Optimization of water procurement decisions in an irrigation district: the role of option contracts

Water supply instability is one of the main risks faced by irrigation districts and farmers. Water procurement decision optimisation is essential in order to increase supply reliability and reduce costs. Water markets, such as spot purchases or water supply option contracts, can make this decision process more flexible. We analyse the potential interest in an option contract for an irrigation district that has access to several water sources. We apply a stochastic recursive mathematical programming model to simulate the water procurement decisions of an irrigation district?s board operating in a context of water supply uncertainty in south-eastern Spain. We analyse what role different option contracts could play in securing its water supply. Results suggest that the irrigation district would be willing to accept the proposed option contract in most cases subject to realistic values of the option contract financial terms. Of nine different water sources, desalination and the option contract are the main substitutes, where the use of either depends on the contract parameters. The contract premium and optioned volume are the variables that have a greater impact on the irrigation district?s decisions. Key words: Segura Basin, stochastic recursive programming, water markets, water supply option contract, water supply risk.

Impactos de regulações ambientais sobre o transporte de cargas no Brasil: uma análise para o transporte de soja

O aumento da concentração de gases de efeito estufa na atmosfera levou a uma preocupação de como se reduzir as emissões destes gases. Desta preocupação surgiram instrumentos de regulação a fim de reduzir ou controlar os níveis de poluição. Dentro deste contexto, esta pesquisa analisou o setor de transportes de cargas, com ênfase no transporte de soja. No Brasil, o setor de transportes é um dos principais responsáveis pelas emissões de gases de efeito estufa provenientes da queima de combustíveis fósseis. No setor de transportes, as emissões diferem entre os modais, sendo que as ferrovias e hidrovias poluem menos que as rodovias. Desta forma, simulou-se por meio de um modelo de programação linear se a adoção de medidas regulatórias sobre as emissões de CO2 traria uma alteração no uso das ferrovias e hidrovias. Uma das constatações, ao se utilizar o modelo de Minimização de Fluxo de Custo Mínimo para o transporte de soja em 2013, foi que a capacidade de embarque nos terminais ferroviários e hidroviários desempenha um papel fundamental na redução das emissões de CO2. Se não houver capacidade suficiente, a adoção de uma taxa pode não provocar a redução das emissões. No caso do sistema de compra e crédito de carbono, seria necessária a compra de créditos de carbono, numa situação em que a capacidade de embarque nos terminais intermodais seja limitada. Verificou-se, ainda, que melhorias na infraestrutura podem desempenhar um papel mitigador das emissões. Um aumento da capacidade dos terminais ferroviários e hidroviários existentes, bem como o aumento da capacidade dos portos, pode provocar a redução das emissões de CO2. Se os projetos de expansão das ferrovias e hidrovias desenvolvidos por órgãos governamentais saírem do papel, pode-se chegar a uma redução de pouco mais de 50% das emissões de CO2. Consideraram-se ainda quais seriam os efeitos do aumento do uso de biodiesel como combustível e percebeu-se que seria possível obter reduções tanto das emissões quanto do custo de transporte. Efeitos semelhantes foram encontrados quando se simulou um aumento da eficiência energética. Por fim, percebeu-se nesta pesquisa que a adoção de uma taxa não traria tantos benefícios, econômicos e ambientais, quanto a melhoria da infraestrutura logística do país.

Optimización (curso 2011-2012)

Material docente de la asignatura «Simulación y Optimización de procesos químicos». Parte de Optimización OPTIMIZACIÓN TEMA 6. Conceptos Básicos 6.1 Introducción. Desarrollo histórico de la optimización de procesos. 6.2 Funciones y regiones cóncavas y convexas. 6.3 Optimización sin restricciones. 6.4 Optimización con restricciones de igualdad y desigualdad. Condiciones de optimalidad de Karush Khun Tucker 6.5 Interpretación de los Multiplicadores de Lagrange. TEMA 7. Programación lineal 7.1 Introducción. Planteamiento del problema en forma canónica y forma estándar. 7.2 Teoremas de la programación lineal 7.3 Resolución gráfica 7.4 Resolución en forma de tabla. El método simplex. 7.5 Variables artificiales. Método de la Gran M y método de las dos fases. 7.6 Conceptos básicos de dualidad. TEMA 8. Programación no lineal 8.1 Repaso de métodos numéricos de optimización sin restricciones 8.2 Optimización con restricciones. Fundamento de los métodos de programación cuadrática sucesiva y de gradiente reducido. TEMA 9. Introducción a la programación lineal y no lineal con variables discretas. 9.1 Conceptos básicos para la resolución de problemas lineales con variables discretas.(MILP, mixed integer linear programming) 9.2 Introducción a la programación no lineal con variables continuas y discretas (MINLP mixed integer non linear programming) 9.3 Modelado de problemas con variables binarias: 9.3.1 Conceptos básicos de álgebra de Boole 9.3.2 Transformación de expresiones lógicas a expresiones algebraicas 9.3.3 Modelado con variables discretas y continuas. Formulación de envolvente convexa y de la gran M.

Combined simulation-optimization methodology for the design of environmental conscious absorption systems

This work addresses the optimization of ammonia–water absorption cycles for cooling and refrigeration applications with economic and environmental concerns. Our approach combines the capabilities of process simulation, multi-objective optimization (MOO), cost analysis and life cycle assessment (LCA). The optimization task is posed in mathematical terms as a multi-objective mixed-integer nonlinear program (moMINLP) that seeks to minimize the total annualized cost and environmental impact of the cycle. This moMINLP is solved by an outer-approximation strategy that iterates between primal nonlinear programming (NLP) subproblems with fixed binaries and a tailored mixed-integer linear programming (MILP) model. The capabilities of our approach are illustrated through its application to an ammonia–water absorption cycle used in cooling and refrigeration applications.

Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.

Isobutane Alkylation Process Synthesis by means of Hybrid Simulation-Multiobjective Optimization

Multiobjective Generalized Disjunctive Programming (MO-GDP) optimization has been used for the synthesis of an important industrial process, isobutane alkylation. The two objective functions to be simultaneously optimized are the environmental impact, determined by means of LCA (Life Cycle Assessment), and the economic potential of the process. The main reason for including the minimization of the environmental impact in the optimization process is the widespread environmental concern by the general public. For the resolution of the problem we employed a hybrid simulation- optimization methodology, i.e., the superstructure of the process was developed directly in a chemical process simulator connected to a state of the art optimizer. The model was formulated as a GDP and solved using a logic algorithm that avoids the reformulation as MINLP -Mixed Integer Non Linear Programming-. Our research gave us Pareto curves compounded by three different configurations where the LCA has been assessed by two different parameters: global warming potential and ecoindicator-99.

An analog input-output system for the ILLIAC II.

Issued also as thesis, University of Illinois.

A feasibility conjecture related to the Hirsch conjecture.

Photocopy. [Washington?] Clearinghouse for Federal Scientific and Technical Information of the U. S. Dept. of Commerce [1966?]

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06

Optimal eradication: when to stop looking for an invasive plant

The notion of being sure that you have completely eradicated an invasive species is fanciful because of imperfect detection and persistent seed banks. Eradication is commonly declared either on an ad hoc basis, on notions of seed bank longevity, or on setting arbitrary thresholds of 1% or 5% confidence that the species is not present. Rather than declaring eradication at some arbitrary level of confidence, we take an economic approach in which we stop looking when the expected costs outweigh the expected benefits. We develop theory that determines the number of years of absent surveys required to minimize the net expected cost. Given detection of a species is imperfect, the optimal stopping time is a trade-off between the cost of continued surveying and the cost of escape and damage if eradication is declared too soon. A simple rule of thumb compares well to the exact optimal solution using stochastic dynamic programming. Application of the approach to the eradication programme of Helenium amarum reveals that the actual stopping time was a precautionary one given the ranges for each parameter.

Prioritizing global conservation efforts

One of the most pressing issues facing the global conservation community is how to distribute limited resources between regions identified as priorities for biodiversity conservation(1-3). Approaches such as biodiversity hotspots(4), endemic bird areas(5) and ecoregions(6) are used by international organizations to prioritize conservation efforts globally(7). Although identifying priority regions is an important first step in solving this problem, it does not indicate how limited resources should be allocated between regions. Here we formulate how to allocate optimally conservation resources between regions identified as priorities for conservation - the 'conservation resource allocation problem'. Stochastic dynamic programming is used to find the optimal schedule of resource allocation for small problems but is intractable for large problems owing to the curse of dimensionality(8). We identify two easy- to- use and easy- to- interpret heuristics that closely approximate the optimal solution. We also show the importance of both correctly formulating the problem and using information on how investment returns change through time. Our conservation resource allocation approach can be applied at any spatial scale. We demonstrate the approach with an example of optimal resource allocation among five priority regions in Wallacea and Sundaland, the transition zone between Asia and Australasia.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.

Density evolution and power profile optimization for iterative multiuser decoders based on individually optimum multiuser detectors

Iterative multiuser joint decoding based on exact Belief Propagation (BP) is analyzed in the large system limit by means of the replica method. It is shown that performance can be improved by appropriate power assignment to the users. The optimum power assignment can be found by linear programming in most technically relevant cases. The performance of BP iterative multiuser joint decoding is compared to suboptimum approximations based on Interference Cancellation (IC). While IC receivers show a significant loss for equal-power users, they yield performance close to BP under optimum power assignment.

Optimization of physical distribution problem in logistics management

Physical distribution plays an imporant role in contemporary logistics management. Both satisfaction level of of customer and competitiveness of company can be enhanced if the distribution problem is solved optimally. The multi-depot vehicle routing problem (MDVRP) belongs to a practical logistics distribution problem, which consists of three critical issues: customer assignment, customer routing, and vehicle sequencing. According to the literatures, the solution approaches for the MDVRP are not satisfactory because some unrealistic assumptions were made on the first sub-problem of the MDVRP, ot the customer assignment problem. To refine the approaches, the focus of this paper is confined to this problem only. This paper formulates the customer assignment problem as a minimax-type integer linear programming model with the objective of minimizing the cycle time of the depots where setup times are explicitly considered. Since the model is proven to be MP-complete, a genetic algorithm is developed for solving the problem. The efficiency and effectiveness of the genetic algorithm are illustrated by a numerical example.

Selecting the most preferable alternatives in a group decision making problem using DEA

Group decision making is the study of identifying and selecting alternatives based on the values and preferences of the decision maker. Making a decision implies that there are several alternative choices to be considered. This paper uses the concept of Data Envelopment Analysis to introduce a new mathematical method for selecting the best alternative in a group decision making environment. The introduced model is a multi-objective function which is converted into a multi-objective linear programming model from which the optimal solution is obtained. A numerical example shows how the new model can be applied to rank the alternatives or to choose a subset of the most promising alternatives.