Accelerating benders decomposition with heuristicmaster problem solutions

In this paper, a general scheme for generating extra cuts during the execution of a Benders decomposition algorithm is presented. These cuts are based on feasible and infeasible master problem solutions generated by means of a heuristic. This article includes general guidelines and a case study with a fixed charge network design problem. Computational tests with instances of this problem show the efficiency of the strategy. The most important aspect of the proposed ideas is their generality, which allows them to be used in virtually any Benders decomposition implementation.

Computing primal solutions with exact arithmetics in SCIP.

The research for exact solutions of mixed integer problems is an active topic in the scientific community. State-of-the-art MIP solvers exploit a floating- point numerical representation, therefore introducing small approximations. Although such MIP solvers yield reliable results for the majority of problems, there are cases in which a higher accuracy is required. Indeed, it is known that for some applications floating-point solvers provide falsely feasible solutions, i.e. solutions marked as feasible because of approximations that would not pass a check with exact arithmetic and cannot be practically implemented. The framework of the current dissertation is SCIP, a mixed integer programs solver mainly developed at Zuse Institute Berlin. In the same site we considered a new approach for exactly solving MIPs. Specifically, we developed a constraint handler to plug into SCIP, with the aim to analyze the accuracy of provided floating-point solutions and compute exact primal solutions starting from floating-point ones. We conducted a few computational experiments to test the exact primal constraint handler through the adoption of two main settings. Analysis mode allowed to collect statistics about current SCIP solutions' reliability. Our results confirm that floating-point solutions are accurate enough with respect to many instances. However, our analysis highlighted the presence of numerical errors of variable entity. By using the enforce mode, our constraint handler is able to suggest exact solutions starting from the integer part of a floating-point solution. With the latter setting, results show a general improvement of the quality of provided final solutions, without a significant loss of performances.

A model for setting services on auxiliary bus lines under congestion

In this paper, a mathematical programming model and a heuristically derived solution is described to assist with the efficient planning of services for a set of auxiliary bus lines (a bus-bridging system) during disruptions of metro and rapid transit lines. The model can be considered static and takes into account the average flows of passengers over a given period of time (i.e., the peak morning traffic hour) Auxiliary bus services must accommodate very high demand levels, and the model presented is able to take into account the operation of a bus-bridging system under congested conditions. A general analysis of the congestion in public transportation lines is presented, and the results are applied to the design of a bus-bridging system. A nonlinear integer mathematical programming model and a suitable approximation of this model are then formulated. This approximated model can be solved by a heuristic procedure that has been shown to be computationally viable. The output of the model is as follows: (a) the number of bus units to assign to each of the candidate lines of the bus-bridging system; (b) the routes to be followed by users passengers of each of the origin–destination pairs; (c) the operational conditions of the components of the bus-bridging system, including the passenger load of each of the line segments, the degree of saturation of the bus stops relative to their bus input flows, the bus service times at bus stops and the passenger waiting times at bus stops. The model is able to take into account bounds with regard to the maximum number of passengers waiting at bus stops and the space available at bus stops for the queueing of bus units. This paper demonstrates the applicability of the model with two realistic test cases: a railway corridor in Madrid and a metro line in Barcelona Planificación de los servicios de lineas auxiliares de autobuses durante las incidencias de las redes de metro y cercanías. El modelo estudia el problema bajo condiciones de alta demanda y condiciones de congestión. El modelo no lineal resultante es resuelto mediante heurísticas que demuestran su utilidad. Se demuestran los resultados en dos corredores de las ciudades de Barcelona y Madrid.

Optimización de operaciones unitarias utilizando líquidos iónicos como agentes materiales de separación

En este trabajo se estudia la modelización y optimización de procesos industriales de separación mediante el empleo de mezclas de líquidos iónicos como disolventes. Los disolventes habitualmente empleados en procesos de absorción o extracción suelen ser componentes orgánicos muy volátiles y dañinos para la salud humana. Las innovadoras propiedades que presentan los líquidos iónicos, los convierten en alternativas adecuadas para solucionar estos problemas. La presión de vapor de estos compuestos es muy baja y apenas varía con la temperatura. Por tanto, estos compuestos apenas se evaporan incluso a temperaturas altas. Esto supone una gran ventaja en cuanto al empleo de estos compuestos como disolventes industriales ya que permite el reciclaje continuo del disolvente al final del proceso sin necesidad de introducir disolvente fresco debido a la evaporación del mismo. Además, al no evaporarse, estos compuestos no suponen un peligro para la salud humana por inhalación; al contrario que otros disolventes como el benceno. El único peligro para la salud que tienen estos compuestos es por tanto el de contacto directo o ingesta, aunque de hecho muchos Líquidos Iónicos son inocuos con lo cual no existe peligro para la salud ni siquiera a través de estas vías. Los procesos de separación estudiados en este trabajo, se rigen por la termodinámica de fases, concretamente el equilibrio líquido-vapor. Para la predicción de los equilibrios se ha optado por el empleo de modelos COSMO (COnductor-like Screening MOdel). Estos modelos tienen su origen en el empleo de la termodinámica de solvatación y en la mecánica cuántica. En el desarrollo de procesos y productos, químicos e ingenieros frecuentemente precisan de la realización de cálculos de predicción de equilibrios de fase. Previamente al desarrollo de los modelos COSMO, se usaban métodos de contribución de grupos como UNIFAC o modelos de coeficientes de actividad como NRTL.La desventaja de estos métodos, es que requieren parámetros de interacción binaria que únicamente pueden obtenerse mediante ajustes por regresión a partir de resultados experimentales. Debido a esto, estos métodos apenas tienen aplicabilidad para compuestos con grupos funcionales novedosos debido a que no se dispone de datos experimentales para llevar a cabo los ajustes por regresión correspondientes. Una alternativa a estos métodos, es el empleo de modelos de solvatación basados en la química cuántica para caracterizar las interacciones moleculares y tener en cuenta la no idealidad de la fase líquida. Los modelos COSMO, permiten la predicción de equilibrios sin la necesidad de ajustes por regresión a partir de resultados experimentales. Debido a la falta de resultados experimentales de equilibrios líquido-vapor de mezclas en las que se ven involucrados los líquidos iónicos, el empleo de modelos COSMO es una buena alternativa para la predicción de equilibrios de mezclas con este tipo de materiales. Los modelos COSMO emplean las distribuciones superficiales de carga polarizada (sigma profiles) de los compuestos involucrados en la mezcla estudiada para la predicción de los coeficientes de actividad de la misma, definiéndose el sigma profile de una molécula como la distribución de probabilidad de densidad de carga superficial de dicha molécula. Dos de estos modelos son COSMO-RS (Realistic Solvation) y COSMO-SAC (Segment Activity Coefficient). El modelo COSMO-RS fue la primera extensión de los modelos de solvatación basados en continuos dieléctricos a la termodinámica de fases líquidas mientras que el modelo COSMO-SAC es una variación de este modelo, tal y como se explicará posteriormente. Concretamente en este trabajo se ha empleado el modelo COSMO-SAC para el cálculo de los coeficientes de actividad de las mezclas estudiadas. Los sigma profiles de los líquidos iónicos se han obtenido mediante el empleo del software de química computacional Turbomole y el paquete químico-cuántico COSMOtherm. El software Turbomole permite optimizar la geometría de la molécula para hallar la configuración más estable mientras que el paquete COSMOtherm permite la obtención del perfil sigma del compuesto mediante el empleo de los datos proporcionados por Turbomole. Por otra parte, los sigma profiles del resto de componentes se han obtenido de la base de datos Virginia Tech-2005 Sigma Profile Database. Para la predicción del equilibrio a partir de los coeficientes de actividad se ha empleado la Ley de Raoult modificada. Se ha supuesto por tanto que la fracción de cada componente en el vapor es proporcional a la fracción del mismo componente en el líquido, dónde la constante de proporcionalidad es el coeficiente de actividad del componente en la mezcla multiplicado por la presión de vapor del componente y dividido por la presión del sistema. Las presiones de vapor de los componentes se han obtenido aplicando la Ley de Antoine. Esta ecuación describe la relación entre la temperatura y la presión de vapor y se deduce a partir de la ecuación de Clausius-Clapeyron. Todos estos datos se han empleado para la modelización de una separación flash usando el algoritmo de Rachford-Rice. El valor de este modelo reside en la deducción de una función que relaciona las constantes de equilibrio, composición total y fracción de vapor. Para llevar a cabo la implementación del modelado matemático descrito, se ha programado un código empleando el software MATLAB de análisis numérico. Para comprobar la fiabilidad del código programado, se compararon los resultados obtenidos en la predicción de equilibrios de mezclas mediante el código con los resultados obtenidos mediante el simulador ASPEN PLUS de procesos químicos. Debido a la falta de datos relativos a líquidos iónicos en la base de datos de ASPEN PLUS, se han introducido estos componentes como pseudocomponentes, de manera que se han introducido únicamente los datos necesarios de estos componentes para realizar las simulaciones. El modelo COSMO-SAC se encuentra implementado en ASPEN PLUS, de manera que introduciendo los sigma profiles, los volúmenes de la cavidad y las presiones de vapor de los líquidos iónicos, es posible predecir equilibrios líquido-vapor en los que se ven implicados este tipo de materiales. De esta manera pueden compararse los resultados obtenidos con ASPEN PLUS y como el código programado en MATLAB y comprobar la fiabilidad del mismo. El objetivo principal del presente Trabajo Fin de Máster es la optimización de mezclas multicomponente de líquidos iónicos para maximizar la eficiencia de procesos de separación y minimizar los costes de los mismos. La estructura de este problema es la de un problema de optimización no lineal con variables discretas y continuas, es decir, un problema de optimización MINLP (Mixed Integer Non-Linear Programming). Tal y como se verá posteriormente, el modelo matemático de este problema es no lineal. Por otra parte, las variables del mismo son tanto continuas como binarias. Las variables continuas se corresponden con las fracciones molares de los líquidos iónicos presentes en las mezclas y con el caudal de la mezcla de líquidos iónicos. Por otra parte, también se ha introducido un número de variables binarias igual al número de líquidos iónicos presentes en la mezcla. Cada una de estas variables multiplican a las fracciones molares de sus correspondientes líquidos iónicos, de manera que cuando dicha variable es igual a 1, el líquido se encuentra en la mezcla mientras que cuando dicha variable es igual a 0, el líquido iónico no se encuentra presente en dicha mezcla. El empleo de este tipo de variables obliga por tanto a emplear algoritmos para la resolución de problemas de optimización MINLP ya que si todas las variables fueran continuas, bastaría con el empleo de algoritmos para la resolución de problemas de optimización NLP (Non-Linear Programming). Se han probado por tanto diversos algoritmos presentes en el paquete OPTI Toolbox de MATLAB para comprobar cuál es el más adecuado para abordar este problema. Finalmente, una vez validado el código programado, se han optimizado diversas mezclas de líquidos iónicos para lograr la máxima recuperación de compuestos aromáticos en un proceso de absorción de mezclas orgánicas. También se ha usado este código para la minimización del coste correspondiente a la compra de los líquidos iónicos de la mezcla de disolventes empleada en la operación de absorción. En este caso ha sido necesaria la introducción de restricciones relativas a la recuperación de aromáticos en la fase líquida o a la pureza de la mezcla obtenida una vez separada la mezcla de líquidos iónicos. Se han modelizado los dos problemas descritos previamente (maximización de la recuperación de Benceno y minimización del coste de operación) empleando tanto únicamente variables continuas (correspondientes a las fracciones o cantidades molares de los líquidos iónicos) como variables continuas y binarias (correspondientes a cada uno de los líquidos iónicos implicados en las mezclas).

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.

A multi-depot travelling salesman problem and its iterative and integrated approaches

This paper formulates a logistics distribution problem as the multi-depot travelling salesman problem (MDTSP). The decision makers not only have to determine the travelling sequence of the salesman for delivering finished products from a warehouse or depot to a customer, but also need to determine which depot stores which type of products so that the total travelling distance is minimised. The MDTSP is similar to the combination of the travelling salesman and quadratic assignment problems. In this paper, the two individual hard problems or models are formulated first. Then, the problems are integrated together, that is, the MDTSP. The MDTSP is constructed as both integer nonlinear and linear programming models. After formulating the models, we verify the integrated models using commercial packages, and most importantly, investigate whether an iterative approach, that is, solving the individual models repeatedly, can generate an optimal solution to the MDTSP. Copyright © 2006 Inderscience Enterprises Ltd.

Zero weights and non-zero slacks:different solution to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.

Method of Linear Programming as an Idea for High School Curriculum

2010 Mathematics Subject Classification: 97D40, 97M10, 97M40, 97N60, 97N80, 97R80

Cooperative routing for collision minimization in wireless sensor networks

Cooperative communication has gained much interest due to its ability to exploit the broadcasting nature of the wireless medium to mitigate multipath fading. There has been considerable amount of research on how cooperative transmission can improve the performance of the network by focusing on the physical layer issues. During the past few years, the researchers have started to take into consideration cooperative transmission in routing and there has been a growing interest in designing and evaluating cooperative routing protocols. Most of the existing cooperative routing algorithms are designed to reduce the energy consumption; however, packet collision minimization using cooperative routing has not been addressed yet. This dissertation presents an optimization framework to minimize collision probability using cooperative routing in wireless sensor networks. More specifically, we develop a mathematical model and formulate the problem as a large-scale Mixed Integer Non-Linear Programming problem. We also propose a solution based on the branch and bound algorithm augmented with reducing the search space (branch and bound space reduction). The proposed strategy builds up the optimal routes from each source to the sink node by providing the best set of hops in each route, the best set of relays, and the optimal power allocation for the cooperative transmission links. To reduce the computational complexity, we propose two near optimal cooperative routing algorithms. In the first near optimal algorithm, we solve the problem by decoupling the optimal power allocation scheme from optimal route selection. Therefore, the problem is formulated by an Integer Non-Linear Programming, which is solved using a branch and bound space reduced method. In the second near optimal algorithm, the cooperative routing problem is solved by decoupling the transmission power and the relay node se- lection from the route selection. After solving the routing problems, the power allocation is applied in the selected route. Simulation results show the algorithms can significantly reduce the collision probability compared with existing cooperative routing schemes.

How effective are heuristic solutions for electricity planning in developing countries

Acknowledgement The first author would like to acknowledge the University of Aberdeen and the Henderson Economics Research Fund for funding his PhD studies in the period 2011-2014 which formed the basis for the research presented in this paper. The first author would also like to acknowledge the Macaulay Development Trust which funds his postdoctoral fellowship with The James Hutton Institute, Aberdeen, Scotland. The authors thank two anonymous referees for valuable comments and suggestions on earlier versions of this paper. All usual caveats apply

Optimal Capital Requirements in Financial Networks with Fire Sales

I explore and analyze a problem of finding the socially optimal capital requirements for financial institutions considering two distinct channels of contagion: direct exposures among the institutions, as represented by a network and fire sales externalities, which reflect the negative price impact of massive liquidation of assets.These two channels amplify shocks from individual financial institutions to the financial system as a whole and thus increase the risk of joint defaults amongst the interconnected financial institutions; this is often referred to as systemic risk. In the model, there is a trade-off between reducing systemic risk and raising the capital requirements of the financial institutions. The policymaker considers this trade-off and determines the optimal capital requirements for individual financial institutions. I provide a method for finding and analyzing the optimal capital requirements that can be applied to arbitrary network structures and arbitrary distributions of investment returns.

In particular, I first consider a network model consisting only of direct exposures and show that the optimal capital requirements can be found by solving a stochastic linear programming problem. I then extend the analysis to financial networks with default costs and show the optimal capital requirements can be found by solving a stochastic mixed integer programming problem. The computational complexity of this problem poses a challenge, and I develop an iterative algorithm that can be efficiently executed. I show that the iterative algorithm leads to solutions that are nearly optimal by comparing it with lower bounds based on a dual approach. I also show that the iterative algorithm converges to the optimal solution.

Finally, I incorporate fire sales externalities into the model. In particular, I am able to extend the analysis of systemic risk and the optimal capital requirements with a single illiquid asset to a model with multiple illiquid assets. The model with multiple illiquid assets incorporates liquidation rules used by the banks. I provide an optimization formulation whose solution provides the equilibrium payments for a given liquidation rule.

I further show that the socially optimal capital problem using the ``socially optimal liquidation" and prioritized liquidation rules can be formulated as a convex and convex mixed integer problem, respectively. Finally, I illustrate the results of the methodology on numerical examples and

discuss some implications for capital regulation policy and stress testing.

Effektiviseringsmöjligheter avseende fyllnadsgrad : En jämförande analys mellan nuläge och optimerat resultat

The study aims to provide information on efficiency opportunities on SCA's northbound cassettes. It has been made by examining the capacity utilization rate on the northbound cassettes on SCA's vessels for a period of two weeks. The cargo loaded in the ports of Rotterdam and Sheerness consists of external cargo of varying shape. The cargo is shipped northbound to Holmsund and Sundsvall. Measurements have been made on the cargo to the final destinations Sundsvall, Holmsund and Finland. The measurements have been used in a mathematical optimization model created to optimize the loading of the cassettes. The model is based on placing boxes in a grid where the boxes that are placed representing the cargo and the grids representing the cassettes. The aim of the model is to reduce the number of cassettes and thereby increase the capacity utilization rate. The study resulted in an increase in capacity utilization rate for both area and volume to all destinations. The overall improvement for all cassettes examined resulted in an increase in the area capacity utilization rate by 9.02 percentage points and 5.72 percentage points for the volume capacity utilization rate. It also resulted in a decrease of 22 cassettes in total on the four voyages that were examined which indicate that there are opportunities to improve the capacity utilization rate. The study also shows that the model can be used as a basis for similar problems.

Exact solutions to the short sea shipping distribution problem

Short sea shipping has several advantages over other means of transportation, recognized by EU members. The maritime transportation could be dealt like a combination of two well-known problems: the container stowage problem and routing planning problem. The integration of these two well-known problems results in a new problem CSSRP (Container stowage and ship routing problem) that is also an hard combinatorial optimization problem. The aim of this work is to solve the CSSRP using a mixed integer programming model. It is proved that regardless the complexity of this problem, optimal solutions could be achieved in a reduced computational time. For testing the mathematical model some problems based on real data were generated and a sensibility analysis was performed.

Airport ground movement

Worldwide air traffic tends to increase and for many airports it is no longer an op-tion to expand terminals and runways, so airports are trying to maximize their op-erational efficiency. Many airports already operate near their maximal capacity. Peak hours imply operational bottlenecks and cause chained delays across flights impacting passengers, airlines and airports. Therefore there is a need for the opti-mization of the ground movements at the airports. The ground movement prob-lem consists of routing the departing planes from the gate to the runway for take-off, and the arriving planes from the runway to the gate, and to schedule their movements. The main goal is to minimize the time spent by the planes during their ground movements while respecting all the rules established by the Ad-vanced Surface Movement, Guidance and Control Systems of the International Civil Aviation. Each aircraft event (arrival or departing authorization) generates a new environment and therefore a new instance of the Ground Movement Prob-lem. The optimization approach proposed is based on an Iterated Local Search and provides a fast heuristic solution for each real-time event generated instance granting all safety regulations. Preliminary computational results are reported for real data comparing the heuristic solutions with the solutions obtained using a mixed-integer programming approach.

Train scheduling and rolling stock assignment in high speed trains

The Train Timetabling Problem (TTP) has been widely studied for freight and passenger rail systems. A lesser effort has been devoted to the study of high-speed rail systems. A modeling issue that has to be addressed is to model departure time choice of passengers on railway services. Passengers who use these systems attempt to travel at predetermined hours due to their daily life necessities (e.g., commuter trips). We incorporate all these features into TTP focusing on high-speed railway systems. We propose a Rail Scheduling and Rolling Stock (RSch-RS) model for timetable planning of high-speed railway systems. This model is composed of two essential elements: i) an infrastructure model for representing the railway network: it includes capacity constraints of the rail network and the Rolling-Stock constraints; and ii) a demand model that defines how the passengers choose the departure time. The resulting model is a mixed-integer programming model which objective function attempts to maximize the profit for the rail operator