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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


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Journal ArticleDOI
Gurcan Comert1
TL;DR: In this paper, the authors introduced simple analytical estimation models for queue lengths from tracked or probe vehicles at traffic signals using stochastic modeling approach, which can estimate cycle-to-cycle queue lengths by using primary parameters such as arrival rate, probe vehicle proportions and signal phase durations.
Abstract: As mobile traffic sensor technology gets more attention, mathematical models are being developed that utilize this new data type in various intelligent transportation systems applications. This study introduces simple analytical estimation models for queue lengths from tracked or probe vehicles at traffic signals using stochastic modeling approach. Developed models estimate cycle-to-cycle queue lengths by using primary parameters such as arrival rate, probe vehicle proportions, and signal phase durations. Valuable probability distributions and moment generating functions for probe information types are formulated. Fully analytical closed-form expressions are given for the case ignoring the overflow queue and approximation models are presented for the overflow case. Derived models are compared with the results from VISSIM-microscopic simulation. Analytical steady-state and cycle-to-cycle estimation errors are also derived. Numerical examples are shown for the errors of these estimators that change with probe vehicle market penetration levels, arrival rates, and volume-to-capacity ratios.

96 citations

01 Jan 2014
TL;DR: This article places a variety of competing strategies into a common framework, which makes it easier to see the close relationship between communities such as stochastic programming, (approximate) dynamic programming, simulation, and Stochastic search.
Abstract: Whereas deterministic optimization enjoys an almost universally accepted canonical form, stochastic optimization is a jungle of competing notational systems and algorithmic strategies. This is especially problematic in the context of sequential (multistage) stochastic optimization problems, which is the focus of our presentation. In this article, we place a variety of competing strategies into a common framework, which makes it easier to see the close relationship between communities such as stochastic programming, (approximate) dynamic programming, simulation, and stochastic search. What have previously been viewed as competing approaches (e.g., simulation versus optimization, stochastic programming versus dynamic programming) can be reduced to four fundamental classes of policies that are evaluated in a simulation-based setting we call the base model. The result is a single coherent framework that encompasses all of these methods, which can often be combined to create powerful hybrid policies to address complex problems.

96 citations

BookDOI
05 Dec 2007
TL;DR: The author explains the development of Simulation Simulation Languages and Software Simulation Projects- the Bigger Picture and Metaheuristics for Discrete Optimization Problems, as well as some definitions of "Best Solution" and some recommendations for future research.
Abstract: I OR/MS Models and Methods Linear Programming, K. G. Murty Brief History of Algorithms for Solving Linear Equations, Linear Inequalities, and LPs Applicability of the LP Model: Classical Examples of Direct Applications LP Models Involving Transformations of Variables Intelligent Modeling Essential to Get Good Results, an Example from Container Shipping Planning Uses of LP Models Brief Introduction to Algorithms for Solving LP Models Software Systems Available for Solving LP Models Multiobjective LP Models Nonlinear Programming, T.B. Trafalis and R.C. Gilbert Introduction Unconstrained Optimization Constrained Optimization Conclusion Integer Programming, M. Weng Introduction Formulation of IP Models Branch and Bound Method Cutting Plane Method Other Solution Methods and Computer Solution Network Optimization, M.B. Yildirim Introduction Notation Minimum Cost Flow Problem Shortest Path Problem Maximum Flow Problem Assignment Problem Minimum Spanning Tree Problem Minimum Cost Multicommodity Flow Problem Conclusions Multiple Criteria Decision Making, A.S. M. Masud and A. R. Ravindran Some Definitions The Concept of "Best Solution" Criteria Normalization Computing Criteria Weights Multiple Criteria Methods for Finite Alternatives Multiple Criteria Mathematical Programming Problems Goal Programming Method of Global Criterion and Compromise Programming Interactive Methods MCDM Applications MCDM Software Further Readings Decision Analysis, C. M. Klein Introduction Terminology for Decision Analysis Decision Making under Risk Decision Making under Uncertainty Practical Decision Analysis Conclusions Resources Dynamic Programming, J. A. Ventura Introduction Deterministic Dynamic Programming Models Stochastic Dynamic Programming Models Conclusions Stochastic Processes, S. H. Xu Introduction Poisson Processes Discrete-Time Markov Chains Continuous-Time Markov Chains Renewal Theory Software Products Available for Solving Stochastic Models Queueing Theory, N. Gautam Introduction Queueing Theory Basics Single-Station and Single-Class Queues Single-Station and Multiclass Queues Multistation and Single-Class Queues Multistation and Multiclass Queues Concluding Remarks Inventory Control, F. Azadivar and A. Rangarajan Introduction Design of Inventory Systems Deterministic Inventory Systems Stochastic Inventory Systems Inventory Control at Multiple Locations Inventory Management in Practice Conclusions Current and Future Research Complexity and Large-Scale Networks, H. P. Thadakamalla, S. R.T. Kumara, and R. Albert Introduction Statistical Properties of Complex Networks Modeling of Complex Networks Why "Complex" Networks Optimization in Complex Networks Conclusions Simulation, C. M. Harmonosky Introduction Basics of Simulation Simulation Languages and Software Simulation Projects-The Bigger Picture Summary Metaheuristics for Discrete Optimization Problems, R.K. Kincaid Mathematical Framework for Single Solution Metaheuristics Network Location Problems Multistart Local Search Simulated Annealing Plain Vanilla Tabu Search Active Structural Acoustic Control (ASAC) Nature Reserve Site Selection Damper Placement in Flexible Truss Structures Reactive Tabu Search Discussion Robust Optimization, H. J. Greenberg and T. Morrison Introduction Classical Models Robust Optimization Models More Applications Summary II OR/MS Applications Project Management, A. B. Badiru Introduction Critical Path Method PERT Network Analysis Statistical Analysis of Project Duration Precedence Diagramming Method Software Tools for Project Management Conclusion Quality Control, Q. Feng and K. C. Kapur Introduction Quality Control and Product Life Cycle New Trends and Relationship to Six Sigma Statistical Process Control Process Capability Studies Advanced Control Charts 16.7 Limitations of Acceptance Sampling 16.8 Conclusions Reliability, L. M. Leemis Introduction Reliability in System Design Lifetime Distributions Parametric Models Parameter Estimation in Survival Analysis Nonparametric Methods Assessing Model Adequacy Summary Production Systems, B. L. Foote and K. G. Murty Production Planning Problem Demand Forecasting Models for Production Layout Design Scheduling of Production and Service Systems Energy Systems, C. R. Hudson and A. B. Badiru Introduction Definition of Energy Harnessing Natural Energy Mathematical Modeling of Energy Systems Linear Programming Model of Energy Resource Combination Integer Programming Model for Energy Investment Options Simulation and Optimization of Distributed Energy Systems Point-of-Use Energy Generation Modeling of CHP Systems Economic Optimization Methods Design of a Model for Optimization of CHP System Capacities Capacity Optimization Implementation of the Computer Model Other Scenarios Airline Optimization, J. L. Snowdon and G. Paleologo Introduction Schedule Planning Revenue Management Aircraft Load Planning Future Research Directions and Conclusions Financial Engineering, A. R. Heching and A. J. King Introduction Return Estimating an Asset's Mean and Variance Diversification Efficient Frontier Utility Analysis Black-Litterman Asset Allocation Model Risk Management Options Valuing Options Dynamic Programming Pricing American Options Using Dynamic Programming Comparison of Monte Carlo Simulation and Dynamic Programming Multi-Period Asset Liability Management Conclusions Supply Chain Management, D. P. Warsing Introduction Managing Inventories in the Supply Chain Managing Transportation in the Supply Chain Managing Locations in the Supply Chain Managing Dyads in the Supply Chain Discussion and Conclusions E-Commerce, S. Sadagopan Introduction Evolution of E-Commerce OR/MS and E-Commerce OR Applications in E-Commerce Tools-Applications Matrix Way Forward Summary Water Resources, G.V. Loganathan Introduction Optimal Operating Policy for Reservoir Systems Water Distribution Systems Optimization Preferences in Choosing Domestic Plumbing Materials Stormwater Management Groundwater Management Summary Military Applications, J. D. Weir and M. U. Thomas Introduction Background on Military OR Current Military Applications of OR Concluding Remarks Future of OR/MS Applications: A Practitioner's Perspective, P. Balasubramanian Past as a Guide to the Future Impact of the Internet Emerging Opportunities Index

96 citations

Journal ArticleDOI
TL;DR: In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized.
Abstract: The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is al...

95 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a stochastic mathematical model and proposed a new replenishment policy in a centralized supply chain for deteriorating items, considering inventory and transportation costs, as well as the environmental impacts under uncertain demand.

95 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532