Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study

This paper aims to develop a capacitated vehicle routing solution to evacuate short-notice evacuees with time windows and disruption risks under uncertainties during a bushfire. A heuristic solution technique is applied to solve the triangular possibilistic model to optimise emergency delivery service. The effectiveness of the proposed algorithm is evaluated by comparing it with a designed genetic algorithm on sets of 20 numerical examples. The model is then applied to the real case study of 2009 Black Saturday bushfires in Victoria, Australia. The results show that it is possible to transfer the last-minute evacuees during the Black Saturday bushfires under the hard time window constraint. Network disruptions however have impact on resource utilisation. The modelling outputs will be useful in the development of emergency plans and evacuation strategies to enhance rapid response to last-minute evacuation in a bushfire emergency.

[1]  Jared L. Cohon,et al.  Multiobjective programming and planning , 2004 .

[2]  Todd Litman,et al.  Lessons From Katrina and Rita: What Major Disasters Can Teach Transportation Planners , 2006 .

[3]  Babak Abbasi,et al.  Multi-Objective Decision Analytics for Short-Notice Bushfire Evacuation: An Australian Case Study , 2015, Australas. J. Inf. Syst..

[4]  Li Zhang,et al.  Optimum Transit Operations during the Emergency Evacuations , 2009 .

[5]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..

[6]  C. Hwang,et al.  A new approach to some possibilistic linear programming problems , 1992 .

[7]  Tien-Fu Liang,et al.  Distribution planning decisions using interactive fuzzy multi-objective linear programming , 2006, Fuzzy Sets Syst..

[8]  George B. Dantzig,et al.  The Truck Dispatching Problem , 1959 .

[9]  Hui Li,et al.  Computing efficient solutions to fuzzy multiple objective linear programming problems , 2006, Fuzzy Sets Syst..

[10]  Dimitris Bertsimas,et al.  A Vehicle Routing Problem with Stochastic Demand , 1992, Oper. Res..

[11]  Paolo Toth,et al.  The Vehicle Routing Problem , 2002, SIAM monographs on discrete mathematics and applications.

[12]  Babak Abbasi,et al.  Enhancing emergency evacuation response of late evacuees: Revisiting the case of Australian Black Saturday bushfire , 2016 .

[13]  Gilbert Laporte,et al.  STOCHASTIC VEHICLE ROUTING. , 1996 .

[14]  Cheng-Chieh Chen,et al.  Modeling and Performance Assessment of a Transit-Based Evacuation Plan within a Contraflow Simulation Environment , 2009 .

[15]  Cjh Cees Midden,et al.  Complex evacuation; effects of motivation level and slope of stairs on emergency egress time in a sports stadium , 1999 .

[16]  Martha A. Centeno,et al.  Hurricane Evacuation Decision-Support Model for Bus Dispatch , 2006 .

[17]  Evangelos I. Kaisar,et al.  An Emergency Evacuation Planning Model for Special Needs Populations , 2012 .

[18]  Yingyan Lou,et al.  Pickup Locations and Bus Allocation for Transit-Based Evacuation Planning with Demand Uncertainty , 2014 .

[19]  Henry X. Liu,et al.  Model Reference Adaptive Control Framework for Real-Time Traffic Management Under Emergency Evacuation , 2007 .

[20]  Mark S. Daskin,et al.  Strategic facility location: A review , 1998, Eur. J. Oper. Res..

[21]  Fatemeh Sayyady,et al.  Optimizing the use of public transit system during no-notice evacuation of urban areas , 2010, Comput. Ind. Eng..

[22]  Baoding Liu,et al.  Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm , 2006, Appl. Math. Comput..

[23]  Lee D. Han,et al.  MODELING TRANSIT ISSUES UNIQUE TO HURRICANE EVACUATIONS: NORTH CAROLINA'S SMALL URBAN AND RURAL AREAS , 2001 .

[24]  Baher Abdulhai,et al.  Large-Scale Evacuation Using Subway and Bus Transit: Approach and Application in City of Toronto , 2012 .

[25]  C. Waters.  Vehicle-scheduling Problems with Uncertainty and Omitted Customers , 1989 .

[26]  Baher Abdulhai,et al.  Managing Large-Scale Multimodal Emergency Evacuations , 2010 .

[27]  Xin Zhang,et al.  A Transit-Based Evacuation Model for Metropolitan Areas , 2014 .

[28]  John Handmer,et al.  Australian bushfire fatalities 1900–2008: exploring trends in relation to the ‘Prepare, stay and defend or leave early’ policy , 2010 .

[29]  Dusan Teodorovic,et al.  The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain , 1996, Fuzzy Sets Syst..

[30]  Baoding Liu,et al.  Theory and Practice of Uncertain Programming , 2003, Studies in Fuzziness and Soft Computing.

[31]  F. Southworth,et al.  Regional Evacuation Modeling: A State of the Art Reviewing , 1991 .

[32]  T Urbanik,et al.  Evacuation time estimates for nuclear power plants. , 2000, Journal of hazardous materials.

[33]  Babak Abbasi,et al.  Vehicle routing and scheduling for bushfire emergency evacuation , 2015, 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[34]  Marc Goerigk,et al.  A robust bus evacuation model with delayed scenario information , 2014, OR Spectr..

[35]  Marc Goerigk,et al.  Branch and bound algorithms for the bus evacuation problem , 2013, Comput. Oper. Res..

[36]  Gabriel Gutiérrez-Jarpa,et al.  A single vehicle routing problem with fixed delivery and optional collections , 2009 .

[37]  A. Kaufman,et al.  Introduction to the Theory of Fuzzy Subsets. , 1977 .

[38]  Hisashi Kubota,et al.  Short-Notice Bus-Based Evacuation under Dynamic Demand Conditions , 2016 .

[39]  Shih-Pin Chen,et al.  A mathematical programming approach to supply chain models with fuzzy parameters , 2006 .

[40]  R. Jayakrishnan,et al.  Real-Time Mass Passenger Transport Network Optimization Problems , 2006 .

[41]  Yanfeng Ouyang,et al.  Location planning for transit-based evacuation under the risk of service disruptions , 2013 .

[42]  Parham Pahlavani,et al.  Dynamic Evacuation Routing Plan after an Earthquake , 2015 .

[43]  Tadeusz Sawik,et al.  Selection of supply portfolio under disruption risks , 2011 .

[44]  Michael K. Lindell.  Assessing emergency preparedness in support of hazardous facility risk analyses: Application to siting a US hazardous waste incinerator , 1995 .

[45]  Douglas R. Bish,et al.  Planning for a bus-based evacuation , 2011, OR Spectr..

[46]  S.A. Torabi,et al.  An interactive possibilistic programming approach for multiple objective supply chain master planning , 2008, Fuzzy Sets Syst..

[47]  T. Sawik.  Integrated supply, production and distribution scheduling under disruption risks , 2016 .

[48]  Rachel A. Davidson,et al.  Shelter location and transportation planning under hurricane conditions , 2012 .

[49]  Burak Eksioglu,et al.  The vehicle routing problem: A taxonomic review , 2009, Comput. Ind. Eng..

[50]  Reay-Chen Wang,et al.  Applying possibilistic linear programming to aggregate production planning , 2005 .

[51]  H. Zimmermann.  Fuzzy programming and linear programming with several objective functions , 1978 .

[52]  Di Wu,et al.  Optimal Transit Routing Problem for Emergency Evacuations , 2009 .

[53]  Pamela Murray-Tuite,et al.  Evacuation transportation modeling: An overview of research, development, and practice , 2013 .

[54]  Marcos Negreiros,et al.  The capacitated centred clustering problem , 2006, Comput. Oper. Res..

[55]  Hani S. Mahmassani,et al.  Optimal Scheduling of Evacuation Operations , 2006 .

[56]  Chi Pak Chan,et al.  Large scale evacuation of carless people during short- and long-notice emergency , 2010 .

[57]  Irem Ozkarahan,et al.  A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach , 2008 .

[58]  Baoding Liu,et al.  Redundancy optimization problems with uncertainty of combining randomness and fuzziness , 2004, Eur. J. Oper. Res..

[59]  Linet Özdamar,et al.  A dynamic logistics coordination model for evacuation and support in disaster response activities , 2007, Eur. J. Oper. Res..

[60]  Wang Guangyuan,et al.  Linear programming with fuzzy random variable coefficients , 1993 .

[61]  G. Clarke,et al.  Scheduling of Vehicles from a Central Depot to a Number of Delivery Points , 1964 .

[62]  Jian Wang,et al.  Modeling of evacuations to no-notice event by public transit system , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[63]  Hong Zheng,et al.  Optimization of bus routing strategies for evacuation , 2014 .

[64]  İbrahim Akgün,et al.  Risk based facility location by using fault tree analysis in disaster management , 2015 .

[65]  Yi Zhu,et al.  An investigation into the vehicle routing problem with time windows and link capacity constraints , 2012 .

[66]  Serge P. Hoogendoorn,et al.  A review on travel behaviour modelling in dynamic traffic simulation models for evacuations , 2012 .

[67]  Babak Abbasi,et al.  Fleet routing and scheduling in bushfire emergency evacuation: A regional case study of the Black Saturday bushfires in Australia , 2017, Transportation Research Part D: Transport and Environment.

[68]  Dimitris Bertsimas,et al.  Computational Approaches to Stochastic Vehicle Routing Problems , 1995, Transp. Sci..

[69]  F. Drews,et al.  Modeling Evacuate versus Shelter-in-Place Decisions in Wildfires , 2011 .

[70]  Susan Pascoe,et al.  Interim report: 2009 Victorian Bushfires Royal Commission , 2009 .

[71]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[72]  Bruce L. Golden,et al.  Stochastic vehicle routing: A comprehensive approach , 1983 .

[73]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[74]  Didier Dubois,et al.  Possibility Theory: Qualitative and Quantitative Aspects , 1998 .