Relevant bibliographies by topics / Myopic optimality

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Myopic optimality'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Myopic optimality"

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Fristedt, Bert, and Donald A. Berry. "Optimality of myopic stopping times for geometric discounting." Journal of Applied Probability 25, no. 2 (June 1988): 437–43. http://dx.doi.org/10.2307/3214454.

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Consider a sequence of conditionally independent Bernoulli random variables taking on the values 1 and − 1. The objective is to stop the sequence in order to maximize the discounted sum. Suppose the Bernoulli parameter has a beta distribution with integral parameters. It is optimal to stop when the conditional expectation of the next random variable is negative provided the discount factor is less than or equal to . Moreover, is best possible. The case where the parameters of the beta distribution are arbitrary positive numbers is also treated.
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Fristedt, Bert, and Donald A. Berry. "Optimality of myopic stopping times for geometric discounting." Journal of Applied Probability 25, no. 02 (June 1988): 437–43. http://dx.doi.org/10.1017/s0021900200041103.

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Consider a sequence of conditionally independent Bernoulli random variables taking on the values 1 and − 1. The objective is to stop the sequence in order to maximize the discounted sum. Suppose the Bernoulli parameter has a beta distribution with integral parameters. It is optimal to stop when the conditional expectation of the next random variable is negative provided the discount factor is less than or equal to . Moreover, is best possible. The case where the parameters of the beta distribution are arbitrary positive numbers is also treated.
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Wang, Kehao, Lin Chen, Jihong Yu, and Duzhong Zhang. "Optimality of Myopic Policy for Multistate Channel Access." IEEE Communications Letters 20, no. 2 (February 2016): 300–303. http://dx.doi.org/10.1109/lcomm.2015.2503770.

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Ahmad, Sahand Haji Ali, Mingyan Liu, Tara Javidi, Qing Zhao, and Bhaskar Krishnamachari. "Optimality of Myopic Sensing in Multichannel Opportunistic Access." IEEE Transactions on Information Theory 55, no. 9 (September 2009): 4040–50. http://dx.doi.org/10.1109/tit.2009.2025561.

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Rosenfield, Donald B. "Optimality of Myopic Policies in Disposing Excess Inventory." Operations Research 40, no. 4 (August 1992): 800–803. http://dx.doi.org/10.1287/opre.40.4.800.

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Leahy, J. V. "Investment in Competitive Equilibrium: The Optimality of Myopic Behavior." Quarterly Journal of Economics 108, no. 4 (November 1, 1993): 1105–33. http://dx.doi.org/10.2307/2118461.

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Wang, Kehao, Lin Chen, and Jihong Yu. "On Optimality of Myopic Policy in Multi-Channel Opportunistic Access." IEEE Transactions on Communications 65, no. 2 (February 2017): 677–90. http://dx.doi.org/10.1109/tcomm.2016.2628899.

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Ouyang, Yi, and Demosthenis Teneketzis. "On the Optimality of Myopic Sensing in Multi-State Channels." IEEE Transactions on Information Theory 60, no. 1 (January 2014): 681–96. http://dx.doi.org/10.1109/tit.2013.2288636.

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Benhenni, Robert A. "Monotone stopping-allocation problems." Advances in Applied Probability 23, no. 01 (March 1991): 24–45. http://dx.doi.org/10.1017/s0001867800023326.

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Stopping-allocation problems are concerned with how best to allocate observations among some K competing stochastic populations and when to stop the observation process. The goal of the decision-maker is to choose a stopping–allocation rule to maximize the expected value of a payoff function. First the stopping rule is fixed, and the local and global optimality of the myopic allocation rule are derived under some monotonicity conditions. An application is considered, namely the inspection problem and its use in solving a computer scheduling problem. Next, optimization is done with respect to both the allocation rule and the stopping rule. For any given stopping-allocation rule, it is shown that under some monotonicity conditions, the decision-maker can improve on it by using a ‘partial' myopic allocation rule and a generalized one-stage-look-ahead stopping rule; this result is then extended, under the same conditions and other monotonicity requirements, to derive the joint optimality of the myopic allocation rule and the one-stage-look-ahead stopping rule. Finally this latter result is applied to the inspection problem.
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Benhenni, Robert A. "Monotone stopping-allocation problems." Advances in Applied Probability 23, no. 1 (March 1991): 24–45. http://dx.doi.org/10.2307/1427510.

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Stopping-allocation problems are concerned with how best to allocate observations among some K competing stochastic populations and when to stop the observation process. The goal of the decision-maker is to choose a stopping–allocation rule to maximize the expected value of a payoff function. First the stopping rule is fixed, and the local and global optimality of the myopic allocation rule are derived under some monotonicity conditions. An application is considered, namely the inspection problem and its use in solving a computer scheduling problem. Next, optimization is done with respect to both the allocation rule and the stopping rule. For any given stopping-allocation rule, it is shown that under some monotonicity conditions, the decision-maker can improve on it by using a ‘partial' myopic allocation rule and a generalized one-stage-look-ahead stopping rule; this result is then extended, under the same conditions and other monotonicity requirements, to derive the joint optimality of the myopic allocation rule and the one-stage-look-ahead stopping rule. Finally this latter result is applied to the inspection problem.
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Dissertations / Theses on the topic "Myopic optimality"

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Ulku, M. Ali. "ANALYSIS OF SHIPMENT CONSOLIDATION IN THE LOGISTICS SUPPLY CHAIN." Thesis, 2009. http://hdl.handle.net/10012/4562.

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Shipment Consolidation (SCL) is a logistics strategy that combines two or more orders or shipments so that a larger quantity can be dispatched on the same vehicle to the same market region. This dissertation aims to emphasize the importance and substantial cost saving opportunities that come with SCL in a logistics supply chain, by offering new models or by improving on the current body of literature. Our research revolves around "three main axes" in SCL: Single-Item Shipment Consolidation (SISCL), Multi-Item Shipment Consolidation (MISCL), and Pricing and Shipment Consolidation. We investigate those topics by employing various Operations Research concepts or techniques such as renewal theory, dynamic optimization, and simulation. In SISCL, we focus on analytical models, when the orders arrive randomly. First, we examine the conditions under which an SCL program enables positive savings. Then, in addition to the current SCL policies used in practice and studied in the literature, i.e. Quantity-Policy (Q-P), Time-Policy (T-P) and Hybrid Policy (H-P), we introduce a new one that we call the Controlled Dispatch Policy (CD-P). Moreover, we provide a cost-based comparison of those policies. We show that the Q-P yields the lowest cost per order amongst the others, yet with the highest randomness in dispatch times. On the other hand, we also show that, between the service-level dependent policies (i.e. the CD-P, H-P and T-P), H-P provides the lowest cost per order, while CD-P turns out to be more flexible and responsive to dispatch times, again with a lower cost than the T-P. In MISCL, we construct dispatch decision rules. We employ a myopic analysis, and show that it is optimal, when costs and the order-arrival processes are dependent on the type of items. In a dynamic setting, we apply the concept of time-varying probability to integrate the dispatching and load planning decisions. For the most common dispatch objectives such as cost per order, cost per unit time or cost per unit weight, we use simulation and observe that the variabilities in both cost and the optimal consolidation cycle are smaller for the objective of cost per unit weight. Finally on our third axis, we study the joint optimization of pricing and time-based SCL policy. We do this for a price- and time-sensitive logistics market, both for common carriage (transport by a public, for-hire trucking company) and private carriage (employing one's own fleet of trucks). The main motivation for introducing pricing in SCL decisions stems from the fact that transportation is a service, and naturally demand is affected by price. Suitable pricing decisions may influence the order-arrival rates, enabling extra savings. Those savings emanate from two sources: Scale economies (in private carriage) or discount economies (in common carriage) that come with SCL, and additional revenue generated by employing an appropriate pricing scheme. Throughout the dissertation, we offer numerical examples and as many managerial insights as possible. Suggestions for future research are offered.
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Book chapters on the topic "Myopic optimality"

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Policardo, Laura. "Myopia of Governments and Optimality of Irreversible Pollution Accumulation." In Springer Proceedings in Mathematics & Statistics, 331–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74086-7_17.

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Conference papers on the topic "Myopic optimality"

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Javidi, T., B. Krishnamachari, Q. Zhao, and M. Liu. "Optimality of Myopic Sensing in Multi-Channel Opportunistic Access." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.404.

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Zhao, Q., and B. Krishnamachari. "Structure and Optimality of Myopic Sensing for Opportunistic Spectrum Access." In 2007 IEEE International Conference on Communications. IEEE, 2007. http://dx.doi.org/10.1109/icc.2007.1071.

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Wang, Kehao, Lin Chen, and Jihong Yu. "On optimality of myopic policy in multi-channel opportunistic access." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511001.

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Wang, Kehao. "Optimality of Myopic Policy for Restless Multiarmed Bandit with Imperfect Observation." In GLOBECOM 2016 - 2016 IEEE Global Communications Conference. IEEE, 2016. http://dx.doi.org/10.1109/glocom.2016.7842101.

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Iannello, Fabio, Osvaldo Simeone, and Umberto Spagnolini. "Optimality of myopic scheduling and whittle indexability for energy harvesting sensors." In 2012 46th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2012. http://dx.doi.org/10.1109/ciss.2012.6310816.

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Ouyang, Yi, and Demosthenis Teneketzis. "On the optimality of a myopic policy in multi-state channel probing." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483238.

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Mansourifard, Parisa, Tara Javidi, and Bhaskar Krishnamachari. "Optimality of myopic policy for a class of monotone affine restless multi-armed bandits." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6425858.

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You might also be interested in the extended bibliographies on the topic 'Myopic optimality' for particular source types:
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