (2009). Google Scholar

[39]

UNDAC, Handbook for Emergencies,, 2006., (). Google Scholar

[40]

P. Yi, Y. Zhang, Y. Tang and S. Li, An incident information management framework based on data integration, data mining, and multi-criteria decision making,, Decision Support Systems, 51 (2011), 316. Google Scholar

[41]

L. Yu and K. Lai, A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support,, Decision Support Systems, 51 (2011), 307. doi: 10.1016/j.dss.2010.11.024. Google Scholar

[42]

Y. Yuan and D. Wang, Path selection model and algorithm for emergency logistics management,, Computers and Industrial Engineering, 56 (2009), 1081. doi: 10.1016/j.cie.2008.09.033. Google Scholar

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