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Agent-Based System Design for Service Process Scheduling: Challenges, Approaches and Opportunities


Agent-Based System Design for Service Process Scheduling: Challenges, Approaches and Opportunities

Dargahi, Farnaz, Wang, Chun, Bhuiyan, Mohammad Fozlul Haque and Mehrizi, Hamidreza (2012) Agent-Based System Design for Service Process Scheduling: Challenges, Approaches and Opportunities. Journal of Integrated Design and Process Science, 16 (2). pp. 15-32. ISSN 1875-8959

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Official URL: http://dx.doi.org/10.3233/jid-2012-0011


Compared with traditional manufacturing scheduling, service process scheduling poses additional challenges attributable to the significant customer involvement in service processes. In services, there are typically no inventoried products, which make the service provider's capacity more sensitive to dynamic changes. Service process scheduling objectives are also more complicated due to the consideration of customer preferences, customer waiting costs and human resource costs. After describing the Unified Services Theory and analysing its scheduling implications, this paper reviews the research literature on service process scheduling system design with a particular emphasis on agent-based approaches. Major issues in agent-based service process scheduling systems design are discussed and research opportunities are identified. The survey of the literature reveals that despite of many domain-specific designs in agent-based service process scheduling, there is a lack of general problem formulations, classifications, solution frameworks, and test beds. Constructing these general models for service process scheduling system design will facilitate the collaboration of researchers in this area and guide the effective development of integrated service process scheduling systems.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Article
Authors:Dargahi, Farnaz and Wang, Chun and Bhuiyan, Mohammad Fozlul Haque and Mehrizi, Hamidreza
Journal or Publication:Journal of Integrated Design and Process Science
Digital Object Identifier (DOI):10.3233/jid-2012-0011
Keywords:services, agent-based systems, decentralized scheduling, dynamic scheduling, auctions
ID Code:976898
Deposited By: Danielle Dennie
Deposited On:19 Feb 2013 16:50
Last Modified:18 Jan 2018 17:43


Abdennadher, S., & Schlenker, H. (1999). INTERDIP-an interactive constraint based nurse scheduler. Proceedings of the Eleventh Conference on Innovative Applications of Artificial Intelligence, Menlo Park, CA, 838-843

Aickelin, U., & Dowsland, K. A. (2001). Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. Journal of Scheduling 3(3), 139-153

An, B., Lesser, V., Irwin, D., & Zink, M. (2010). Automated negotiation with decommitment for dynamic resource allocation in cloud computing. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, 981–988

Azaiez, M. N., & Sharif, S. (2005). A 0-1 goal programming model for nurses cheduling. Computers and Operations Research 32(3), 491-507

Bailey, R. N., Garner, K. M., & Hobbs, M. F. (1997). Using Simulated Annealing and Genetic Algorithms to Solve Staff Scheduling Problems. Asia-Pacific Journal of Operational Research 14(2), 27-43

Bannock, G., Baxter, R. E., & Reese, R. (1982). The Penguin Dictionary of Economics. Penguin Books, Ltd., Harmondsworth, Middlesex England

Becker, M., & Hans, C. (2006). Artificial Software Agents as Representatives of Their Human Principals in Operating-Room-Team-Forming. Multi-agnet Engineering International Handbooks on Information Systems, 221-237

Berger, S., & Bierwirth, C. (2010). Solutions to the request reassignment problem in collaborative carrier networks. Transportation research Part E,Volume 46,No.5, 627-638

Burke, E. K., Elliman, D. G., & Weare, R. F. (1995). A hybrid genetic algorithm for highly constrained timetabling problems. Proceedings of the 6th International Conference on Genetic Algorithms, Pittsburgh, USA,Morgan Kaufmann, Los Altos, CA, 605-610

Burke, E. K., & Newall, J. P. (1999). A multi-stage evolutionary algorithm for the timetable problem. IEEE Transactions on Evolutionary Computation 3 (1), 63–74

Burke, E. K., Newall, J. P., & Weare, R. F. (1996). A memetic algorithm for University exam timetabling. Burke and Ross, 241-250

Burke, E. K., Newall, J. P., & Weare, R. F. (1998). Initialisation strategies and diversity in evolutionary timetabling. Evolutionary Computation 6 (1), 81-103 (special issue on Scheduling)

Crawford, E., & Veloso, M. (2004). Mechanism Design for Multi-Agent Meeting Scheduling Including Time Preferences, Availability, and Value of Presence. Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT)

Davidsson, P., Henesey, L., Ramstedt, L., T¨ornquist, J., & Wernstedt, F. (2005). An analysis of agent-based approaches to transport logistics. Transportation Research, Part C, 13, 255–271

Davis, L. (1985). Job shop scheduling with genetic algorithms. Proc. 1st int. Conf. on Genetic algorithms and their Applications, Pittsburgh, PA, 130-140

Demeester, P., Souffriau, W., De Causmaecker, P., & Vanden Berghe, G. (2010). A hybrid tabu search algorithm for automatically assigning patients to beds. Artif. Intell. Med. 48(1), 61–70

Dowsland, K. (1998). Nurse scheduling with tabu search andstrategic oscillation. European Journal of Operational Research 106 (2–3), 393–407

Fischer, K., Müller J. P. and Pischel, M. (1995).Cooperative transportation scheduling: an application domain for DAI. Journal of Applied Artificial Intelligence

Franzin, M. S., Freuder, E. C., & Rossi, F. (2002). Multi-agent meeting scheduling with preferences: efficiency, privacy loss, and solution quality. American Association for Artificial Intelligence AAAI

Gagliano, R. A., Fraser, M. D., & Schaefer, M. E. (1995). Auction allocation of computing resources. Communications of the ACM, 38 (6), 88–102

Garg, S., and Buyya, R. (2011). Market-Oriented Resource Management and Scheduling: A Taxonomy and Survey, Cooperative Networking 277-306, M. S. Obaidat and S. Misra (eds), ISBN: 978-0-470-74915-9, Wiley Press, New York, USA

Ghaemi, M.,Vakili, M., & Aghagolzadeh, A. (2007). Using a genetic algorithm optimizer tool to solve university timetable scheduling problem. 9th international symposium on signal processing and its Application

Gomber, P., Schmidt, C., Weinhardt, C. (1997). Elektronische Märkte für die dezentrale Transportplanung, Wirschaftsinformatik 39(2),137-145

Grano, M., Medeiros, D. J., & Eitel, D. (2009). Accommodating individual preferences in nurse scheduling via auctions and optimization. Health Care Manage Science, Volume 12,228-242

Groothuis, S., & Merode, G. (2001). Simulation as decision tool for capacity planning. Journal of Computer Methods and Programs in Biomedicine 66 , 139–151

Gueret, C., Jussien, N., Boizumault, P., & Prins, C. (1995). Building University Timetables Using Constraint Logic Programming. Proc. of the 1st Int. Conf. on the Practice and Theory of Automated Timetabling, 393- 408

Gujo, O., Schwind, M., & Vykoukal, J. (2009). A combinatorial intra-enterprise exchange for logistics services. Information systems and e-business management,Volume 7,No 4,447-471

Gunawan, A., Ming, K., & Poh, K. (2007). Solving the teacher assignment-course scheduling problem by hybrid Algorithm. International journal of Computer, information and system science and engineering, 1(2),139-141

Hancock, W.M., & Walter, P. F. (1984). The use of admissions simulation to stabilize ancillary workloads. Simulation journals, 88-94

Hannebauer, M., & Muller, S. (2001). Distributed Constraint Optimization for Medical Appointment Scheduling. Proceedings of the fifth international conference on autonomous agents,139 -140

Harvey, J. (1998). Service quality: A tutotial. Journal of Operations Management 16(5), 583-597

Hassine, A. B., Defago, X., & Ho, T. B. (2004). Agent-Based Approach to Dynamic Meeting Scheduling Problems. Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems,Volume 3, 1132 -1139

Henz M., & Wurtz J. (1995). Using Oz for college timetabling. Proceedings of the 1st Int. Conference on the Practice and Theory of Automated Timetabling, 283- 296

Hertz, A. (1991). Tabu Search for Large Scale Timetabling Problems. European Journal of Operational Research 54, 39-47

Hertz, A. (1992). Finding a Feasible Course Schedule Using Tabu Search. Discrete Applied Mathematics 35(3), 255-270

Ho, Ch., & Lau, H. (1999). Evaluating the impact of operating conditions on the performance of appointment scheduling rules in service systems. European Journal of Operational Research 112 ,542-553

Hosseini, H., Hoey, J., & Cohen, R. (2011). Multi-Agent Patient Scheduling Through Auctioned Decentralized MDPs. Proceedings of the 6th InformsWorkshop on Data Mining and Health Informatics

Hur, D., Mabert, V. A., & Bretthauer, K. M.(2004). Real-time work schedule adjustment decisions: An investigation and evaluation. Production and Operations Management 13(4), 322

Jack, E. P., & Powers, T. L. (2004). Volume flexible strategies in health services: A research framework. Production and Operations Management 13(3), 230

Jaumard, B., Semet, F., & Vovor, T. (1998). A generalized linear programming model for nurse scheduling. European Journal of Operational Research 107(1),1-18

Jennings, N. R. (2001). An agent-based approach for building complex software systems. Communications of the ACM, 44(4),35- 41

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. American Association for the Advancement of Science New Series, Vol. 220, No. 4598., 671-680

Kotler, P., & Keller, K. (2006). Marketing management, Twelfth edition. Prentice-Hall, Upper Saddle River, New Jersey

Krajewska, M. A., & Kopfer, H. (2006a). Profit sharing approaches for freight forwarders: An overview”, Proceedings of the 5th International Conference on Modern Trends in Logistics,157-161

Krajewska, M. A., & Kopfer, H. (2006b). Collaborating freight forwarding enterprises, request allocation and profit sharing. OR spectrum, Volume 28, No2, 301-317

Krishna, A., & Ünver, M. U. (2007). Improving the Efficiency of Course Bidding at Business Schools: An Experimental Study. Marketing Science, forthcoming

Kwon, R. H., Lee, C., & Ma, Z. (2005). An integrated combinatorial auction mechanism for truckload transportation procurement. Technical Report, Mechanical and Industrial Engineering, the University of Toronto, Ontario, Canada

Lang, N., Moonen, H. M., Srour, F. J., & Zuidwijk, R. A. (2008). Multi Agent Systems in Logistics: A Literature and State-of-the art Review. ERIM Report Series, Reference No. ERS-2008-043-LIS

Meisels, A., & Kaplansky, E. (2003). Scheduling Agents – Distributed Timetabling Problems. Lecture Notes in Computer Science, Practice and Theory of automated timetabling IV,Volume 2740/2003, 166-177

Modi, P.,Veloso, M., Smith, S. F., & Oh, J. (2004). CMRadar: A Personal Assistant Agent for Calendar Management. Lecture Notes in Computer Science, LNCS 3508,169–181

Paechter, B., Cumming, A., & Luchian, H., (1995). The use of local search suggestion lists for improving the solution of timetabling problems with evolutionary algorithms. Proceedings of the AISB Workshop on Evolutionary Computing, Sheffield, England.

Paechter, B., Cumming, A., Norman, M. G., & Luchian, H. (1996). Extensions to a memetic timetabling system. The Practice and Theory of Automated Timetabling, volume 1153 of Lecture Notes in Computer Science. Springer Verlag, 251–265

Paulussen, T. O., Jennings, N. R., Decker, K. S., & Heinzl, A. (2003). Distributed patient scheduling in Hospital. Coordination and Agent Technology in Value Networks, GITO

Pearce, D. W. (1981). The dictionary of modern economics. The MIT Press, Cambridge, Massachusetts

Petrovic, D., Morshed, M., & Petrovic, S. (2011). Multi-objective genetic algorithms for scheduling of radiotherapy treatments for categorized cancer patients. Journal of Expert Systems with Applications,38(6), 6994-7002

Pinedo, M.L (2009). Planning and scheduling in manufacturing and services (2nd ed.). Springer, New York. doi: 10.1007/978-1-4419-0910-7

Sampson, S. E. & Froehle, C. M. (2006). Foundations and implications of a proposed unified services theory. Production and Operations Management, 329-343

Sampson, S. E. (2001). Understanding service businesses: Applying principles of the unified services theory (2nd ed.). John Wiley & Sons, New York, New York

Schönberger, J. (2005). Operational Freight Carrieer Planning. Springer, Berlin

Schönsleben P., Hieber R. (2004). Gestaltung von effizienten Wertschöpfungspartnerschaften im Supply Chain Management. Busch A., Dangelmaier W., Integriertes Supply Chain Management, Wiesbaden.

Sheffi, Y. (2004). Combinatorial Auctions in the Procurement of Transportation services. Interfaces,Volume.34 , 245-252

Shen, W., Wang, L., & Hao, Q. (2006). Agent-based distributed manufacturing process planning and scheduling : a state-of-the-art survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 36(4), 563-577

Sim, K. M. (2012). Complex and Concurrent Negotiations for Multiple Interrelated e-Markets. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, PP(99), doi: 10.1109/TSMCB.2012.2204742, 1-16

Singh, A., & Malhotra, M. (2012). Agent Based Framework for Scalability in Cloud Computing. International Journal of Computer Science & Engineering Technology (IJCSET), 3(4), 41-45

Song, J., & Regan, A. C. (2003). An Auction Based Collaborative Carrier Network.Technical report: UCI-ITS-WP-03-6, Institute of Transportation Studies, University of California, Irvine

Sönmez, T., & Ünver, U. (2007). Course Bidding at Business Schools. Retrieved from http://ssrn.com/abstract=1079525 2007

Wainer, J., Ferreira, P., & Constantino, E. R. (2007). Scheduling meetings through multi-agent negotiations. Decision Support Systems 44, 285–297

Wall, B. M. (1996). A Genetic Algorithm for Resource-Constrained Scheduling, Ph.D. thesis, Massachusetts institute of technology

Wang, C. (2007). Economic Models for Decentralized Scheduling. Ph.D thesis. University of Western Ontario.

Wang, W., & Gupta, D. (2011). Adaptive Appointment Systems with Patient Preferences. Manufacturing and Service Operations Management 13(3), 373-389

Wemmerlov, U. (1990). A taxonomy for service processes and its implications for system design. International Journal of Service Industry Management 1(3), 13–27

Wolski, R., Plank, J. S., Brevik, J., & Bryan, T. (2001). Analyzing market-based resource allocation strategies for the computational grid. International Journal of High Performance Computing Applications, 15 (3), 258-281

Zaman, S., & Grosu, D. (2011). Combinatorial Auction-Based Dynamic VM Provisioning and Allocation in Clouds. IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom),107-114

Zhiming, Z. (2011). A Two-stage Scheduling Approach of Operation Rooms Considering Uncertain Operation Time. International Conference on Information Science and Technology,Nanjing, Jiangsu, China 250. doi: 10.1115/DETC2011-48263
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