Reverse production systems are often comprised of several tiers with independent members competing at each tier. This research develops and designs a decision-making process for decentralized reverse production systems where each participant in the network determines its decisions in a self-interested way. This dissertation includes three major parts. The first part develops a prototype model for a decentralized reverse production system with two tiers, collectors and processors, focusing on the coordination of transactions of recycled items between these two tiers. The collectors determine the individual material flow allocation mechanisms, based on predictions of the range of prices from the processors, that relate the flow amount to the overall vector of acquisition prices that will be offered by the processors to all the collectors. The processors compete for the flow from the collectors and reach an equilibrium state where no entity is willing to change its decisions. In the second part, we extend the prototype model for a general multi-tiered recycling network comprised of the upstream boundary tier, several intermediate tiers, and the downstream boundary tier where each of the tiers has multiple independent entities. Recycled items flow from the top tier to the downstream tier, but acquisition prices are set from the downstream tier back to the upstream tier. Finally the third part provides a comparison of centralized and decentralized models for reverse production systems and addresses several numerical insights of different system subsidy schemes.

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Jessica L. Heier Stamm / In many humanitarian and public health settings, multiple organizations act independently to locate facilities to serve an affected population. As a result of this decentralized decision-making environment, individuals’ access to facility resources may suffer in comparison to a hypothetical system in which a single planner locates the facilities to optimize access for all. Furthermore, due to the unanticipated nature of humanitarian events and the urgency of the need, responders often must cope with a high level of uncertainty regarding the future supply of resources and demand for relief. The contributions of this thesis address the challenges that arise due to the decentralized and dynamic nature of humanitarian response. The first goal of this research is to quantify the difference between decentralized system performance and that possible with a centralized planner. The second goal is to demonstrate the value and feasibility of using a dynamic, rolling-horizon framework to optimize facility location decisions over time. This work compares individuals’ access to health facilities resulting from location decisions made by decentralized decision-makers to the access achieved by a centralized model that optimizes access for all. Access is measured using a special case of the gravity model, the Enhanced Two-Step Floating Catchment Area (E2SFCA) method, which is a distance-weighted ratio of capacity to demand. The E2SFCA method is integrated with integer programming to optimize public access to health facilities. This method is applied to the location of cholera treatment facilities in Haiti, which has been afflicted with a cholera epidemic since October 2010. This research finds that access varied significantly across Haiti, and in the month of February 2011, thirty-seven of the 570 sections, representing 474,286 persons (4.8 percent of the population), did not have adequate access to cholera treatment facilities. Using centralized models to optimize accessibility, performance can be improved but no single model is dominant. This paper recommends use of an efficiency-oriented model in conjunction with an equity constraint to make facility location decisions in future responses. Finally, this work successfully integrates measures of access and equity into a rolling-horizon facility location model and demonstrates that these measures can be incorporated in a full-scale implementation to provide dynamic decision support to planners. This paper advocates for greater awareness of the impact of decentralization in humanitarian response and recommends that future work be undertaken to discover incentives and strategies to mitigate the impact of decentralization in future responses.

Dagens samhälle ställer allt högre krav på att företag ska agera utifrån etthållbarhetsperspektiv. I takt med detta väljer allt fler företag att vara transparenta genom att hållbarhetsrapportera sitt sociala ansvar, trots att hållbarhetsrapporteringen inte är ett lagstadgat krav. Tidigare studier av bankernas sociala rapportering har till stor del gjorts med stöd av legitimitetsteorin. Studierna har funnit att företagens rapportering av socialt ansvar påverkas av förklaringsvariabler som exempelvis närhet till kunder, medieexponering, storlek på företaget och intressenternas värderingar.Vår studie tar avstamp utifrån tidigare forskning och syftar till att beskriva och analysera om bankernas rapportering av socialt ansvar vad gäller arbetsförhållande och produktansvar bidrar till en ökad medieexponering. Studien analyserar också hur de bakomliggande faktorerna storlek, diversifiering och decentralisering påverkar bankernas rapportering avarbetsförhållande och produktansvar vad gäller socialt ansvar. Studien omfattar hållbarhetsrapporter från 110 banker som fanns upptagna i GRI:s databas. Den beroende variabeln var medieexponering som mättes genom antal nyhetsträffar på bankerna i Googles databas. De övriga variablerna var rapportering av arbetsförhållanden vad gäller socialt ansvar, produktrapportering av socialt ansvar, bankens storlek, bankens decentralisering och bankens diversifiering.Resultatet av studien visar att bankernas rapportering av arbetsförhållanden inte bidrar till en ökad medieexponering, däremot innebär en mer utförlig rapportering av produktansvar att bankerna uppmärksammas mer i media. Resultatet visar också att produktrapporteringen påverkas av bankens storlek och diversifiering. Vidare visar resultatet att banker som tillämpar ett decentraliserat beslutsfattande är mer benägna att rapportera om dess socialaansvar vad gäller bankens arbetsförhållande. / Today's society is increasing demands on companies to act from a sustainability perspective. In line with this, more and more companies choose to be transparent through sustainability reporting, although sustainability reporting is not a legal requirement. Previous studies of banks' social reporting has largely been supported by the legitimacy theory. The studies have found that corporate reporting of social responsibility is affected by the variables such as; proximity to customers, media exposure, size of company and stakeholder values.Our study is based on previous research and aims to describe and analyze whether the banks' reporting of social responsibility regarding their working conditions and product contributes to greater media exposure. Furthermore, this study aims to describe and analyze how the underlying factors of the bank size, diversification and decentralized decision-making affect the sustainability reports subcategories: product responsibility and labor practices and decent work.The study includes sustainability reports from 110 banks in the GRI database. Data for five variables was collected and compared with the variable media exposure, defined as the number of hits for the banks in Googles search engine for news. The variables used in the study of banks are: reporting of labor practices and decent work, product responsibility reporting, size, decentralization and diversification. The results of the study demonstrate thata more detailed reporting of product responsibility contributes to the bank gaining more media attention. Furthermore, the result shows that extent of the product reporting is influenced by the bank´s size and diversification. The results of the study also show that the bank's reporting of employment does not contribute to increased media exposure. However, the results reveal that if the organization applies more decentralized decision-making, it is more likely that the bank reports on the social responsibility for working conditions to a greater extent.