Mineral Supply Chain Optimization Under Uncertainty Using Approximate Dynamic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mineral Supply Chain Optimization Under Uncertainty Using Approximate Dynamic Programming write by Cosmin Paduraru. This book was released on 2015. Mineral Supply Chain Optimization Under Uncertainty Using Approximate Dynamic Programming available in PDF, EPUB and Kindle.
Mining Supply Chain Optimization Under Geological Uncertainty
Mining Supply Chain Optimization Under Geological Uncertainty - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mining Supply Chain Optimization Under Geological Uncertainty write by Ryan Goodfellow. This book was released on 2013. Mining Supply Chain Optimization Under Geological Uncertainty available in PDF, EPUB and Kindle.
Dynamic Simultaneous Optimization of Mineral Value Chains Under Resource Uncertainty
Dynamic Simultaneous Optimization of Mineral Value Chains Under Resource Uncertainty - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamic Simultaneous Optimization of Mineral Value Chains Under Resource Uncertainty write by Maria Del Castillo Suarez. This book was released on 2018. Dynamic Simultaneous Optimization of Mineral Value Chains Under Resource Uncertainty available in PDF, EPUB and Kindle. "Mining complexes are mineral value chains where extracted material from different mines is transformed into sellable products through a set of processing streams. This value chain is governed by uncertainties at different levels, from the geological attributes of the orebody at the mine(s), to the different operational and processing components that lead the sellable products to the market. Stochastic simultaneous optimization formulations for industrial mining complexes have proven to be effective in generating reliable strategic plans that maximize net present value and, at the same time, manage and reduce risk. However, because of the uncertainties governing a mining complex, particularly the ones related to the geological attributes which define the supply of the system, it has become a priority to integrate flexibility mechanisms that allow a mining project to change and adapt as more information becomes available. Within this adaptability, optimizing the investment timing of high-magnitude capital expenditures throughout the life-of-mine is a priority, due to their high impact on the annual cash-flows and on their effects over the physical mining schedule. Additionally, to improve a mining complex's ability to meet production targets and overall performance, advanced mechanisms should be developed to ensure complex blending constraints are met, managing the geometallurgical variables of the deposit.This thesis presents a methodology to embed flexibility into mineral value chains, by allowing the strategic mine plan of a mining complex to dynamically consider possible options and alternatives for reacting and adapting to future changes. For this, first, a study on extraction capacity optimization is presented, followed by the development of a mechanism to deal with complex variables of the deposit to meet blending constraints and production targets. These two components are later integrated into a dynamic optimization model, which optimizes the mining complex's mine plan under geological uncertainty, integrating flexible investment alternatives, as well as operational modes.The dynamic model developed produces a unique initial extraction sequence, while keeping a viable flexible long-term plan for future investment decisions, as may be needed. The flexible long-term plan is obtained through a dynamic optimization which allows making transitioning plans upfront to facilitate change. This method introduces a new adapted multistage stochastic programming model which expands upon the two-stage framework by performing multiple recourse stages that are solved iteratively, allowing parallel designs to be generated in a scenario-tree structure. In this model, dynamic decisions over capital expenditures are made sequentially over time, based on information that becomes available over production time. The above model is subsequently extended to include alternatives over operating modes at different levels of the mineral value chain. More specifically, optimal operating modes are chosen per period, selecting blasting patterns at the mine, and processing relations of throughput and recovery at the plant. The practical implications of the proposed method are demonstrated through an application over a copper-gold mining complex, where the dynamic model presents a 10.5% increase in net present value compared to a traditional two-stage stochastic formulation.The dynamic mining complex formulation proposed is able to include flexibility into the optimization of the strategic plan of a mineral value chain. This enables possible developments within the feasible set of alternatives that can be taken, considering the mining complex's configuration, capacities, and constraints. The proposed model is able to generate feasible, operational schedules, while providing a wider view of the mining complex's performance, easing the transition to possible changes due to the periodic unveiling of uncertainty." --
Approximate Dynamic Programming
Approximate Dynamic Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Approximate Dynamic Programming write by Warren B. Powell. This book was released on 2007-10-05. Approximate Dynamic Programming available in PDF, EPUB and Kindle. A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Quantitative Models for Reverse Logistics
Quantitative Models for Reverse Logistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Quantitative Models for Reverse Logistics write by Moritz Fleischmann. This book was released on 2012-12-06. Quantitative Models for Reverse Logistics available in PDF, EPUB and Kindle. Economic, marketing, and legislative considerations are increasingly leading companies to take back and recover their products after use. From a logistics perspective, these initiatives give rise to new goods flows from the user back to the producer. The management of these goods flows opposite to the traditional supply chain flows is addressed in the recently emerged field of Reverse Logistics. This monograph considers quantitative models that support decision making in Reverse Logistics. To this end, several recent case studies are reviewed. Moreover, first hand insight from a study on used electronic equipment is reported on. On this basis, logistics issues arising in the management of "reverse" goods flows are identified. Moreover, differences between Reverse Logistics and more traditional logistics contexts are highlighted. Finally, attention is paid to capturing the characteristics of Reverse Logistics in appropriate quantitative models.
