Social Value Propagation for Supply Chain Formation

Supply Chain Formation is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized supply chain formation appears as a highly intricate task because agents only possess local information, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. State-of-the-art decentralized supply chain formation approaches can either: (i) #12;find supply chains of high value at the expense of high resources usage; or (ii) fi#12;nd supply chains of low value with low resources usage. This work presents chainme, a novel decentralized supply chain formation algorithm. Our results show that chainme fi#12;nds supply chains with higher value than state-of-the-art decentralized algorithms whilst decreasing the amount of resources required from one up to four orders of magnitude.Peer Reviewe

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Approximate algorithms for decentralized Supply Chain Formation

[eng] Supply chain formation involves determining the participants and the exchange of goods within a production network. Today’s companies operate autonomously, making local decisions, and coordinating with other companies to buy and sell goods along their supply chains. Decentralized decision making is well suited to this scenario since it better preserves the privacy of the participants, offers better scalability on large-scale scenarios, and is more resilient to failure. Moreover, decentralized supply chain formation can be tackled either by means of peer-to-peer communication between supply chain participants or by introducing local markets that mediate the trading of goods. Unfortunately, current approaches to decentralized supply chain formation, both in the peer- to-peer and the mediated scenario, are unable to provide computationally and economically efficient solutions to the supply chain formation problem. The main goal of this dissertation is to provide computationally and economically efficient methods for decentralized supply chain formation both in the peer-to-peer and the mediated scenario. This is achieved by means of two optimized max-sum based methods for supply chain formation. On the one hand, we contribute to peer-to-peer supply chain formation via the so-called Reduced Binarized Loopy Belief Propagation (rb-lbp) algorithm. The rb-lbp algorithm is run by a multi-agent system in which each of the participants in the supply chain is represented by a computational agent. Moreover, rb-lbp’s message computation mechanisms allow the efficient computation of max-sum messages. This results in an algorithm that is able to find solutions to the supply chain formation problem of higher value than the state of the art while reducing the memory, bandwidth and computational resources required by several orders of magnitude. On the other hand, we contribute to mediated supply chain formation via the so-called CHaining Agents IN Mediated Environments (chainme) algorithm. The chainme algorithm is run by a multi-agent system in which each of the participants and each of the goods in the supply chain is represented by a computational agent. In chainme participant agents communicate exclusively with the agents representing the goods who act as mediators. Likewise rb-lbp, chainme is also endowed with a message computation mechanism for the efficient computation of max-sum messages. This results in an algorithm that is able to find economically efficient solutions while requiring a fraction of the computa- tional resources needed by the state-of-the-art methods for both peer-to-peer and mediated supply chain formation. Finally, the design and implementation of both of our contributions to decentralized supply chain formation follow the same methodology. That is, we first map the problem at hand into a local term graph over which max-sum can operate. Then, we assign each max-sum local term to a computational agent. Last, we derive computationally efficient expressions to assess the max-sum messages exchanged between these agents. Although our methodology proved to be valid for the design of SCF algorithms, its generality makes it appear as a promising candidate for other multi-agent coordination problems

Operational research:methods and applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Assignment Algorithms for Multi-Robot Task Allocation in Uncertain and Dynamic Environments

Multi-robot task allocation is a general approach to coordinate a team of robots to complete a set of tasks collectively. The classical works adopt relevant theories from other disciplines (e.g., operations research, economics), but oftentimes they are not adequately rich to deal with the properties from the robotics domain such as perception that is local and communication which is limited. This dissertation reports the efforts on relaxing the assumptions, making problems simpler and developing new methods considering the constraints or uncertainties in robot problems. We aim to solve variants of classical multi-robot task allocation problems where the team of robots operates in dynamic and uncertain environments. In some of these problems, it is adequate to have a precise model of nondeterministic costs (e.g., time, distance) subject to change at run-time. In some other problems, probabilistic or stochastic approaches are adequate to incorporate uncertainties into the problem formulation. For these settings, we propose algorithms that model dynamics owing to robot interactions, new cost representations incorporating uncertainty, algorithms specialized for the representations, and policies for tasks arriving in an online manner. First, we consider multi-robot task assignment problems where costs for performing tasks are interrelated, and the overall team objective need not be a standard sum-of costs (or utilities) model, enabling straightforward treatment of the additional costs incurred by resource contention. In the model we introduce, a team may choose one of a set of shared resources to perform a task (e.g., several routes to reach a destination), and resource contention is modeled when multiple robots use the same resource. We propose efficient task assignment algorithms that model this contention with different forms of domain knowledge and compute an optimal assignment under such a model. Second, we address the problem of finding the optimal assignment of tasks to a team of robots when the associated costs may vary, which arises when robots deal with uncertain situations. We propose a region-based cost representation incorporating the cost uncertainty and modeling interrelationships among costs. We detail how to compute a sensitivity analysis that characterizes how much costs may change before optimality is violated. Using this analysis, robots are able to avoid unnecessary re-assignment computations and reduce global communication when costs change. Third, we consider multi-robot teams operating in probabilistic domains. We represent costs by distributions capturing the uncertainty in the environment. This representation also incorporates inter-robot couplings in planning the team’s coordination. We do not have the assumption that costs are independent, which is frequently used in probabilistic models. We propose algorithms that help in understanding the effects of different characterizations of cost distributions such as mean and Conditional Value-at-Risk (CVaR), in which the latter assesses the risk of the outcomes from distributions. Last, we study multi-robot task allocation in a setting where tasks are revealed sequentially and where it is possible to execute bundles of tasks. Particularly, we are interested in tasks that have synergies so that the greater the number of tasks executed together, the larger the potential performance gain. We provide an analysis of bundling, giving an understanding of the important bundle size parameter. Based on the qualitative basis, we propose multiple simple bundling policies that determine how many tasks the robots bundle for a batched planning and execution

Operational Research: methods and applications

This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes