Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

Conflicting Objectives in Decisions

This book deals with quantitative approaches in making decisions when conflicting objectives are present. This problem is central to many applications of decision analysis, policy analysis, operational research, etc. in a wide range of fields, for example, business, economics, engineering, psychology, and planning. The book surveys different approaches to the same problem area and each approach is discussed in considerable detail so that the coverage of the book is both broad and deep. The problem of conflicting objectives is of paramount importance, both in planned and market economies, and this book represents a cross-cultural mixture of approaches from many countries to the same class of problem

Confidence Intervals for the Shapley–Shubik Power Index in Markovian Games

International audienceWe consider simple Markovian games, in which several states succeed each other over time, following an exogenous discrete-time Markov chain. In each state, a different simple static game is played by the same set of players. We investigate the approximation of the Shapley–Shubik power index in simple Markovian games (SSM). We prove that an exponential number of queries on coalition values is necessary for any deterministic algorithm even to approximate SSM with polynomial accuracy. Motivated by this, we propose and study three randomized approaches to compute a confidence interval for SSM. They rest upon two different assumptions, static and dynamic, about the process through which the estimator agent learns the coalition values. Such approaches can also be utilized to compute confidence intervals for the Shapley value in any Markovian game. The proposed methods require a number of queries, which is polynomial in the number of players in order to achieve a polynomial accuracy

Deep Learning of the Order Flow for Modelling Price Formation

The objective of this thesis is to apply deep learning to order flow data in novel ways, in order to improve price prediction models, and thus improve on current deep price formation models. A survey of previous work in the deep modelling of price formation revealed the importance of utilising the order flow for the deep learning of price formation had previously been over looked. Previous work in the statistical modelling of the price formation process in contrast has always focused on order flow data. To demonstrate the advantage of utilising order flow data for learning deep price formation models, the thesis first benchmarks order flow trained Recurrent Neural Networks (RNNs), against methods used in previous work for predicting directional mid-price movements. To further improve the price modelling capability of the RNN, a novel deep mixture model extension to the model architecture is then proposed. This extension provides a more realistically uncertain prediction of the mid-price, and also jointly models the direction and size of the mid-price movements. Experiments conducted showed that this novel architecture resulted in an improved model compared to common benchmarks. Lastly, a novel application of Generative Adversarial Networks (GANs) was introduced for generative modelling of the order flow sequences that induce the mid-price movements. Experiments are presented that show the GAN model is able to generate more realistic sequences than a well-known benchmark model. Also, the mid-price time-series resulting from the SeqGAN generated order flow is able to better reproduce the statistical behaviour of the real mid-price time-series

Decentralised Multi-Robot Systems Towards Coordination in Real World Settings

In recent years, Multi-Robot Systems (MRS) have gained significant interest in research and in industry (Khandelwal and Stone, 2017; E. Schneider et al., 2016; Amato et al., 2015; Alonso-Mora et al., 2015b; Enright and Wurman, 2011). Manufacturers are moving away from large one-size-fits-all productions to more customisable on demand production, which result in smaller and smaller batch sizes. Additionally, in order to be able to increase productivity even further, more and more tasks in the production process have to be automated. To accommodate these changes, industry is facing major shifts in how the products are produced and in particular the role robotic platforms are playing. Previously, robots have mainly been used in a static manner, i.e. performing a singular repetitive task over and over again with high precision and speed. When multiple robots are employed in such a setup, each robot performs a dedicated task, with no interaction with the other robots. While this approach was suitable for large-scale productions, it cannot maintain the same productivity for highly customisable products. Additionally, many tasks in the production process require that the robots are mobile, since they are spatially distributed. One example is for instance retrieving items from different locations in a warehouse. Furthermore, another requirement is that every robot should be able to handle many different tasks and more importantly, many robots should work together in a team towards a common goal. These new requirements introduce various new challenges. As an example, since the robots are mobile, they should be able to perform the tasks alongside the human workers. Likewise, since multiple robots have to work together, a new challenge is to coordinate such MRS. The work presented in this thesis focuses on the core issues when deploying MRS in the physical world. We focus on the task of warehouse commissioning as a running example. The environment for this task is highly dynamic, adaptive and complex, since new orders can appear at any time and priorities might change. A major issue is to coordinate the robots, while taking current and possible future tasks into account. One solution is a centralised planning entity, which knows about all tasks and robots in the team and assigns the tasks accordingly. While in the case of a handful robots, a good assignment can usually be calculated in a straight forward manner, a problem with a centralised system arises when more and more robots are added to the system. The number of possible assignments rises exponentially with every additional robot. Thus, planning times increase and it might become infeasible to provide an optimal plan in time or to respond quickly to changes. On the other hand, in a decentralised solution, each robot decides on its own. Thus, it accumulates all necessary information, and calculates a plan based on this information. While the robots might not have all information available, this is in many cases not necessary. The planning robot is mainly interested in its own actions. While the robot should take the other robots into account, this effect can be approximated, and not every single action of the other robots is needed. This results in a much less complex planning problem, which allows the robot to re-plan online, as soon as the environment changes. In this thesis, we focus on such decentralised solutions for MRS that can run online on the robots. We investigate navigation, decision making and planning algorithms that are suitable for problems in which the tasks are highly dynamic and spatially distributed, such as the warehouse commissioning example. We explore how a team of robots can navigate safely in a shared environment with humans. We apply Monte Carlo sampling techniques and trajectory rollouts as used in the commonly used Dynamic Window Approach (DWA) (Fox et al., 1997), while taking the localisation uncertainty into account. We show that our resulting navigation method is robust and able to run decentralised on the robots. To facilitate formal evaluation of planning and decision making algorithms, a formal framework called Spatial Task Allocation Problems (SPATAPs) is introduced, that enables us to capture and analyse these problems in the well known Markov Decision Process (MDP) (Puterman, 1994) and Multi-Agent Markov Decision Process (MMDP) (Boutilier, 1996) frameworks. The commonly used MDP solution methods, i.e. value iteration and dynamic programming, fail to provide a solution, due to the large problem space. We investigate whether we can exploit the structure of these problems and introduce approximations to enable planning using the common solution methods. We further refine the framework to formally capture the warehouse commissioning task. A solution method based on Monte Carlo Tree Search (MCTS) (Kocsis and Szepesvári, 2006) is introduced, using computationally cheap greedy roll-out strategies. We show that the resulting approach can yield significantly higher performance than previous approaches, while still being able to plan within the magnitude of seconds, which allows for online re-planning on the robots. Finally, the decision making algorithm and the navigation approach are combined in a proof-of-concept application, in which three youBots are used in a physical warehouse commissioning setup

Mathematical programming based approaches for classes of complex network problems : economical and sociological applications

The thesis deals with the theoretical and practical study of mathematical programming methodologies to the analysis complex networks and their application in economic and social problems. More specifically, it applies models and methods for solving linear and integer programming problems to network models exploiting the matrix structure of such models, resulting in efficient computational procedures and small processing time. As a consequence, it allows the study of larger and more complex networks models that arise in many economical and sociological applications. The main efforts have been addressed to the development of a rigorous mathematical programming based framework, which is able to capture many classes of complex network problems. Such a framework involves a general and flexible modeling approach, based on linear and integer programmin, as well as a collection of efficient probabilistic procedures to deal with these models. The computer implementation has been carried out by high level programming languages, such as Java, MatLab, R and AMPL. The final chapter of the thesis introduced an extension of the analyzed model to the case of microeconomic interaction, providing a fruitful mathematical linkage between its optimization-like properties and its multi-agents properties. The theoretical and practical use of optimization methods represents the trait-de-union of the different chapters. The overall structure of the thesis manuscript contains three parts: Part I: The fine-grained structure of complex networks: theories, models and methods; Chapter 1 and Chapter 2. Part II: Mathematical Programming based approaches for random models of network formation; Chapter 3, Chapter 4 and Chapter 5. Part III: Strategic models of network formation. Chapter 6. Results of this research have generated four working papers in quality scientific journals: one has been accepted and three are under review. Some results have been also presented in four international conferences.La tesis aborda el estudio teórico y práctico de las metodologías de programación matemática para el análisis de redes complejas y su aplicación a problemas económicos y sociales. Más específicamente, se aplica modelos y métodos para resolver problemas de programación lineal y de programación lineal entera explotando las estructuras matriciales de tales modelos, lo que resulta en procedimientos computacionales eficientes y bajo coste de procesamiento. Como consecuencia de ello, las metodologías propuestas permiten el estudio de modelos complejos de gran dimensión, para redes complejas que surgen en muchas aplicaciones económicas y sociológicas. Los principales esfuerzos se han dirigido al desarrollo de un marco teórico basado en la programación matemática, que es capaz de capturar muchas clases de problemas de redes complejas. Dicho marco teórico envuelve un sistema general y flexible de modelado y una colección de procedimientos probabilísticos para solucionar eficientemente dichos modelos, basados en la programación linear y entera. Las implementaciones informáticas se han llevado a cabo mediante lenguajes de programación de alto nivel, como Java, Matlab, R y AMPL. El último capítulo de la tesis introduce una extensión de los modelos analizados, para el caso de la interacción microeconómica, con el objetivo de establecer un nexo metodológico entre sus propiedades de optimización y sus propiedades multi-agentes. El uso teórico y práctico de los métodos de optimización representa el elemento de conjunción de los distintos capítulos. Parte I: The fine-grained structure of complex networks: theories, models and methods; - Capitulo 1 y Capitulo 2. Parte II: Mathematical Programming based approaches for random models of network formation; - Capitulo 3, Capitulo 4 y Capitulo 5. Parte III: Strategic models of network formation. - Capitulo 6. Los resultados de esta investigación han generado cuatro papers en revistas científicas indexadas: uno ha sido aceptado, tres están en revisión. Algunos resultados han sido también presentados en cuatro conferencias internacionale