Abstract
Numerous papers based on various search methods across a wide variety of applications have appeared in the literature over recent years. Most of these methods apply the following same approach to address the problems at hand: at each iteration of the search, they first apply their search methods to generate new solutions, then they calculate the objective values (or costs) by taking some constraints into account, and finally they use some strategies to determine the acceptance or rejection of these solutions based upon the calculated objective values. However, the premise of this paper is that calculating the exact objective value of every resulting solution is not a must, particularly for highly constrained problems where such a calculation is costly and the feasible regions are small and disconnected. Furthermore, we believe that for newly-generated solutions, evaluating the quality purely by their objective values is sometimes not the most efficient approach. In many combinatorial problems, there are poor-cost solutions where possibly just one component is misplaced and all others work well. Although these poor-cost solutions can be the intermediate states towards the search of a high quality solution, any cost-oriented criteria for solution acceptance would deem them as inferior and consequently probably suggest a rejection. To address the above issues, we propose a pattern recognition-based framework with the target of designing more intelligent and more flexible search systems. The role of pattern recognition is to classify the quality of resulting solutions, based on the solution structure rather than the solution cost. Hence, the general contributions of this work are in the line of “insights” and recommendations. Two real-world cases of the assignment problem, i.e. the hospital personnel scheduling and educational timetabling, are used as the case studies. For each case, we apply neural networks as the tool for pattern recognition. In addition, we present our theoretical and experimental results in terms of runtime speedup.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Alfares HK (2004) Survey, categorization, and comparison of recent tour scheduling literature. Ann Oper Res 127:145–175
Bishop CM (2005) Neural networks for pattern recognition. Oxford University Publisher, Oxford
Burke EK, De Causmaecker P, Vanden Berghe G, Landeghem H (2004) The state of the art of nurse rostering. J Sched 7:441–499
Burke EK, De Werra D, Kingston J (2004) Applications to timetabling. In: Cross J, Yellen J (eds) Handbook of graph theory. Chapman Hall/CRC Press, London/Boca Raton, pp 445–474. Sect. 5.6
Edmund EK, Graham K (eds) (2005) Search methodologies: introductory tutorials in optimization and decision support techniques. Springer, Berlin
Burke EK, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger G (eds) Handbook of meta-heuristics. Kluwer Academic, Norwell, pp 457–474
Burke EK, Li J, Qu R (2010) A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems. Eur J Oper Res 2003:484–493
Burke EK, Curtois T, Post G, Qu R, Veltman B (2008) A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem. Eur J Oper Res 188:330–341
Burke EK, McCollum B, Meisel A, Petrovic S, Qu R (2007) A graph-based hyper-heuristic for educational timetabling problems. Eur J Oper Res 176:177–192
Burke EK, Cowling P, De Causmaecker P, Vanden Berghe G (2001) A memetic approach to the nurse rostering problem. Appl Intell 15:199–214
Cattrysse D, Van Wassenhove LN (1992) A survey of algorithms for the generalized assignment problem. Eur J Oper Res 60:260–272
Cheang B, Li H, Lim A, Rodrigues B (2003) Nurse rostering problems—a bibliographic survey. Eur J Oper Res 151:447–460
Curtois T (2007) Novel heuristic and metaheuristic approaches to the automated scheduling of healthcare personnel. PhD thesis, School of Computer Science, University of Nottingham
Easton K, Nemhauser G, Trick M (2004) Sports scheduling. In: Leung J (ed) Handbook of scheduling: algorithms, models, and performance analysis, Chap 52. CRC Press, Boca Raton
Fisher M, Jaikumar R, Van Wassenhove L (1986) A multiplier adjustment method for the generalized assignment problem. Manag Sci 32:1095–1103
Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New York
Gans N, Koole G, Mandelbaum A (2003) Telephone call centers: tutorial, review, and research prospects. Manuf Serv Oper Manag 5:79–141
Glover F, Laguna M (1997) Tabu search. Kluwer Academic, Norwell
Guyon I, Gunn S, Nikravesh M, Zadeh LA (2006) Feature extraction: foundations and applications. Springer, Berlin
Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680
Kohl N, Karisch SE (2004) Airline crew rostering: problem types, modeling, and optimization. Ann Oper Res 127:223–257
Kulkarni AD, Cavanaugh CD (2000) Fuzzy neural network models for classification. Appl Intell 12:207–215
Kwan RSK (2004) Bus and train driver scheduling. In: Leung J (ed) Handbook of scheduling: algorithms, models, and performance analysis, Chap 51. CRC Press, Boca Raton
Li JJ, Aickelin U, Burke EK (2009) Self-adjusting search for hospital personnel scheduling. INFORMS J Comput 21:468–479
Li J, Kwan RSK (2003) A fuzzy genetic algorithm for driver scheduling. Eur J Oper Res 147:334–344
Li J, Kwan RSK (2005) A self-adjusting algorithm for driver scheduling. J Heuristics 11:351–367
Mansour N, Isahakian V, Ghalayini I (2009) Scatter search technique for exam timetabling. Appl Intell. doi:10.1007/s10489-009-0196-5
Martello S, Toth P (1990) Knapsack problems: algorithms and computer implementations. Wiley, New York
Osman IH (1995) Heuristics for the generalized assignment problem: simulated annealing and tabu search approaches. OR Spektrum 17:211–225
Petrovic S, Burke EK (2004) University timetabling. In: Leung J (ed) Handbook of scheduling: algorithms, models, and performance analysis, Chap 45. CRC Press, Boca Raton
Qu R, Burke EK (2009) Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. J Oper Res Soc 60:1273–1285
Qu R, Burke EK, McCollum B, Merlot LTG, Lee SY (2009) A survey of search methodologies and automated system development for examination timetabling. J Sched 12:55–89
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318–362
Schaerf A (1999) A survey of automated timetabling. Artif Intell Rev 13:87–127
Salewski F, Bottcher L, Drex LA (1996) Operational audittask assignment and staff scheduling. OR Spektrum 18:29–41
Sitompul D, Randhawa S (1990) Nurse scheduling models: a state-of-the-art review. J Soc Health Syst 2:62–72
Salcedo-Sanz S, Bousoño-Calzón C (2005) A hybrid neural-genetic algorithm for the frequency assignment problem in satellite communications. Appl Intell 22:207–217
Theodoridis S, Koutroumbas K (2006) Pattern recognition. Academic Press, San Diego
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, J., Burke, E.K. & Qu, R. A pattern recognition based intelligent search method and two assignment problem case studies. Appl Intell 36, 442–453 (2012). https://doi.org/10.1007/s10489-010-0270-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-010-0270-z