Decentralized beneficiary behavior in humanitarian supply chains: models, performance bounds, and coordination mechanisms

Effectiveness in humanitarian supply chain operations depends on the critical last mile between beneficiaries and needed supplies or services. Often, the last mile is traveled by the beneficiaries themselves. This paper’s focus is on systems in which beneficiaries make autonomous decisions about where to seek supplies or services using a utility function that captures distance, congestion, and the relative importance of the two factors. We model beneficiary behavior as a network congestion game where the resources are a set of facilities from which individuals choose. Importantly, our models capture the fact that the relative importance of distance and congestion may be specific to both the individual and the facility; we represent this using a factor called the congestion weight. We prove new bounds on the system performance that results from decentralized beneficiary decisions in comparison to centralized optimal assignments, and we introduce mechanisms for achieving centrally optimal outcomes even in the presence of decentralization. We demonstrate the methods with data from the international public health response to the Haiti cholera epidemic.

[1]  Özlem Ergun,et al.  Mechanism design for a multicommodity flow game in service network alliances , 2008, Oper. Res. Lett..

[2]  Kurt Mehlhorn,et al.  Improving the Price of Anarchy for Selfish Routing via Coordination Mechanisms , 2011, ESA.

[3]  Paul R. Harper,et al.  Selfish routing in public services , 2013, Eur. J. Oper. Res..

[4]  Christos H. Papadimitriou,et al.  Worst-case equilibria , 1999 .

[5]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[6]  Yoav Shoham,et al.  Fast and Compact: A Simple Class of Congestion Games , 2005, AAAI.

[7]  Luk N. Van Wassenhove,et al.  Humanitarian aid logistics: supply chain management in high gear , 2006, J. Oper. Res. Soc..

[8]  Joel Sokol,et al.  Designing Mechanisms for the Management of Carrier Alliances , 2011, Transp. Sci..

[9]  Benita M. Beamon,et al.  Last Mile Distribution in Humanitarian Relief , 2008, J. Intell. Transp. Syst..

[10]  Martin Gairing,et al.  Routing (un-) splittable flow in games with player-specific affine latency functions , 2011, TALG.

[11]  Rajan Batta,et al.  Review of recent developments in OR/MS research in disaster operations management , 2013, Eur. J. Oper. Res..

[12]  Heier Stamm,et al.  Design and analysis of humanitarian and public health logistics systems , 2010 .

[13]  Patrick Hirsch,et al.  Agent-based simulation optimization for dynamic disaster relief distribution , 2018, Central Eur. J. Oper. Res..

[14]  Elias Koutsoupias,et al.  Coordination mechanisms , 2009, Theor. Comput. Sci..

[15]  D. Hearn,et al.  Congestion Toll Pricing of Traffic Networks , 1997 .

[16]  Julie Swann,et al.  Quantifying and explaining accessibility with application to the 2009 H1N1 vaccination campaign , 2017, Health care management science.

[17]  Dale M. Pfrimmer Cholera in Haiti. , 2010, Journal of continuing education in nursing.

[18]  Yossi Azar,et al.  The Price of Routing Unsplittable Flow , 2005, STOC '05.

[19]  Martin Gairing,et al.  Total Latency in Singleton Congestion Games , 2007, WINE.

[20]  I. Milchtaich,et al.  Congestion Games with Player-Specific Payoff Functions , 1996 .

[21]  Linet Özdamar,et al.  Models, solutions and enabling technologies in humanitarian logistics , 2015, Eur. J. Oper. Res..

[22]  R. Rosenthal A class of games possessing pure-strategy Nash equilibria , 1973 .

[23]  Paul G. Spirakis,et al.  Atomic Selfish Routing in Networks: A Survey , 2005, WINE.

[24]  Adriana Leiras,et al.  Literature review of humanitarian logistics research: trends and challenges , 2014 .

[25]  Luke Muggy,et al.  Game theory applications in humanitarian operations: a review , 2014 .

[26]  Jessica L. Heier Stamm,et al.  Dynamic, robust models to quantify the impact of decentralization in post-disaster health care facility location decisions , 2017 .

[27]  J. Nash Equilibrium Points in N-Person Games. , 1950, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Timothy Luke Muggy Quantifying and mitigating decentralized decision making in humanitarian logistics systems , 2015 .

[29]  Benita M. Beamon,et al.  Facility location in humanitarian relief , 2008 .

[30]  Marianne Jahre,et al.  Coordination in humanitarian logistics through clusters , 2010 .

[31]  Mark A. Turnquist,et al.  Pre-positioning of emergency supplies for disaster response , 2010 .

[32]  Csaba D. Tóth,et al.  Congestion Games, Load Balancing, and Price of Anarchy , 2004, CAAN.

[33]  Michal Tzur,et al.  Designing humanitarian supply chains by incorporating actual post-disaster decisions , 2018, Eur. J. Oper. Res..

[34]  Berthold Vöcking,et al.  On the Impact of Combinatorial Structure on Congestion Games , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[35]  Robert V. Tauxe,et al.  Lessons Learned during Public Health Response to Cholera Epidemic in Haiti and the Dominican Republic , 2011, Emerging infectious diseases.

[36]  Walter J. Gutjahr,et al.  Modelling beneficiaries’ choice in disaster relief logistics , 2017, Ann. Oper. Res..

[37]  Jiuh-Biing Sheu,et al.  An emergency logistics distribution approach for quick response to urgent relief demand in disasters , 2007 .

[38]  Elias Koutsoupias,et al.  The price of anarchy of finite congestion games , 2005, STOC '05.

[39]  Christos H. Papadimitriou,et al.  Algorithms, games, and the internet , 2001, STOC '01.

[40]  John F. Nash,et al.  EQUILIBRIUM POINTS IN 𝑛-PERSON GAMES , 2020 .