The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Symbolic quantum programming for supporting applications of quantum computing technologies

The goal of this paper is to deliver the overview of the current state of the art, to provide experience report on developing quantum software tools, and to outline the perspective for developing quantum programming tools supporting symbolic programming for the needs of quantum computing technologies. The main focus of this paper is on quantum computing technologies, as they can in the most direct way benefit from developing tools enabling the symbolic manipulation of quantum circuits and providing software tools for creating, optimizing, and testing quantum programs. We deliver a short survey of the most popular approaches in the field of quantum software development and we aim at pointing their strengths and weaknesses. This helps to formulate a list of desirable characteristics which should be included in quantum computing frameworks. Next, we describe a software architecture and its preliminary implementation supporting the development of quantum programs using symbolic approach, encouraging the functional programming paradigm, and, at the same, time enabling the integration with high-performance and cloud computing. The described software consists of several packages developed to address different needs, but nevertheless sharing common design concepts. We also outline how the presented approach could be used in tasks in quantum software engineering, namely quantum software testing and quantum circuit construction.Comment: 14 pages, contribution to QP2023 Workshop, Programming'23, Tokyo, JP, March 13-17, 202

A framework for closed-loop supply chains of reusable articles

Reuse practices contribute to the environmental and economical sustainability of production and distribution systems. Surprisingly, reuse closed-loop supply chains (CLSC) have not been widely researched for the moment. In this paper, we explore the scientific literature on reuse and we propose a framework for reusable articles. This conceptual structure includes a typology integrating under the reusable articles term different categories of articles (transportation items, packaging materials, tools) and addresses the management issues that arise in reuse CLSC. We ground our results in a set of case studies developed in real industrial settings, which have also been contrasted with cases available in existing literature.reverse logistics;case studies;closed-loop supply chains;returns managment

A conceptual framework of control, learn, and knowledge for computer power management

This conceptual paper observes the human inactivity in computer power management and discovers that; the efficiency of the computer power management (CPM)can be achieved by the eligibility of the human inactivity period. This period reduces the efficiency of CPM. This study examines the self-adaptation(SA) and the knowledge repository (KR)concepts, to model the framework of a new approach in computer power management. The essential elements and features from theseconceptswere adapted and applied as a techniqueto a new implementation of CLK-CPM. As a result, this study has proposed a modelof thetheoretical framework and demonstratesit through its conceptual framework for the technique