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Journal articles on the topic 'Stochastic program'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Schkufza, Eric, Rahul Sharma, and Alex Aiken. "Stochastic program optimization." Communications of the ACM 59, no. 2 (2016): 114–22. http://dx.doi.org/10.1145/2863701.

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2

Bortolussi, L., and A. Policriti. "(Hybrid) automata and (stochastic) programs * The hybrid automata lattice of a stochastic program." Journal of Logic and Computation 23, no. 4 (2011): 761–98. http://dx.doi.org/10.1093/logcom/exr045.

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3

Alizadeh, Hosein, and S. Jamshid Mousavi. "Stochastic order-based optimal design of a surface reservoir–irrigation district system." Journal of Hydroinformatics 15, no. 2 (2012): 591–606. http://dx.doi.org/10.2166/hydro.2012.223.

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This paper explores the effects of streamflow uncertainty and the type of stochastic order, which is used for comparing stochastic variables, on optimal design of a reservoir multi-crop irrigation district system. Four nonlinear mathematical programs with an economic objective function including deterministic, stochastic-EXP with expected value order, stochastic-SD with stochastic dominance (SD) order, and stochastic-EGCL with expected gain-confidence limit (EGCL) order were developed. Afterwards, the approaches of successive linear programming (SLP) and PSO–MC, which combines particle swarm o

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4

Jiang, Ruiwei, and Yongpei Guan. "Data-driven chance constrained stochastic program." Mathematical Programming 158, no. 1-2 (2015): 291–327. http://dx.doi.org/10.1007/s10107-015-0929-7.

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5

Easton, Fred F., and Donald F. Rossin. "A Stochastic Goal Program for Employee Scheduling." Decision Sciences 27, no. 3 (1996): 541–68. http://dx.doi.org/10.1111/j.1540-5915.1996.tb01825.x.

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6

Gulwani, Sumit. "Technical Perspective: Program synthesis using stochastic techniques." Communications of the ACM 59, no. 2 (2016): 113. http://dx.doi.org/10.1145/2863699.

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7

Gen, Mitsuo, Baoding Liu, and Kenichi Ida. "Evolution program for deterministic and stochastic optimizations." European Journal of Operational Research 94, no. 3 (1996): 618–25. http://dx.doi.org/10.1016/0377-2217(95)00138-7.

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8

Park, M., K. Krishnamoorthy, S. Darbha, P. Chandler, and M. Pachter. "State partitioning based linear program for stochastic dynamic programs: An invariance property." Operations Research Letters 40, no. 6 (2012): 487–91. http://dx.doi.org/10.1016/j.orl.2012.08.006.

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9

Davarnia, Danial, Burak Kocuk, and Gérard Cornuéjols. "Computational Aspects of Bayesian Solution Estimators in Stochastic Optimization." INFORMS Journal on Optimization 2, no. 4 (2020): 256–72. http://dx.doi.org/10.1287/ijoo.2019.0035.

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We study a class of stochastic programs in which some of the elements in the objective function are random and their probability distribution has unknown parameters. The goal is to find a good estimate for the optimal solution of the stochastic program using data sampled from the distribution of the random elements. We investigate two common optimization criteria for evaluating the quality of a solution estimator—one based on the difference in objective values and the other based on the Euclidean distance between solutions. We use risk as the expected value of such criteria over the sample spa

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10

Bosch, Paul. "A Numerical Method for Two-Stage Stochastic Programs under Uncertainty." Mathematical Problems in Engineering 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/840137.

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Motivated by problems coming from planning and operational management in power generation companies, this work extends the traditional two-stage linear stochastic program by adding probabilistic constraints in the second stage. In this work we describe, under special assumptions, how the two-stage stochastic programs with mixed probabilities can be treated computationally. We obtain a convex conservative approximations of the chance constraints defined in second stage of our model and use Monte Carlo simulation techniques for approximating the expectation function in the first stage by the ave

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11

Goh, Siew, Chao-Chyuan Shih, Mark J. Cochran, and Rob Raskin. "A Generalized Stochastic Dominance Program for the IBM PC." Journal of Agricultural and Applied Economics 21, no. 2 (1989): 175–82. http://dx.doi.org/10.1017/s008130520000128x.

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AbstractA microcomputer program to perform Generalized Stochastic Dominance (GSD), Quasi-Second Degree Dominance (SSD), and Quasi-First Degree Stochastic Dominance (FSD) is described. The program is designed to run on IBM-compatible personal computers with a Hercules or CGA graphics adapter. It is menu-driven and has options for GSD, quasi-FSD, quasi-SSD, graphics, and calculations of premiums associated with use of dominant distributions.

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12

Chen, Jun, and Dengfeng Sun. "Stochastic Ground-Delay-Program Planning in a Metroplex." Journal of Guidance, Control, and Dynamics 41, no. 1 (2018): 231–39. http://dx.doi.org/10.2514/1.g002964.

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13

Panjaitan, Togi. "Transformation of Nonlinear Mixture Chopped Stochastic Program Model." Applied and Computational Mathematics 4, no. 2 (2015): 69. http://dx.doi.org/10.11648/j.acm.20150402.16.

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14

Ben Abdelaziz, F., and M. Masmoudi. "A multiobjective stochastic program for hospital bed planning." Journal of the Operational Research Society 63, no. 4 (2012): 530–38. http://dx.doi.org/10.1057/jors.2011.39.

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15

Singh, Vikas Vikram, and N. Hemachandra. "A Characterization of Stationary Nash Equilibria of Single Controller Constrained Stochastic Games." International Game Theory Review 17, no. 02 (2015): 1540018. http://dx.doi.org/10.1142/s0219198915400186.

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We consider a two player finite state-action general sum single controller constrained stochastic game with both discounted and average cost criteria. We consider the situation where player 1 has subscription-based constraints and player 2, who controls the transition probabilities, has realization-based constraints which can also depend on the strategies of player 1. It is known that a stationary Nash equilibrium for discounted case exists under strong Slater condition, while, for the average case, stationary Nash equilibrium exists if additionally the Markov chain is unichain. For each case

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16

Menh, Nguyen Cao, and Tran Duong Tri. "On the simulation technique of stochastic processes and nonlinear vibrations." Vietnam Journal of Mechanics 16, no. 3 (1994): 23–31. http://dx.doi.org/10.15625/0866-7136/10171.

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In this paper the procedure and program for simulation of stochastic processes are represented. The program is applied to nonlinear mechanical systems subjected to stochastic stationary excitation. The results obtained are compared with the ones from other methods which are used for estimating the exactitude of simulation technique.

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17

Xiao, Ning. "An Efficiency Algorithm for SDCP Program." Advanced Materials Research 971-973 (June 2014): 1533–36. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1533.

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For more effectively solving SDCP,in the paper,using BP neural networks to approximate chance function,training samples are produced by random simulation,and a hybrid intelligent algorithm for SDCP combined stochastic particle swarm optimization and BP neural network is proposed.The experimental results show that the algorithm is more preferable.

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18

BLACKBURN, PAUL, and MIGUEL ORSZAG. "A SINGLE-ION STOCHASTIC QUANTUM PROCESSOR." International Journal of Modern Physics B 20, no. 11n13 (2006): 1679–89. http://dx.doi.org/10.1142/s0217979206034212.

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We propose a scheme for implementing a single-ion Stochastic Quantum Processor using a single cold trapped ion. The processor implements an arbitrary rotation around the z axis of the Bloch sphere of a data qubit, given two program qubits, that is, the operation realized on the data is determined by using different program qubits and not by varying the gate itself. Unfortunately this cannot be done deterministically, and must be necessarily stochastic. In this proposal the operation is applied successfully with probability p = 3/4.

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19

Alhadi, Mohammad Alabed, and Baha Alzalg. "Stochastic Second-Order Cone Programming: The Equivalent Convex Program." Applied Mathematics & Information Sciences 12, no. 3 (2018): 601–6. http://dx.doi.org/10.18576/amis/120315.

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20

Kingma, Johannes, and Johan Reuvekamp. "MOKKEN Scale: A Pascal Program for Nonparametric Stochastic Scaling." Educational and Psychological Measurement 46, no. 3 (1986): 667–77. http://dx.doi.org/10.1177/0013164486463023.

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21

Martinez, J. A., and J. Martin-Arnedo. "Voltage Sag Stochastic Prediction Using an Electromagnetic Transients Program." IEEE Transactions on Power Delivery 19, no. 4 (2004): 1975–82. http://dx.doi.org/10.1109/tpwrd.2004.829125.

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22

Le, Vu, Chengnian Sun, and Zhendong Su. "Finding deep compiler bugs via guided stochastic program mutation." ACM SIGPLAN Notices 50, no. 10 (2015): 386–99. http://dx.doi.org/10.1145/2858965.2814319.

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23

Ayadi, Mohamed A., Hatem Ben-Ameur, Tymur Kirillov, and Robert Welch. "A Stochastic Dynamic Program for Valuing Options on Futures." Journal of Futures Markets 34, no. 12 (2013): 1185–201. http://dx.doi.org/10.1002/fut.21645.

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24

Jiang, Ruiwei, and Yongpei Guan. "Risk-Averse Two-Stage Stochastic Program with Distributional Ambiguity." Operations Research 66, no. 5 (2018): 1390–405. http://dx.doi.org/10.1287/opre.2018.1729.

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25

DePaolo, Concetta A., and David J. Rader. "A heuristic algorithm for a chance constrained stochastic program." European Journal of Operational Research 176, no. 1 (2007): 27–45. http://dx.doi.org/10.1016/j.ejor.2005.08.008.

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26

Wang, Jian, Wei Deng, and Jinbao Zhao. "ROAD NETWORK RESERVE CAPACITY WITH STOCHASTIC USER EQUILIBRIUM." TRANSPORT 30, no. 1 (2015): 103–16. http://dx.doi.org/10.3846/16484142.2015.1020870.

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To relax the strong assumption associated with User Equilibrium (UE) in the previous research of network reserve capacity conducted by Gao and Song (2002), this paper assumes that the drivers all make route choices based on Stochastic User Equilibrium (SUE) principle. Similarly, two bi-level programs are formulated to study the network reserve capacity with SUE problem. The first bi-level program is developed to maximize the network reserve capacity by optimizing signal settings while the traffic demands are reassigned by SUE model. The second program extends the research with Continuous Netwo

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27

Chen, Sheng-I., and Wei-Fu Chen. "The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices." Sustainability 13, no. 17 (2021): 9660. http://dx.doi.org/10.3390/su13179660.

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This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares dete

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28

Ming, Liu, Wu Yongping, and Qiao Xinzhou. "Reliability of Arch Retaining Structure with Stochastic Parameters." Open Civil Engineering Journal 9, no. 1 (2015): 140–44. http://dx.doi.org/10.2174/1874149501509010140.

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Aimed at the reliability problems of arch retaining structure with stochastic parameters, in consideration of the influences of the randomness of the structural parameters and loads, the concrete paper applies program building relevant arch retaining structural model. The simulation programs are based on the response surface method and Monte Carlo method hybrid simulation analysis method. The structure failure probability and the probability distribution of the maximum stress of arch retaining structure are obtained in this study: get the influence of different random parameters on the structu

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29

Wang, Bin, and Tao Yang. "Stochastic Optimization of Empty Container Repositioning of Sea Carriage." Advanced Materials Research 340 (September 2011): 324–30. http://dx.doi.org/10.4028/www.scientific.net/amr.340.324.

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To improve the efficiency of empty container repositioning for a shipping company, a stochastic optimization model of empty container repositioning of sea carriage was established by chance-constrained programming. The objective function was to minimize the cost of empty container repositioning including shipping, rening and shortage cost. In the model, shipping cost was decided by the number of ship used for empty container repositioning. The constraints of the model included meeting the need of empty containers, limit to the number of empty containers provided and the capacity of shipping. T

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30

Wang, Yan, Rui Gao, and Ling Qiang Yang. "Stochastic Analysis to Truss Spar Platform in Deep Sea." Applied Mechanics and Materials 477-478 (December 2013): 809–12. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.809.

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Truss spar model was tested using regular waves in a wave basin and the responses in surge, heave and pitch were measured. A program was developed to determine the responses using numerical method. This program was run using the model parameters. The results are agreed well with the corresponding results obtained from the test measurements. Then this program was applied to a prototype spar simulated results were compared with the corresponding numerical results and test measurements.

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31

Banker, Rajiv D., Srikant M. Datar, and Madhav V. Rajan. "Measurement of Productivity Improvements: An Empirical Analysis." Journal of Accounting, Auditing & Finance 2, no. 4 (1987): 319–47. http://dx.doi.org/10.1177/0148558x8700200401.

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In this paper, we test for productivity gains resulting from the introduction of a productivity-based incentive program in a large manufacturing plant of a Fortune 500 corporation. We develop a methodology based on a stochastic nonparametric frontier estimation technique to evaluate productivity in the postincentive plan period relative to the pre-incentive plan period. We also test for productivity gains using stochastic parametric frontier approaches. The results of both the nonparametric and parametric stochastic frontier analyses indicate that the incentive program has a positive effect on

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32

Masahiro Inuiguchi and Masatoshi Sakawa. "A possibilistic linear program is equivalent to a stochastic linear program in a special case." Fuzzy Sets and Systems 76, no. 3 (1995): 309–17. http://dx.doi.org/10.1016/0165-0114(94)00364-7.

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33

Tleubergenov, Marat I., and Gulmira K. Vassilina. "On stochastic inverse problem of construction of stable program motion." Open Mathematics 19, no. 1 (2021): 157–62. http://dx.doi.org/10.1515/math-2021-0005.

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Abstract One of the inverse problems of dynamics in the presence of random perturbations is considered. This is the problem of the simultaneous construction of a set of first-order Ito stochastic differential equations with a given integral manifold, and a set of comparison functions. The given manifold is stable in probability with respect to these comparison functions.

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34

Zhang, Wenzhao. "Discrete-Time Constrained Average Stochastic Games with Independent State Processes." Mathematics 7, no. 11 (2019): 1089. http://dx.doi.org/10.3390/math7111089.

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In this paper, we consider the discrete-time constrained average stochastic games with independent state processes. The state space of each player is denumerable and one-stage cost functions can be unbounded. In these game models, each player chooses an action each time which influences the transition probability of a Markov chain controlled only by this player. Moreover, each player needs to pay some costs which depend on the actions of all the players. First, we give an existence condition of stationary constrained Nash equilibria based on the technique of average occupation measures and the

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35

Choung, Joon-Mo, Jang-Hyun Joung, Myung-Hun Choo, and Ki-Young Yoon. "Development of Fully Stochastic Fatigue Analysis Program for Offshore Floaters." Journal of the Society of Naval Architects of Korea 44, no. 4 (2007): 425–38. http://dx.doi.org/10.3744/snak.2007.44.4.425.

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36

Coomes, P. A. "PLEM: A Computer Program for Passive Learning Stochastic Control Experiments." IFAC Proceedings Volumes 19, no. 10 (1986): 385–89. http://dx.doi.org/10.1016/s1474-6670(17)59697-4.

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37

TASDOGAN, Celal. "An Evaluation of New Incentive Program by Stochastic Frontier Analysis." Ekonomik Yaklasim 24, no. 89 (2013): 1. http://dx.doi.org/10.5455/ey.35300.

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38

Chen, Zhiping, and Zongben Xu. "Continuity and Stability of a Quadratic Mixed-Integer Stochastic Program." Numerical Functional Analysis and Optimization 30, no. 5-6 (2009): 462–77. http://dx.doi.org/10.1080/01630560902920668.

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39

Zhao, Wei, Lin Zhao, Weidong Wu, Sigen Chen, Shaohui Sun, and Yong Cao. "Loading-balance relay-selective strategy based on stochastic dynamic program." Tsinghua Science and Technology 23, no. 4 (2018): 493–500. http://dx.doi.org/10.26599/tst.2018.9010077.

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40

Chan, Wenyaw, Asha S. Kapadia, and Alice Z. Chuang. "A Stochastic Model of Smoking Behavior Under a Cessation Program." Biometrical Journal 43, no. 1 (2001): 53–62. http://dx.doi.org/10.1002/1521-4036(200102)43:1<53::aid-bimj53>3.0.co;2-2.

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41

Keqin Li. "Stochastic bounds for parallel program execution times with processor constraints." IEEE Transactions on Computers 46, no. 5 (1997): 630–36. http://dx.doi.org/10.1109/12.589243.

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42

Coomes, Paul A. "PLEM: A computer program for passive learning, stochastic control experiments." Journal of Economic Dynamics and Control 11, no. 2 (1987): 223–27. http://dx.doi.org/10.1016/0165-1889(87)90012-1.

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43

Sivazlian, B. D. "Optimum scheduling of a new maintenance program under stochastic degradation." Microelectronics Reliability 29, no. 1 (1989): 57–71. http://dx.doi.org/10.1016/0026-2714(89)90011-5.

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44

Rostampour, Vahab, and Tamás Keviczky. "Distributed Computational Framework for Large-Scale Stochastic Convex Optimization." Energies 14, no. 1 (2020): 23. http://dx.doi.org/10.3390/en14010023.

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This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and common uncertainties. Due to the large number of required scenarios to approximate the stochasticity of these problems, the stochastic optimization involves formulating a large-scale scenario program, which is in general computationally demanding. We present two novel ideas in this paper to address this issue. We first develop a technique to decomp

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45

Vardanyan, Yelena, and Henrik Madsen. "Stochastic Bilevel Program for Optimal Coordinated Energy Trading of an EV Aggregator." Energies 12, no. 20 (2019): 3813. http://dx.doi.org/10.3390/en12203813.

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Gradually replacing fossil-fueled vehicles in the transport sector with Electric Vehicles (EVs) may help ensure a sustainable future. With regard to the charging electric load of EVs, optimal scheduling of EV batteries, controlled by an aggregating agent, may provide flexibility and increase system efficiency. This work proposes a stochastic bilevel optimization problem based on the Stackelberg game to create price incentives that generate optimal trading plans for an EV aggregator in day-ahead, intra-day and real-time markets. The upper level represents the profit maximizer EV aggregator who

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46

Li, Yu-xin, Jie Zhang, and Zun-quan Xia. "A Regularization SAA Scheme for a Stochastic Mathematical Program with Complementarity Constraints." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/321010.

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To reflect uncertain data in practical problems, stochastic versions of the mathematical program with complementarity constraints (MPCC) have drawn much attention in the recent literature. Our concern is the detailed analysis of convergence properties of a regularization sample average approximation (SAA) method for solving a stochastic mathematical program with complementarity constraints (SMPCC). The analysis of this regularization method is carried out in three steps: First, the almost sure convergence of optimal solutions of the regularized SAA problem to that of the true problem is establ

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47

Roush, W. B., J. Purswell, and S. L. Branton. "Microsoft Excel sensitivity analysis for linear and stochastic program feed formulation." Journal of Applied Poultry Research 18, no. 1 (2009): 85–89. http://dx.doi.org/10.3382/japr.2008-00102.

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48

Sparacino, Giovanni, Gianluigi Pillonetto, Massimo Capello, Giuseppe De Nicolao, and Claudio Cobelli. "winstodec: a stochastic deconvolution interactive program for physiological and pharmacokinetic systems." Computer Methods and Programs in Biomedicine 67, no. 1 (2002): 67–77. http://dx.doi.org/10.1016/s0169-2607(00)00151-6.

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49

Saadouli, Nasreddine. "Computationally efficient solution algorithm for a large scale stochastic dynamic program." Procedia Computer Science 1, no. 1 (2010): 1397–405. http://dx.doi.org/10.1016/j.procs.2010.04.155.

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50

Herring, William L., and Jeffrey W. Herrmann. "A stochastic dynamic program for the single-day surgery scheduling problem." IIE Transactions on Healthcare Systems Engineering 1, no. 4 (2011): 213–25. http://dx.doi.org/10.1080/19488300.2011.628638.

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