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Gregory V. Frazier

Researcher at University of Texas at Arlington

Publications -  27
Citations -  1722

Gregory V. Frazier is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Supply chain & Radio-frequency identification. The author has an hindex of 18, co-authored 27 publications receiving 1643 citations. Previous affiliations of Gregory V. Frazier include University of Texas at Austin & University of Alaska Anchorage.

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Future impacts of RFID on e‐supply chains in grocery retailing

TL;DR: In this paper, the authors place the research on radio frequency identification (RFID) usage in supply chains within a specific business and market context; in this case, the grocery industry.
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Outsourcing effects on firms' operational performance: An empirical study

TL;DR: In this article, the authors investigate the effect of outsourcing on firm level performance metrics, providing evidence about outsourcing influences on a firm's cost efficiency, productivity, and profitability, using publicly available accounting data.
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An analysis of job dissatisfaction and turnover to reduce global supply chain risk: Evidence from China

TL;DR: In this paper, a combination of quantitative techniques were applied to determine the reasons why workers leave China's export factories, trying to identify the root causes of job dissatisfaction leading to turnover and providing managerial implications that may assist managers in dealing with labor-related supply chain risks.
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Using simulated annealing to solve a multiobjective assembly line balancing problem with parallel workstations

TL;DR: In this paper, a Simulated Annealing-based technique was used to address the assembly line balancing problem for multiple objective problems when paralleling of workstations is permitted, and the resulting performance of each solution was studied through a simulation experiment.
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A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line

TL;DR: In this article, a Simulated Annealing based heuristic that simultaneously considers both setups and the stability of parts usage rates when sequencing jobs for production in a just-in-time environment is presented.