Index Policies for Shooting Problems

References

• Barkdoll T. C., Gaver D. P., Glazebrook K. D., Jacobs P. A., Posadas S. Suppression of Enemy Air Defences (SEAD) as an information duel. Naval Res. Logist. (2002) 49:723–742Crossref, Google Scholar
• Bertsimas D., Niño-Mora J. Conservation laws, extended polymatroids and multi-armed bandit problems: A polyhedral approach to indexable systems. Math. Oper. Res. (1996) 21:257–306Link, Google Scholar
• Crosbie J. H., Glazebrook K. D. Evaluating policies for generalized bandits via a notion of duality. J. Appl. Probab. (2000a) 37:540–546Crossref, Google Scholar
• Crosbie J. H., Glazebrook K. D. Index policies and a novel performance space structure for a class of generalised branching bandit problems. Math. Oper. Res. (2000b) 25:281–297Link, Google Scholar
• Dumitriu I., Tetali P., Winkler P. On playing golf with two balls. SIAM J. Discrete Math. (2003) 16:604–615Crossref, Google Scholar
• Fay N. A., Glazebrook K. D. On a “no arrivals” heuristic for single-machine stochastic scheduling. Oper. Res. (1992) 40:168–177Link, Google Scholar
• Fay N. A., Walrand J. C. On approximately index strategies for generalized arm problems. J. Appl. Probab. (1991) 28:602–612Crossref, Google Scholar
• Gaver D. P., Glazebrook K. D., Pilnick S. E. Optimal sequential replenishment of ships during combat. Naval. Res. Logist. (1991) 38:637–668Crossref, Google Scholar
• Gittins J. C. Bandit processes and dynamic allocation indices (with discussion). J. Roy. Statist. Soc. (1979) B41:148–177Google Scholar
• Gittins J. C.Multi-Armed Bandit Allocation Indices (1989) (Wiley, Chichester, UK) Google Scholar
• Gittins J. C., Jones D. M., Gani J., Vince I. A dynamic allocation index for the sequential design of experiments. Progress in Statistics (1974) (North-Holland, Amsterdam, The Netherlands) 241–266Google Scholar
• Glazebrook K. D., Greatrix S. On transforming an index for generalised bandit problems. J. Appl. Probab. (1995) 32:168–182Crossref, Google Scholar
• Glazebrook K. D., Washburn A. Shoot-look-shoot: A review and extension. Oper. Res. (2004) 52:454–463Link, Google Scholar
• Glazebrook K. D., Kirkbride C., Ruiz D. Some families of indexable restless bandit problems. Adv. Appl. Probab. (2006) 38:643–672Crossref, Google Scholar
• Glazebrook K. D., Ansell P. S., Dunn R. T., Lumley R. R. On the optimal allocation of service to impatient tasks. J. Appl. Probab. (2004) 41:51–72Crossref, Google Scholar
• Katehakis M. N., Veinott A. F. The multi-armed bandit problem – decomposition and computation. Math. Oper. Res. (1987) 12:262–268Link, Google Scholar
• Katta A., Sethuraman J. A note on bandits with a twist. SIAM J. Discrete Math. (2004) 18:110–113Crossref, Google Scholar
• Manor G., Kress M. Optimality of the greedy shooting strategy in the presence of incomplete damage information. Naval Res. Logist. (1997) 44:613–622Crossref, Google Scholar
• Nash P. Optimal allocation of resources between research projects. (1973) . Ph.D. thesis, Cambridge University, Cambridge, UKGoogle Scholar
• Nash P. A generalised bandit problem. J. Roy. Statist. Soc. (1980) B42:165–169Google Scholar
• Papadimitriou C. H., Tsitsiklis J. N. The complexity of optimal queueing network control. Math. Oper. Res. (1999) 24:293–305Link, Google Scholar
• Puterman M. L.Markov Decision Processes: Discrete Stochastic Dynamic Programming (1994) (Wiley, New York) Crossref, Google Scholar
• Robinson D. Algorithms for evaluating the dynamic allocation index. Oper. Res. Lett. (1982) 1:72–74Crossref, Google Scholar
• U.S. Marine Corps Suppression of Enemy Air Defenses (SEAD). MCWP 3–22⋯2 (2001) . Marine Corps Combat Development Command, Doctrine Division, Quantico, VAGoogle Scholar
• Weber R. R. On the Gittins index for multi-armed bandits. Ann. Appl. Probab. (1992) 2:1024–1035Crossref, Google Scholar
• Whittle P. Multi-armed bandits and the Gittins index. J. Roy. Statist. Soc. (1980) B42:143–149Google Scholar
• Whittle P. Restless bandits: Activity allocation in a changing world. J. Appl. Probab. (1988) A25:287–398Crossref, Google Scholar