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John M. Wilson

Other affiliations: Aberystwyth University
Bio: John M. Wilson is an academic researcher from Loughborough University. The author has contributed to research in topics: Integer programming & Generalized assignment problem. The author has an hindex of 25, co-authored 74 publications receiving 4160 citations. Previous affiliations of John M. Wilson include Aberystwyth University.


Papers
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Journal ArticleDOI
TL;DR: In this paper, an introduction to stochastic programming is presented, which is based on the idea of Stochastic Programming (SPP) and is used in our work.
Abstract: (1998). Introduction to Stochastic Programming. Journal of the Operational Research Society: Vol. 49, No. 8, pp. 897-898.

1,274 citations

Journal ArticleDOI
TL;DR: In this article, the authors consider single-level lot sizing problems, their variants and solution approaches, together with exact and heuristic approaches for their solution, and conclude with some suggestions for future research.
Abstract: Lot sizing is one of the most important and also one of the most difficult problems in production planning. This subject has been studied extensively in the literature. In this article, we consider single-level lot sizing problems, their variants and solution approaches. After introducing factors affecting formulation and the complexity of production planning problems, and introducing different variants of lot sizing and scheduling problems, we discuss single-level lot sizing problems, together with exact and heuristic approaches for their solution. We conclude with some suggestions for future research.

670 citations

Book
01 Jun 1995
TL;DR: The Process Modellera s needs, needs, and techniques for modelling large processes: basic Concepts in Process Modelling and Managing the Modelling.
Abstract: The Process Modellera s Needs. Basic Concepts in Process Modelling. Modelling with RADs. Animating a Process Model. Micro--Modelling of Processes. Modelling Large Processes. Process Patterns. Modelling the Materials in the Process. Analysing a Process Model. Managing the Modelling. Epilogue. Index.

519 citations

Journal ArticleDOI
TL;DR: A literature review of the cell formation (CF) problem concentrating on formulations proposed in the last decade such as mathematical programming, heuristic and metaheuristic methodologies and artificial intelligence strategies is presented.

209 citations

Journal ArticleDOI
TL;DR: A Historical Sketch on Sensitivity Analysis and Parametric Programming T.J. Greenberg and the Optimal Set and Optimal Partition Approach.
Abstract: Foreword. Preface. 1. A Historical Sketch on Sensitivity Analysis and Parametric Programming T. Gal. 2. A Systems Perspective: Entity Set Graphs H. Muller-Merbach. 3. Linear Programming 1: Basic Principles H.J. Greenberg. 4. Linear Programming 2: Degeneracy Graphs T. Gal. 5. Linear Programming 3: The Tolerance Approach R.E. Wendell. 6. The Optimal Set and Optimal Partition Approach A.B. Berkelaar, et al. 7. Network Models G.L. Thompson. 8. Qualitative Sensitivity Analysis A. Gautier, et al. 9. Integer and Mixed-Integer Programming C. Blair. 10. Nonlinear Programming A.S. Drud, L. Lasdon. 11. Multi-Criteria and Goal Programming J. Dauer, Yi-Hsin Liu. 12. Stochastic Programming and Robust Optimization H. Vladimirou, S.A. Zenios. 13. Redundancy R.J. Caron, et al. 14. Feasibility and Viability J.W. Chinneck. 15. Fuzzy Mathematical Programming H.-J. Zimmermann. Subject Index.

195 citations


Cited by
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Journal ArticleDOI
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.

1,728 citations

Journal ArticleDOI
TL;DR: The main focus will be on the different approaches to perform robust optimization in practice including the methods of mathematical programming, deterministic nonlinear optimization, and direct search methods such as stochastic approximation and evolutionary computation.

1,435 citations

Journal ArticleDOI
TL;DR: An extensive review of the scheduling literature on models with setup times (costs) from then to date covering more than 300 papers is provided, which classifies scheduling problems into those with batching and non-batching considerations, and with sequence-independent and sequence-dependent setup times.

1,264 citations

Book
27 Jul 2017
TL;DR: Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.
Abstract: Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

1,142 citations

Journal ArticleDOI
TL;DR: This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale and integrates a recently proposed sampling strategy, the sample average approximation scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions.

1,044 citations