Ambulance location and relocation problems with time-dependent travel times

Emergency service providers are facing the following problem: how and where to locate vehicles in order to cover potential future demand effectively. Ambulances are supposed to be located at designated locations such that in case of an emergency the patients can be reached in a time-efficient manner. A patient is said to be covered by a vehicle if (s)he can be reached by an ambulance within a predefined time limit. Due to variations in speed and the resulting travel times it is not sufficient to solve the static ambulance location problem once using fixed average travel times, as the coverage areas themselves change throughout the day. Hence we developed a multi-period version, taking into account time-varying coverage areas, where we allow vehicles to be repositioned in order to maintain a certain coverage standard throughout the planning horizon. We have formulated a mixed integer program for the problem at hand, which tries to optimize coverage at various points in time simultaneously. The problem is solved metaheuristically using variable neighborhood search. We show that it is essential to consider time-dependent variations in travel times and coverage respectively. When ignoring them the resulting objective will be overestimated by more than 24%. By taking into account these variations explicitly the solution on average can be improved by more than 10%.

[1]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[2]  Pierre Hansen,et al.  Improvement and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem , 2000, Oper. Res..

[3]  Cem Saydam,et al.  A multiperiod set covering location model for dynamic redeployment of ambulances , 2008, Comput. Oper. Res..

[4]  Armann Ingolfsson,et al.  Empirical Analysis of Ambulance Travel Times: The Case of Calgary Emergency Medical Services , 2010, Manag. Sci..

[5]  Michel Gendreau,et al.  Vehicle dispatching with time-dependent travel times , 2003, Eur. J. Oper. Res..

[6]  F Weichenmeier,et al.  Evaluation of speed estimation by floating car data within the research project Dmotion , 2007 .

[7]  Peter J. Kolesar,et al.  Determining the Relation between Fire Engine Travel Times and Travel Distances in New York City , 1975, Oper. Res..

[8]  Michel Gendreau,et al.  The maximal expected coverage relocation problem for emergency vehicles , 2006, J. Oper. Res. Soc..

[9]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

[10]  John J. Bernardo,et al.  Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky , 1994 .

[11]  Bernhard Fleischmann,et al.  Dynamic Vehicle Routing Based on Online Traffic Information , 2004, Transp. Sci..

[12]  Pierre Hansen,et al.  Solving the p‐Center problem with Tabu Search and Variable Neighborhood Search , 2000, Networks.

[13]  Richard F. Hartl,et al.  Heuristic Solution of an Extended Double-Coverage Ambulance Location Problem for Austria , 2005 .

[14]  Arthur V. Hill,et al.  Modelling Intra-City Time-Dependent Travel Speeds for Vehicle Scheduling Problems , 1992 .

[15]  Pierre Hansen,et al.  Cooperative Parallel Variable Neighborhood Search for the p-Median , 2004, J. Heuristics.

[16]  Mark S. Daskin,et al.  A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution , 1983 .

[17]  Richard L. Church,et al.  The maximal covering location problem , 1974 .

[18]  Richard F. Hartl,et al.  Variable neighborhood search for the dial-a-ride problem , 2010, Comput. Oper. Res..

[19]  Gilbert Laporte,et al.  Solving an ambulance location model by tabu search , 1997 .

[20]  Gilbert Laporte,et al.  Application of the Double Standard Model for Ambulance Location , 2009 .

[21]  Gilbert Laporte,et al.  Ambulance location and relocation models , 2000, Eur. J. Oper. Res..

[22]  Mark E. T. Horn,et al.  Efficient modeling of travel in networks with time-varying link speeds , 2000, Networks.

[23]  Olli Bräysy,et al.  A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows , 2003, INFORMS J. Comput..

[24]  H. Kunzi,et al.  Lectu re Notes in Economics and Mathematical Systems , 1975 .

[25]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[26]  Michel Gendreau,et al.  A dynamic model and parallel tabu search heuristic for real-time ambulance relocation , 2001, Parallel Comput..

[27]  Timothy J. Lowe,et al.  Aggregation error for location models: survey and analysis , 2009, Ann. Oper. Res..

[28]  Roberto Montemanni,et al.  Integration of a robust shortest path algorithm with a time dependent vehicle routing model and applications , 2003, The 3rd International Workshop on Scientific Use of Submarine Cables and Related Technologies, 2003..

[29]  Bernhard Fleischmann,et al.  Time-Varying Travel Times in Vehicle Routing , 2004, Transp. Sci..

[30]  Mark S. Daskin,et al.  Time Dependent Vehicle Routing Problems: Formulations, Properties and Heuristic Algorithms , 1992, Transp. Sci..

[31]  Richard F. Hartl,et al.  A Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows , 2004, J. Heuristics.

[32]  Charles ReVelle,et al.  Concepts and applications of backup coverage , 1986 .

[33]  Charles S. ReVelle,et al.  The Location of Emergency Service Facilities , 1971, Oper. Res..

[34]  M Linauer,et al.  TRAVEL TIME ESTIMATION BASED ON INCOMPLETE PROBE CAR INFORMATION , 2006 .

[35]  M. Chiani Error Detecting and Error Correcting Codes , 2012 .

[36]  Roberto Montemanni,et al.  Time dependent vehicle routing problem with a multi ant colony system , 2008, Eur. J. Oper. Res..