MS-E2191 - Graduate Seminar on Operations Research (V) D, 11.09.2020-04.12.2020

Topic outline

• Material
  • Bertsekas, D. P. (2000). Dynamic programming and optimal control (Vol. 1, 2nd ed.). Belmont, MA: Athena scientific.
    
  • Bertsekas, D. P. (2012). Dynamic programming and optimal control (Vol. 2, 4th ed.) Approximate Dynamic Programming. Belmont, MA: Athena scientific.
  • Chen, D., & Trivedi, K. S. (2005). Optimization for condition-based maintenance with semi-Markov decision process. Reliability engineering & system safety, 90(1), 25-29.
    
  • Corotis, R. B., Hugh Ellis, J., & Jiang, M. (2005). Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision processes. Structure and Infrastructure Engineering, 1(1), 75-84.
    
  • Eisenführ, F., Weber, M., Langer, T., (2010). Rational Decision Making,  Springer-Verlag Berlin Heidelberg.
  • Elgesem, A. S., Skogen, E. S., Wang, X., & Fagerholt, K. (2018). A traveling salesman problem with pickups and deliveries and stochastic travel times: An application from chemical shipping. European Journal of Operational Research, 269(3), 844-859.
    
  • Gustafsson, J., Salo, A. (2005). Contingent portfolio programming for the management of risky projects. Operations Research 53: 946-956.
  • Hauskrecht, M., & Fraser, H. (2000). Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artificial Intelligence in Medicine, 18(3), 221-244.
    
  • Howard, R., Matheson, J., (2005). Influence Diagrams, Decision Analysis 2: 127-143.
  • Howard, R. A. (1960). Dynamic programming and markov processes. John Wiley & Sons
    
  • Hynninen, Y., Vilkkumaa, E., Salo, A. (2020). Operationalization of Utilitarian and Egalitarian Objectives for Optimal Allocation of Health Care Resources. Decision Sciences.
    
  • Jensen, F. V. (2001). Bayesian Networks and Decision Graphs. Springer, New York
  • Jing, C., Jingqi, F. (2012). Fire alarm system based on multi-sensor bayes network. Procedia Engineering 29: 2551-2555.
  • Leskelä, L. (2018). Stokastiset prosessit (luentomateriaali). From: https://math.aalto.fi/~lleskela/papers/Leskela_2018-08-07_Stokastiset_prosessit.pdf
  • Liesiö, J., Salo, A. (2012). Scenario-based portfolio selection of investment projects with incomplete probability and utility information. European Journal of Operational Research 217: 162-172.
  • Leppinen, J. (2020). A Dynamic Optimization Model for Main-tenance Scheduling of a Multi-Component System (Doctoral dissertation, Aalto University). From: https://sal.aalto.fi/publications/pdf-files/theses/mas/tlep20_public.pdf
  • Mancuso, A., Compare, M., Salo, A., Zio, E., & Laakso, T. (2016). Risk-based optimization of pipe inspections in large underground networks with imprecise information. Reliability Engineering & System Safety, 152, 228-238.
    
  • Marcot, B. G., Holthausen, R. S., Raphael, M. G., Rowland, M. M., Wisdom, M. J. (2001). Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management, 153: 29-42
  • Puteman, M. L. (1994) Markov Decision Process: Discrete Stochastic Dynamic Programming. John Wiley & Sons
  • Qiu, Q., & Pedram, M. (1999, June). Dynamic power management based on continuous-time Markov decision processes. In Proceedings of the 36th annual ACM/IEEE Design Automation Conference (pp. 555-561).
  • Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European journal of operational research, 219(3), 611-621.
    
  • Seidscher, A., & Minner, S. (2013). A Semi-Markov decision problem for proactive and reactive transshipments between multiple warehouses. European Journal of Operational Research, 230(1), 42-52.
  • Simao, H. P., Day, J., George, A. P., Gifford, T., Nienow, J., & Powell, W. B. (2009). An approximate dynamic programming algorithm for large-scale fleet management: A case application. Transportation Science, 43(2), 178-197.
    
  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press. From: http://incompleteideas.net/book/RLbook2018.pdf
  • Urbani, M., Brunelli, M., & Collan, M. (2020). A Comparison of Maintenance Policies for Multi-Component Systems Through Discrete Event Simulation of Faults. IEEE Access, 8, 143654-143664.