Supply chain design under uncertainty using sample average approximation and dual decomposition
TL;DR: A supply chain design problem modeled as a sequence of splitting and combining processes, where the first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions.
About: This article is published in European Journal of Operational Research.The article was published on 2009-12-01. It has received 260 citations till now. The article focuses on the topics: Supply chain & Stochastic programming.
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TL;DR: In this article, a robust optimization model for handling the inherent uncertainty of input data in a closed-loop supply chain network design problem is proposed, and the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory.
571 citations
TL;DR: A comprehensive review of studies in the fields of SCND and reverse logistics network design under uncertainty and existing optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, risk-averse stochastics, robust optimization, and fuzzy mathematical programming are explored.
442 citations
Cites background from "Supply chain design under uncertain..."
...…and Aït-Kadi (2008), Kiya and Davoudpour 2012), Klibi and Martel (2012a), Klibi and Martel (2012b), Lee and ong (2009), Lee, Dong, and Bian (2010), Lee, Dong, Bian, and seng (2007), Santoso, Ahmed, Goetschalckx, and Shapiro (2005), chütz et al. (2009) , and Ayvaz, Bolat, and Aydın (2015) ....
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...…a w e c 5 b o m " t m s p o A few studies (e.g., Fattahi & Govindan, 2016; Govindan & Fatahi, 2017; Keyvanshokooh et al., 2016; Klibi & Martel, 2012b; chütz et al., 2009 ) developed an appropriate scenario generation rocedure to obtain a set of scenarios, and typically most reference apers…...
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TL;DR: In this article, the authors present the Ripple effect in supply chains and summarise recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective, identifying gaps in current research and delineating future research avenues.
Abstract: This study aims at presenting the Ripple effect in supply chains It develops different dimensions of the Ripple effect and summarises recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective It structures and classifies existing research streams and applications areas of different quantitative methods to the Ripple effect analysis as well as identifying gaps in current research and delineating future research avenues The analysis shows that different frameworks already exist implicitly for tackling the Ripple effect in the SC dynamics, control and disruption management domain However, quantitative analysis tools are still rarely applied in praxis We conclude that the Ripple effect can be the phenomenon that is able to consolidate research in SC disruption management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations This may build the agenda for future research on SC dynamics, control, continuity and disrup
417 citations
Cites background from "Supply chain design under uncertain..."
...…and reactive have been developed to coping with uncertainty in recent years (Santoso et al. 2005; Tomlin 2006; Chopra, Reinhardt, and Mohan 2007; Schütz, Tomasgard, and Ahmed 2009; Knemeyer, Zinn, and Eroglu 2009; Klibi, Martel, and Guitouni 2010; Georgiadis et al. 2011; Peng et al. 2011;…...
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TL;DR: A stochastic mathematical formulation for designing a network of multi-product supply chains comprising several capacitated production facilities, distribution centres and retailers in markets under uncertainty and incorporates the cut-set concept in reliability theory and also the robust optimisation concept is developed.
382 citations
Cites background from "Supply chain design under uncertain..."
...Schütz et al. (2009) formulated the multi-commodity SC design problem as a two-stage stochastic model....
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TL;DR: In this paper, the authors provide a review of the competitive supply chain network design literature and highlight the effects of competitive environment on SCND, and develop a general framework for modeling the competitive SCND problems considering managerial insight and propose potential areas for future research.
Abstract: Supply chain network design (SCND) determines the structure of a chain and affects its costs and performance. SCND deals with a variety of decisions such as determining number, size and location of facilities in a supply chain (SC) and may include tactical decisions (such as distribution, transportation and inventory management policies) as well as operational decisions (such as fulfilling customers demand). SCND has a voluminous literature. Most of the literature deals with a single SC and ignores the existing competitor SCs and future emerging ones. However, SCs compete together to capture more market shares. Even if there is not any competitor at the moment, SCs should be prepared for possible future competitive situation at the SCND stage. On the other hand, many competitive models assume that the supply chain network (SCN) and its structure already exist. Few research papers consider both aspects of design and competition. In this paper, we provide a review of SCND literature and highlight the effects of competitive environment on SCND. We review, classify, and introduce the major features of the proposed models in both SCND and competition literature. After investigating proposed competitive SCND models we develop a general framework for modeling the competitive SCND problems considering managerial insight and propose potential areas for future research.
367 citations
References
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TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
Abstract: Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792
10,011 citations
27 Jun 2011
TL;DR: This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability to help students develop an intuition on how to model uncertainty into mathematical problems.
Abstract: The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods.The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition:"The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)
5,398 citations
TL;DR: A Monte Carlo simulation--based approach to stochastic discrete optimization problems, where a random sample is generated and the expected value function is approximated by the corresponding sample average function.
Abstract: In this paper we study a Monte Carlo simulation--based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and the expected value function is approximated by the corresponding sample average function. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping criterion is satisfied. We discuss convergence rates, stopping rules, and computational complexity of this procedure and present a numerical example for the stochastic knapsack problem.
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