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Mechanism design : a linear programming approach
\"Mechanism design is an analytical framework for thinking clearly and carefully about what exactly a given institution can achieve when the information necessary to make decisions is dispersed and privately held. This analysis provides an account of the underlying mathematics of mechanism design based on linear programming. Three advantages characterize the approach. The first is simplicity: arguments based on linear programming are both elementary and transparent. The second is unity: the machinery of linear programming provides a way to unify results from disparate areas of mechanism design. The third is reach: the technique offers the ability to solve problems that appear to be beyond solutions offered by traditional methods. No claim is made that the approach advocated should supplant traditional mathematical machinery. Rather, the approach represents an addition to the tools of the economic theorist who proposes to understand economic phenomena through the lens of mechanism design\"-- Provided by publisher.
Book
Fundamentals of Object Tracking
Kalman filter, particle filter, IMM, PDA, ITS, random sets... The number of useful object-tracking methods is exploding. But how are they related? How do they help track everything from aircraft, missiles and extra-terrestrial objects to people and lymphocyte cells? How can they be adapted to novel applications? Fundamentals of Object Tracking tells you how. Starting with the generic object-tracking problem, it outlines the generic Bayesian solution. It then shows systematically how to formulate the major tracking problems – maneuvering, multiobject, clutter, out-of-sequence sensors – within this Bayesian framework and how to derive the standard tracking solutions. This structured approach makes very complex object-tracking algorithms accessible to the growing number of users working on real-world tracking problems and supports them in designing their own tracking filters under their unique application constraints. The book concludes with a chapter on issues critical to successful implementation of tracking algorithms, such as track initialization and merging.
eBook
The golden ticket : P, NP, and the search for the impossible
\"The P-NP problem is the most important open problem in computer science, if not all of mathematics. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. In this informative and entertaining book, Lance Fortnow traces how the problem arose during the Cold War on both sides of the Iron Curtain, and gives examples of the problem from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. But difficulty also has its advantages. Hard problems allow us to safely conduct electronic commerce and maintain privacy in our online lives.The Golden Ticket explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of the P-NP problem\"-- Provided by publisher.
Book
Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach
Magnified resource consumption and depletion of natural resources calls for non-flexible or strict regulations and penalties on industrial operations, increased rate of processing and reuse of waste material as a substitute for raw material and political and legal interventions at global scale. Product recovery involves reuse, repair, refurbishing, remanufacturing and materials recycling, requires an efficient network design known as reverse logistic network and offers economical benefits in terms of fewer procurement of raw material, inventory management and less disposal. In current study, a mixed integer linear programming model designed on a multi-stage reverse logistics network for product recovery is proposed which considers different recovery options-product remanufacturing, component reprocessing and material recycling for sustainable outcomes. The model is designed to find optimal solutions for fulfilling demand and revenue needs by focusing on strategic locations for collection centers, reprocessing centers, remanufacturing plants and transportation options and simultaneously achieving sustainability goals. The model is applied on an Indian based manufacturing unit of a Saudi Arabian Industrial Air conditioner manufacturing organization and the case is presented here. The model is converted into a multi-objective programming model in accordance with the importance of each objective suiting the business needs. All relevant objective functions are evaluated using BWM, AHP and FAHP methods to obtain weights for integration into a fuzzy linear programming model which eventually provides three separate results. The model applied has originality and uniqueness for applications to solve multi-objective problems under uncertain environment and tends to strike a balance between economic and environmental objectives. The study provides for a base for further scope covering uncertainty about the amount and quality of returned products and even can be implemented by practitioners and academics for making a significant contribution in improving the efficiency of supply chains.
Journal Article
Agent zero : toward neurocognitive foundations for generative social science
\"The Final Volume of the Groundbreaking Trilogy on Agent-Based ModelingIn this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent Zero. This software individual, or \"agent,\" is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics.Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his \"computational parables.\" Agent Zero is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, Agent Zero presents a groundbreaking vision and the tools to realize it\"-- Provided by publisher.
Book
A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation
Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.
Journal Article
An efficient 3D topology optimization code written in Matlab
This paper presents an efficient and compact
Matlab
code to solve three-dimensional topology optimization problems. The 169 lines comprising this code include finite element analysis, sensitivity analysis, density filter, optimality criterion optimizer, and display of results. The basic code solves minimum compliance problems. A systematic approach is presented to easily modify the definition of supports and external loads. The paper also includes instructions to define multiple load cases, active and passive elements, continuation strategy, synthesis of compliant mechanisms, and heat conduction problems, as well as the theoretical and numerical elements to implement general non-linear programming strategies such as SQP and MMA. The code is intended for students and newcomers in the topology optimization. The complete code is provided in Appendix
C
and it can be downloaded from
http://top3dapp.com
.
Journal Article
Transformation and Linearization Techniques in Optimization: A State-of-the-Art Survey
To formulate a real-world optimization problem, it is sometimes necessary to adopt a set of non-linear terms in the mathematical formulation to capture specific operational characteristics of that decision problem. However, the use of non-linear terms generally increases computational complexity of the optimization model and the computational time required to solve it. This motivates the scientific community to develop efficient transformation and linearization approaches for the optimization models that have non-linear terms. Such transformations and linearizations are expected to decrease the computational complexity of the original non-linear optimization models and, ultimately, facilitate decision making. This study provides a detailed state-of-the-art review focusing on the existing transformation and linearization techniques that have been used for solving optimization models with non-linear terms within the objective functions and/or constraint sets. The existing transformation approaches are analyzed for a wide range of scenarios (multiplication of binary variables, multiplication of binary and continuous variables, multiplication of continuous variables, maximum/minimum operators, absolute value function, floor and ceiling functions, square root function, and multiple breakpoint function). Furthermore, a detailed review of piecewise approximating functions and log-linearization via Taylor series approximation is presented. Along with a review of the existing methods, this study proposes a new technique for linearizing the square root terms by means of transformation. The outcomes of this research are anticipated to reveal some important insights to researchers and practitioners, who are closely working with non-linear optimization models, and assist with effective decision making.
Journal Article
Matheuristic algorithms for the parallel drone scheduling traveling salesman problem
In a near future drones are likely to become a viable way of distributing parcels in a urban environment. In this paper we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of minimizing the total time required to service all the customers. We present a set of matheuristic methods for the problem. The new approaches are validated via an experimental campaign on two sets of benchmarks available in the literature. It is shown that the approaches we propose perform very well on small/medium size instances. Solving a mixed integer linear programming model to optimality leads to the first optimality proof for all the instances with 20 customers considered, while the heuristics are shown to be fast and effective on the same dataset. When considering larger instances with 48 to 229 customers, the results are competitive with state-of-the-art methods and lead to 28 new best known solutions out of the 90 instances considered.
Journal Article
Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications
Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is the base for the price per kW. The results demonstrate that batteries in peak shaving applications can shorten the payback period when used for large industrial loads. They also show the impacts of peak shaving variation on the return of investment and battery aging of the system.
Journal Article