Facility location problem for emergency and on-demand transportation systems

Although they have different objectives, emergency response systems and on-demand transportation systems are two similar systems in the sense that both deal with stochastic demand and service time which create congestions for moderate level of demand. Emergency response system location problems are one of the early problems immensely dealt in the literature. These problems are modeled by either set covering or transportation models which do not give much attention to the stochastic nature of the problem. On-demand transportation is a newly developing type of transportation system and literature is not broad enough but has similarities with emergency response systems. In this research, our aim is to solve facility location problem with stochastic demand and service time. Specifically we are dealing with temporal and spatial stochasticity which emerge because of the uncertainty in demand and service time. Recently we have developed a mixed aggregate hypercube model which are extensions to Larson (1974) and Boyaci and Geroliminis (2012). Results are promising and applicable to real life instances.

[1]  A. S. Manne CAPACITY EXPANSION AND PROBABILISTIC GROWTH , 1961 .

[2]  Jiuh-Biing Sheu,et al.  Challenges of emergency logistics management , 2007 .

[3]  Richard C. Larson,et al.  Approximating the Performance of Urban Emergency Service Systems , 1975, Oper. Res..

[4]  Pierre Hansen,et al.  The p-median problem: A survey of metaheuristic approaches , 2005, Eur. J. Oper. Res..

[5]  Vladimir Marianov,et al.  The Queueing Maximal availability location problem: A model for the siting of emergency vehicles , 1996 .

[6]  Alexander Skabardonis,et al.  A spatial queuing model for the emergency vehicle districting and location problem , 2009 .

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

[8]  Reinaldo Morabito,et al.  Emergency service systems: The use of the hypercube queueing model in the solution of probabilistic location problems , 2008, Int. Trans. Oper. Res..

[9]  RICHARD C. LARSON,et al.  A hypercube queuing model for facility location and redistricting in urban emergency services , 1974, Comput. Oper. Res..

[10]  Nikolas Geroliminis,et al.  A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network , 2011, Eur. J. Oper. Res..

[11]  Nikolas Geroliminis,et al.  Extended Hypercube Models for Large-Scale Spatial Queueing Systems. , 2012 .

[12]  Richard C. Larson,et al.  Urban Operations Research , 1981 .

[13]  Reinaldo Morabito,et al.  A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways , 2007 .

[14]  S. L. Hakimi,et al.  Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph , 1964 .

[15]  Mark S. Daskin,et al.  A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment , 1981 .

[16]  Matthew G. Karlaftis,et al.  Genetic Algorithm-Based Approach for Optimal Location of Transit Repair Vehicles on a Large Urban Network , 2004 .

[17]  Reinaldo Morabito,et al.  A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways , 2007, Ann. Oper. Res..

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

[19]  Igor N. Kovalenko,et al.  A hypercube queueing loss model with customer-dependent service rates , 2008, Eur. J. Oper. Res..

[20]  Charles S. ReVelle,et al.  The Maximum Availability Location Problem , 1989, Transp. Sci..

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

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