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Double hedonic price-characteristics frontier estimation for IoT service providers in the industry 5.0 era: A nonconvex perspective accommodating ratios

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期319卷 222-233页

作者： Kerstens, Kristiaan Azadi, Majid Matin, Reza Kazemi Saen, Reza Farzipoor Univ Lille IESEG Sch Management CNRS UMR 9221LEM Lille Econ Management F-59000 Lille France Aston Univ Aston Business Sch Operat & Informat Management Dept Birmingham England Sultan Qaboos Univ Coll Sci Dept Math Muscat Oman Sultan Qaboos Univ Coll Econ & Polit Sci Dept Operat Management & Business Stat Muscat Oman

The advent of advanced digital technologies, including the Internet of Things (IoT), image processing, artificial intelligence (AI), blockchain, robotics and cognitive computing that have been embedded in Industry 5.0, is considerably improving the sustainability, resilience, and human-centric performance of industrial organizations. Despite the increasing use of Industry 5.0 technologies in smart product platforming in industrial organizations, a critical issue remains how to assess the providers/suppliers of such technologies in highly competitive markets to fulfil personalized products and services. Following Lancaster's characteristics approach to consumer theory, in this study we contribute to assess digital technologies service providers in the Industry 5.0 era by focusing on both theoretical and empirical evidence inquiring about the convexity of conventional nonparametric frontier estimation methods. To do so, a nonparametric double frontier estimation of the hedonic price characteristics relation is developed from both the buyer's and seller's perspectives. Moreover, a separable directional distance function-based optimization model is developed for the efficiency estimation. Furthermore, a comparable estimation of the convex and nonconvex hedonic price function is proposed. We also explicitly test the impact of convexity in evaluating the efficiency of IoT service providers in the Industry 5.0 context. In this study, we also show that the hypothesis of convexity in assessing the efficiency of IoT service providers is rejected using the Li-test comparing entire densities in the case of the seller's perspective without ratio data. Differences are less pronounced for the buyer's perspective and in the case with ratio data.

关键词： Industry 5.0 Sustainability convexity and nonconvexity Free disposal hull (FDH) technology Hedonic price

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Distributed Optimization in Learning Algorithms with Parsimony in Communications

Distributed Optimization in Learning Algorithms with Parsimo...

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作者： Ren, Jineng University of Minnesota

学位级别：Ph.D.

As we have seen, today machine learning and big data technologies are transforming both our daily life and economies fundamentally. An important factor that fuels the progress of learning algorithms is the abundance of data generated everyday. In many scenarios, including internet of things, intelligent transportation systems, mobile/edge computing, and smart grids, the datasets are often generated and stored locally in different locations. Traditional centralized (concentrated) algorithms, however, are facing challenges in these settings because they usually require much higher computation cost on a single machine, more communications for collecting raw local data, and are more vulnerable to possible failure of the host. Therefore the distributed learning and optimization algorithms, which are essentially exempted from those problems, are becoming promising alternatives that attract growing interest in recent years. Generally speaking, distributed algorithms describe the approaches that solve problems in a collaborative manner over multiple agents (machines, nodes, computation units or cores) based on communications among them. The main theme of this work is the identification of efficient and effective ways to exploit distributed procedures and communication structures in this type of settings and applications. The first part of this work contains the discussions of a communication-efficient distributed optimization framework for general nonconvex nonsmooth signal processing and machine learning problems under an asynchronous protocol. As we know, an important criterion for preferable distributed algorithms in latency and communication sensitive applications is that they can complete tasks fast with as less communication resources as possible. Thus in this part we present an asynchronous efficient distributed algorithm with reduced waiting time based on the updates utilizing local higher-order information and investigate the theoretical guarantee for the convergen

关键词： communication efficiency communication patterns convexity and nonconvexity distributed optimization

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