Security personnel routing and rostering: a hyper-heuristic approach

In the present study, a large scale, structured problem regarding the routing and rostering of security personnel is investigated. Structured problems are combinatorial optimization problems that encompass characteristics of more than one known problem in operational research. The problem deals with assigning the available personnel to visits associated with a set of customers. This objective just described, reflects the rostering characteristic of the problem. In addition, the different geographic locations of the customers indicate the requirement of routing. A new benchmark dataset for this complex problem is presented. A group of high-level problem-independent methods, i.e. hyper-heuristics, is used to solve this novel problem. The performance and behaviour of different hyper-heuristics for the presented benchmark dataset are analysed.

[1]  Graham Kendall,et al.  An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-Heuristic , 2005 .

[2]  Patrick De Causmaecker,et al.  A hyperheuristic approach to Belgian nurse rostering problems , 2009 .

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

[4]  Michel Gendreau,et al.  A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows , 1997, Transp. Sci..

[5]  Hendrik Van Landeghem,et al.  The State of the Art of Nurse Rostering , 2004, J. Sched..

[6]  Graham Kendall,et al.  An investigation of a tabu assisted hyper-heuristic genetic algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[7]  Éric D. Taillard,et al.  POPMUSIC for a real-world large-scale vehicle routing problem with time windows , 2009, J. Oper. Res. Soc..

[8]  Sanja Petrovic,et al.  Knowledge Discovery in a Hyper-heuristic for Course Timetabling Using Case-Based Reasoning , 2002, PATAT.

[9]  Greet Van den Berghe,et al.  A Hyper-heuristic with Learning Automata for the Traveling Tournament Problem , 2012 .

[10]  Alexander Nareyek,et al.  Choosing search heuristics by non-stationary reinforcement learning , 2004 .

[11]  Graham Kendall,et al.  A Classification of Hyper-heuristic Approaches , 2010 .

[12]  Peter Demeester,et al.  One hyper-heuristic approach to two timetabling problems in health care , 2012, J. Heuristics.

[13]  Graham Kendall,et al.  A Tabu-Search Hyperheuristic for Timetabling and Rostering , 2003, J. Heuristics.

[14]  David M. Miller,et al.  An Integrated Spatial DSS for Scheduling and Routing Home-Health-Care Nurses , 1997 .

[15]  Graham Kendall,et al.  Monte Carlo hyper-heuristics for examination timetabling , 2012, Ann. Oper. Res..

[16]  Edmund K. Burke,et al.  A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling , 2010, Int. J. Appl. Metaheuristic Comput..

[17]  Graham Kendall,et al.  Evolving Bin Packing Heuristics with Genetic Programming , 2006, PPSN.

[18]  Graham Kendall,et al.  A Hyperheuristic Approach to Scheduling a Sales Summit , 2000, PATAT.

[19]  Gilbert Laporte,et al.  A unified tabu search heuristic for vehicle routing problems with time windows , 2001, J. Oper. Res. Soc..

[20]  Katja Verbeeck,et al.  A New Learning Hyper-heuristic for the Traveling Tournament Problem , 2009 .

[21]  Torsten Fahle,et al.  A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem , 2006, Comput. Oper. Res..